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Classe « ndarray »

Méthode numpy.matlib.ndarray.view

Signature de la méthode view

Description

help(ndarray.view)

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('float64')``.

    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 last axis of ``a`` must be
    contiguous. This axis will be resized in the result.

    .. versionchanged:: 1.23.0
       Only the last axis needs to be contiguous. Previously, the entire array
       had to be C-contiguous.

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

    Viewing array data using a different type and dtype:

    >>> nonneg = np.dtype([("a", np.uint8), ("b", np.uint8)])
    >>> y = x.view(dtype=nonneg, type=np.recarray)
    >>> x["a"]
    array([-1], dtype=int8)
    >>> y.a
    array([255], dtype=uint8)

    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]
    np.record((9, 10), dtype=[('a', 'i1'), ('b', 'i1')])

    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[:, ::2]
    >>> y
    array([[1, 3],
           [4, 6]], 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 last axis must be contiguous
    >>> z = y.copy()
    >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
    array([[(1, 3)],
           [(4, 6)]], dtype=[('width', '<i2'), ('length', '<i2')])

    However, views that change dtype are totally fine for arrays with a
    contiguous last axis, even if the rest of the axes are not C-contiguous:

    >>> x = np.arange(2 * 3 * 4, dtype=np.int8).reshape(2, 3, 4)
    >>> x.transpose(1, 0, 2).view(np.int16)
    array([[[ 256,  770],
            [3340, 3854]],
    <BLANKLINE>
           [[1284, 1798],
            [4368, 4882]],
    <BLANKLINE>
           [[2312, 2826],
            [5396, 5910]]], dtype=int16)

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