lint

hippunfold/workflow/scripts/laplace-beltrami.py

@@ -5,38 +5,46 @@
 from collections import defaultdict
 import networkx as nx
 
+
 def largest_connected_component(vertices: np.ndarray, faces: np.ndarray):
     """
     Returns the vertices, faces, and original indices of the largest connected component of a 3D surface mesh.
-    
+
     Parameters:
     vertices (np.ndarray): Array of vertex coordinates (N x 3).
     faces (np.ndarray): Array of face indices (M x 3).
-    
+
     Returns:
     tuple: (new_vertices, new_faces, largest_component), where new_vertices are the vertex coordinates of the largest component,
            new_faces are the face indices adjusted to the new vertex order, and largest_component contains the indices of the original vertices.
     """
     # Build adjacency graph from face connectivity
     G = nx.Graph()
     for face in faces:
-        G.add_edges_from([(face[i], face[j]) for i in range(3) for j in range(i + 1, 3)])
-
+        G.add_edges_from(
+            [(face[i], face[j]) for i in range(3) for j in range(i + 1, 3)]
+        )
+
     # Find connected components
     components = list(nx.connected_components(G))
-    
+
     # Select the largest connected component
     largest_component = max(components, key=len)
     largest_component = np.array(list(largest_component))
-    
+
     # Create a mapping from old vertex indices to new ones
     index_map = {old_idx: new_idx for new_idx, old_idx in enumerate(largest_component)}
-    
+
     # Filter the vertices and faces
     new_vertices = vertices[largest_component]
-    new_faces = np.array([[index_map[v] for v in face] for face in faces 
-                           if all(v in index_map for v in face)])
-
+    new_faces = np.array(
+        [
+            [index_map[v] for v in face]
+            for face in faces
+            if all(v in index_map for v in face)
+        ]
+    )
+
     return new_vertices, new_faces, largest_component
 
 
@@ -149,7 +157,7 @@ def solve_laplace_beltrami_open_mesh(vertices, faces, boundary_conditions=None):
 vertices_orig = surf.agg_data("NIFTI_INTENT_POINTSET")
 faces = surf.agg_data("NIFTI_INTENT_TRIANGLE")
 
-vertices, faces, i_concomp = largest_connected_component(vertices_orig,faces)
+vertices, faces, i_concomp = largest_connected_component(vertices_orig, faces)
 
 
 # get source/sink vertices by nearest neighbour (only of edge vertices)
@@ -185,11 +193,15 @@ def solve_laplace_beltrami_open_mesh(vertices, faces, boundary_conditions=None):
 APinds = np.array(np.where(boundary_values < 12)[0]).astype(int)
 boundary_conditions = dict(zip(boundary_vertices[APinds], boundary_values[APinds] - 10))
 APcoords = np.nans((len(vertices_orig)))
-APcoords[i_concomp] = solve_laplace_beltrami_open_mesh(vertices, faces, boundary_conditions)
+APcoords[i_concomp] = solve_laplace_beltrami_open_mesh(
+    vertices, faces, boundary_conditions
+)
 PDinds = np.array(np.where(boundary_values > 12)[0]).astype(int)
 boundary_conditions = dict(zip(boundary_vertices[PDinds], boundary_values[PDinds] - 20))
 PDcoords = np.nans((len(vertices_orig)))
-PDcoords[i_concomp] = solve_laplace_beltrami_open_mesh(vertices, faces, boundary_conditions)
+PDcoords[i_concomp] = solve_laplace_beltrami_open_mesh(
+    vertices, faces, boundary_conditions
+)
 
 data_array = nib.gifti.GiftiDataArray(data=APcoords.astype(np.float32))
 image = nib.gifti.GiftiImage()

hippunfold/workflow/scripts/rewrite_vertices_to_flat.py

@@ -6,10 +6,12 @@
 ap = nib.load(snakemake.input.coords_AP).darrays[0].data
 pd = nib.load(snakemake.input.coords_PD).darrays[0].data
 
-vertices[:, 0] = ap * -float(snakemake.params.vertspace["extent"][0]
-)  - float(snakemake.params.vertspace["origin"][0])
-vertices[:, 1] = pd * float(snakemake.params.vertspace["extent"][1]
-)  - float(snakemake.params.vertspace["origin"][1])
+vertices[:, 0] = ap * -float(snakemake.params.vertspace["extent"][0]) - float(
+    snakemake.params.vertspace["origin"][0]
+)
+vertices[:, 1] = pd * float(snakemake.params.vertspace["extent"][1]) - float(
+    snakemake.params.vertspace["origin"][1]
+)
 vertices[:, 2] = snakemake.params.z_level
 
 nib.save(gii, snakemake.output.surf_gii)