Пример #1
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def masked_all_like(arr):
    """Return an empty masked array of the same shape and dtype as
    the array `a`, where all the data are masked.

    """
    a = np.empty_like(arr).view(MaskedArray)
    a._mask = np.ones(a.shape, dtype=make_mask_descr(a.dtype))
    return a
Пример #2
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def masked_all_like(arr):
    """Return an empty masked array of the same shape and dtype as
    the array `a`, where all the data are masked.

    """
    a = np.empty_like(arr).view(MaskedArray)
    a._mask = np.ones(a.shape, dtype=make_mask_descr(a.dtype))
    return a
Пример #3
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def masked_all(shape, dtype=float):
    """Return an empty masked array of the given shape and dtype,
    where all the data are masked.

    Parameters
    ----------
        dtype : dtype, optional
            Data type of the output.

    """
    a = masked_array(np.empty(shape, dtype), mask=np.ones(shape, make_mask_descr(dtype)))
    return a
Пример #4
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def masked_all_like(arr):
    """
    Empty masked array with the properties of an existing array.

    Return an empty masked array of the same shape and dtype as
    the array `arr`, where all the data are masked.

    Parameters
    ----------
    arr : ndarray
        An array describing the shape and dtype of the required MaskedArray.

    Returns
    -------
    a : MaskedArray
        A masked array with all data masked.

    Raises
    ------
    AttributeError
        If `arr` doesn't have a shape attribute (i.e. not an ndarray)

    See Also
    --------
    masked_all : Empty masked array with all elements masked.

    Examples
    --------
    >>> import numpy.ma as ma
    >>> arr = np.zeros((2, 3), dtype=np.float32)
    >>> arr
    array([[ 0.,  0.,  0.],
           [ 0.,  0.,  0.]], dtype=float32)
    >>> ma.masked_all_like(arr)
    masked_array(data =
     [[-- -- --]
     [-- -- --]],
          mask =
     [[ True  True  True]
     [ True  True  True]],
          fill_value=1e+20)

    The dtype of the masked array matches the dtype of `arr`.

    >>> arr.dtype
    dtype('float32')
    >>> ma.masked_all_like(arr).dtype
    dtype('float32')

    """
    a = np.empty_like(arr).view(MaskedArray)
    a._mask = np.ones(a.shape, dtype=make_mask_descr(a.dtype))
    return a
Пример #5
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def masked_all(shape, dtype=float):
    """Return an empty masked array of the given shape and dtype,
    where all the data are masked.

    Parameters
    ----------
        dtype : dtype, optional
            Data type of the output.

    """
    a = masked_array(np.empty(shape, dtype),
                     mask=np.ones(shape, make_mask_descr(dtype)))
    return a
Пример #6
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def masked_all(shape, dtype=float):
    """
    Empty masked array with all elements masked.

    Return an empty masked array of the given shape and dtype, where all the
    data are masked.

    Parameters
    ----------
    shape : tuple
        Shape of the required MaskedArray.
    dtype : dtype, optional
        Data type of the output.

    Returns
    -------
    a : MaskedArray
        A masked array with all data masked.

    See Also
    --------
    masked_all_like : Empty masked array modelled on an existing array.

    Examples
    --------
    >>> import numpy.ma as ma
    >>> ma.masked_all((3, 3))
    masked_array(data =
     [[-- -- --]
     [-- -- --]
     [-- -- --]],
          mask =
     [[ True  True  True]
     [ True  True  True]
     [ True  True  True]],
          fill_value=1e+20)

    The `dtype` parameter defines the underlying data type.

    >>> a = ma.masked_all((3, 3))
    >>> a.dtype
    dtype('float64')
    >>> a = ma.masked_all((3, 3), dtype=np.int32)
    >>> a.dtype
    dtype('int32')

    """
    a = masked_array(np.empty(shape, dtype),
                     mask=np.ones(shape, make_mask_descr(dtype)))
    return a