Exemplo n.º 1
0
def getcount(arr, hits, shots=10):
    """Gets a summary of accuracies"""
    assert arr.shape==(batch_size, (seq_size +1)), \
    "array input shape does not match expected shape: (%d,%d)"%(batch_size, (seq_size +1))
    current_count = np.zeros((batch_size, seq_size + 1, num_classes))
    success_count = np.zeros_like(current_count)
    shot_count = np.zeros((num_classes, shots))
    shot_success_count = np.zeros((num_classes, shots))
    for I in ndi.ndindex(arr.shape):
        current_count[I][arr[I]] = 1
        success_count[I][arr[I]] = hits[I]

    temp = np.cumsum(current_count, axis=1)
    for batch in range(batch_size):
        for instance in range(seq_size + 1):
            for class_label in range(num_classes):
                num_shot = int(temp[batch, instance, class_label])
                if num_shot != 0 and num_shot - 1 < shots:
                    shot_count[class_label, num_shot - 1] += 1
                    shot_success_count[class_label,
                                       num_shot - 1] += hits[batch, instance]

    total_count = np.sum(current_count, axis=0).T
    success_count = (np.sum(success_count, axis=0).T)
    return total_count, success_count, shot_count, shot_success_count
Exemplo n.º 2
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def apply_along_axis(func1d, axis, arr, flux, *args, **kwargs):
    # handle negative axes
    arr = asanyarray(arr)
    flux = asanyarray(flux)
    nd = arr.ndim
    axis = normalize_axis_index(axis, nd)

    # arr, with the iteration axis at the end
    in_dims = list(range(nd))
    inarr_view = transpose(arr, in_dims[:axis] + in_dims[axis + 1:] + [axis])
    flux_view = transpose(flux, in_dims[:axis] + in_dims[axis + 1:] + [axis])

    # compute indices for the iteration axes, and append a trailing ellipsis to
    # prevent 0d arrays decaying to scalars, which fixes gh-8642
    inds = ndindex(inarr_view.shape[:-1])
    inds = (ind + (Ellipsis, ) for ind in inds)

    # invoke the function on the first item
    try:
        ind0 = next(inds)
    except StopIteration:
        raise ValueError(
            'Cannot apply_along_axis when any iteration dimensions are 0')
    res = asanyarray(func1d(inarr_view[ind0], flux_view[ind0], *args,
                            **kwargs))

    # build a buffer for storing evaluations of func1d.
    # remove the requested axis, and add the new ones on the end.
    # laid out so that each write is contiguous.
    # for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
    buff = zeros(inarr_view.shape[:-1] + res.shape, res.dtype)

    # permutation of axes such that out = buff.transpose(buff_permute)
    buff_dims = list(range(buff.ndim))
    buff_permute = (buff_dims[0:axis] +
                    buff_dims[buff.ndim - res.ndim:buff.ndim] +
                    buff_dims[axis:buff.ndim - res.ndim])

    # matrices have a nasty __array_prepare__ and __array_wrap__
    if not isinstance(res, matrix):
        buff = res.__array_prepare__(buff)

    # save the first result, then compute and save all remaining results
    buff[ind0] = res
    for ind in inds:
        buff[ind] = asanyarray(
            func1d(inarr_view[ind], flux_view[ind], *args, **kwargs))

    if not isinstance(res, matrix):
        # wrap the array, to preserve subclasses
        buff = res.__array_wrap__(buff)

        # finally, rotate the inserted axes back to where they belong
        return transpose(buff, buff_permute)

    else:
        # matrices have to be transposed first, because they collapse dimensions!
        out_arr = transpose(buff, buff_permute)

        return res.__array_wrap__(out_arr)
Exemplo n.º 3
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def counter(y_seq, n=num_classes):
    u, v, w = y_seq.shape
    class_label = np.argmax(y_seq, axis=2)
    g = np.cumsum(y_seq, axis=1)
    return np.array([
        g[I][class_label[I]] for I in ndi.ndindex(class_label.shape)
    ]).reshape(u, v)
Exemplo n.º 4
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def apply_along_axis(func1d, axis, arr, *args, **kwargs):
    """Apply a function to 1-D slices along the given axis.

    Args:
        func1d (function (M,) -> (Nj...)): This function should accept 1-D
            arrays. It is applied to 1-D slices of ``arr`` along the specified
            axis. It must return a 1-D ``cupy.ndarray``.
        axis (integer): Axis along which ``arr`` is sliced.
        arr (cupy.ndarray (Ni..., M, Nk...)): Input array.
        args: Additional arguments for ``func1d``.
        kwargs: Additional keyword arguments for ``func1d``.

    Returns:
        cupy.ndarray: The output array. The shape of ``out`` is identical to
        the shape of ``arr``, except along the ``axis`` dimension. This
        axis is removed, and replaced with new dimensions equal to the
        shape of the return value of ``func1d``. So if ``func1d`` returns a
        scalar ``out`` will have one fewer dimensions than ``arr``.

