Add type hinting for all_coords parameter in validation functions and

src/ect/dect.py

@@ -4,7 +4,7 @@
 from .results import ECTResult
 from typing import Optional, Union
 import numpy as np
-from numba import njit
+from numba import njit  # type: ignore[attr-defined]
 
 
 class DECT(ECT):
@@ -86,18 +86,19 @@ def _compute_directional_transform(
     def calculate(
         self,
         graph: Union[EmbeddedGraph, EmbeddedCW],
-        scale: Optional[float] = None,
         theta: Optional[float] = None,
         override_bound_radius: Optional[float] = None,
+        *,
+        scale: Optional[float] = None,
     ) -> ECTResult:
         """
         Calculate the Differentiable Euler Characteristic Transform (DECT) for a given embedded complex.
 
         Args:
             graph (EmbeddedGraph or EmbeddedCW): The embedded complex to analyze.
-            scale (Optional[float]): Slope parameter for the sigmoid function. If None, uses the instance's scale.
             theta (Optional[float]): Specific direction angle to use. If None, uses all directions.
             override_bound_radius (Optional[float]): Override for bounding radius in threshold generation.
+            scale (Optional[float]): Slope parameter for the sigmoid function. If None, uses the instance's scale.
 
         Returns:
             ECTResult: Result object containing the DECT matrix, directions, and thresholds.

src/ect/ect.py

@@ -1,5 +1,5 @@
 import numpy as np
-from numba import prange, njit
+from numba import prange, njit  # type: ignore[attr-defined]
 from numba.typed import List
 from typing import Optional
 
@@ -16,7 +16,7 @@ class ECT:
     The result is a matrix where entry ``M[i,j]`` is :math:`\chi(K_{a_i})` for the direction :math:`\omega_j`
     where :math:`a_i` is the ith entry in ``self.thresholds``, and :math:`\omega_j` is the jth entry in ``self.directions``.
 
-    
+
 
     Example:
         >>> from ect import ECT, EmbeddedComplex
@@ -106,14 +106,15 @@ def _ensure_thresholds(self, graph, override_bound_radius=None):
     def calculate(
         self,
         graph: EmbeddedComplex,
-        theta: float = None,
-        override_bound_radius: float = None,
+        theta: Optional[float] = None,
+        override_bound_radius: Optional[float] = None,
     ):
         self._ensure_directions(graph.dim, theta)
         self._ensure_thresholds(graph, override_bound_radius)
         directions = (
             self.directions if theta is None else Directions.from_angles([theta])
         )
+        assert self.thresholds is not None
         ect_matrix = self._compute_ect(graph, directions, self.thresholds, self.dtype)
 
         return ECTResult(ect_matrix, directions, self.thresholds)

src/ect/embed_complex.py

@@ -655,9 +655,8 @@ def pca_projection(self, target_dim=2):
         pca = PCA(n_components=target_dim)
         self._coord_matrix = pca.fit_transform(self._coord_matrix)
 
-
+    @staticmethod
     def validate_plot_parameters(func):
-        # decorator to check plotting requirements
         @functools.wraps(func)
         def wrapper(self, *args, **kwargs):
             bounding_center_type = kwargs.get("bounding_center_type", "bounding_box")
@@ -706,7 +705,6 @@ def plot_faces(self, ax=None, **kwargs):
 
         return ax
 
-
     @validate_plot_parameters
     def plot(
         self,
@@ -722,7 +720,7 @@ def plot(
         face_color: str = "lightblue",
         face_alpha: float = 0.3,
         **kwargs,
-        ) -> plt.Axes:
+    ) -> plt.Axes:
         """
         Visualize the embedded complex in 2D or 3D
 
