Python fromnumeric.reshape方法代码示例

# 需要导入模块: from numpy.core import fromnumeric [as 别名]
# 或者: from numpy.core.fromnumeric import reshape [as 别名]
def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

# 需要导入模块: from numpy.core import fromnumeric [as 别名]
# 或者: from numpy.core.fromnumeric import reshape [as 别名]
def hsplit(ary, indices_or_sections):
    """
    Split an array into multiple sub-arrays horizontally (column-wise).

    Please refer to the `split` documentation.  `hsplit` is equivalent
    to `split` with ``axis=1``, the array is always split along the second
    axis regardless of the array dimension.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(4, 4)
    >>> x
    array([[  0.,   1.,   2.,   3.],
           [  4.,   5.,   6.,   7.],
           [  8.,   9.,  10.,  11.],
           [ 12.,  13.,  14.,  15.]])
    >>> np.hsplit(x, 2)
    [array([[  0.,   1.],
           [  4.,   5.],
           [  8.,   9.],
           [ 12.,  13.]]),
     array([[  2.,   3.],
           [  6.,   7.],
           [ 10.,  11.],
           [ 14.,  15.]])]
    >>> np.hsplit(x, np.array([3, 6]))
    [array([[  0.,   1.,   2.],
           [  4.,   5.,   6.],
           [  8.,   9.,  10.],
           [ 12.,  13.,  14.]]),
     array([[  3.],
           [  7.],
           [ 11.],
           [ 15.]]),
     array([], dtype=float64)]

    With a higher dimensional array the split is still along the second axis.

    >>> x = np.arange(8.0).reshape(2, 2, 2)
    >>> x
    array([[[ 0.,  1.],
            [ 2.,  3.]],
           [[ 4.,  5.],
            [ 6.,  7.]]])
    >>> np.hsplit(x, 2)
    [array([[[ 0.,  1.]],
           [[ 4.,  5.]]]),
     array([[[ 2.,  3.]],
           [[ 6.,  7.]]])]

    """
    if _nx.ndim(ary) == 0:
        raise ValueError('hsplit only works on arrays of 1 or more dimensions')
    if ary.ndim > 1:
        return split(ary, indices_or_sections, 1)
    else:
        return split(ary, indices_or_sections, 0) 

# 需要导入模块: from numpy.core import fromnumeric [as 别名]
# 或者: from numpy.core.fromnumeric import reshape [as 别名]
def vsplit(ary, indices_or_sections):
    """
    Split an array into multiple sub-arrays vertically (row-wise).

    Please refer to the ``split`` documentation.  ``vsplit`` is equivalent
    to ``split`` with `axis=0` (default), the array is always split along the
    first axis regardless of the array dimension.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(4, 4)
    >>> x
    array([[  0.,   1.,   2.,   3.],
           [  4.,   5.,   6.,   7.],
           [  8.,   9.,  10.,  11.],
           [ 12.,  13.,  14.,  15.]])
    >>> np.vsplit(x, 2)
    [array([[ 0.,  1.,  2.,  3.],
           [ 4.,  5.,  6.,  7.]]),
     array([[  8.,   9.,  10.,  11.],
           [ 12.,  13.,  14.,  15.]])]
    >>> np.vsplit(x, np.array([3, 6]))
    [array([[  0.,   1.,   2.,   3.],
           [  4.,   5.,   6.,   7.],
           [  8.,   9.,  10.,  11.]]),
     array([[ 12.,  13.,  14.,  15.]]),
     array([], dtype=float64)]

    With a higher dimensional array the split is still along the first axis.

    >>> x = np.arange(8.0).reshape(2, 2, 2)
    >>> x
    array([[[ 0.,  1.],
            [ 2.,  3.]],
           [[ 4.,  5.],
            [ 6.,  7.]]])
    >>> np.vsplit(x, 2)
    [array([[[ 0.,  1.],
            [ 2.,  3.]]]),
     array([[[ 4.,  5.],
            [ 6.,  7.]]])]

    """
    if _nx.ndim(ary) < 2:
        raise ValueError('vsplit only works on arrays of 2 or more dimensions')
    return split(ary, indices_or_sections, 0) 

# 需要导入模块: from numpy.core import fromnumeric [as 别名]
# 或者: from numpy.core.fromnumeric import reshape [as 别名]
def dsplit(ary, indices_or_sections):
    """
    Split array into multiple sub-arrays along the 3rd axis (depth).

