From 3beebbc0164afbbcc2b6840cf56174c6c073bb40 Mon Sep 17 00:00:00 2001
From: Charles Harris <charlesr.harris@gmail.com>
Date: Sun, 18 Aug 2013 18:40:28 -0600
Subject: DEP: Remove deprecated modules numarray and oldnumeric.

They were deprecated in 1.8 and scheduled for removal in 1.9.

Closes #3637.
---
 numpy/oldnumeric/random_array.py | 269 ---------------------------------------
 1 file changed, 269 deletions(-)
 delete mode 100644 numpy/oldnumeric/random_array.py

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

diff --git a/numpy/oldnumeric/random_array.py b/numpy/oldnumeric/random_array.py
deleted file mode 100644
index c43a49cdb..000000000
--- a/numpy/oldnumeric/random_array.py
+++ /dev/null
@@ -1,269 +0,0 @@
-"""Backward compatible module for RandomArray
-
-"""
-from __future__ import division, absolute_import, print_function
-
-__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',
-           'randint', 'random', 'random_integers', 'seed', 'standard_normal',
-           'uniform']
-
-ArgumentError = ValueError
-
-import numpy.random.mtrand as mt
-import numpy as np
-
-def seed(x=0, y=0):
-    if (x == 0 or y == 0):
-        mt.seed()
-    else:
-        mt.seed((x, y))
-
-def get_seed():
-    raise NotImplementedError(
-          "If you want to save the state of the random number generator.\n"
-          "Then you should use obj = numpy.random.get_state() followed by.\n"
-          "numpy.random.set_state(obj).")
-
-def random(shape=[]):
-    "random(n) or random([n, m, ...]) returns array of random numbers"
-    if shape == []:
-        shape = None
-    return mt.random_sample(shape)
-
-def uniform(minimum, maximum, shape=[]):
-    """uniform(minimum, maximum, shape=[]) returns array of given shape of random reals
-    in given range"""
-    if shape == []:
-        shape = None
-    return mt.uniform(minimum, maximum, shape)
-
-def randint(minimum, maximum=None, shape=[]):
-    """randint(min, max, shape=[]) = random integers >=min, < max
-    If max not given, random integers >= 0, <min"""
-    if not isinstance(minimum, int):
-        raise ArgumentError("randint requires first argument integer")
-    if maximum is None:
-        maximum = minimum
-        minimum = 0
-    if not isinstance(maximum, int):
-        raise ArgumentError("randint requires second argument integer")
-    a = ((maximum-minimum)* random(shape))
-    if isinstance(a, np.ndarray):
-        return minimum + a.astype(np.int)
-    else:
-        return minimum + int(a)
-
-def random_integers(maximum, minimum=1, shape=[]):
-    """random_integers(max, min=1, shape=[]) = random integers in range min-max inclusive"""
-    return randint(minimum, maximum+1, shape)
-
-def permutation(n):
-    "permutation(n) = a permutation of indices range(n)"
-    return mt.permutation(n)
-
-def standard_normal(shape=[]):
-    """standard_normal(n) or standard_normal([n, m, ...]) returns array of
-           random numbers normally distributed with mean 0 and standard
-           deviation 1"""
-    if shape == []:
-        shape = None
-    return mt.standard_normal(shape)
-
-def normal(mean, std, shape=[]):
-    """normal(mean, std, n) or normal(mean, std, [n, m, ...]) returns
-    array of random numbers randomly distributed with specified mean and
-    standard deviation"""
-    if shape == []:
-        shape = None
-    return mt.normal(mean, std, shape)
-
-def multivariate_normal(mean, cov, shape=[]):
-    """multivariate_normal(mean, cov) or multivariate_normal(mean, cov, [m, n, ...])
-    returns an array containing multivariate normally distributed random numbers
-    with specified mean and covariance.
-
-    mean must be a 1 dimensional array. cov must be a square two dimensional
-    array with the same number of rows and columns as mean has elements.
-
-    The first form returns a single 1-D array containing a multivariate
-    normal.
-
-    The second form returns an array of shape (m, n, ..., cov.shape[0]).
-    In this case, output[i,j,...,:] is a 1-D array containing a multivariate
-    normal."""
-    if shape == []:
-        shape = None
-    return mt.multivariate_normal(mean, cov, shape)
-
-def exponential(mean, shape=[]):
-    """exponential(mean, n) or exponential(mean, [n, m, ...]) returns array
-      of random numbers exponentially distributed with specified mean"""
-    if shape == []:
-        shape = None
-    return mt.exponential(mean, shape)
-
-def beta(a, b, shape=[]):
-    """beta(a, b) or beta(a, b, [n, m, ...]) returns array of beta distributed random numbers."""
