FM / lib

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

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

Name	Size	Mode	Actions
benchmarks/	-	0755	rm
tests/	-	0755	rm
arraysetops.py	13704	0644	edit dl rm
arraysetops.pyc	13380	0644	edit dl rm
arraysetops.pyo	13380	0644	edit dl rm
arrayterator.py	6437	0644	edit dl rm
arrayterator.pyc	7022	0644	edit dl rm
arrayterator.pyo	7022	0644	edit dl rm
financial.py	20872	0644	edit dl rm
financial.pyc	21546	0644	edit dl rm
financial.pyo	21546	0644	edit dl rm
format.py	19309	0644	edit dl rm
format.pyc	17279	0644	edit dl rm
format.pyo	17279	0644	edit dl rm
function_base.py	100524	0644	edit dl rm
function_base.pyc	99169	0644	edit dl rm
function_base.pyo	99169	0644	edit dl rm
getlimits.py	8790	0644	edit dl rm
getlimits.pyc	10053	0644	edit dl rm
getlimits.pyo	10053	0644	edit dl rm
index_tricks.py	26790	0644	edit dl rm
index_tricks.pyc	28008	0644	edit dl rm
index_tricks.pyo	28008	0644	edit dl rm
info.py	6240	0644	edit dl rm
info.pyc	6425	0644	edit dl rm
info.pyo	6425	0644	edit dl rm
io.py	53226	0644	edit dl rm
io.pyc	44102	0644	edit dl rm
io.pyo	44102	0644	edit dl rm
machar.py	10658	0644	edit dl rm
machar.pyc	8688	0644	edit dl rm
machar.pyo	8688	0644	edit dl rm
polynomial.py	35562	0644	edit dl rm
polynomial.pyc	38139	0644	edit dl rm
polynomial.pyo	38139	0644	edit dl rm
recfunctions.py	32038	0644	edit dl rm
recfunctions.pyc	29451	0644	edit dl rm
recfunctions.pyo	29451	0644	edit dl rm
scimath.py	13997	0644	edit dl rm
scimath.pyc	15867	0644	edit dl rm
scimath.pyo	15867	0644	edit dl rm
setup.py	588	0644	edit dl rm
setup.pyc	918	0644	edit dl rm
setup.pyo	918	0644	edit dl rm
setupscons.py	470	0644	edit dl rm
setupscons.pyc	824	0644	edit dl rm
setupscons.pyo	824	0644	edit dl rm
shape_base.py	24387	0644	edit dl rm
shape_base.pyc	25291	0644	edit dl rm
shape_base.pyo	25291	0644	edit dl rm
stride_tricks.py	3972	0644	edit dl rm
stride_tricks.pyc	3863	0644	edit dl rm
stride_tricks.pyo	3863	0644	edit dl rm
twodim_base.py	22946	0644	edit dl rm
twodim_base.pyc	25464	0644	edit dl rm
twodim_base.pyo	25464	0644	edit dl rm
type_check.py	17075	0644	edit dl rm
type_check.pyc	18392	0644	edit dl rm
type_check.pyo	18392	0644	edit dl rm
ufunclike.py	5452	0644	edit dl rm
ufunclike.pyc	6311	0644	edit dl rm
ufunclike.pyo	6311	0644	edit dl rm
user_array.py	7475	0644	edit dl rm
user_array.pyc	15727	0644	edit dl rm
user_array.pyo	15727	0644	edit dl rm
utils.py	34034	0644	edit dl rm
utils.pyc	30422	0644	edit dl rm
utils.pyo	30422	0644	edit dl rm
_compiled_base.so	19720	0755	edit dl rm
_datasource.py	20638	0644	edit dl rm
_datasource.pyc	20986	0644	edit dl rm
_datasource.pyo	20986	0644	edit dl rm
_iotools.py	27712	0644	edit dl rm
_iotools.pyc	26916	0644	edit dl rm
_iotools.pyo	26916	0644	edit dl rm
__init__.py	936	0644	edit dl rm
__init__.pyc	1038	0644	edit dl rm
__init__.pyo	1038	0644	edit dl rm

Edit: /usr/lib64/python2.6/site-packages/numpy/lib/stride_tricks.py (3972B)

"""
Utilities that manipulate strides to achieve desirable effects.

