ENH: Add ext parameter to UnivariateSpline call

Pass parameter to fitpack.splev to control how to handle out of range
evaluations. Added test to scipy/interpolate/tests/test_fitpack2.py.

See ticket 3557.

closes scipygh-3557

scipy/interpolate/fitpack2.py

@@ -228,11 +228,28 @@ def set_smoothing_factor(self, s):
         self._data = data
         self._reset_class()
 
-    def __call__(self, x, nu=0):
-        """ Evaluate spline (or its nu-th derivative) at positions x.
+    def __call__(self, x, nu=0, ext=0):
+        """ 
+        Evaluate spline (or its nu-th derivative) at positions x.
+
+        Parameters
+        ----------
+        x : array_like
+            A 1-D array of points at which to return the value of the smoothed
+            spline or its derivatives. Note: x can be unordered but the 
+            evaluation is more efficient if x is (partially) ordered.
+        nu  : int
+            The order of derivative of the spline to compute.
+        ext : int
+            Controls the value returned for elements of ``x`` not in the
+            interval defined by the knot sequence.
+
+            * if ext=0, return the extrapolated value.
+            * if ext=1, return 0
+            * if ext=2, raise a ValueError
+
+            The default value is 0.
 
-        Note: x can be unordered but the evaluation is more efficient
-        if x is (partially) ordered.
         """
         x = np.asarray(x)
         # empty input yields empty output
@@ -241,7 +258,7 @@ def __call__(self, x, nu=0):
 #        if nu is None:
 #            return dfitpack.splev(*(self._eval_args+(x,)))
 #        return dfitpack.splder(nu=nu,*(self._eval_args+(x,)))
-        return fitpack.splev(x, self._eval_args, der=nu)
+        return fitpack.splev(x, self._eval_args, der=nu, ext=ext)
 
     def get_knots(self):
         """ Return positions of (boundary and interior) knots of the spline.

scipy/interpolate/tests/test_fitpack2.py

@@ -78,6 +78,21 @@ def test_resize_regression(self):
         desired = array([0.35100374, 0.51715855, 0.87789547, 0.98719344])
         assert_allclose(spl([0.1, 0.5, 0.9, 0.99]), desired, atol=5e-4)
 
+    def test_out_of_range_regression(self):
+        """Regression test for #3557."""
+        x_in_range = [-3., -2.33333333, -1.66666667, -1., -0.33333333,
+                      0.33333333, 1., 1.66666667, 2.33333333, 3.]
+        x_out_of_range = [-3.66666667, -3., -2.33333333, -1.66666667, -1.,
+                          -0.33333333, 0.33333333, 1., 1.66666667, 2.33333333,
+                          3., 3.66666667]
+        y = [-0.03443365, 0.02969204, 0.16251805, 0.33279394, 1.07995049, 
+             0.88203547,  0.31274008, -0.10215527, -0.0274716, -0.06661292]
+        desired = array([0., -0.21332128, 0.16590419, 0.41822373, 0.55728419,
+                         0.59673238, 0.55021514, 0.43137928, 0.25387164, 
+                         0.03133904, -0.22257169, 0.])
+        spl = UnivariateSpline(x=x_in_range, y=y)
+        assert_allclose(spl(x_out_of_range, ext=1), desired, atol=5e-4)
+
     def test_derivative_and_antiderivative(self):
         # Thin wrappers to splder/splantider, so light smoke test only.
         x = np.linspace(0, 1, 70)**3