Ran black

aicscytoparam/alignment/generic_2d_shape.py

@@ -5,10 +5,11 @@
 from skimage import measure as skmeasure
 
 
-class Generic2DShape():
+class Generic2DShape:
     """
     Generic class for 2D shapes
     """
+
     def __init__():
         pass
 
@@ -112,8 +113,9 @@ def get_contour_from_3d_image(image, pad=5, center=True):
 
 class ElongatedHexagonalShape(Generic2DShape):
     """
-    Elongated hexagonal shape    
+    Elongated hexagonal shape
     """
+
     def __init__(self, base, elongation, pad=5):
         self._pad = pad
         self._base = base

aicscytoparam/alignment/shape_library_2d.py

@@ -3,10 +3,11 @@
 from aicscytoparam.alignment.generic_2d_shape import Generic2DShape
 
 
-class ShapeLibrary2D():
+class ShapeLibrary2D:
     """
     Define a library of 2D shapes
     """
+
     def __init__(self):
         pass
 
@@ -80,7 +81,7 @@ def display(self, xlim=[-150, 150], ylim=[-50, 50], contours_to_match=None):
             axs[j, i].set_xlim(xlim[0], xlim[1])
             axs[j, i].set_ylim(ylim[0], ylim[1])
         if contours_to_match is not None:
-            for (cx, cy) in contours_to_match:
+            for cx, cy in contours_to_match:
                 pid, p, angle = self.find_best_match(cx, cy)
                 cxrot, cyrot = Generic2DShape.rotate_contour(cx, cy, angle)
                 axs[j, i].plot(cxrot, cyrot, color="magenta")

aicscytoparam/utils.py

@@ -21,7 +21,7 @@ def load_image_and_align_based_on_shape_library(path, library, nuc_channel=1):
     Returns
     -------
     image_rot: np.ndarray
-        aligned image    
+        aligned image
     """
     image = skio.imread(path)
     (cx, cy) = Generic2DShape.get_contour_from_3d_image(image[0])
@@ -34,11 +34,12 @@ def load_image_and_align_based_on_shape_library(path, library, nuc_channel=1):
     xpx = (x - x.mean()) * px / px.sum()
     if xpx.sum() > 0:
         image_rot = shtools.rotate_image_2d(image_rot, 180)
-    return image_rot  
+    return image_rot
 
 
 def get_voxelized_image_of_mean_shape(
-        vec: List, coeffs_mem: List, coeffs_nuc: List, return_meshes=False):
+    vec: List, coeffs_mem: List, coeffs_nuc: List, return_meshes=False
+):
     """
     Get the voxelized image of the mean shape
     Parameters
@@ -59,8 +60,8 @@ def get_voxelized_image_of_mean_shape(
     vec = np.array(vec).mean(axis=0)
     df_coeffs_mem = pd.DataFrame(coeffs_mem)
     df_coeffs_nuc = pd.DataFrame(coeffs_nuc)
-    df_coeffs_mem_avg = df_coeffs_mem.select_dtypes(include='number').mean(axis=0)
-    df_coeffs_nuc_avg = df_coeffs_nuc.select_dtypes(include='number').mean(axis=0)
+    df_coeffs_mem_avg = df_coeffs_mem.select_dtypes(include="number").mean(axis=0)
+    df_coeffs_nuc_avg = df_coeffs_nuc.select_dtypes(include="number").mean(axis=0)
     avg_coeffs_mem = shtools.convert_coeffs_dict_to_matrix(df_coeffs_mem_avg, lmax=32)
     avg_coeffs_nuc = shtools.convert_coeffs_dict_to_matrix(df_coeffs_nuc_avg, lmax=32)
     avg_mesh_mem, _ = shtools.get_reconstruction_from_coeffs(avg_coeffs_mem)
@@ -69,7 +70,8 @@ def get_voxelized_image_of_mean_shape(
     # centroid relative to cell centroid
     pts = vtknp.vtk_to_numpy(avg_mesh_nuc.GetPoints().GetData())
     shtools.update_mesh_points(
-        avg_mesh_nuc, pts[:, 0] - vec[0], pts[:, 1] - vec[1], pts[:, 2] - vec[2])
+        avg_mesh_nuc, pts[:, 0] - vec[0], pts[:, 1] - vec[1], pts[:, 2] - vec[2]
+    )
     # Convert average cell shape into an image
     avg_image, _ = shtools.voxelize_meshes([avg_mesh_mem, avg_mesh_nuc])
     if return_meshes: