Classe « ndarray »

Méthode numpy.matlib.ndarray.view

Signature de la méthode view

Description

view.__doc__

a.view([dtype][, type])

    New view of array with the same data.

    .. note::
        Passing None for ``dtype`` is different from omitting the parameter,
        since the former invokes ``dtype(None)`` which is an alias for
        ``dtype('float_')``.

    Parameters
    ----------
    dtype : data-type or ndarray sub-class, optional
        Data-type descriptor of the returned view, e.g., float32 or int16.
        Omitting it results in the view having the same data-type as `a`.
        This argument can also be specified as an ndarray sub-class, which
        then specifies the type of the returned object (this is equivalent to
        setting the ``type`` parameter).
    type : Python type, optional
        Type of the returned view, e.g., ndarray or matrix.  Again, omission
        of the parameter results in type preservation.

    Notes
    -----
    ``a.view()`` is used two different ways:

    ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
    of the array's memory with a different data-type.  This can cause a
    reinterpretation of the bytes of memory.

    ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
    returns an instance of `ndarray_subclass` that looks at the same array
    (same shape, dtype, etc.)  This does not cause a reinterpretation of the
    memory.

    For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
    bytes per entry than the previous dtype (for example, converting a
    regular array to a structured array), then the behavior of the view
    cannot be predicted just from the superficial appearance of ``a`` (shown
    by ``print(a)``). It also depends on exactly how ``a`` is stored in
    memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus
    defined as a slice or transpose, etc., the view may give different
    results.


    Examples
    --------
    >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])

    Viewing array data using a different type and dtype:

    >>> y = x.view(dtype=np.int16, type=np.matrix)
    >>> y
    matrix([[513]], dtype=int16)
    >>> print(type(y))
    <class 'numpy.matrix'>

    Creating a view on a structured array so it can be used in calculations

    >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
    >>> xv = x.view(dtype=np.int8).reshape(-1,2)
    >>> xv
    array([[1, 2],
           [3, 4]], dtype=int8)
    >>> xv.mean(0)
    array([2.,  3.])

    Making changes to the view changes the underlying array

    >>> xv[0,1] = 20
    >>> x
    array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])

    Using a view to convert an array to a recarray:

    >>> z = x.view(np.recarray)
    >>> z.a
    array([1, 3], dtype=int8)

    Views share data:

    >>> x[0] = (9, 10)
    >>> z[0]
    (9, 10)

    Views that change the dtype size (bytes per entry) should normally be
    avoided on arrays defined by slices, transposes, fortran-ordering, etc.:

    >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
    >>> y = x[:, 0:2]
    >>> y
    array([[1, 2],
           [4, 5]], dtype=int16)
    >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
    Traceback (most recent call last):
        ...
    ValueError: To change to a dtype of a different size, the array must be C-contiguous
    >>> z = y.copy()
    >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
    array([[(1, 2)],
           [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])