[Feat] Add type hints

.github/workflows/flake8.yml

@@ -21,6 +21,6 @@ jobs:
       - name: flake8 Lint
         uses: py-actions/flake8@v2
         with:
-          ignore: "E501,W503"
+          ignore: "E501,W503,E252"
           exclude: "__init__.py, input/__init__.py"
           path: "pyerrors"

.github/workflows/pytest.yml

@@ -39,7 +39,7 @@ jobs:
         run: |
           uv pip install wheel --system
           uv pip install . --system
-          uv pip install pytest pytest-cov pytest-benchmark hypothesis --system
+          uv pip install pytest pytest-cov pytest-benchmark hypothesis typing_extensions --system
           uv pip freeze --system
 
       - name: Run tests with -Werror

pyerrors/covobs.py

@@ -1,9 +1,12 @@
+from __future__ import annotations
 import numpy as np
+from numpy import ndarray
+from typing import Optional, Union
 
 
 class Covobs:
 
-    def __init__(self, mean, cov, name, pos=None, grad=None):
+    def __init__(self, mean: Union[float, int], cov: Union[list, ndarray], name: str, pos: Optional[int]=None, grad: Optional[Union[ndarray, list[float]]]=None):
         """ Initialize Covobs object.
 
         Parameters
@@ -39,12 +42,12 @@ def __init__(self, mean, cov, name, pos=None, grad=None):
             self._set_grad(grad)
         self.value = mean
 
-    def errsq(self):
+    def errsq(self) -> float:
         """ Return the variance (= square of the error) of the Covobs
         """
         return np.dot(np.transpose(self.grad), np.dot(self.cov, self.grad)).item()
 
-    def _set_cov(self, cov):
+    def _set_cov(self, cov: Union[list, ndarray]):
         """ Set the covariance matrix of the covobs
 
         Parameters
@@ -79,7 +82,7 @@ def _set_cov(self, cov):
             if ev < 0:
                 raise Exception('Covariance matrix is not positive-semidefinite!')
 
-    def _set_grad(self, grad):
+    def _set_grad(self, grad: Union[list[float], ndarray]):
         """ Set the gradient of the covobs
 
         Parameters
@@ -96,9 +99,9 @@ def _set_grad(self, grad):
             raise Exception('Invalid dimension of grad!')
 
     @property
-    def cov(self):
+    def cov(self) -> ndarray:
         return self._cov
 
     @property
-    def grad(self):
+    def grad(self) -> ndarray:
         return self._grad

pyerrors/dirac.py

@@ -1,4 +1,6 @@
+from __future__ import annotations
 import numpy as np
+from numpy import ndarray
 
 
 gammaX = np.array(
@@ -22,7 +24,7 @@
     dtype=complex)
 
 
-def epsilon_tensor(i, j, k):
+def epsilon_tensor(i: int, j: int, k: int) -> int:
     """Rank-3 epsilon tensor
 
     Based on https://codegolf.stackexchange.com/a/160375
@@ -36,10 +38,10 @@ def epsilon_tensor(i, j, k):
     if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))):
         raise ValueError("Unexpected input", i, j, k)
 
-    return (i - j) * (j - k) * (k - i) / 2
+    return int((i - j) * (j - k) * (k - i) / 2)
 
 
-def epsilon_tensor_rank4(i, j, k, o):
+def epsilon_tensor_rank4(i: int, j: int, k: int, o: int) -> int:
     """Rank-4 epsilon tensor
 
     Extension of https://codegolf.stackexchange.com/a/160375
@@ -54,10 +56,10 @@ def epsilon_tensor_rank4(i, j, k, o):
     if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))):
         raise ValueError("Unexpected input", i, j, k, o)
 
-    return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12
+    return int((i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12)
 
 
-def Grid_gamma(gamma_tag):
+def Grid_gamma(gamma_tag: str) -> ndarray:
     """Returns gamma matrix in Grid labeling."""
     if gamma_tag == 'Identity':
         g = identity