    .. seealso:: :func:`numpy.apply_along_axis`
    """
    ndim = arr.ndim
    axis = internal._normalize_axis_index(axis, ndim)
    inarr_view = cupy.moveaxis(arr, axis, -1)

    # compute indices for the iteration axes, and append a trailing ellipsis to
    # prevent 0d arrays decaying to scalars
    inds = index_tricks.ndindex(inarr_view.shape[:-1])
    inds = (ind + (Ellipsis,) for ind in inds)

    # invoke the function on the first item
    try:
        ind0 = next(inds)
    except StopIteration:
        raise ValueError(
            'Cannot apply_along_axis when any iteration dimensions are 0'
        )
    res = func1d(inarr_view[ind0], *args, **kwargs)
    if cupy.isscalar(res):
        # scalar outputs need to be transfered to a device ndarray
        res = cupy.asarray(res)

    # build a buffer for storing evaluations of func1d.
    # remove the requested axis, and add the new ones on the end.
    # laid out so that each write is contiguous.
    # for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
    buff = cupy.empty(inarr_view.shape[:-1] + res.shape, res.dtype)

    # save the first result, then compute and save all remaining results
    buff[ind0] = res
    for ind in inds:
        buff[ind] = func1d(inarr_view[ind], *args, **kwargs)

    # restore the inserted axes back to where they belong
    for i in range(res.ndim):
        buff = cupy.moveaxis(buff, -1, axis)

    return buff
def test_ndindex():
    x = list(ndindex(1, 2, 3))
    expected = [ix for ix, e in ndenumerate(np.zeros((1, 2, 3)))]
    assert_array_equal(x, expected)

    x = list(ndindex((1, 2, 3)))
    assert_array_equal(x, expected)

    # Test use of scalars and tuples
    x = list(ndindex((3, )))
    assert_array_equal(x, list(ndindex(3)))

    # Make sure size argument is optional
    x = list(ndindex())
    assert_equal(x, [()])

    x = list(ndindex(()))
    assert_equal(x, [()])

    # Make sure 0-sized ndindex works correctly
    x = list(ndindex(*[0]))
    assert_equal(x, [])
Exemplo n.º 6
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def test_ndindex():
    x = list(ndindex(1, 2, 3))
    expected = [ix for ix, e in ndenumerate(np.zeros((1, 2, 3)))]
    assert_array_equal(x, expected)

    x = list(ndindex((1, 2, 3)))
    assert_array_equal(x, expected)

    # Test use of scalars and tuples
    x = list(ndindex((3,)))
    assert_array_equal(x, list(ndindex(3)))

    # Make sure size argument is optional
    x = list(ndindex())
    assert_equal(x, [()])

    x = list(ndindex(()))
    assert_equal(x, [()])

    # Make sure 0-sized ndindex works correctly
    x = list(ndindex(*[0]))
    assert_equal(x, [])
Exemplo n.º 7
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def apply_along_axis(func1d, axis, arr, *args, **kwargs):
    """
    Apply a function to 1-D slices along the given axis.

    Execute `func1d(a, *args, **kwargs)` where `func1d` operates on 1-D arrays
    and `a` is a 1-D slice of `arr` along `axis`.

    This is equivalent to (but faster than) the following use of `ndindex` and
    `s_`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of indices::

        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
        for ii in ndindex(Ni):
            for kk in ndindex(Nk):
                f = func1d(arr[ii + s_[:,] + kk])
                Nj = f.shape
                for jj in ndindex(Nj):
                    out[ii + jj + kk] = f[jj]

    Equivalently, eliminating the inner loop, this can be expressed as::

        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
        for ii in ndindex(Ni):
            for kk in ndindex(Nk):
                out[ii + s_[...,] + kk] = func1d(arr[ii + s_[:,] + kk])

    Parameters
    ----------
    func1d : function (M,) -> (Nj...)
        This function should accept 1-D arrays. It is applied to 1-D
        slices of `arr` along the specified axis.
    axis : integer
        Axis along which `arr` is sliced.
    arr : ndarray (Ni..., M, Nk...)
        Input array.
    args : any
        Additional arguments to `func1d`.
    kwargs : any
        Additional named arguments to `func1d`.

        .. versionadded:: 1.9.0


    Returns
    -------
    out : ndarray  (Ni..., Nj..., Nk...)
        The output array. The shape of `out` is identical to the shape of
        `arr`, except along the `axis` dimension. This axis is removed, and
        replaced with new dimensions equal to the shape of the return value
        of `func1d`. So if `func1d` returns a scalar `out` will have one
        fewer dimensions than `arr`.

    See Also
    --------
    apply_over_axes : Apply a function repeatedly over multiple axes.

    Examples
    --------
    >>> def my_func(a):
    ...     \"\"\"Average first and last element of a 1-D array\"\"\"
    ...     return (a[0] + a[-1]) * 0.5
    >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
    >>> np.apply_along_axis(my_func, 0, b)
    array([4., 5., 6.])
    >>> np.apply_along_axis(my_func, 1, b)
    array([2.,  5.,  8.])

    For a function that returns a 1D array, the number of dimensions in
    `outarr` is the same as `arr`.

    >>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
    >>> np.apply_along_axis(sorted, 1, b)
    array([[1, 7, 8],
           [3, 4, 9],
           [2, 5, 6]])

    For a function that returns a higher dimensional array, those dimensions
    are inserted in place of the `axis` dimension.

