Fixed python 3.8/3.9 support (#2)

* some typing thing

* trying to fix for python 3.8

Author: jacotay7

.github/workflows/ci.yml

@@ -25,4 +25,4 @@ jobs:
         pip install -e .[dev]
     - name: Run tests
       run: |
-        pytest
+        pytest --cov=src/aobasis --cov-fail-under=90

pyproject.toml

@@ -24,6 +24,7 @@ requires-python = ">=3.8"
 [project.optional-dependencies]
 dev = [
     "pytest",
+    "pytest-cov",
     "imageio",
 ]
 

src/aobasis/base.py

@@ -1,7 +1,7 @@
 from abc import ABC, abstractmethod
 import numpy as np
 from pathlib import Path
-from typing import Tuple, Optional
+from typing import Tuple, Optional, Union
 from .utils import plot_basis_modes
 
 class BasisGenerator(ABC):
@@ -31,7 +31,7 @@ def generate(self, n_modes: int, **kwargs) -> np.ndarray:
         """
         pass
 
-    def save(self, filepath: str | Path) -> None:
+    def save(self, filepath: Union[str, Path]) -> None:
         """
         Save the generated basis and actuator positions to a .npz file.
         """
@@ -46,7 +46,7 @@ def save(self, filepath: str | Path) -> None:
         )
 
     @classmethod
-    def load(cls, filepath: str | Path) -> 'BasisGenerator':
+    def load(cls, filepath: Union[str, Path]) -> 'BasisGenerator':
         """
         Load a basis from a .npz file. 
         Note: This returns a generic container or re-instantiates the specific class if possible.
@@ -62,7 +62,7 @@ def load(cls, filepath: str | Path) -> 'BasisGenerator':
         instance.modes = modes
         return instance
 
-    def plot(self, count: int = 6, outfile: Optional[str | Path] = None, **kwargs):
+    def plot(self, count: int = 6, outfile: Optional[Union[str, Path]] = None, **kwargs):
         """Plot the generated modes."""
         if self.modes is None:
             raise ValueError("No modes to plot.")

src/aobasis/utils.py

@@ -1,6 +1,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from pathlib import Path
+from typing import Union
 import math
 from scipy.interpolate import griddata
 
@@ -20,7 +21,7 @@ def plot_basis_modes(
     modes: np.ndarray,
     positions: np.ndarray,
     count: int = 6,
-    outfile: Path | str | None = None,
+    outfile: Union[Path, str, None] = None,
     cmap: str = "coolwarm",
     title_prefix: str = "Mode",
     interpolate: bool = False,

src/aobasis/zernike.py

@@ -1,5 +1,6 @@
 import numpy as np
 import math
+from typing import Tuple
 from .base import BasisGenerator
 
 class ZernikeBasisGenerator(BasisGenerator):
@@ -62,7 +63,7 @@ def generate(self, n_modes: int, ignore_piston: bool = False, **kwargs) -> np.nd
         self.modes = np.column_stack(modes_list)
         return self.modes
 
-    def _noll_to_nm(self, j: int) -> tuple[int, int]:
+    def _noll_to_nm(self, j: int) -> Tuple[int, int]:
         """
         Convert Noll index j to radial order n and azimuthal frequency m.
         Based on Noll, J. Opt. Soc. Am. 66, 207 (1976).

tests/test_utils.py

@@ -0,0 +1,100 @@
+import numpy as np
+import pytest
+from pathlib import Path
+from unittest.mock import patch, MagicMock
+from aobasis.utils import make_circular_actuator_grid, make_concentric_actuator_grid, plot_basis_modes
+
+def test_make_circular_actuator_grid():
+    diameter = 10.0
+    grid_size = 10
+    positions = make_circular_actuator_grid(diameter, grid_size)
+    
+    assert isinstance(positions, np.ndarray)
+    assert positions.shape[1] == 2
+    
+    # Check that all points are within the radius
+    radius = diameter / 2
+    distances = np.linalg.norm(positions, axis=1)
+    assert np.all(distances <= radius * 1.0000001)
+
+def test_make_concentric_actuator_grid():
+    diameter = 10.0
+    n_rings = 3
+    n_points_innermost = 6
+    positions = make_concentric_actuator_grid(diameter, n_rings, n_points_innermost)
+    
+    assert isinstance(positions, np.ndarray)
+    assert positions.shape[1] == 2
+    
+    # Expected number of points: 1 (center) + 6*1 + 6*2 + 6*3 = 1 + 6 + 12 + 18 = 37
+    expected_points = 1 + sum(n_points_innermost * i for i in range(1, n_rings + 1))
+    assert positions.shape[0] == expected_points
+
+@patch("aobasis.utils.plt")
+def test_plot_basis_modes(mock_plt):
+    # Setup mock data
+    n_actuators = 20
+    n_modes = 5
+    positions = np.random.rand(n_actuators, 2)
+    modes = np.random.rand(n_actuators, n_modes)
+    
+    # Configure mock to return a tuple
+    mock_fig = MagicMock()
+    mock_axes = MagicMock()
+    mock_plt.subplots.return_value = (mock_fig, mock_axes)
+    
+    # Test basic plotting
+    plot_basis_modes(modes, positions, count=3)
+    
+    assert mock_plt.subplots.called
+    assert mock_plt.show.called
+
+@patch("aobasis.utils.plt")
+def test_plot_basis_modes_save(mock_plt, tmp_path):
+    # Setup mock data
+    n_actuators = 20
+    n_modes = 5
+    positions = np.random.rand(n_actuators, 2)
+    modes = np.random.rand(n_actuators, n_modes)
+    outfile = tmp_path / "test_plot.png"
+    
+    # Configure mock to return a tuple
+    mock_fig = MagicMock()
+    mock_axes = MagicMock()
+    mock_plt.subplots.return_value = (mock_fig, mock_axes)
+    
+    # Test saving to file
+    plot_basis_modes(modes, positions, count=3, outfile=outfile)
+    
+    assert mock_plt.subplots.called
+    mock_plt.savefig.assert_called_with(outfile, dpi=150)
+    assert mock_plt.close.called
+
+@patch("aobasis.utils.plt")
+def test_plot_basis_modes_interpolate(mock_plt):
+    # Setup mock data
+    n_actuators = 20
+    n_modes = 5
+    positions = np.random.rand(n_actuators, 2)
+    modes = np.random.rand(n_actuators, n_modes)
+    
+    # Configure mock to return a tuple
+    mock_fig = MagicMock()
+    mock_axes = MagicMock()
+    mock_plt.subplots.return_value = (mock_fig, mock_axes)
+    
+    # Test interpolation
+    plot_basis_modes(modes, positions, count=3, interpolate=True)
+    
+    assert mock_plt.subplots.called
+    # We can't easily check if imshow was called on the axes objects without more complex mocking,
+    # but we can check that no errors were raised.
+
+def test_plot_basis_modes_invalid_shape():
+    n_actuators = 20
+    n_modes = 5
+    positions = np.random.rand(n_actuators, 2)
+    modes = np.random.rand(n_actuators + 1, n_modes) # Mismatch
+    
+    with pytest.raises(ValueError, match="Mode dimension 0"):
+        plot_basis_modes(modes, positions)