@@ -739,7 +737,7 @@ def plot(
             face_color (str): Color for faces (2-cells)
             face_alpha (float): Transparency for faces (2-cells)
             **kwargs: Additional keyword arguments for plotting functions
-        
+
         Returns:
             matplotlib.axes.Axes: The axes object with the plot.
         """
@@ -991,20 +989,20 @@ def _build_incidence_csr(self) -> tuple:
 
         cell_vertex_pointers = np.empty(n_cells + 1, dtype=np.int64)
         cell_euler_signs = np.empty(n_cells, dtype=np.int32)
-        cell_vertex_indices_flat = []
+        list_flat: List[int] = []
 
         cell_vertex_pointers[0] = 0
         cell_index = 0
         for dim in dimensions:
             cells_in_dim = cells_by_dimension[dim]
             euler_sign = 1 if (dim % 2 == 0) else -1
             for cell_vertices in cells_in_dim:
-                cell_vertex_indices_flat.extend(cell_vertices)
+                list_flat.extend(cell_vertices)
                 cell_euler_signs[cell_index] = euler_sign
                 cell_index += 1
-                cell_vertex_pointers[cell_index] = len(cell_vertex_indices_flat)
+                cell_vertex_pointers[cell_index] = len(list_flat)
 
-        cell_vertex_indices_flat = np.asarray(cell_vertex_indices_flat, dtype=np.int32)
+        cell_vertex_indices_flat = np.asarray(list_flat, dtype=np.int32)
         return (
             cell_vertex_pointers,
             cell_vertex_indices_flat,

src/ect/results.py

@@ -2,7 +2,7 @@
 import numpy as np
 from ect.directions import Sampling
 from scipy.spatial.distance import cdist, pdist, squareform
-from typing import Union, List, Callable
+from typing import Union, List, Callable, cast
 
 
 # ---------- CSR <-> Dense helpers (prefix-difference over thresholds) ----------
@@ -352,14 +352,15 @@ def dist(
             >>> # Batch distances with custom function
             >>> dists = ect1.dist([ect2, ect3, ect4], metric=my_distance)
         """
-        # normalize input to list
         single = isinstance(other, ECTResult)
-        others = [other] if single else other
+        others_list: List["ECTResult"] = cast(
+            List["ECTResult"], [other] if single else other
+        )
 
-        if not others:
+        if not others_list:
             return np.array([])
 
-        for i, ect in enumerate(others):
+        for i, ect in enumerate(others_list):
             if ect.shape != self.shape:
                 raise ValueError(
                     f"Shape mismatch at index {i}: {self.shape} vs {ect.shape}"
@@ -370,13 +371,15 @@ def dist(
             if single:
                 b = np.asarray(other, dtype=np.float64)
                 return float(np.sqrt(np.sum((a - b) ** 2)))
-            b = np.stack([np.asarray(ect, dtype=np.float64) for ect in others], axis=0)
+            b = np.stack(
+                [np.asarray(ect, dtype=np.float64) for ect in others_list], axis=0
+            )
             diff = b - a
             return np.sqrt(np.sum(diff * diff, axis=(1, 2)))
 
         distances = cdist(
             self.ravel()[np.newaxis, :],
-            np.vstack([ect.ravel() for ect in others]),
+            np.vstack([ect.ravel() for ect in others_list]),
             metric=metric,
             **kwargs,
         )[0]
@@ -399,13 +402,25 @@ def dist_matrix(
                 raise ValueError(f"Shape mismatch at index {i}: {shape0} vs {r.shape}")
 
         if isinstance(metric, str) and metric.lower() in ("frobenius", "fro"):
-            return np.vstack([results[i].dist(results, metric="frobenius") for i in range(len(results))])
+            return np.vstack(
+                [
+                    results[i].dist(results, metric="frobenius")
+                    for i in range(len(results))
+                ]
+            )
 
         if isinstance(metric, str):
-            X = np.stack([np.asarray(r, dtype=np.float64).ravel() for r in results], axis=0)
+            X = np.stack(
+                [np.asarray(r, dtype=np.float64).ravel() for r in results], axis=0
+            )
             try:
                 return squareform(pdist(X, metric=metric, **kwargs))
             except TypeError:
                 return cdist(X, X, metric=metric, **kwargs)
 
-        return np.vstack([results[i].dist(results, metric=metric, **kwargs) for i in range(len(results))])
+        return np.vstack(
+            [
+                results[i].dist(results, metric=metric, **kwargs)
+                for i in range(len(results))
+            ]
+        )