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Module « numpy.matlib »

Fonction ravel - module numpy.matlib

Signature de la fonction ravel

def ravel(a, order='C') 

Description

help(numpy.matlib.ravel)

Return a contiguous flattened array.

A 1-D array, containing the elements of the input, is returned.  A copy is
made only if needed.

As of NumPy 1.10, the returned array will have the same type as the input
array. (for example, a masked array will be returned for a masked array
input)

Parameters
----------
a : array_like
    Input array.  The elements in `a` are read in the order specified by
    `order`, and packed as a 1-D array.
order : {'C','F', 'A', 'K'}, optional

    The elements of `a` are read using this index order. 'C' means
    to index the elements in row-major, C-style order,
    with the last axis index changing fastest, back to the first
    axis index changing slowest.  'F' means to index the elements
    in column-major, Fortran-style order, with the
    first index changing fastest, and the last index changing
    slowest. Note that the 'C' and 'F' options take no account of
    the memory layout of the underlying array, and only refer to
    the order of axis indexing.  'A' means to read the elements in
    Fortran-like index order if `a` is Fortran *contiguous* in
    memory, C-like order otherwise.  'K' means to read the
    elements in the order they occur in memory, except for
    reversing the data when strides are negative.  By default, 'C'
    index order is used.

Returns
-------
y : array_like
    y is a contiguous 1-D array of the same subtype as `a`,
    with shape ``(a.size,)``.
    Note that matrices are special cased for backward compatibility,
    if `a` is a matrix, then y is a 1-D ndarray.

See Also
--------
ndarray.flat : 1-D iterator over an array.
ndarray.flatten : 1-D array copy of the elements of an array
                  in row-major order.
ndarray.reshape : Change the shape of an array without changing its data.

Notes
-----
In row-major, C-style order, in two dimensions, the row index
varies the slowest, and the column index the quickest.  This can
be generalized to multiple dimensions, where row-major order
implies that the index along the first axis varies slowest, and
the index along the last quickest.  The opposite holds for
column-major, Fortran-style index ordering.

When a view is desired in as many cases as possible, ``arr.reshape(-1)``
may be preferable. However, ``ravel`` supports ``K`` in the optional
``order`` argument while ``reshape`` does not.

Examples
--------
It is equivalent to ``reshape(-1, order=order)``.

>>> import numpy as np
>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> np.ravel(x)
array([1, 2, 3, 4, 5, 6])

>>> x.reshape(-1)
array([1, 2, 3, 4, 5, 6])

>>> np.ravel(x, order='F')
array([1, 4, 2, 5, 3, 6])

When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:

>>> np.ravel(x.T)
array([1, 4, 2, 5, 3, 6])
>>> np.ravel(x.T, order='A')
array([1, 2, 3, 4, 5, 6])

When ``order`` is 'K', it will preserve orderings that are neither 'C'
nor 'F', but won't reverse axes:

>>> a = np.arange(3)[::-1]; a
array([2, 1, 0])
>>> a.ravel(order='C')
array([2, 1, 0])
>>> a.ravel(order='K')
array([2, 1, 0])

>>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
array([[[ 0,  2,  4],
        [ 1,  3,  5]],
       [[ 6,  8, 10],
        [ 7,  9, 11]]])
>>> a.ravel(order='C')
array([ 0,  2,  4,  1,  3,  5,  6,  8, 10,  7,  9, 11])
>>> a.ravel(order='K')
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])

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