diff --git a/wafo/kdetools/demo.py b/wafo/kdetools/demo.py
index 29780b4..aeaf6bf 100644
--- a/wafo/kdetools/demo.py
+++ b/wafo/kdetools/demo.py
@@ -32,9 +32,9 @@ def kde_demo1():
     data = np.random.normal(loc=0, scale=1.0, size=7)
     kernel = Kernel('gauss')
     hs = kernel.hns(data)
-    hVec = [hs / 2, hs, 2 * hs]
+    h_vec = [hs / 2, hs, 2 * hs]
 
-    for ix, h in enumerate(hVec):
+    for ix, h in enumerate(h_vec):
         plt.figure(ix)
         kde = KDE(data, hs=h, kernel=kernel)
         f2 = kde(x, output='plot', title='h_s = {0:2.2f}'.format(float(h)),
@@ -176,7 +176,8 @@ def kde_demo5(N=500):
 
 
 def kreg_demo1(hs=None, fast=False, fun='hisj'):
-    """"""
+    """Compare KRegression to KernelReg from statsmodels.nonparametric
+    """
     N = 100
     # ei = np.random.normal(loc=0, scale=0.075, size=(N,))
     ei = np.array([
@@ -236,6 +237,9 @@ def kreg_demo1(hs=None, fast=False, fun='hisj'):
 
 
 def _get_data(n=100, symmetric=False, loc1=1.1, scale1=0.6, scale2=1.0):
+    """
+    Return test data for binomial regression demo.
+    """
     st = scipy.stats
     dist = st.norm
 
@@ -262,6 +266,9 @@ def _get_data(n=100, symmetric=False, loc1=1.1, scale1=0.6, scale2=1.0):
 
 
 def check_bkregression():
+    """
+    Check binomial regression
+    """
     plt.ion()
     k = 0
     for _i, n in enumerate([50, 100, 300, 600]):
diff --git a/wafo/kdetools/kdetools.py b/wafo/kdetools/kdetools.py
index c7e2268..3e8f251 100644
--- a/wafo/kdetools/kdetools.py
+++ b/wafo/kdetools/kdetools.py
@@ -412,27 +412,6 @@ class TKDE(_KDE):
         Check the KDE for spurious spikes''')
         return pdf
 
-    def eval_grid_fast2(self, *args, **kwds):
-        """Evaluate the estimated pdf on a grid.
-
-        Parameters
-        ----------
-        arg_0,arg_1,... arg_d-1 : vectors
-           Alternatively, if no vectors is passed in then
-            arg_i = gauss2dat(linspace(dat2gauss(self.xmin[i]),
-                                       dat2gauss(self.xmax[i]), self.inc))
-        output : string optional
-            'value' if value output
-            'data' if object output
-
-        Returns
-        -------
-        values : array-like
-           The values evaluated at meshgrid(*args).
-
-        """
-        return self.eval_grid_fun(self._eval_grid_fast, *args, **kwds)
-
     def _interpolate(self, points, f, *args, **kwds):
         ipoints = meshgrid(*args)  # if self.d > 1 else args
         for i in range(self.d):
diff --git a/wafo/kdetools/tests/test_kdetools.py b/wafo/kdetools/tests/test_kdetools.py
index b3fa582..195240d 100644
--- a/wafo/kdetools/tests/test_kdetools.py
+++ b/wafo/kdetools/tests/test_kdetools.py
@@ -103,15 +103,12 @@ class TestKde(unittest.TestCase):
         assert_allclose(f, [1.03982714,  0.45839018,  0.39514782,  0.32860602,
                             0.26433318, 0.20717946,  0.15907684,  0.1201074,
                             0.08941027,  0.06574882])
-
+        f = kde.eval_grid(x)
+        assert_allclose(f, [1.03982714,  0.45839018,  0.39514782,  0.32860602,
+                            0.26433318, 0.20717946,  0.15907684,  0.1201074,
+                            0.08941027,  0.06574882])
         assert_allclose(np.trapz(f, x), 0.94787730659349068)
         f = kde.eval_grid_fast(x)
-        assert_allclose(f, [1.0401892415290148, 0.45838973393693677,
-                            0.39514689240671547, 0.32860531818532457,
-                            0.2643330110605783, 0.20717975528556506,
-                            0.15907696844388747, 0.12010770443337843,
-                            0.08941129458260941, 0.06574899139165799])
-        f = kde.eval_grid_fast2(x)
         assert_allclose(f, [1.0401892415290148, 0.45838973393693677,
                             0.39514689240671547, 0.32860531818532457,
                             0.2643330110605783, 0.20717975528556506,
@@ -340,7 +337,7 @@ class TestRegression(unittest.TestCase):
 
         bkreg = wk.BKRegression(x, y, a=0.05, b=0.05)
         fbest = bkreg.prb_search_best(hsfun='hste', alpha=0.05, color='g')
-        print(fbest.data[::10].tolist())
+        # print(fbest.data[::10].tolist())
         assert_allclose(fbest.data[::10],
 
                         [1.80899736e-15, 0,  6.48351162e-16,  6.61404311e-15,
@@ -355,6 +352,27 @@ class TestRegression(unittest.TestCase):
                          2.68633448e-04,  1.68974054e-05,  5.73040143e-07,
                          1.11994760e-08,  1.36708818e-10,  1.09965904e-12,
                          5.43806309e-15, 0.0, 0, 0], atol=1e-10)
+        bkreg = wk.BKRegression(x, y, method='wilson')
+        fbest = bkreg.prb_search_best(hsfun='hste', alpha=0.05, color='g')
+        assert_allclose(fbest.data[::10],
+                        [3.2321397702105376e-15, 4.745626420805898e-17,
+                         6.406118940191104e-16, 5.648884668051452e-16,
+                         3.499875381296387e-16, 1.0090442883241678e-13,
+                         4.264723863193633e-11, 9.29288388831705e-09,
+                         9.610074789043923e-07, 4.086642453634508e-05,
+                         0.0008305202502773989, 0.00909121197102206,
+                         0.05490768364395013, 0.1876637145781381,
+                         0.4483015169104682, 0.8666709816557657,
+                         0.9916656713022183, 0.9996648903706271,
+                         0.999990921956741, 0.9999909219567404,
+                         0.999664890370625, 0.9916656713022127,
+                         0.8666709816557588, 0.4483015169104501,
+                         0.18766371457812697, 0.054907683643947366,
+                         0.009091211971022042, 0.0008305202502770367,
+                         4.086642453593762e-05, 9.610074786590158e-07,
+                         9.292883469982049e-09, 4.264660017463372e-11,
+                         1.005284921271869e-13, -0.0, -0.0, -0.0, -0.0, -0.0],
+                        atol=1e-10)
 
 if __name__ == "__main__":
     # import sys;sys.argv = ['', 'Test.testName']