small update on reading from multiple files

src/sc3D/sc3D.py

@@ -25,6 +25,7 @@
 import anndata
 from sc3D.transformations import transformations as tr
 
+
 class Embryo:
     """
     Embryo class to handle samples from 3D spatial
@@ -993,14 +994,19 @@ def registration_3d(
 
             all_cs_names = sorted(self.anndata.obs[self.array_id].unique())
             exp = re.compile("[0-9]+")
-            init_cs_numbers = [int(exp.findall(x)[self.array_id_num_pos]) for x in all_cs_names]
+            init_cs_numbers = [
+                int(exp.findall(x)[self.array_id_num_pos])
+                for x in all_cs_names
+            ]
             num_to_name = dict(zip(init_cs_numbers, all_cs_names))
-            M_raw_filtered.obsm['spatial'] = M_raw_filtered.obsm[self.pos_id]
+            M_raw_filtered.obsm["spatial"] = M_raw_filtered.obsm[self.pos_id]
             slices_id = sorted(cs_to_treat)
             slices = []
             for s_id in slices_id:
                 slices.append(
-                    M_raw_filtered[M_raw_filtered.obs[self.array_id] == num_to_name[s_id]]
+                    M_raw_filtered[
+                        M_raw_filtered.obs[self.array_id] == num_to_name[s_id]
+                    ]
                 )
             if timing:
                 start = time()
@@ -2322,7 +2328,10 @@ def __init__(
         self.pos_id = pos_id
         self.__diff_expr_processed = {}
 
-        if Path(data_path).suffix in [".h5ad", ".h5", ".csv"]:
+        if Path(data_path).suffix in [".h5ad", ".h5", ".csv"] or (
+            0 < len(sample_list)
+            and Path(sample_list[0]).suffix in [".h5ad", ".h5", ".csv"]
+        ):
             self.read_anndata(
                 data_path,
                 xy_resolution=xy_resolution,

src/sc3D/transformations.py

@@ -13,6 +13,7 @@
 import numpy as np
 import math
 
+
 class transformations:
     _EPS = np.finfo(float).eps * 4.0
 
@@ -60,7 +61,7 @@ def unit_vector(clf, data, axis=None, out=None):
         if out is None:
             return data
         return None
-    
+
     @classmethod
     def rotation_matrix(clf, angle, direction, point=None):
         """Return matrix to rotate about axis defined by point and direction.
@@ -88,6 +89,7 @@ def rotation_matrix(clf, angle, direction, point=None):
 
         """
         import math
+
         sina = math.sin(angle)
         cosa = math.cos(angle)
         direction = clf.unit_vector(direction[:3])
@@ -109,7 +111,7 @@ def rotation_matrix(clf, angle, direction, point=None):
             point = np.array(point[:3], dtype=np.float64, copy=False)
             M[:3, 3] = point - np.dot(R, point)
         return M
-    
+
     @classmethod
     def vector_norm(clf, data, axis=None, out=None):
         """Return length, i.e. Euclidean norm, of ndarray along axis.
@@ -195,7 +197,9 @@ def quaternion_matrix(clf, quaternion):
         )
 
     @classmethod
-    def affine_matrix_from_points(clf, v0, v1, shear=True, scale=True, usesvd=True):
+    def affine_matrix_from_points(
+        clf, v0, v1, shear=True, scale=True, usesvd=True
+    ):
         """Return affine transform matrix to register two point sets.
 
         v0 and v1 are shape (ndims, -1) arrays of at least ndims non-homogeneous
@@ -241,7 +245,7 @@ def affine_matrix_from_points(clf, v0, v1, shear=True, scale=True, usesvd=True):
 
         ndims = v0.shape[0]
         if ndims < 2 or v0.shape[1] < ndims or v0.shape != v1.shape:
-            raise ValueError('input arrays are of wrong shape or type')
+            raise ValueError("input arrays are of wrong shape or type")
 
         # move centroids to origin
         t0 = -np.mean(v0, axis=1)
@@ -253,7 +257,6 @@ def affine_matrix_from_points(clf, v0, v1, shear=True, scale=True, usesvd=True):
         M1[:ndims, ndims] = t1
         v1 += t1.reshape(ndims, 1)
 
-
         if shear:
             # Affine transformation
             A = np.concatenate((v0, v1), axis=0)
@@ -304,4 +307,4 @@ def affine_matrix_from_points(clf, v0, v1, shear=True, scale=True, usesvd=True):
         # move centroids back
         M = np.dot(np.linalg.inv(M1), np.dot(M, M0))
         M /= M[ndims, ndims]
-        return M
+        return M