diff --git a/gaussian.py b/gaussian.py
index 2225c20..bbc6e96 100644
--- a/gaussian.py
+++ b/gaussian.py
@@ -30,14 +30,14 @@ def _to_cov(x,n):
 
 _two_pi = 2*math.pi
 
-def gaussian (x, mean, var):
+def gaussian(x, mean, var):
     """returns normal distribution for x given a gaussian with the specified
     mean and variance. All must be scalars
     """
     return math.exp((-0.5*(x-mean)**2)/var) / math.sqrt(_two_pi*var)
 
 
-def multivariate_gaussian (x, mu, cov):
+def multivariate_gaussian(x, mu, cov):
     """ This is designed to work the same as scipy.stats.multivariate_normal
     which is not available before version 0.14. You may either pass in a
     multivariate set of data:
@@ -56,20 +56,20 @@ def multivariate_gaussian (x, mu, cov):
     x = _to_array(x)
     mu = _to_array(mu)
     n = mu.size
-    cov = _to_cov (cov, n)
+    cov = _to_cov(cov, n)
 
     det = np.sqrt(np.prod(np.diag(cov)))
     frac = _two_pi**(-n/2.) * (1./det)
     fprime = (x - mu)**2
     return frac * np.exp(-0.5*np.dot(fprime, 1./np.diag(cov)))
 
-def norm_plot (mean, var):
+def norm_plot(mean, var):
     min_x = mean - var * 1.5
     max_x = mean + var * 1.5
 
-    xs = np.arange (min_x, max_x, 0.1)
-    ys = [gaussian (x,23,5) for x in xs]
-    plt.plot (xs,ys)
+    xs = np.arange(min_x, max_x, 0.1)
+    ys = [gaussian(x,23,5) for x in xs]
+    plt.plot(xs,ys)
 
 if __name__ == '__main__':
     from scipy.stats import norm
@@ -80,9 +80,9 @@ if __name__ == '__main__':
     assert x == x2
 
     # test univarate case
-    rv = norm (loc = 1., scale = np.sqrt(2.3))
-    x2 = multivariate_gaussian (1.2, 1., 2.3)
-    x3 = gaussian (1.2, 1., 2.3)
+    rv = norm(loc = 1., scale = np.sqrt(2.3))
+    x2 = multivariate_gaussian(1.2, 1., 2.3)
+    x3 = gaussian(1.2, 1., 2.3)
 
     assert rv.pdf(1.2) == x2
     assert abs(x2- x3) < 0.00000001