diff --git a/doc/examples/plot_ransac3D.py b/doc/examples/plot_ransac3D.py
index d3d60605..af2fa373 100644
--- a/doc/examples/plot_ransac3D.py
+++ b/doc/examples/plot_ransac3D.py
@@ -32,7 +32,9 @@ outliers = inliers == False
 
 fig = plt.figure()
 ax = fig.add_subplot(111, projection='3d')
-ax.scatter(xyz[inliers][:, 0], xyz[inliers][:, 1], xyz[inliers][:, 2], c='b', marker='o', label='Inlier data')
-ax.scatter(xyz[outliers][:, 0], xyz[outliers][:, 1], xyz[outliers][:, 2], c='r', marker='o', label='Outlier data')
+ax.scatter(xyz[inliers][:, 0], xyz[inliers][:, 1], xyz[inliers][:, 2], c='b',
+           marker='o', label='Inlier data')
+ax.scatter(xyz[outliers][:, 0], xyz[outliers][:, 1], xyz[outliers][:, 2], c='r',
+           marker='o', label='Outlier data')
 ax.legend(loc='lower left')
 plt.show()
diff --git a/skimage/measure/fit.py b/skimage/measure/fit.py
index 49d55d0f..9849c996 100644
--- a/skimage/measure/fit.py
+++ b/skimage/measure/fit.py
@@ -188,7 +188,8 @@ class LineModel3D(BaseModel):
         elif data.shape[0] > 2:  # over-determined
             data = data - X0
             # first principal component
-            # Note: without full_matrices=False Python dies with joblib parallel_for.
+            # Note: without full_matrices=False Python dies with joblib
+            # parallel_for.
             _, _, u = np.linalg.svd(data, full_matrices=False)
             u = u[0]
         else:  # under-determined
@@ -200,8 +201,8 @@ class LineModel3D(BaseModel):
 
     def residuals(self, data):
         """Determine residuals of data to model.
-        For each point the shortest distance to the line is returned. It is obtained by projecting the data onto the
-        line.
+        For each point the shortest distance to the line is returned.
+        It is obtained by projecting the data onto the line.
 
         Parameters
         ----------
@@ -214,7 +215,8 @@ class LineModel3D(BaseModel):
             Residual for each data point.
         """
         X0, u = self.params
-        return np.linalg.norm((data - X0) - np.dot(data - X0, u)[..., np.newaxis] * u, axis=1)
+        return np.linalg.norm((data - X0) -
+                              np.dot(data - X0, u)[..., np.newaxis] * u, axis=1)
 
 
 class CircleModel(BaseModel):
diff --git a/skimage/measure/tests/test_fit.py b/skimage/measure/tests/test_fit.py
index 32963cb0..b9c77031 100644
--- a/skimage/measure/tests/test_fit.py
+++ b/skimage/measure/tests/test_fit.py
@@ -56,10 +56,12 @@ def test_line_model_under_determined():
 def test_line_model3D_estimate():
     # generate original data without noise
     model0 = LineModel3D()
-    model0.params = (np.array([0,0,0], dtype='float'), np.array([1,1,1], dtype='float')/np.sqrt(3))
-    # we scale the unit vector with a factor 10 when generating points on the line
-    # in order to compensate for the scale of the random noise
-    data0 = model0.params[0] + 10 * np.arange(-100,100)[...,np.newaxis] * model0.params[1]
+    model0.params = (np.array([0,0,0], dtype='float'),
+                     np.array([1,1,1], dtype='float')/np.sqrt(3))
+    # we scale the unit vector with a factor 10 when generating points on the
+    # line in order to compensate for the scale of the random noise
+    data0 = (model0.params[0] +
+             10 * np.arange(-100,100)[...,np.newaxis] * model0.params[1])
 
     # add gaussian noise to data
     np.random.seed(1234)
@@ -70,9 +72,11 @@ def test_line_model3D_estimate():
     model_est.estimate(data)
 
     # test whether estimated parameters are correct
-    # we use the following geometric property: two aligned vectors have a cross-product equal to zero
+    # we use the following geometric property: two aligned vectors have
+    # a cross-product equal to zero
     # test if direction vectors are aligned
-    assert_almost_equal(np.linalg.norm(np.cross(model0.params[1], model_est.params[1])), 0, 1)
+    assert_almost_equal(np.linalg.norm(np.cross(model0.params[1],
+                                                model_est.params[1])), 0, 1)
     # test if origins are aligned with the direction
     a = model_est.params[0] - model0.params[0]
     if np.linalg.norm(a) > 0: