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Fonction asanyarray - module numpy.matlib

Signature de la fonction asanyarray

def asanyarray(a, dtype=None, order=None, *, like=None) 

Description

asanyarray.__doc__

Convert the input to an ndarray, but pass ndarray subclasses through.

    Parameters
    ----------
    a : array_like
        Input data, in any form that can be converted to an array.  This
        includes scalars, lists, lists of tuples, tuples, tuples of tuples,
        tuples of lists, and ndarrays.
    dtype : data-type, optional
        By default, the data-type is inferred from the input data.
    order : {'C', 'F', 'A', 'K'}, optional
        Memory layout.  'A' and 'K' depend on the order of input array a.
        'C' row-major (C-style), 
        'F' column-major (Fortran-style) memory representation.
        'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise
        'K' (keep) preserve input order
        Defaults to 'C'.
    like : array_like
        Reference object to allow the creation of arrays which are not
        NumPy arrays. If an array-like passed in as ``like`` supports
        the ``__array_function__`` protocol, the result will be defined
        by it. In this case, it ensures the creation of an array object
        compatible with that passed in via this argument.

        .. note::
            The ``like`` keyword is an experimental feature pending on
            acceptance of :ref:`NEP 35 <NEP35>`.

        .. versionadded:: 1.20.0

    Returns
    -------
    out : ndarray or an ndarray subclass
        Array interpretation of `a`.  If `a` is an ndarray or a subclass
        of ndarray, it is returned as-is and no copy is performed.

    See Also
    --------
    asarray : Similar function which always returns ndarrays.
    ascontiguousarray : Convert input to a contiguous array.
    asfarray : Convert input to a floating point ndarray.
    asfortranarray : Convert input to an ndarray with column-major
                     memory order.
    asarray_chkfinite : Similar function which checks input for NaNs and
                        Infs.
    fromiter : Create an array from an iterator.
    fromfunction : Construct an array by executing a function on grid
                   positions.

    Examples
    --------
    Convert a list into an array:

    >>> a = [1, 2]
    >>> np.asanyarray(a)
    array([1, 2])

    Instances of `ndarray` subclasses are passed through as-is:

    >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
    >>> np.asanyarray(a) is a
    True