Code style cleanup

docs/requirements.txt

@@ -1,4 +1,4 @@
 jupyter-book>=0.7.0b
 napari
-PyQt5
+qtpy
 napari-stress

pyproject.toml

@@ -1,3 +1,9 @@
 [build-system]
 requires = ["setuptools", "wheel"]
 build-backend = "setuptools.build_meta"
+
+[tool.ruff]
+ignore = ["F821"]
+
+[tool.ruff.per-file-ignores]
+"_napari_plugin.py" = ["F401"]

src/napari_stress/__init__.py

@@ -35,3 +35,32 @@
 
 from . import types
 from . import _vectors as vectors
+
+__all__ = [
+    "__version__",
+    "measurements",
+    "approximation",
+    "reconstruction",
+    "sample_data",
+    "plotting",
+    "utils",
+    "stress_backend",
+    "rescale",
+    "smooth_sinc",
+    "smoothMLS2D",
+    "reconstruct_surface",
+    "decimate",
+    "extract_vertex_points",
+    "fit_ellipsoid_to_pointcloud_points",
+    "fit_ellipsoid_to_pointcloud_vectors",
+    "TimelapseConverter",
+    "frame_by_frame",
+    "fit_spherical_harmonics",
+    "lebedev_quadrature",
+    "create_manifold",
+    "get_droplet_point_cloud",
+    "get_droplet_point_cloud_4d",
+    "get_droplet_4d",
+    "types",
+    "vectors",
+]

src/napari_stress/_approximation/__init__.py

@@ -5,3 +5,9 @@
     expand_points_on_ellipse,
     normals_on_ellipsoid,
 )
+
+__all__ = [
+    "least_squares_ellipsoid",
+    "expand_points_on_ellipse",
+    "normals_on_ellipsoid",
+]

src/napari_stress/_approximation/fit_ellipsoid.py

@@ -64,15 +64,15 @@ def normals_on_ellipsoid(points: PointsData) -> VectorsData:
     F = coefficients.flatten()[5]
     G = coefficients.flatten()[6]
     H = coefficients.flatten()[7]
-    I = coefficients.flatten()[8]
+    J = coefficients.flatten()[8]
 
     xx = points[:, 0][:, None]
     yy = points[:, 1][:, None]
     zz = points[:, 2][:, None]
 
     grad_F_x = 2.0 * A * xx + D * yy + E * zz + G
     grad_F_y = 2.0 * B * yy + D * xx + F * zz + H
-    grad_F_z = 2.0 * C * zz + E * xx + F * yy + I
+    grad_F_z = 2.0 * C * zz + E * xx + F * yy + J
 
     grad_F_X = np.hstack((grad_F_x, grad_F_y, grad_F_z))
     Vec_Norms = np.sqrt(np.sum(np.multiply(grad_F_X, grad_F_X), axis=1)).reshape(