DEP: Remove deprecated modules numarray and oldnumeric.

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

Closes #3637.

1 files changed, 0 insertions, 127 deletions

diff --git a/numpy/oldnumeric/functions.py b/numpy/oldnumeric/functions.py
deleted file mode 100644
index 156a09a43..000000000
--- a/numpy/oldnumeric/functions.py
+++ /dev/null
@@ -1,127 +0,0 @@
-"""Functions that should behave the same as Numeric and need changing
-
-"""
-from __future__ import division, absolute_import, print_function
-
-import numpy as np
-import numpy.core.multiarray as mu
-import numpy.core.numeric as nn
-from .typeconv import convtypecode, convtypecode2
-
-__all__ = ['take', 'repeat', 'sum', 'product', 'sometrue', 'alltrue',
-           'cumsum', 'cumproduct', 'compress', 'fromfunction',
-           'ones', 'empty', 'identity', 'zeros', 'array', 'asarray',
-           'nonzero', 'reshape', 'arange', 'fromstring', 'ravel', 'trace',
-           'indices', 'where', 'sarray', 'cross_product', 'argmax', 'argmin',
-           'average']
-
-def take(a, indicies, axis=0):
-    return np.take(a, indicies, axis)
-
-def repeat(a, repeats, axis=0):
-    return np.repeat(a, repeats, axis)
-
-def sum(x, axis=0):
-    return np.sum(x, axis)
-
-def product(x, axis=0):
-    return np.product(x, axis)
-
-def sometrue(x, axis=0):
-    return np.sometrue(x, axis)
-
-def alltrue(x, axis=0):
-    return np.alltrue(x, axis)
-
-def cumsum(x, axis=0):
-    return np.cumsum(x, axis)
-
-def cumproduct(x, axis=0):
-    return np.cumproduct(x, axis)
-
-def argmax(x, axis=-1):
-    return np.argmax(x, axis)
-
-def argmin(x, axis=-1):
-    return np.argmin(x, axis)
-
-def compress(condition, m, axis=-1):
-    return np.compress(condition, m, axis)
-
-def fromfunction(args, dimensions):
-    return np.fromfunction(args, dimensions, dtype=int)
-
-def ones(shape, typecode='l', savespace=0, dtype=None):
-    """ones(shape, dtype=int) returns an array of the given
-    dimensions which is initialized to all ones.
-    """
-    dtype = convtypecode(typecode, dtype)
-    a = mu.empty(shape, dtype)
-    a.fill(1)
-    return a
-
-def zeros(shape, typecode='l', savespace=0, dtype=None):
-    """zeros(shape, dtype=int) returns an array of the given
-    dimensions which is initialized to all zeros
-    """
-    dtype = convtypecode(typecode, dtype)
-    return mu.zeros(shape, dtype)
-
-def identity(n,typecode='l', dtype=None):
-    """identity(n) returns the identity 2-d array of shape n x n.
-    """
-    dtype = convtypecode(typecode, dtype)
-    return nn.identity(n, dtype)
-
-def empty(shape, typecode='l', dtype=None):
-    dtype = convtypecode(typecode, dtype)
-    return mu.empty(shape, dtype)
-
-def array(sequence, typecode=None, copy=1, savespace=0, dtype=None):
-    dtype = convtypecode2(typecode, dtype)
-    return mu.array(sequence, dtype, copy=copy)
-
-def sarray(a, typecode=None, copy=False, dtype=None):
-    dtype = convtypecode2(typecode, dtype)
-    return mu.array(a, dtype, copy)
-
-def asarray(a, typecode=None, dtype=None):
-    dtype = convtypecode2(typecode, dtype)
-    return mu.array(a, dtype, copy=0)
-
-def nonzero(a):
-    res = np.nonzero(a)
-    if len(res) == 1:
-        return res[0]
-    else:
-        raise ValueError("Input argument must be 1d")
-
-def reshape(a, shape):
-    return np.reshape(a, shape)
-
-def arange(start, stop=None, step=1, typecode=None, dtype=None):
-    dtype = convtypecode2(typecode, dtype)
-    return mu.arange(start, stop, step, dtype)
-
-def fromstring(string, typecode='l', count=-1, dtype=None):
-    dtype = convtypecode(typecode, dtype)
-    return mu.fromstring(string, dtype, count=count)
-
-def ravel(m):
-    return np.ravel(m)
-
-def trace(a, offset=0, axis1=0, axis2=1):
-    return np.trace(a, offset=0, axis1=0, axis2=1)
-
-def indices(dimensions, typecode=None, dtype=None):
-    dtype = convtypecode(typecode, dtype)
-    return np.indices(dimensions, dtype)
-
-def where(condition, x, y):
-    return np.where(condition, x, y)
-
-def cross_product(a, b, axis1=-1, axis2=-1):
-    return np.cross(a, b, axis1, axis2)
-
-def average(a, axis=0, weights=None, returned=False):
-    return np.average(a, axis, weights, returned)