    Please refer to the `split` documentation.  `dsplit` is equivalent
    to `split` with ``axis=2``, the array is always split along the third
    axis provided the array dimension is greater than or equal to 3.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(2, 2, 4)
    >>> x
    array([[[  0.,   1.,   2.,   3.],
            [  4.,   5.,   6.,   7.]],
           [[  8.,   9.,  10.,  11.],
            [ 12.,  13.,  14.,  15.]]])
    >>> np.dsplit(x, 2)
    [array([[[  0.,   1.],
            [  4.,   5.]],
           [[  8.,   9.],
            [ 12.,  13.]]]),
     array([[[  2.,   3.],
            [  6.,   7.]],
           [[ 10.,  11.],
            [ 14.,  15.]]])]
    >>> np.dsplit(x, np.array([3, 6]))
    [array([[[  0.,   1.,   2.],
            [  4.,   5.,   6.]],
           [[  8.,   9.,  10.],
            [ 12.,  13.,  14.]]]),
     array([[[  3.],
            [  7.]],
           [[ 11.],
            [ 15.]]]),
     array([], dtype=float64)]

    """
    if _nx.ndim(ary) < 3:
        raise ValueError('dsplit only works on arrays of 3 or more dimensions')
    return split(ary, indices_or_sections, 2) 

# 需要导入模块: from numpy.core import fromnumeric [as 别名]
# 或者: from numpy.core.fromnumeric import reshape [as 别名]
def expand_dims(a, axis):
    """
    Expand the shape of an array.

    Insert a new axis, corresponding to a given position in the array shape.

    Parameters
    ----------
    a : array_like
        Input array.
    axis : int
        Position (amongst axes) where new axis is to be inserted.

    Returns
    -------
    res : ndarray
        Output array. The number of dimensions is one greater than that of
        the input array.

    See Also
    --------
    doc.indexing, atleast_1d, atleast_2d, atleast_3d

    Examples
    --------
    >>> x = np.array([1,2])
    >>> x.shape
    (2,)

    The following is equivalent to ``x[np.newaxis,:]`` or ``x[np.newaxis]``:

    >>> y = np.expand_dims(x, axis=0)
    >>> y
    array([[1, 2]])
    >>> y.shape
    (1, 2)

    >>> y = np.expand_dims(x, axis=1)  # Equivalent to x[:,newaxis]
    >>> y
    array([[1],
           [2]])
    >>> y.shape
    (2, 1)

    Note that some examples may use ``None`` instead of ``np.newaxis``.  These
    are the same objects:

    >>> np.newaxis is None
    True

    """
    a = asarray(a)
    shape = a.shape
    if axis < 0:
        axis = axis + len(shape) + 1
    return a.reshape(shape[:axis] + (1,) + shape[axis:]) 

# 需要导入模块: from numpy.core import fromnumeric [as 别名]
# 或者: from numpy.core.fromnumeric import reshape [as 别名]
def vsplit(ary, indices_or_sections):
    """
    Split an array into multiple sub-arrays vertically (row-wise).

    Please refer to the ``split`` documentation.  ``vsplit`` is equivalent
    to ``split`` with `axis=0` (default), the array is always split along the
    first axis regardless of the array dimension.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(4, 4)
    >>> x
    array([[  0.,   1.,   2.,   3.],
           [  4.,   5.,   6.,   7.],
           [  8.,   9.,  10.,  11.],
           [ 12.,  13.,  14.,  15.]])
    >>> np.vsplit(x, 2)
    [array([[ 0.,  1.,  2.,  3.],
           [ 4.,  5.,  6.,  7.]]),
     array([[  8.,   9.,  10.,  11.],
           [ 12.,  13.,  14.,  15.]])]
    >>> np.vsplit(x, np.array([3, 6]))
    [array([[  0.,   1.,   2.,   3.],
           [  4.,   5.,   6.,   7.],
           [  8.,   9.,  10.,  11.]]),
     array([[ 12.,  13.,  14.,  15.]]),
     array([], dtype=float64)]

    With a higher dimensional array the split is still along the first axis.

    >>> x = np.arange(8.0).reshape(2, 2, 2)
    >>> x
    array([[[ 0.,  1.],
            [ 2.,  3.]],
           [[ 4.,  5.],
            [ 6.,  7.]]])
    >>> np.vsplit(x, 2)
    [array([[[ 0.,  1.],
            [ 2.,  3.]]]),
     array([[[ 4.,  5.],
            [ 6.,  7.]]])]

    """
    if len(_nx.shape(ary)) < 2:
        raise ValueError('vsplit only works on arrays of 2 or more dimensions')
    return split(ary, indices_or_sections, 0) 

# 需要导入模块: from numpy.core import fromnumeric [as 别名]
# 或者: from numpy.core.fromnumeric import reshape [as 别名]
def dsplit(ary, indices_or_sections):
    """
    Split array into multiple sub-arrays along the 3rd axis (depth).