    >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
    >>> np.apply_along_axis(np.diag, -1, b)
    array([[[1, 0, 0],
            [0, 2, 0],
            [0, 0, 3]],
           [[4, 0, 0],
            [0, 5, 0],
            [0, 0, 6]],
           [[7, 0, 0],
            [0, 8, 0],
            [0, 0, 9]]])
    """
    # handle negative axes
    arr = asanyarray(arr)
    nd = arr.ndim
    axis = normalize_axis_index(axis, nd)

    # arr, with the iteration axis at the end
    in_dims = list(range(nd))
    inarr_view = transpose(arr, in_dims[:axis] + in_dims[axis + 1 :] + [axis])

    # compute indices for the iteration axes, and append a trailing ellipsis to
    # prevent 0d arrays decaying to scalars, which fixes gh-8642
    inds = ndindex(inarr_view.shape[:-1])
    inds = (ind + (Ellipsis,) for ind in inds)

    # invoke the function on the first item
    try:
        ind0 = next(inds)
    except StopIteration as e:
        raise ValueError(
            "Cannot apply_along_axis when any iteration dimensions are 0"
        ) from None
    res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))

    # build a buffer for storing evaluations of func1d.
    # remove the requested axis, and add the new ones on the end.
    # laid out so that each write is contiguous.
    # for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
    buff = zeros(inarr_view.shape[:-1] + res.shape, res.dtype)

    # permutation of axes such that out = buff.transpose(buff_permute)
    buff_dims = list(range(buff.ndim))
    buff_permute = (
        buff_dims[0:axis]
        + buff_dims[buff.ndim - res.ndim : buff.ndim]
        + buff_dims[axis : buff.ndim - res.ndim]
    )

    # matrices have a nasty __array_prepare__ and __array_wrap__
    if not isinstance(res, matrix):
        buff = res.__array_prepare__(buff)

    # save the first result, then compute and save all remaining results
    buff[ind0] = res
    for ind in inds:
        buff[ind] = asanyarray(func1d(inarr_view[ind], *args, **kwargs))

    if not isinstance(res, matrix):
        # wrap the array, to preserve subclasses
        buff = res.__array_wrap__(buff)

        # finally, rotate the inserted axes back to where they belong
        return transpose(buff, buff_permute)

    else:
        # matrices have to be transposed first, because they collapse dimensions!
        out_arr = transpose(buff, buff_permute)
        return res.__array_wrap__(out_arr)
Exemplo n.º 8
0
def apply_along_axis(func1d, axis, arr, *args, **kwargs):
    """
    Apply a function to 1-D slices along the given axis.

    Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a`
    is a 1-D slice of `arr` along `axis`.

    Parameters
    ----------
    func1d : function
        This function should accept 1-D arrays. It is applied to 1-D
        slices of `arr` along the specified axis.
    axis : integer
        Axis along which `arr` is sliced.
    arr : ndarray
        Input array.
    args : any
        Additional arguments to `func1d`.
    kwargs : any
        Additional named arguments to `func1d`.

        .. versionadded:: 1.9.0


    Returns
    -------
    apply_along_axis : ndarray
        The output array. The shape of `outarr` is identical to the shape of
        `arr`, except along the `axis` dimension. This axis is removed, and
        replaced with new dimensions equal to the shape of the return value
        of `func1d`. So if `func1d` returns a scalar `outarr` will have one
        fewer dimensions than `arr`.

    See Also
    --------
    apply_over_axes : Apply a function repeatedly over multiple axes.

    Examples
    --------
    >>> def my_func(a):
    ...     \"\"\"Average first and last element of a 1-D array\"\"\"
    ...     return (a[0] + a[-1]) * 0.5
    >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
    >>> np.apply_along_axis(my_func, 0, b)
    array([ 4.,  5.,  6.])
    >>> np.apply_along_axis(my_func, 1, b)
    array([ 2.,  5.,  8.])

    For a function that returns a 1D array, the number of dimensions in
    `outarr` is the same as `arr`.

    >>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
    >>> np.apply_along_axis(sorted, 1, b)
    array([[1, 7, 8],
           [3, 4, 9],
           [2, 5, 6]])

    For a function that returns a higher dimensional array, those dimensions
    are inserted in place of the `axis` dimension.

    >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
    >>> np.apply_along_axis(np.diag, -1, b)
    array([[[1, 0, 0],
            [0, 2, 0],
            [0, 0, 3]],
           [[4, 0, 0],
            [0, 5, 0],
            [0, 0, 6]],
           [[7, 0, 0],
            [0, 8, 0],
            [0, 0, 9]]])
    """
    # handle negative axes
    arr = asanyarray(arr)
    nd = arr.ndim
    axis = normalize_axis_index(axis, nd)

    # arr, with the iteration axis at the end
    in_dims = list(range(nd))
    inarr_view = transpose(arr, in_dims[:axis] + in_dims[axis+1:] + [axis])

    # compute indices for the iteration axes, and append a trailing ellipsis to
    # prevent 0d arrays decaying to scalars, which fixes gh-8642
    inds = ndindex(inarr_view.shape[:-1])
    inds = (ind + (Ellipsis,) for ind in inds)

    # invoke the function on the first item
    try:
        ind0 = next(inds)
    except StopIteration:
        raise ValueError('Cannot apply_along_axis when any iteration dimensions are 0')
    res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))