An explanation of strides can be found in the "ndarray.rst" file in the
NumPy reference guide.

"""
import numpy as np

__all__ = ['broadcast_arrays']

class DummyArray(object):
    """ Dummy object that just exists to hang __array_interface__ dictionaries
    and possibly keep alive a reference to a base array.
    """
    def __init__(self, interface, base=None):
        self.__array_interface__ = interface
        self.base = base

def as_strided(x, shape=None, strides=None):
    """ Make an ndarray from the given array with the given shape and strides.
    """
    interface = dict(x.__array_interface__)
    if shape is not None:
        interface['shape'] = tuple(shape)
    if strides is not None:
        interface['strides'] = tuple(strides)
    return np.asarray(DummyArray(interface, base=x))

def broadcast_arrays(*args):
    """
    Broadcast any number of arrays against each other.

Parameters
    ----------
    `*args` : arrays
        The arrays to broadcast.

Returns
    -------
    broadcasted : list of arrays
        These arrays are views on the original arrays. They are typically not
        contiguous. Furthermore, more than one element of a broadcasted array
        may refer to a single memory location. If you need to write to the
        arrays, make copies first.

Examples
    --------
    >>> x = np.array([[1,2,3]])
    >>> y = np.array([[1],[2],[3]])
    >>> np.broadcast_arrays(x, y)
    [array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]]), array([[1, 1, 1],
           [2, 2, 2],
           [3, 3, 3]])]

Here is a useful idiom for getting contiguous copies instead of
    non-contiguous views.

>>> map(np.array, np.broadcast_arrays(x, y))
    [array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]]), array([[1, 1, 1],
           [2, 2, 2],
           [3, 3, 3]])]

"""
    args = map(np.asarray, args)
    shapes = [x.shape for x in args]
    if len(set(shapes)) == 1:
        # Common case where nothing needs to be broadcasted.
        return args
    shapes = [list(s) for s in shapes]
    strides = [list(x.strides) for x in args]
    nds = [len(s) for s in shapes]
    biggest = max(nds)
    # Go through each array and prepend dimensions of length 1 to each of the
    # shapes in order to make the number of dimensions equal.
    for i in range(len(args)):
        diff = biggest - nds[i]
        if diff > 0:
            shapes[i] = [1] * diff + shapes[i]
            strides[i] = [0] * diff + strides[i]
    # Chech each dimension for compatibility. A dimension length of 1 is
    # accepted as compatible with any other length.
    common_shape = []
    for axis in range(biggest):
        lengths = [s[axis] for s in shapes]
        unique = set(lengths + [1])
        if len(unique) > 2:
            # There must be at least two non-1 lengths for this axis.
            raise ValueError("shape mismatch: two or more arrays have "
                "incompatible dimensions on axis %r." % (axis,))
        elif len(unique) == 2:
            # There is exactly one non-1 length. The common shape will take this
            # value.
            unique.remove(1)
            new_length = unique.pop()
            common_shape.append(new_length)
            # For each array, if this axis is being broadcasted from a length of
            # 1, then set its stride to 0 so that it repeats its data.
            for i in range(len(args)):
                if shapes[i][axis] == 1:
                    shapes[i][axis] = new_length
                    strides[i][axis] = 0
        else:
            # Every array has a length of 1 on this axis. Strides can be left
            # alone as nothing is broadcasted.
            common_shape.append(1)

# Construct the new arrays.
    broadcasted = [as_strided(x, shape=sh, strides=st) for (x,sh,st) in
        zip(args, shapes, strides)]
    return broadcasted

cmd: