Module « numpy.matlib »

Fonction load - module numpy.matlib

Signature de la fonction load

def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True, encoding='ASCII') 

Description

load.__doc__

    Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.

    .. warning:: Loading files that contain object arrays uses the ``pickle``
                 module, which is not secure against erroneous or maliciously
                 constructed data. Consider passing ``allow_pickle=False`` to
                 load data that is known not to contain object arrays for the
                 safer handling of untrusted sources.

    Parameters
    ----------
    file : file-like object, string, or pathlib.Path
        The file to read. File-like objects must support the
        ``seek()`` and ``read()`` methods. Pickled files require that the
        file-like object support the ``readline()`` method as well.
    mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
        If not None, then memory-map the file, using the given mode (see
        `numpy.memmap` for a detailed description of the modes).  A
        memory-mapped array is kept on disk. However, it can be accessed
        and sliced like any ndarray.  Memory mapping is especially useful
        for accessing small fragments of large files without reading the
        entire file into memory.
    allow_pickle : bool, optional
        Allow loading pickled object arrays stored in npy files. Reasons for
        disallowing pickles include security, as loading pickled data can
        execute arbitrary code. If pickles are disallowed, loading object
        arrays will fail. Default: False

        .. versionchanged:: 1.16.3
            Made default False in response to CVE-2019-6446.

    fix_imports : bool, optional
        Only useful when loading Python 2 generated pickled files on Python 3,
        which includes npy/npz files containing object arrays. If `fix_imports`
        is True, pickle will try to map the old Python 2 names to the new names
        used in Python 3.
    encoding : str, optional
        What encoding to use when reading Python 2 strings. Only useful when
        loading Python 2 generated pickled files in Python 3, which includes
        npy/npz files containing object arrays. Values other than 'latin1',
        'ASCII', and 'bytes' are not allowed, as they can corrupt numerical
        data. Default: 'ASCII'

    Returns
    -------
    result : array, tuple, dict, etc.
        Data stored in the file. For ``.npz`` files, the returned instance
        of NpzFile class must be closed to avoid leaking file descriptors.

    Raises
    ------
    IOError
        If the input file does not exist or cannot be read.
    ValueError
        The file contains an object array, but allow_pickle=False given.

    See Also
    --------
    save, savez, savez_compressed, loadtxt
    memmap : Create a memory-map to an array stored in a file on disk.
    lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.

    Notes
    -----
    - If the file contains pickle data, then whatever object is stored
      in the pickle is returned.
    - If the file is a ``.npy`` file, then a single array is returned.
    - If the file is a ``.npz`` file, then a dictionary-like object is
      returned, containing ``{filename: array}`` key-value pairs, one for
      each file in the archive.
    - If the file is a ``.npz`` file, the returned value supports the
      context manager protocol in a similar fashion to the open function::

        with load('foo.npz') as data:
            a = data['a']

      The underlying file descriptor is closed when exiting the 'with'
      block.

    Examples
    --------
    Store data to disk, and load it again:

    >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
    >>> np.load('/tmp/123.npy')
    array([[1, 2, 3],
           [4, 5, 6]])

    Store compressed data to disk, and load it again:

    >>> a=np.array([[1, 2, 3], [4, 5, 6]])
    >>> b=np.array([1, 2])
    >>> np.savez('/tmp/123.npz', a=a, b=b)
    >>> data = np.load('/tmp/123.npz')
    >>> data['a']
    array([[1, 2, 3],
           [4, 5, 6]])
    >>> data['b']
    array([1, 2])
    >>> data.close()

    Mem-map the stored array, and then access the second row
    directly from disk:

    >>> X = np.load('/tmp/123.npy', mmap_mode='r')
    >>> X[1, :]
    memmap([4, 5, 6])