    # build a buffer for storing evaluations of func1d.
    # remove the requested axis, and add the new ones on the end.
    # laid out so that each write is contiguous.
    # for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
    buff = zeros(inarr_view.shape[:-1] + res.shape, res.dtype)

    # permutation of axes such that out = buff.transpose(buff_permute)
    buff_dims = list(range(buff.ndim))
    buff_permute = (
        buff_dims[0 : axis] +
        buff_dims[buff.ndim-res.ndim : buff.ndim] +
        buff_dims[axis : buff.ndim-res.ndim]
    )

    # matrices have a nasty __array_prepare__ and __array_wrap__
    if not isinstance(res, matrix):
        buff = res.__array_prepare__(buff)

    # save the first result, then compute and save all remaining results
    buff[ind0] = res
    for ind in inds:
        buff[ind] = asanyarray(func1d(inarr_view[ind], *args, **kwargs))

    if not isinstance(res, matrix):
        # wrap the array, to preserve subclasses
        buff = res.__array_wrap__(buff)

        # finally, rotate the inserted axes back to where they belong
        return transpose(buff, buff_permute)

    else:
        # matrices have to be transposed first, because they collapse dimensions!
        out_arr = transpose(buff, buff_permute)
        return res.__array_wrap__(out_arr)
Exemplo n.º 9
0
def pad(array, pad_width, mode='constant', **kwargs):
    """
    Pad an array.

    Parameters
    ----------
    array : array_like of rank N
        The array to pad.
    pad_width : {sequence, array_like, int}
        Number of values padded to the edges of each axis.
        ((before_1, after_1), ... (before_N, after_N)) unique pad widths
        for each axis.
        ((before, after),) yields same before and after pad for each axis.
        (pad,) or int is a shortcut for before = after = pad width for all
        axes.
    mode : str or function, optional
        One of the following string values or a user supplied function.

        'constant' (default)
            Pads with a constant value.
        'edge'
            Pads with the edge values of array.
        'linear_ramp'
            Pads with the linear ramp between end_value and the
            array edge value.
        'maximum'
            Pads with the maximum value of all or part of the
            vector along each axis.
        'mean'
            Pads with the mean value of all or part of the
            vector along each axis.
        'median'
            Pads with the median value of all or part of the
            vector along each axis.
        'minimum'
            Pads with the minimum value of all or part of the
            vector along each axis.
        'reflect'
            Pads with the reflection of the vector mirrored on
            the first and last values of the vector along each
            axis.
        'symmetric'
            Pads with the reflection of the vector mirrored
            along the edge of the array.
        'wrap'
            Pads with the wrap of the vector along the axis.
            The first values are used to pad the end and the
            end values are used to pad the beginning.
        'empty'
            Pads with undefined values.

            .. versionadded:: 1.17

        <function>
            Padding function, see Notes.
    stat_length : sequence or int, optional
        Used in 'maximum', 'mean', 'median', and 'minimum'.  Number of
        values at edge of each axis used to calculate the statistic value.

        ((before_1, after_1), ... (before_N, after_N)) unique statistic
        lengths for each axis.

        ((before, after),) yields same before and after statistic lengths
        for each axis.

        (stat_length,) or int is a shortcut for before = after = statistic
        length for all axes.

        Default is ``None``, to use the entire axis.
    constant_values : sequence or scalar, optional
        Used in 'constant'.  The values to set the padded values for each
        axis.

        ``((before_1, after_1), ... (before_N, after_N))`` unique pad constants
        for each axis.

        ``((before, after),)`` yields same before and after constants for each
        axis.

        ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
        all axes.

        Default is 0.
    end_values : sequence or scalar, optional
        Used in 'linear_ramp'.  The values used for the ending value of the
        linear_ramp and that will form the edge of the padded array.

        ``((before_1, after_1), ... (before_N, after_N))`` unique end values
        for each axis.

        ``((before, after),)`` yields same before and after end values for each
        axis.

        ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
        all axes.

        Default is 0.
    reflect_type : {'even', 'odd'}, optional
        Used in 'reflect', and 'symmetric'.  The 'even' style is the
        default with an unaltered reflection around the edge value.  For
        the 'odd' style, the extended part of the array is created by
        subtracting the reflected values from two times the edge value.

    Returns
    -------
    pad : ndarray
        Padded array of rank equal to `array` with shape increased
        according to `pad_width`.

    Notes
    -----
    .. versionadded:: 1.7.0

    For an array with rank greater than 1, some of the padding of later
    axes is calculated from padding of previous axes.  This is easiest to
    think about with a rank 2 array where the corners of the padded array
    are calculated by using padded values from the first axis.

    The padding function, if used, should modify a rank 1 array in-place. It
    has the following signature::

        padding_func(vector, iaxis_pad_width, iaxis, kwargs)

    where

        vector : ndarray
            A rank 1 array already padded with zeros.  Padded values are
            vector[:iaxis_pad_width[0]] and vector[-iaxis_pad_width[1]:].
        iaxis_pad_width : tuple
            A 2-tuple of ints, iaxis_pad_width[0] represents the number of
            values padded at the beginning of vector where
            iaxis_pad_width[1] represents the number of values padded at
            the end of vector.
        iaxis : int
            The axis currently being calculated.
        kwargs : dict
            Any keyword arguments the function requires.

