verified type changes

Author: peterdsharpe

aerosandbox/numpy/array.py

@@ -839,13 +839,13 @@ def full_like(
 
 
 def assert_equal_shape(
-    arrays: list[_onp.ndarray] | dict[str, _onp.ndarray],
+    arrays: list[Array] | dict[str, Array],
 ) -> None:
     """Assert that all of the given arrays have the same shape.
 
     Parameters
     ----------
-    arrays : list of ndarray, or dict of str to ndarray
+    arrays : list of Array, or dict of str to Array
         The arrays to be evaluated. Can be provided as:
 
         - A list, in which case a generic ValueError is thrown on mismatch.

aerosandbox/numpy/finite_difference_operators.py

@@ -1,12 +1,13 @@
-from aerosandbox.numpy.array import array, length
+from aerosandbox.numpy.array import array, asarray, length
+from aerosandbox.numpy.typing import ArrayLike, Array
 import numpy as _onp
 
 
 def finite_difference_coefficients(
-    x: _onp.ndarray,
+    x: ArrayLike,
     x0: float = 0,
     derivative_degree: int = 1,
-) -> _onp.ndarray:
+) -> Array:
     """
     Computes the weights (coefficients) in compact finite differece formulas for any order of derivative
     and to any order of accuracy on one-dimensional grids with arbitrary spacing.
@@ -51,6 +52,8 @@ def finite_difference_coefficients(
     grid points `x`.
 
     """
+    x = asarray(x)
+
     ### Check inputs
     if derivative_degree < 1:
         raise ValueError("The parameter derivative_degree must be an integer >= 1.")

aerosandbox/numpy/interpolate.py

@@ -4,7 +4,7 @@
 from aerosandbox.numpy.array import array, zeros_like
 from aerosandbox.numpy.conditionals import where
 from aerosandbox.numpy.logicals import all, any, logical_or
-from aerosandbox.numpy.typing import Vectorizable, ConcreteVector, ConcreteArray
+from aerosandbox.numpy.typing import Vectorizable, ArrayLike, ConcreteVector, ConcreteArray
 from typing import Literal, Sequence
 from scipy import interpolate as _interpolate
 
@@ -110,11 +110,11 @@ def is_data_structured(
 
 
 def interpn(
-    points: Sequence[ConcreteVector],
+    points: Sequence[ArrayLike],
     values: ConcreteArray,
     xi: Vectorizable,
     method: Literal["linear", "bspline", "nearest"] = "linear",
-    bounds_error: bool = True,
+    bounds_error: bool | None = True,
     fill_value: float | None = _onp.nan,
 ) -> Vectorizable:
     """

aerosandbox/numpy/rotations.py

@@ -180,7 +180,7 @@ def rotation_matrix_from_euler_angles(
         return rot
 
 
-def is_valid_rotation_matrix(a: _onp.ndarray, tol: float = 1e-9) -> bool:
+def is_valid_rotation_matrix(a: Array, tol: float = 1e-9) -> bool:
     """Check whether a matrix satisfies the properties of a rotation matrix.
 
     Tests for:
@@ -191,7 +191,7 @@ def is_valid_rotation_matrix(a: _onp.ndarray, tol: float = 1e-9) -> bool:
 
     Parameters
     ----------
-    a : ndarray
+    a : Array
         The matrix to test.
     tol : float, optional
         Tolerance for floating-point comparisons. Default is 1e-9.

aerosandbox/numpy/surrogate_model_tools.py

@@ -7,13 +7,14 @@
 import aerosandbox.numpy as _np
 import casadi as _cas
 from typing import Literal
+from aerosandbox.numpy.typing import Vectorizable
 
 
 def softmax(
-    *args: float | _np.ndarray,
+    *args: Vectorizable,
     softness: float | None = None,
     hardness: float | None = None,
-) -> float | _np.ndarray:
+) -> Vectorizable:
     """Compute element-wise soft maximum of two or more arrays.
 
     Also known as the log-sum-exp (LSE) function. Useful for optimization
@@ -89,10 +90,10 @@ def softmax(
 
 
 def softmin(
-    *args: float | _np.ndarray,
+    *args: Vectorizable,
     softness: float | None = None,
     hardness: float | None = None,
-) -> float | _np.ndarray:
+) -> Vectorizable:
     """Compute element-wise soft minimum of two or more arrays.
 
     Related to the log-sum-exp function. Useful for optimization because
@@ -132,10 +133,10 @@ def softmin(
 
 
 def softmax_scalefree(
-    *args: float | _np.ndarray,
+    *args: Vectorizable,
     relative_softness: float | None = None,
     relative_hardness: float | None = None,
-) -> float | _np.ndarray:
+) -> Vectorizable:
     """Compute scale-free soft maximum of two or more arrays.
 