    Please refer to the `split` documentation.  `dsplit` is equivalent
    to `split` with ``axis=2``, the array is always split along the third
    axis provided the array dimension is greater than or equal to 3.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(2, 2, 4)
    >>> x
    array([[[  0.,   1.,   2.,   3.],
            [  4.,   5.,   6.,   7.]],
           [[  8.,   9.,  10.,  11.],
            [ 12.,  13.,  14.,  15.]]])
    >>> np.dsplit(x, 2)
    [array([[[  0.,   1.],
            [  4.,   5.]],
           [[  8.,   9.],
            [ 12.,  13.]]]),
     array([[[  2.,   3.],
            [  6.,   7.]],
           [[ 10.,  11.],
            [ 14.,  15.]]])]
    >>> np.dsplit(x, np.array([3, 6]))
    [array([[[  0.,   1.,   2.],
            [  4.,   5.,   6.]],
           [[  8.,   9.,  10.],
            [ 12.,  13.,  14.]]]),
     array([[[  3.],
            [  7.]],
           [[ 11.],
            [ 15.]]]),
     array([], dtype=float64)]

    """
    if len(_nx.shape(ary)) < 3:
        raise ValueError('dsplit only works on arrays of 3 or more dimensions')
    return split(ary, indices_or_sections, 2) 

# 需要导入模块: from numpy.core import fromnumeric [as 别名]
# 或者: from numpy.core.fromnumeric import reshape [as 别名]
def hsplit(ary, indices_or_sections):
    """
    Split an array into multiple sub-arrays horizontally (column-wise).

    Please refer to the `split` documentation.  `hsplit` is equivalent
    to `split` with ``axis=1``, the array is always split along the second
    axis regardless of the array dimension.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(4, 4)
    >>> x
    array([[  0.,   1.,   2.,   3.],
           [  4.,   5.,   6.,   7.],
           [  8.,   9.,  10.,  11.],
           [ 12.,  13.,  14.,  15.]])
    >>> np.hsplit(x, 2)
    [array([[  0.,   1.],
           [  4.,   5.],
           [  8.,   9.],
           [ 12.,  13.]]),
     array([[  2.,   3.],
           [  6.,   7.],
           [ 10.,  11.],
           [ 14.,  15.]])]
    >>> np.hsplit(x, np.array([3, 6]))
    [array([[  0.,   1.,   2.],
           [  4.,   5.,   6.],
           [  8.,   9.,  10.],
           [ 12.,  13.,  14.]]),
     array([[  3.],
           [  7.],
           [ 11.],
           [ 15.]]),
     array([], dtype=float64)]

    With a higher dimensional array the split is still along the second axis.

    >>> x = np.arange(8.0).reshape(2, 2, 2)
    >>> x
    array([[[ 0.,  1.],
            [ 2.,  3.]],
           [[ 4.,  5.],
            [ 6.,  7.]]])
    >>> np.hsplit(x, 2)
    [array([[[ 0.,  1.]],
           [[ 4.,  5.]]]),
     array([[[ 2.,  3.]],
           [[ 6.,  7.]]])]

    """
    if len(_nx.shape(ary)) == 0:
        raise ValueError('hsplit only works on arrays of 1 or more dimensions')
    if len(ary.shape) > 1:
        return split(ary, indices_or_sections, 1)
    else:
        return split(ary, indices_or_sections, 0) 

# 需要导入模块: from numpy.core import fromnumeric [as 别名]
# 或者: from numpy.core.fromnumeric import reshape [as 别名]
def dsplit(ary, indices_or_sections):
    """
    Split array into multiple sub-arrays along the 3rd axis (depth).

    Please refer to the `split` documentation.  `dsplit` is equivalent
    to `split` with ``axis=2``, the array is always split along the third
    axis provided the array dimension is greater than or equal to 3.

    See Also
    --------
    split : Split an array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(16.0).reshape(2, 2, 4)
    >>> x
    array([[[  0.,   1.,   2.,   3.],
            [  4.,   5.,   6.,   7.]],
           [[  8.,   9.,  10.,  11.],
            [ 12.,  13.,  14.,  15.]]])
    >>> np.dsplit(x, 2)
    [array([[[  0.,   1.],
            [  4.,   5.]],
           [[  8.,   9.],
            [ 12.,  13.]]]),
     array([[[  2.,   3.],
            [  6.,   7.]],
           [[ 10.,  11.],
            [ 14.,  15.]]])]
    >>> np.dsplit(x, np.array([3, 6]))
    [array([[[  0.,   1.,   2.],
            [  4.,   5.,   6.]],
           [[  8.,   9.,  10.],
            [ 12.,  13.,  14.]]]),
     array([[[  3.],
            [  7.]],
           [[ 11.],
            [ 15.]]]),
     array([], dtype=float64)]

    """
    if len(_nx.shape(ary)) < 3:
        raise ValueError('vsplit only works on arrays of 3 or more dimensions')
    return split(ary, indices_or_sections, 2)