    Examples
    --------
    >>> a = [1, 2, 3, 4, 5]
    >>> np.pad(a, (2, 3), 'constant', constant_values=(4, 6))
    array([4, 4, 1, ..., 6, 6, 6])

    >>> np.pad(a, (2, 3), 'edge')
    array([1, 1, 1, ..., 5, 5, 5])

    >>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))
    array([ 5,  3,  1,  2,  3,  4,  5,  2, -1, -4])

    >>> np.pad(a, (2,), 'maximum')
    array([5, 5, 1, 2, 3, 4, 5, 5, 5])

    >>> np.pad(a, (2,), 'mean')
    array([3, 3, 1, 2, 3, 4, 5, 3, 3])

    >>> np.pad(a, (2,), 'median')
    array([3, 3, 1, 2, 3, 4, 5, 3, 3])

    >>> a = [[1, 2], [3, 4]]
    >>> np.pad(a, ((3, 2), (2, 3)), 'minimum')
    array([[1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [3, 3, 3, 4, 3, 3, 3],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1]])

    >>> a = [1, 2, 3, 4, 5]
    >>> np.pad(a, (2, 3), 'reflect')
    array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])

    >>> np.pad(a, (2, 3), 'reflect', reflect_type='odd')
    array([-1,  0,  1,  2,  3,  4,  5,  6,  7,  8])

    >>> np.pad(a, (2, 3), 'symmetric')
    array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])

    >>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd')
    array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])

    >>> np.pad(a, (2, 3), 'wrap')
    array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])

    >>> def pad_with(vector, pad_width, iaxis, kwargs):
    ...     pad_value = kwargs.get('padder', 10)
    ...     vector[:pad_width[0]] = pad_value
    ...     vector[-pad_width[1]:] = pad_value
    >>> a = np.arange(6)
    >>> a = a.reshape((2, 3))
    >>> np.pad(a, 2, pad_with)
    array([[10, 10, 10, 10, 10, 10, 10],
           [10, 10, 10, 10, 10, 10, 10],
           [10, 10,  0,  1,  2, 10, 10],
           [10, 10,  3,  4,  5, 10, 10],
           [10, 10, 10, 10, 10, 10, 10],
           [10, 10, 10, 10, 10, 10, 10]])
    >>> np.pad(a, 2, pad_with, padder=100)
    array([[100, 100, 100, 100, 100, 100, 100],
           [100, 100, 100, 100, 100, 100, 100],
           [100, 100,   0,   1,   2, 100, 100],
           [100, 100,   3,   4,   5, 100, 100],
           [100, 100, 100, 100, 100, 100, 100],
           [100, 100, 100, 100, 100, 100, 100]])
    """
    array = np.asarray(array)
    pad_width = np.asarray(pad_width)

    if not pad_width.dtype.kind == 'i':
        raise TypeError('`pad_width` must be of integral type.')

    # Broadcast to shape (array.ndim, 2)
    pad_width = _as_pairs(pad_width, array.ndim, as_index=True)

    if callable(mode):
        # Old behavior: Use user-supplied function with np.apply_along_axis
        function = mode
        # Create a new zero padded array
        padded, _ = _pad_simple(array, pad_width, fill_value=0)
        # And apply along each axis

        for axis in range(padded.ndim):
            # Iterate using ndindex as in apply_along_axis, but assuming that
            # function operates inplace on the padded array.

            # view with the iteration axis at the end
            view = np.moveaxis(padded, axis, -1)

            # compute indices for the iteration axes, and append a trailing
            # ellipsis to prevent 0d arrays decaying to scalars (gh-8642)
            inds = ndindex(view.shape[:-1])
            inds = (ind + (Ellipsis, ) for ind in inds)
            for ind in inds:
                function(view[ind], pad_width[axis], axis, kwargs)

        return padded

    # Make sure that no unsupported keywords were passed for the current mode
    allowed_kwargs = {
        'empty': [],
        'edge': [],
        'wrap': [],
        'constant': ['constant_values'],
        'linear_ramp': ['end_values'],
        'maximum': ['stat_length'],
        'mean': ['stat_length'],
        'median': ['stat_length'],
        'minimum': ['stat_length'],
        'reflect': ['reflect_type'],
        'symmetric': ['reflect_type'],
    }
    try:
        unsupported_kwargs = set(kwargs) - set(allowed_kwargs[mode])
    except KeyError:
        raise ValueError("mode '{}' is not supported".format(mode))
    if unsupported_kwargs:
        raise ValueError(
            "unsupported keyword arguments for mode '{}': {}".format(
                mode, unsupported_kwargs))

    stat_functions = {
        "maximum": np.amax,
        "minimum": np.amin,
        "mean": np.mean,
        "median": np.median
    }

