diff --git a/GPy/testing/tp_tests.py b/GPy/testing/tp_tests.py
index 643d67e0..ac35e38f 100644
--- a/GPy/testing/tp_tests.py
+++ b/GPy/testing/tp_tests.py
@@ -1,10 +1,11 @@
-'''
+"""
 Created on 14 Jul 2017, based on gp_tests
 
 @author: javdrher
-'''
+"""
 import unittest
-import numpy as np, GPy
+import numpy as np
+import GPy
 
 
 class Test(unittest.TestCase):
@@ -13,9 +14,9 @@ class Test(unittest.TestCase):
         self.N = 20
         self.N_new = 50
         self.D = 1
-        self.X = np.random.uniform(-3., 3., (self.N, 1))
+        self.X = np.random.uniform(-3.0, 3.0, (self.N, 1))
         self.Y = np.sin(self.X) + np.random.randn(self.N, self.D) * 0.05
-        self.X_new = np.random.uniform(-3., 3., (self.N_new, 1))
+        self.X_new = np.random.uniform(-3.0, 3.0, (self.N_new, 1))
 
     def test_setxy_gp(self):
         k = GPy.kern.RBF(1) + GPy.kern.White(1)
@@ -23,7 +24,7 @@ class Test(unittest.TestCase):
         mu, var = m.predict(m.X)
         X = m.X.copy()
         m.set_XY(m.X[:10], m.Y[:10])
-        assert (m.checkgrad(tolerance=1e-2))
+        assert m.checkgrad(tolerance=1e-2)
         m.set_XY(X, self.Y)
         mu2, var2 = m.predict(m.X)
         np.testing.assert_allclose(mu, mu2)
@@ -34,9 +35,9 @@ class Test(unittest.TestCase):
         from GPy.core.mapping import Mapping
 
         class Parabola(Mapping):
-            def __init__(self, variance, degree=2, name='parabola'):
+            def __init__(self, variance, degree=2, name="parabola"):
                 super(Parabola, self).__init__(1, 1, name)
-                self.variance = Param('variance', np.ones(degree + 1) * variance)
+                self.variance = Param("variance", np.ones(degree + 1) * variance)
                 self.degree = degree
                 self.link_parameter(self.variance)
 
@@ -59,12 +60,17 @@ class Test(unittest.TestCase):
         X = np.linspace(-2, 2, 100)[:, None]
         k = GPy.kern.RBF(1) + GPy.kern.White(1)
         k.randomize()
-        p = Parabola(.3)
+        p = Parabola(0.3)
         p.randomize()
-        Y = p.f(X) + np.random.multivariate_normal(np.zeros(X.shape[0]), k.K(X) + np.eye(X.shape[0]) * 1e-8)[:,
-                     None] + np.random.normal(0, .1, (X.shape[0], 1))
+        Y = (
+            p.f(X)
+            + np.random.multivariate_normal(
+                np.zeros(X.shape[0]), k.K(X) + np.eye(X.shape[0]) * 1e-8
+            )[:, None]
+            + np.random.normal(0, 0.1, (X.shape[0], 1))
+        )
         m = GPy.models.TPRegression(X, Y, kernel=k, mean_function=p)
-        assert (m.checkgrad(tolerance=2e-1))
+        assert m.checkgrad(tolerance=2e-1)
         _ = m.predict(m.X)
 
     def test_normalizer(self):
@@ -73,7 +79,7 @@ class Test(unittest.TestCase):
         mu, std = Y.mean(0), Y.std(0)
         m = GPy.models.TPRegression(self.X, Y, kernel=k, normalizer=True)
         m.optimize()
-        assert (m.checkgrad())
+        assert m.checkgrad()
         k = GPy.kern.RBF(1) + GPy.kern.White(1)
         m2 = GPy.models.TPRegression(self.X, (Y - mu) / std, kernel=k, normalizer=False)
         m2[:] = m[:]
@@ -81,13 +87,13 @@ class Test(unittest.TestCase):
         mu1, var1 = m.predict(m.X, full_cov=True)
         mu2, var2 = m2.predict(m2.X, full_cov=True)
         np.testing.assert_allclose(mu1, (mu2 * std) + mu)
-        np.testing.assert_allclose(var1, var2 * std ** 2)
+        np.testing.assert_allclose(var1, var2 * std**2)
 
         mu1, var1 = m.predict(m.X, full_cov=False)
         mu2, var2 = m2.predict(m2.X, full_cov=False)
 
         np.testing.assert_allclose(mu1, (mu2 * std) + mu)
-        np.testing.assert_allclose(var1, var2 * std ** 2)
+        np.testing.assert_allclose(var1, var2 * std**2)
 
         q50n = m.predict_quantiles(m.X, (50,))
         q50 = m2.predict_quantiles(m2.X, (50,))
@@ -102,8 +108,11 @@ class Test(unittest.TestCase):
         q95 = m2.predict_quantiles(self.X[[c]], qs)
         mu, var = m2.predict(self.X[[c]])
         from scipy.stats import t
-        np.testing.assert_allclose((mu + (t.ppf(qs / 100., m2.nu + m2.num_data) * np.sqrt(var))).flatten(),
-                                   np.array(q95).flatten())
+
+        np.testing.assert_allclose(
+            (mu + (t.ppf(qs / 100.0, m2.nu + m2.num_data) * np.sqrt(var))).flatten(),
+            np.array(q95).flatten(),
+        )
 
     def test_predict_equivalence(self):
         k = GPy.kern.RBF(1) + GPy.kern.White(1)