splines already have a derivative implemented, so there isn't necessarily a need to FD on top of them

Author: pavelkomarov

examples/1_basic_tutorial.ipynb

@@ -200,6 +200,7 @@ def splinediff(x, dt, params=None, options={}, order=3, s=None, num_iterations=1
         spline = scipy.interpolate.UnivariateSpline(t, x_hat, k=order, s=s)
         x_hat = spline(t)
 
-    x_hat, dxdt_hat = finite_difference(x_hat, dt)
+    dspline = spline.derivative()
+    dxdt_hat = dspline(t)
 
     return x_hat, dxdt_hat

examples/2a_optimizing_parameters_with_dxdt_known.ipynb

@@ -137,7 +137,7 @@ def iterated_first_order(*args, **kwargs): return first_order(*args, **kwargs)
                  [(1, 0), (1, 1), (1, 0), (1, 1)],
                  [(2, 2), (3, 2), (2, 2), (3, 2)],
                  [(2, 1), (3, 3), (2, 1), (3, 3)]],
-    splinediff: [[(-14, -15), (-14, -14), (-1, -1), (0, 0)],
+    splinediff: [[(-14, -15), (-14, -15), (-1, -1), (0, 0)],
                  [(-14, -14), (-13, -14), (-1, -1), (0, 0)],
                  [(-14, -14), (-13, -13), (-1, -1), (0, 0)],
                  [(0, 0), (1, 1), (0, 0), (1, 1)],