    # Create array with final shape and original values
    # (padded area is undefined)
    padded, original_area_slice = _pad_simple(array, pad_width)
    # And prepare iteration over all dimensions
    # (zipping may be more readable than using enumerate)
    axes = range(padded.ndim)

    if mode == "constant":
        values = kwargs.get("constant_values", 0)
        values = _as_pairs(values, padded.ndim)
        for axis, width_pair, value_pair in zip(axes, pad_width, values):
            roi = _view_roi(padded, original_area_slice, axis)
            _set_pad_area(roi, axis, width_pair, value_pair)

    elif mode == "empty":
        pass  # Do nothing as _pad_simple already returned the correct result

    elif array.size == 0:
        # Only modes "constant" and "empty" can extend empty axes, all other
        # modes depend on `array` not being empty
        # -> ensure every empty axis is only "padded with 0"
        for axis, width_pair in zip(axes, pad_width):
            if array.shape[axis] == 0 and any(width_pair):
                raise ValueError(
                    "can't extend empty axis {} using modes other than "
                    "'constant' or 'empty'".format(axis))
        # passed, don't need to do anything more as _pad_simple already
        # returned the correct result

    elif mode == "edge":
        for axis, width_pair in zip(axes, pad_width):
            roi = _view_roi(padded, original_area_slice, axis)
            edge_pair = _get_edges(roi, axis, width_pair)
            _set_pad_area(roi, axis, width_pair, edge_pair)

    elif mode == "linear_ramp":
        end_values = kwargs.get("end_values", 0)
        end_values = _as_pairs(end_values, padded.ndim)
        for axis, width_pair, value_pair in zip(axes, pad_width, end_values):
            roi = _view_roi(padded, original_area_slice, axis)
            ramp_pair = _get_linear_ramps(roi, axis, width_pair, value_pair)
            _set_pad_area(roi, axis, width_pair, ramp_pair)

    elif mode in stat_functions:
        func = stat_functions[mode]
        length = kwargs.get("stat_length", None)
        length = _as_pairs(length, padded.ndim, as_index=True)
        for axis, width_pair, length_pair in zip(axes, pad_width, length):
            roi = _view_roi(padded, original_area_slice, axis)
            stat_pair = _get_stats(roi, axis, width_pair, length_pair, func)
            _set_pad_area(roi, axis, width_pair, stat_pair)

    elif mode in {"reflect", "symmetric"}:
        method = kwargs.get("reflect_type", "even")
        include_edge = True if mode == "symmetric" else False
        for axis, (left_index, right_index) in zip(axes, pad_width):
            if array.shape[axis] == 1 and (left_index > 0 or right_index > 0):
                # Extending singleton dimension for 'reflect' is legacy
                # behavior; it really should raise an error.
                edge_pair = _get_edges(padded, axis, (left_index, right_index))
                _set_pad_area(padded, axis, (left_index, right_index),
                              edge_pair)
                continue

            roi = _view_roi(padded, original_area_slice, axis)
            while left_index > 0 or right_index > 0:
                # Iteratively pad until dimension is filled with reflected
                # values. This is necessary if the pad area is larger than
                # the length of the original values in the current dimension.
                left_index, right_index = _set_reflect_both(
                    roi, axis, (left_index, right_index), method, include_edge)

    elif mode == "wrap":
        for axis, (left_index, right_index) in zip(axes, pad_width):
            roi = _view_roi(padded, original_area_slice, axis)
            while left_index > 0 or right_index > 0:
                # Iteratively pad until dimension is filled with wrapped
                # values. This is necessary if the pad area is larger than
                # the length of the original values in the current dimension.
                left_index, right_index = _set_wrap_both(
                    roi, axis, (left_index, right_index))

    return padded
Exemplo n.º 10
0
def apply_along_axis(func1d, axis, arr, *args, **kwargs):
    """
    Apply a function to 1-D slices along the given axis.

    Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a`
    is a 1-D slice of `arr` along `axis`.

    Parameters
    ----------
    func1d : function
        This function should accept 1-D arrays. It is applied to 1-D
        slices of `arr` along the specified axis.
    axis : integer
        Axis along which `arr` is sliced.
    arr : ndarray
        Input array.
    args : any
        Additional arguments to `func1d`.
    kwargs : any
        Additional named arguments to `func1d`.

        .. versionadded:: 1.9.0


    Returns
    -------
    apply_along_axis : ndarray
        The output array. The shape of `outarr` is identical to the shape of
        `arr`, except along the `axis` dimension. This axis is removed, and
        replaced with new dimensions equal to the shape of the return value
        of `func1d`. So if `func1d` returns a scalar `outarr` will have one
        fewer dimensions than `arr`.

    See Also
    --------
    apply_over_axes : Apply a function repeatedly over multiple axes.

    Examples
    --------
    >>> def my_func(a):
    ...     \"\"\"Average first and last element of a 1-D array\"\"\"
    ...     return (a[0] + a[-1]) * 0.5
    >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
    >>> np.apply_along_axis(my_func, 0, b)
    array([ 4.,  5.,  6.])
    >>> np.apply_along_axis(my_func, 1, b)
    array([ 2.,  5.,  8.])

    For a function that returns a 1D array, the number of dimensions in
    `outarr` is the same as `arr`.

    >>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
    >>> np.apply_along_axis(sorted, 1, b)
    array([[1, 7, 8],
           [3, 4, 9],
           [2, 5, 6]])

    For a function that returns a higher dimensional array, those dimensions
    are inserted in place of the `axis` dimension.

    >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
    >>> np.apply_along_axis(np.diag, -1, b)
    array([[[1, 0, 0],
            [0, 2, 0],
            [0, 0, 3]],

           [[4, 0, 0],
            [0, 5, 0],
            [0, 0, 6]],

           [[7, 0, 0],
            [0, 8, 0],
            [0, 0, 9]]])
    """
    # handle negative axes
    arr = asanyarray(arr)
    nd = arr.ndim
    if not (-nd <= axis < nd):
        raise IndexError('axis {0} out of bounds [-{1}, {1})'.format(axis, nd))
    if axis < 0:
        axis += nd

    # arr, with the iteration axis at the end
    in_dims = list(range(nd))
    inarr_view = transpose(arr, in_dims[:axis] + in_dims[axis+1:] + [axis])

    # compute indices for the iteration axes
    inds = ndindex(inarr_view.shape[:-1])

    # invoke the function on the first item
    try:
        ind0 = next(inds)
    except StopIteration:
        raise ValueError('Cannot apply_along_axis when any iteration dimensions are 0')
    res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))

    # build a buffer for storing evaluations of func1d.
    # remove the requested axis, and add the new ones on the end.
    # laid out so that each write is contiguous.
    # for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
    buff = zeros(inarr_view.shape[:-1] + res.shape, res.dtype)

    # permutation of axes such that out = buff.transpose(buff_permute)
    buff_dims = list(range(buff.ndim))
    buff_permute = (
        buff_dims[0 : axis] +
        buff_dims[buff.ndim-res.ndim : buff.ndim] +
        buff_dims[axis : buff.ndim-res.ndim]
    )

    # matrices have a nasty __array_prepare__ and __array_wrap__
    if not isinstance(res, matrix):
        buff = res.__array_prepare__(buff)

    # save the first result, then compute and save all remaining results
    buff[ind0] = res
    for ind in inds:
        buff[ind] = asanyarray(func1d(inarr_view[ind], *args, **kwargs))

    if not isinstance(res, matrix):
        # wrap the array, to preserve subclasses
        buff = res.__array_wrap__(buff)

        # finally, rotate the inserted axes back to where they belong
        return transpose(buff, buff_permute)

    else:
        # matrices have to be transposed first, because they collapse dimensions!
        out_arr = transpose(buff, buff_permute)
        return res.__array_wrap__(out_arr)
Exemplo n.º 11
0
def pad(array, pad_width, mode='constant', **kwargs):
    """
    Pad an array.

    Parameters
    ----------
    array : array_like of rank N
        The array to pad.
    pad_width : {sequence, array_like, int}
        Number of values padded to the edges of each axis.
        ((before_1, after_1), ... (before_N, after_N)) unique pad widths
        for each axis.
        ((before, after),) yields same before and after pad for each axis.
        (pad,) or int is a shortcut for before = after = pad width for all
        axes.
    mode : str or function, optional
        One of the following string values or a user supplied function.

        'constant' (default)
            Pads with a constant value.
        'edge'
            Pads with the edge values of array.
        'linear_ramp'
            Pads with the linear ramp between end_value and the
            array edge value.
        'maximum'
            Pads with the maximum value of all or part of the
            vector along each axis.
        'mean'
            Pads with the mean value of all or part of the
            vector along each axis.
        'median'
            Pads with the median value of all or part of the
            vector along each axis.
        'minimum'
            Pads with the minimum value of all or part of the
            vector along each axis.
        'reflect'
            Pads with the reflection of the vector mirrored on
            the first and last values of the vector along each
            axis.
        'symmetric'
            Pads with the reflection of the vector mirrored
            along the edge of the array.
        'wrap'
            Pads with the wrap of the vector along the axis.
            The first values are used to pad the end and the
            end values are used to pad the beginning.
        'empty'
            Pads with undefined values.

            .. versionadded:: 1.17

        <function>
            Padding function, see Notes.
    stat_length : sequence or int, optional
        Used in 'maximum', 'mean', 'median', and 'minimum'.  Number of
        values at edge of each axis used to calculate the statistic value.

        ((before_1, after_1), ... (before_N, after_N)) unique statistic
        lengths for each axis.

        ((before, after),) yields same before and after statistic lengths
        for each axis.

        (stat_length,) or int is a shortcut for before = after = statistic
        length for all axes.

        Default is ``None``, to use the entire axis.
    constant_values : sequence or scalar, optional
        Used in 'constant'.  The values to set the padded values for each
        axis.

        ``((before_1, after_1), ... (before_N, after_N))`` unique pad constants
        for each axis.

        ``((before, after),)`` yields same before and after constants for each
        axis.

        ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
        all axes.

        Default is 0.
    end_values : sequence or scalar, optional
        Used in 'linear_ramp'.  The values used for the ending value of the
        linear_ramp and that will form the edge of the padded array.

        ``((before_1, after_1), ... (before_N, after_N))`` unique end values
        for each axis.

        ``((before, after),)`` yields same before and after end values for each
        axis.

        ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
        all axes.

        Default is 0.
    reflect_type : {'even', 'odd'}, optional
        Used in 'reflect', and 'symmetric'.  The 'even' style is the
        default with an unaltered reflection around the edge value.  For
        the 'odd' style, the extended part of the array is created by
        subtracting the reflected values from two times the edge value.