     Like ``softmax``, but the softness is automatically scaled based on
@@ -176,10 +177,10 @@ def softmax_scalefree(
 
 
 def softmin_scalefree(
-    *args: float | _np.ndarray,
+    *args: Vectorizable,
     relative_softness: float | None = None,
     relative_hardness: float | None = None,
-) -> float | _np.ndarray:
+) -> Vectorizable:
     """Compute scale-free soft minimum of two or more arrays.
 
     Like ``softmin``, but the softness is automatically scaled based on

aerosandbox/numpy/test_numpy/test_integrate_discrete_intervals.py

@@ -106,7 +106,7 @@ def test_nonuniform_spacing():
 
     ### Compute expected via manual trapezoidal rule
     expected = 0.0
-    for i in range(len(x) - 1):
+    for i in range(np.length(x) - 1):
         expected += (f[i] + f[i + 1]) / 2 * (x[i + 1] - x[i])
 
     assert np.isclose(integral, expected, rtol=1e-10)
@@ -134,7 +134,7 @@ def test_no_x_specified():
 
     result = integrate_discrete_intervals(f, method="trapezoidal")
     expected = integrate_discrete_intervals(
-        f, x=np.arange(len(f)), method="trapezoidal"
+        f, x=np.arange(np.length(f)), method="trapezoidal"
     )
 
     assert np.allclose(result, expected)
@@ -149,7 +149,7 @@ def test_method_endpoints_lower_order():
         f, x, method="cubic", method_endpoints="lower_order"
     )
 
-    assert len(result) == len(x) - 1
+    assert np.length(result) == np.length(x) - 1
     assert not np.any(np.isnan(result))
 
 
@@ -174,9 +174,9 @@ def test_method_endpoints_ignore():
 
     ### With 'ignore', forward_simpson gives N-2 intervals (missing last interval due to endpoint)
     ### With 'lower_order', all N-1 intervals are returned
-    assert len(result_ignore_no_dx) == len(x) - 2
-    assert len(result_ignore_with_dx) == len(x) - 2
-    assert len(result_lower) == len(x) - 1
+    assert np.length(result_ignore_no_dx) == np.length(x) - 2
+    assert np.length(result_ignore_with_dx) == np.length(x) - 2
+    assert np.length(result_lower) == np.length(x) - 1
 
     ### Verify the bug fix: result_ignore_with_dx should be result_ignore_no_dx * dx
     dx = np.diff(x)

aerosandbox/numpy/test_numpy/test_interpolate.py

@@ -59,15 +59,15 @@ def value_func_3d(x, y, z):
         value
         == pytest.approx(value_func_3d(*[point[:, i] for i in range(point.shape[1])]))
     )
-    assert len(value) == 2
+    assert np.length(value) == 2
 
     ### CasADi test
     point = cas.DM(point)
     value = np.interpn(points, values, point)
     value_actual = value_func_3d(
         *[np.array(point[:, i]) for i in range(point.shape[1])]
     )
-    for i in range(len(value)):
+    for i in range(np.length(value)):
         assert value[i] == pytest.approx(float(value_actual[i]))
     assert value.shape == (2,)
 

aerosandbox/numpy/test_numpy/test_logicals.py

@@ -6,7 +6,7 @@ def test_basic_logicals_numpy():
     a = np.array([True, True, False, False])
     b = np.array([True, False, True, False])
 
-    assert np.all(a & b == np.array([True, False, False, False]))
+    assert np.all(a & b == np.array([True, False, False, False]))  # type: ignore[operator]
 
 
 if __name__ == "__main__":

aerosandbox/numpy/test_numpy/test_rotations.py

@@ -10,7 +10,7 @@ def test_euler_angles_equivalence_to_general_3D():
     rot_euler = np.rotation_matrix_from_euler_angles(phi, theta, psi)
     rot_manual = (
         np.rotation_matrix_3D(psi, np.array([0, 0, 1]))
-        @ np.rotation_matrix_3D(theta, np.array([0, 1, 0]))
+        @ np.rotation_matrix_3D(theta, np.array([0, 1, 0]))  # type: ignore[operator]
         @ np.rotation_matrix_3D(phi, np.array([1, 0, 0]))
     )
 

aerosandbox/numpy/trig.py

@@ -6,6 +6,7 @@
 
 import numpy as _onp
 from numpy import pi as _pi
+from aerosandbox.numpy.array import asarray
 from aerosandbox.numpy.typing import Vectorizable, Array
 
 _deg2rad = 180.0 / _pi
@@ -29,7 +30,7 @@ def degrees(x: Vectorizable) -> Array:
     --------
     numpy.degrees : https://numpy.org/doc/stable/reference/generated/numpy.degrees.html
     """
-    return x * _deg2rad
+    return asarray(x * _deg2rad)
 
 
 def radians(x: Vectorizable) -> Array:
@@ -49,7 +50,7 @@ def radians(x: Vectorizable) -> Array:
     --------
     numpy.radians : https://numpy.org/doc/stable/reference/generated/numpy.radians.html
     """
-    return x * _rad2deg
+    return asarray(x * _rad2deg)
 
 
 def sind(x: Vectorizable) -> Array: