replace flatten() by ravel(), and minor changes

src/operators/matrix_product_operator.py

@@ -61,7 +61,7 @@ class MatrixProductOperator(LinearOperator):
     non-participating subspaces of the domain cannot be different in the
     target space, thus the matrix must have enough unsummed axes to stand
     in the places of summed-over axes of the domain, if those summed-over
-    axes are followed by any unsummed(inactive) axes. Example to make
+    axes are followed by any unsummed (inactive) axes. Example to make
     this clear: If the first 2 spaces of the domain are summed over in
     the matrix multiplication and the 3rd space doesn't participate, the
     matrix must have (at least) 2 axes that don't participate in the
@@ -105,9 +105,8 @@ class MatrixProductOperator(LinearOperator):
         domain_dim = len(domain_shape)
 
         # take shortcut for trivial case
-        if spaces is not None:
-            if len(self._domain.shape) == 1 and spaces == (0, ):
-                spaces = None
+        if spaces is not None and len(self._domain.shape) == 1 and spaces == (0, ):
+            spaces = None
 
         if spaces is None:
             self._spaces = None
@@ -157,7 +156,7 @@ class MatrixProductOperator(LinearOperator):
                         target_space_shape.append(self._domain[i].shape[j])
                 else:
                     target_space_shape.append(matrix.shape[matrix_shape_idx])
-                    matrix_shape_idx +=1
+                    matrix_shape_idx += 1
             target_space_shape = tuple(target_space_shape)
 
             self._mat_last_n = tuple([-domain_dim + i for i in range(domain_dim)])
@@ -216,14 +215,14 @@ class MatrixProductOperator(LinearOperator):
         if self._spaces is None:
             if not self._flatten:
                 if times:
-                    res = np.tensordot(m, x.val, axes = len(x.domain.shape))
+                    res = np.tensordot(m, x.val, axes = len(self._domain.shape))
                 else:
                     mat_axes = np.flip(self._mat_last_m)
                     field_axes = self._target_axes
                     res = np.tensordot(m, x.val, axes=(mat_axes, field_axes))
                     res = res.transpose()
             else:
-                res = m.dot(x.val.flatten()).reshape(self._domain.shape)
+                res = m.dot(x.val.ravel()).reshape(self._domain.shape)
             return Field(target, res)
 
         if times: