STY: Giant comma spacing fixup.

Run the 2to3 ws_comma fixer on *.py files. Some lines are now too long
and will need to be broken at some point. OTOH, some lines were already
too long and need to be broken at some point. Now seems as good a time
as any to do this with open PRs at a minimum.

1 files changed, 17 insertions, 17 deletions

diff --git a/numpy/oldnumeric/random_array.py b/numpy/oldnumeric/random_array.py
index ecb3d0b23..c43a49cdb 100644
--- a/numpy/oldnumeric/random_array.py
+++ b/numpy/oldnumeric/random_array.py
@@ -3,7 +3,7 @@
 """
 from __future__ import division, absolute_import, print_function
 
-__all__ = ['ArgumentError','F','beta','binomial','chi_square', 'exponential',
+__all__ = ['ArgumentError', 'F', 'beta', 'binomial', 'chi_square', 'exponential',
            'gamma', 'get_seed', 'mean_var_test', 'multinomial',
            'multivariate_normal', 'negative_binomial', 'noncentral_F',
            'noncentral_chi_square', 'normal', 'permutation', 'poisson',
@@ -19,7 +19,7 @@ def seed(x=0, y=0):
     if (x == 0 or y == 0):
         mt.seed()
     else:
-        mt.seed((x,y))
+        mt.seed((x, y))
 
 def get_seed():
     raise NotImplementedError(
@@ -189,14 +189,14 @@ def poisson(mean, shape=[]):
 
 def mean_var_test(x, type, mean, var, skew=[]):
     n = len(x) * 1.0
-    x_mean = np.sum(x,axis=0)/n
+    x_mean = np.sum(x, axis=0)/n
     x_minus_mean = x - x_mean
-    x_var = np.sum(x_minus_mean*x_minus_mean,axis=0)/(n-1.0)
+    x_var = np.sum(x_minus_mean*x_minus_mean, axis=0)/(n-1.0)
     print("\nAverage of ", len(x), type)
     print("(should be about ", mean, "):", x_mean)
     print("Variance of those random numbers (should be about ", var, "):", x_var)
     if skew != []:
-        x_skew = (np.sum(x_minus_mean*x_minus_mean*x_minus_mean,axis=0)/9998.)/x_var**(3./2.)
+        x_skew = (np.sum(x_minus_mean*x_minus_mean*x_minus_mean, axis=0)/9998.)/x_var**(3./2.)
         print("Skewness of those random numbers (should be about ", skew, "):", x_skew)
 
 def test():
@@ -206,13 +206,13 @@ def test():
     if (obj2[1] - obj[1]).any():
         raise SystemExit("Failed seed test.")
     print("First random number is", random())
-    print("Average of 10000 random numbers is", np.sum(random(10000),axis=0)/10000.)
-    x = random([10,1000])
+    print("Average of 10000 random numbers is", np.sum(random(10000), axis=0)/10000.)
+    x = random([10, 1000])
     if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000:
         raise SystemExit("random returned wrong shape")
     x.shape = (10000,)
-    print("Average of 100 by 100 random numbers is", np.sum(x,axis=0)/10000.)
-    y = uniform(0.5,0.6, (1000,10))
+    print("Average of 100 by 100 random numbers is", np.sum(x, axis=0)/10000.)
+    y = uniform(0.5, 0.6, (1000, 10))
     if len(y.shape) !=2 or y.shape[0] != 1000 or y.shape[1] != 10:
         raise SystemExit("uniform returned wrong shape")
     y.shape = (10000,)
@@ -221,7 +221,7 @@ def test():
     print("randint(1, 10, shape=[50])")
     print(randint(1, 10, shape=[50]))
     print("permutation(10)", permutation(10))
-    print("randint(3,9)", randint(3,9))
+    print("randint(3,9)", randint(3, 9))
     print("random_integers(10, shape=[20])")
     print(random_integers(10, shape=[20]))
     s = 3.0
@@ -232,20 +232,20 @@ def test():
     mean_var_test(x, "normally distributed numbers with mean 2 and variance %f"%(s**2,), 2, s**2, 0)
     x = exponential(3, 10000)
     mean_var_test(x, "random numbers exponentially distributed with mean %f"%(s,), s, s**2, 2)
-    x = multivariate_normal(np.array([10,20]), np.array(([1,2],[2,4])))
+    x = multivariate_normal(np.array([10, 20]), np.array(([1, 2], [2, 4])))
     print("\nA multivariate normal", x)
     if x.shape != (2,): raise SystemExit("multivariate_normal returned wrong shape")
-    x = multivariate_normal(np.array([10,20]), np.array([[1,2],[2,4]]), [4,3])
+    x = multivariate_normal(np.array([10, 20]), np.array([[1, 2], [2, 4]]), [4, 3])
     print("A 4x3x2 array containing multivariate normals")
     print(x)
-    if x.shape != (4,3,2): raise SystemExit("multivariate_normal returned wrong shape")
-    x = multivariate_normal(np.array([-100,0,100]), np.array([[3,2,1],[2,2,1],[1,1,1]]), 10000)
-    x_mean = np.sum(x,axis=0)/10000.
+    if x.shape != (4, 3, 2): raise SystemExit("multivariate_normal returned wrong shape")
+    x = multivariate_normal(np.array([-100, 0, 100]), np.array([[3, 2, 1], [2, 2, 1], [1, 1, 1]]), 10000)
+    x_mean = np.sum(x, axis=0)/10000.
     print("Average of 10000 multivariate normals with mean [-100,0,100]")
     print(x_mean)
     x_minus_mean = x - x_mean
     print("Estimated covariance of 10000 multivariate normals with covariance [[3,2,1],[2,2,1],[1,1,1]]")
-    print(np.dot(np.transpose(x_minus_mean),x_minus_mean)/9999.)
+    print(np.dot(np.transpose(x_minus_mean), x_minus_mean)/9999.)
     x = beta(5.0, 10.0, 10000)
     mean_var_test(x, "beta(5.,10.) random numbers", 0.333, 0.014)
     x = gamma(.01, 2., 10000)
@@ -263,7 +263,7 @@ def test():
     print("\nEach row is the result of 16 multinomial trials with probabilities [0.1, 0.5, 0.1 0.3]:")
     x = multinomial(16, [0.1, 0.5, 0.1], 8)
     print(x)
-    print("Mean = ", np.sum(x,axis=0)/8.)
+    print("Mean = ", np.sum(x, axis=0)/8.)
 
 if __name__ == '__main__':
     test()