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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.
iÿÿÿÿNt broadcast_arrayst
DummyArrayc B s e Z d Z d d „ Z RS( s… Dummy object that just exists to hang __array_interface__ dictionaries
and possibly keep alive a reference to a base array.
c C s | | _ | | _ d S( N( t __array_interface__t base( t selft interfaceR ( ( s= /usr/lib64/python2.6/site-packages/numpy/lib/stride_tricks.pyt __init__ s N( t __name__t
__module__t __doc__t NoneR ( ( ( s= /usr/lib64/python2.6/site-packages/numpy/lib/stride_tricks.pyR s c C sj t | i ƒ } | d j o t | ƒ | d >> 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]])]
i i i sK shape mismatch: two or more arrays have incompatible dimensions on axis %r.R R ( t mapR R R t lent sett listR t maxt ranget
ValueErrort removet popt appendt zipR ( t argst _[1]R t shapest _[2]t st _[3]R t _[4]t ndst biggestt it difft common_shapet axist _[5]t lengthst uniquet
new_lengtht _[6]t sht stt broadcasted( ( s= /usr/lib64/python2.6/site-packages/numpy/lib/stride_tricks.pyR sF '$'*'
!
%
A( R t numpyR t __all__t objectR R
R R ( ( ( s= /usr/lib64/python2.6/site-packages/numpy/lib/stride_tricks.pyt s