hopefully final fix

Author: rflamary

ot/lp/solver_1d.py

@@ -223,8 +223,12 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True,
     ot.lp.emd2_1d : EMD for 1d distributions (returns cost instead of the
         transportation matrix)
     """
-    a, b, x_a, x_b = list_to_array(a, b, x_a, x_b)
+    x_a, x_b = list_to_array(x_a, x_b)
     nx = get_backend(x_a, x_b)
+    if a is not None:
+        a = list_to_array(a, nx=nx)
+    if b is not None:
+        b = list_to_array(b, nx=nx)
 
     assert (x_a.ndim == 1 or x_a.ndim == 2 and x_a.shape[1] == 1), \
         "emd_1d should only be used with monodimensional data"

ot/utils.py

@@ -58,14 +58,20 @@ def laplacian(x):
 
 def list_to_array(*lst, nx=None):
     r""" Convert a list if in numpy format """
+    lst_not_empty = [a for a in lst if len(a) > 0 and not isinstance(a, list)]
     if nx is None:  # find backend
-        lst_not_empty = [a for a in lst if len(a) > 0 and not isinstance(a, list)]
+
         if len(lst_not_empty) == 0:
             type_as = np.zeros(0)
             nx = get_backend(type_as)
         else:
             nx = get_backend(*lst_not_empty)
             type_as = lst_not_empty[0]
+    else:
+        if len(lst_not_empty) == 0:
+            type_as = None
+        else:
+            type_as = lst_not_empty[0]
     if len(lst) > 1:
         return [nx.from_numpy(np.array(a), type_as=type_as)
                 if isinstance(a, list) else a for a in lst]