From 8936ecc8c46b92f4dbc749e294eb5ab88ab0d857 Mon Sep 17 00:00:00 2001
From: Alan McIntyre <alan.mcintyre@local>
Date: Fri, 25 Jul 2008 16:09:26 +0000
Subject: Standardize NumPy import as "import numpy as np".

---
 numpy/oldnumeric/random_array.py | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

(limited to 'numpy/oldnumeric/random_array.py')

diff --git a/numpy/oldnumeric/random_array.py b/numpy/oldnumeric/random_array.py
index 73564811d..e84aedf1e 100644
--- a/numpy/oldnumeric/random_array.py
+++ b/numpy/oldnumeric/random_array.py
@@ -10,7 +10,7 @@ __all__ = ['ArgumentError','F','beta','binomial','chi_square', 'exponential',
 ArgumentError = ValueError
 
 import numpy.random.mtrand as mt
-import numpy as Numeric
+import numpy as np
 
 def seed(x=0, y=0):
     if (x == 0 or y == 0):
@@ -48,8 +48,8 @@ def randint(minimum, maximum=None, shape=[]):
     if not isinstance(maximum, int):
         raise ArgumentError, "randint requires second argument integer"
     a = ((maximum-minimum)* random(shape))
-    if isinstance(a, Numeric.ndarray):
-        return minimum + a.astype(Numeric.int)
+    if isinstance(a, np.ndarray):
+        return minimum + a.astype(np.int)
     else:
         return minimum + int(a)
 
@@ -164,7 +164,7 @@ def multinomial(trials, probs, shape=[]):
            trials is the number of trials in each multinomial distribution.
            probs is a one dimensional array. There are len(prob)+1 events.
            prob[i] is the probability of the i-th event, 0<=i<len(prob).
-           The probability of event len(prob) is 1.-Numeric.sum(prob).
+           The probability of event len(prob) is 1.-np.sum(prob).
 
        The first form returns a single 1-D array containing one multinomially
            distributed vector.
@@ -186,14 +186,14 @@ def poisson(mean, shape=[]):
 
 def mean_var_test(x, type, mean, var, skew=[]):
     n = len(x) * 1.0
-    x_mean = Numeric.sum(x,axis=0)/n
+    x_mean = np.sum(x,axis=0)/n
     x_minus_mean = x - x_mean
-    x_var = Numeric.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 = (Numeric.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():
@@ -203,17 +203,17 @@ 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", Numeric.sum(random(10000),axis=0)/10000.
+    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", Numeric.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 Numeric.minimum.reduce(y) <= 0.5 or Numeric.maximum.reduce(y) >= 0.6:
+    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])
@@ -229,26 +229,26 @@ 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(Numeric.array([10,20]), Numeric.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(Numeric.array([10,20]), Numeric.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(Numeric.array([-100,0,100]), Numeric.array([[3,2,1],[2,2,1],[1,1,1]]), 10000)
-    x_mean = Numeric.sum(x,axis=0)/10000.
+    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 Numeric.dot(Numeric.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)
     mean_var_test(x, "gamma(.01,2.) random numbers", 2*100, 2*100*100)
     x = chi_square(11., 10000)
-    mean_var_test(x, "chi squared random numbers with 11 degrees of freedom", 11, 22, 2*Numeric.sqrt(2./11.))
+    mean_var_test(x, "chi squared random numbers with 11 degrees of freedom", 11, 22, 2*np.sqrt(2./11.))
     x = F(5., 10., 10000)
     mean_var_test(x, "F random numbers with 5 and 10 degrees of freedom", 1.25, 1.35)
     x = poisson(50., 10000)
@@ -260,7 +260,7 @@ 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 = ", Numeric.sum(x,axis=0)/8.
+    print "Mean = ", np.sum(x,axis=0)/8.
 
 if __name__ == '__main__':
     test()
-- 
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