-    if shape == []:
-        shape = None
-    return mt.beta(a, b, shape)
-
-def gamma(a, r, shape=[]):
-    """gamma(a, r) or gamma(a, r, [n, m, ...]) returns array of gamma distributed random numbers."""
-    if shape == []:
-        shape = None
-    return mt.gamma(a, r, shape)
-
-def F(dfn, dfd, shape=[]):
-    """F(dfn, dfd) or F(dfn, dfd, [n, m, ...]) returns array of F distributed random numbers with dfn degrees of freedom in the numerator and dfd degrees of freedom in the denominator."""
-    if shape == []:
-        shape = None
-    return mt.f(dfn, dfd, shape)
-
-def noncentral_F(dfn, dfd, nconc, shape=[]):
-    """noncentral_F(dfn, dfd, nonc) or noncentral_F(dfn, dfd, nonc, [n, m, ...]) returns array of noncentral F distributed random numbers with dfn degrees of freedom in the numerator and dfd degrees of freedom in the denominator, and noncentrality parameter nconc."""
-    if shape == []:
-        shape = None
-    return mt.noncentral_f(dfn, dfd, nconc, shape)
-
-def chi_square(df, shape=[]):
-    """chi_square(df) or chi_square(df, [n, m, ...]) returns array of chi squared distributed random numbers with df degrees of freedom."""
-    if shape == []:
-        shape = None
-    return mt.chisquare(df, shape)
-
-def noncentral_chi_square(df, nconc, shape=[]):
-    """noncentral_chi_square(df, nconc) or chi_square(df, nconc, [n, m, ...]) returns array of noncentral chi squared distributed random numbers with df degrees of freedom and noncentrality parameter."""
-    if shape == []:
-        shape = None
-    return mt.noncentral_chisquare(df, nconc, shape)
-
-def binomial(trials, p, shape=[]):
-    """binomial(trials, p) or binomial(trials, p, [n, m, ...]) returns array of binomially distributed random integers.
-
-           trials is the number of trials in the binomial distribution.
-           p is the probability of an event in each trial of the binomial distribution."""
-    if shape == []:
-        shape = None
-    return mt.binomial(trials, p, shape)
-
-def negative_binomial(trials, p, shape=[]):
-    """negative_binomial(trials, p) or negative_binomial(trials, p, [n, m, ...]) returns
-           array of negative binomially distributed random integers.
-
-           trials is the number of trials in the negative binomial distribution.
-           p is the probability of an event in each trial of the negative binomial distribution."""
-    if shape == []:
-        shape = None
-    return mt.negative_binomial(trials, p, shape)
-
-def multinomial(trials, probs, shape=[]):
-    """multinomial(trials, probs) or multinomial(trials, probs, [n, m, ...]) returns
-           array of multinomial distributed integer vectors.
-
-           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.-np.sum(prob).
-
-       The first form returns a single 1-D array containing one multinomially
-           distributed vector.
-
-           The second form returns an array of shape (m, n, ..., len(probs)).
-           In this case, output[i,j,...,:] is a 1-D array containing a multinomially
-           distributed integer 1-D array."""
-    if shape == []:
-        shape = None
-    return mt.multinomial(trials, probs, shape)
-
-def poisson(mean, shape=[]):
-    """poisson(mean) or poisson(mean, [n, m, ...]) returns array of poisson
-           distributed random integers with specified mean."""
-    if shape == []:
-        shape = None
-    return mt.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_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)
-    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)
-
-def test():
-    obj = mt.get_state()
-    mt.set_state(obj)
-    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.)
-    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))
-    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]))
-    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:
-        raise SystemExit("standard_normal returned wrong shape")
-    x.shape = (10000,)
-    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])))
-    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)
-    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.)
-    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*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)
-    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]:")
-    x = multinomial(16, [0.1, 0.5, 0.1], 8)
-    print(x)
-    print("Mean = ", np.sum(x, axis=0)/8.)
-
-if __name__ == '__main__':
-    test()
-- 
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