FM / oldnumeric

/usr/lib64/python2.6/site-packages/numpy/oldnumeric/

/usr/lib64/python2.6/site-packages/numpy/oldnumeric

Name	Size	Mode	Actions
tests/	-	0755	rm
alter_code1.py	8498	0644	edit dl rm
alter_code1.pyc	9761	0644	edit dl rm
alter_code1.pyo	9761	0644	edit dl rm
alter_code2.py	4635	0644	edit dl rm
alter_code2.pyc	6118	0644	edit dl rm
alter_code2.pyo	6118	0644	edit dl rm
arrayfns.py	2532	0644	edit dl rm
arrayfns.pyc	4431	0644	edit dl rm
arrayfns.pyo	4431	0644	edit dl rm
array_printer.py	456	0644	edit dl rm
array_printer.pyc	669	0644	edit dl rm
array_printer.pyo	669	0644	edit dl rm
compat.py	2837	0644	edit dl rm
compat.pyc	4558	0644	edit dl rm
compat.pyo	4558	0644	edit dl rm
fft.py	869	0644	edit dl rm
fft.pyc	1051	0644	edit dl rm
fft.pyo	1051	0644	edit dl rm
fix_default_axis.py	8035	0644	edit dl rm
fix_default_axis.pyc	7979	0644	edit dl rm
fix_default_axis.pyo	7979	0644	edit dl rm
functions.py	3640	0644	edit dl rm
functions.pyc	7480	0644	edit dl rm
functions.pyo	7480	0644	edit dl rm
linear_algebra.py	2180	0644	edit dl rm
linear_algebra.pyc	3423	0644	edit dl rm
linear_algebra.pyo	3423	0644	edit dl rm
ma.py	75676	0644	edit dl rm
ma.pyc	86241	0644	edit dl rm
ma.pyo	86241	0644	edit dl rm
matrix.py	1604	0644	edit dl rm
matrix.pyc	2381	0644	edit dl rm
matrix.pyo	2381	0644	edit dl rm
misc.py	1045	0644	edit dl rm
misc.pyc	1418	0644	edit dl rm
misc.pyo	1418	0644	edit dl rm
mlab.py	3458	0644	edit dl rm
mlab.pyc	5465	0644	edit dl rm
mlab.pyo	5465	0644	edit dl rm
precision.py	4239	0644	edit dl rm
precision.pyc	3993	0644	edit dl rm
precision.pyo	3993	0644	edit dl rm
random_array.py	11454	0644	edit dl rm
random_array.pyc	14282	0644	edit dl rm
random_array.pyo	14282	0644	edit dl rm
rng.py	3754	0644	edit dl rm
rng.pyc	6926	0644	edit dl rm
rng.pyo	6926	0644	edit dl rm
rng_stats.py	1341	0644	edit dl rm
rng_stats.pyc	1881	0644	edit dl rm
rng_stats.pyo	1881	0644	edit dl rm
setup.py	335	0644	edit dl rm
setup.pyc	666	0644	edit dl rm
setup.pyo	666	0644	edit dl rm
setupscons.py	282	0644	edit dl rm
setupscons.pyc	615	0644	edit dl rm
setupscons.pyo	615	0644	edit dl rm
typeconv.py	1622	0644	edit dl rm
typeconv.pyc	1635	0644	edit dl rm
typeconv.pyo	1635	0644	edit dl rm
ufuncs.py	1231	0644	edit dl rm
ufuncs.pyc	1830	0644	edit dl rm
ufuncs.pyo	1830	0644	edit dl rm
user_array.py	182	0644	edit dl rm
user_array.pyc	370	0644	edit dl rm
user_array.pyo	370	0644	edit dl rm
__init__.py	827	0644	edit dl rm
__init__.pyc	1026	0644	edit dl rm
__init__.pyo	1026	0644	edit dl rm

Edit: /usr/lib64/python2.6/site-packages/numpy/oldnumeric/functions.py (3640B)

# Functions that should behave the same as Numeric and need changing

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)

cmd: