2to3: Apply `print` fixer.

Add `print_function` to all `from __future__ import ...` statements
and use the python3 print function syntax everywhere.

Closes #3078.

1 files changed, 28 insertions, 28 deletions

diff --git a/numpy/oldnumeric/random_array.py b/numpy/oldnumeric/random_array.py
index 4dcddda12..ecb3d0b23 100644
--- a/numpy/oldnumeric/random_array.py
+++ b/numpy/oldnumeric/random_array.py
@@ -1,7 +1,7 @@
 """Backward compatible module for RandomArray
 
 """
-from __future__ import division, absolute_import
+from __future__ import division, absolute_import, print_function
 
 __all__ = ['ArgumentError','F','beta','binomial','chi_square', 'exponential',
            'gamma', 'get_seed', 'mean_var_test', 'multinomial',
@@ -192,12 +192,12 @@ def mean_var_test(x, type, mean, var, skew=[]):
     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)
-    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
+    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.)
-        print "Skewness of those random numbers (should be about ", skew, "):", x_skew
+        print("Skewness of those random numbers (should be about ", skew, "):", x_skew)
 
 def test():
     obj = mt.get_state()
@@ -205,25 +205,25 @@ def test():
     obj2 = mt.get_state()
     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.
+    print("First random number is", random())
+    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.
+    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,)
     if np.minimum.reduce(y) <= 0.5 or np.maximum.reduce(y) >= 0.6:
         raise SystemExit("uniform returned out of desired range")
-    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 "random_integers(10, shape=[20])"
-    print random_integers(10, shape=[20])
+    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("random_integers(10, shape=[20])")
+    print(random_integers(10, shape=[20]))
     s = 3.0
     x = normal(2.0, s, [10, 1000])
     if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000:
@@ -233,19 +233,19 @@ def test():
     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])))
-    print "\nA multivariate normal", x
+    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])
-    print "A 4x3x2 array containing multivariate normals"
-    print x
+    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.
-    print "Average of 10000 multivariate normals with mean [-100,0,100]"
-    print x_mean
+    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("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.)
     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)
@@ -256,14 +256,14 @@ def test():
     mean_var_test(x, "F random numbers with 5 and 10 degrees of freedom", 1.25, 1.35)
     x = poisson(50., 10000)
     mean_var_test(x, "poisson random numbers with mean 50", 50, 50, 0.14)
-    print "\nEach element is the result of 16 binomial trials with probability 0.5:"
-    print binomial(16, 0.5, 16)
-    print "\nEach element is the result of 16 negative binomial trials with probability 0.5:"
-    print negative_binomial(16, 0.5, [16,])
-    print "\nEach row is the result of 16 multinomial trials with probabilities [0.1, 0.5, 0.1 0.3]:"
+    print("\nEach element is the result of 16 binomial trials with probability 0.5:")
+    print(binomial(16, 0.5, 16))
+    print("\nEach element is the result of 16 negative binomial trials with probability 0.5:")
+    print(negative_binomial(16, 0.5, [16,]))
+    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(x)
+    print("Mean = ", np.sum(x,axis=0)/8.)
 
 if __name__ == '__main__':
     test()