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Fonction resize - module numpy.matlib

Signature de la fonction resize

def resize(a, new_shape) 

Description

resize.__doc__

    Return a new array with the specified shape.

    If the new array is larger than the original array, then the new
    array is filled with repeated copies of `a`.  Note that this behavior
    is different from a.resize(new_shape) which fills with zeros instead
    of repeated copies of `a`.

    Parameters
    ----------
    a : array_like
        Array to be resized.

    new_shape : int or tuple of int
        Shape of resized array.

    Returns
    -------
    reshaped_array : ndarray
        The new array is formed from the data in the old array, repeated
        if necessary to fill out the required number of elements.  The
        data are repeated in the order that they are stored in memory.

    See Also
    --------
    np.reshape : Reshape an array without changing the total size.
    np.pad : Enlarge and pad an array.
    np.repeat: Repeat elements of an array.
    ndarray.resize : resize an array in-place.

    Notes
    -----
    When the total size of the array does not change `~numpy.reshape` should
    be used.  In most other cases either indexing (to reduce the size)
    or padding (to increase the size) may be a more appropriate solution.

    Warning: This functionality does **not** consider axes separately,
    i.e. it does not apply interpolation/extrapolation.
    It fills the return array with the required number of elements, taken
    from `a` as they are laid out in memory, disregarding strides and axes.
    (This is in case the new shape is smaller. For larger, see above.)
    This functionality is therefore not suitable to resize images,
    or data where each axis represents a separate and distinct entity.

    Examples
    --------
    >>> a=np.array([[0,1],[2,3]])
    >>> np.resize(a,(2,3))
    array([[0, 1, 2],
           [3, 0, 1]])
    >>> np.resize(a,(1,4))
    array([[0, 1, 2, 3]])
    >>> np.resize(a,(2,4))
    array([[0, 1, 2, 3],
           [0, 1, 2, 3]])