    Returns
    -------
    pad : ndarray
        Padded array of rank equal to `array` with shape increased
        according to `pad_width`.

    Notes
    -----
    .. versionadded:: 1.7.0

    For an array with rank greater than 1, some of the padding of later
    axes is calculated from padding of previous axes.  This is easiest to
    think about with a rank 2 array where the corners of the padded array
    are calculated by using padded values from the first axis.

    The padding function, if used, should modify a rank 1 array in-place. It
    has the following signature::

        padding_func(vector, iaxis_pad_width, iaxis, kwargs)

    where

        vector : ndarray
            A rank 1 array already padded with zeros.  Padded values are
            vector[:iaxis_pad_width[0]] and vector[-iaxis_pad_width[1]:].
        iaxis_pad_width : tuple
            A 2-tuple of ints, iaxis_pad_width[0] represents the number of
            values padded at the beginning of vector where
            iaxis_pad_width[1] represents the number of values padded at
            the end of vector.
        iaxis : int
            The axis currently being calculated.
        kwargs : dict
            Any keyword arguments the function requires.

    Examples
    --------
    >>> a = [1, 2, 3, 4, 5]
    >>> np.pad(a, (2, 3), 'constant', constant_values=(4, 6))
    array([4, 4, 1, ..., 6, 6, 6])

    >>> np.pad(a, (2, 3), 'edge')
    array([1, 1, 1, ..., 5, 5, 5])

    >>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))
    array([ 5,  3,  1,  2,  3,  4,  5,  2, -1, -4])

    >>> np.pad(a, (2,), 'maximum')
    array([5, 5, 1, 2, 3, 4, 5, 5, 5])

    >>> np.pad(a, (2,), 'mean')
    array([3, 3, 1, 2, 3, 4, 5, 3, 3])

    >>> np.pad(a, (2,), 'median')
    array([3, 3, 1, 2, 3, 4, 5, 3, 3])

    >>> a = [[1, 2], [3, 4]]
    >>> np.pad(a, ((3, 2), (2, 3)), 'minimum')
    array([[1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [3, 3, 3, 4, 3, 3, 3],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1]])

    >>> a = [1, 2, 3, 4, 5]
    >>> np.pad(a, (2, 3), 'reflect')
    array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])

    >>> np.pad(a, (2, 3), 'reflect', reflect_type='odd')
    array([-1,  0,  1,  2,  3,  4,  5,  6,  7,  8])

    >>> np.pad(a, (2, 3), 'symmetric')
    array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])

    >>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd')
    array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])

    >>> np.pad(a, (2, 3), 'wrap')
    array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])

    >>> def pad_with(vector, pad_width, iaxis, kwargs):
    ...     pad_value = kwargs.get('padder', 10)
    ...     vector[:pad_width[0]] = pad_value
    ...     vector[-pad_width[1]:] = pad_value
    >>> a = np.arange(6)
    >>> a = a.reshape((2, 3))
    >>> np.pad(a, 2, pad_with)
    array([[10, 10, 10, 10, 10, 10, 10],
           [10, 10, 10, 10, 10, 10, 10],
           [10, 10,  0,  1,  2, 10, 10],
           [10, 10,  3,  4,  5, 10, 10],
           [10, 10, 10, 10, 10, 10, 10],
           [10, 10, 10, 10, 10, 10, 10]])
    >>> np.pad(a, 2, pad_with, padder=100)
    array([[100, 100, 100, 100, 100, 100, 100],
           [100, 100, 100, 100, 100, 100, 100],
           [100, 100,   0,   1,   2, 100, 100],
           [100, 100,   3,   4,   5, 100, 100],
           [100, 100, 100, 100, 100, 100, 100],
           [100, 100, 100, 100, 100, 100, 100]])
    """
    array = np.asarray(array)
    pad_width = np.asarray(pad_width)

    if not pad_width.dtype.kind == 'i':
        raise TypeError('`pad_width` must be of integral type.')

    # Broadcast to shape (array.ndim, 2)
    pad_width = _as_pairs(pad_width, array.ndim, as_index=True)

    if callable(mode):
        # Old behavior: Use user-supplied function with np.apply_along_axis
        function = mode
        # Create a new zero padded array
        padded, _ = _pad_simple(array, pad_width, fill_value=0)
        # And apply along each axis

        for axis in range(padded.ndim):
            # Iterate using ndindex as in apply_along_axis, but assuming that
            # function operates inplace on the padded array.

            # view with the iteration axis at the end
            view = np.moveaxis(padded, axis, -1)

            # compute indices for the iteration axes, and append a trailing
            # ellipsis to prevent 0d arrays decaying to scalars (gh-8642)
            inds = ndindex(view.shape[:-1])
            inds = (ind + (Ellipsis,) for ind in inds)
            for ind in inds:
                function(view[ind], pad_width[axis], axis, kwargs)

        return padded

    # Make sure that no unsupported keywords were passed for the current mode
    allowed_kwargs = {
        'empty': [], 'edge': [], 'wrap': [],
        'constant': ['constant_values'],
        'linear_ramp': ['end_values'],
        'maximum': ['stat_length'],
        'mean': ['stat_length'],
        'median': ['stat_length'],
        'minimum': ['stat_length'],
        'reflect': ['reflect_type'],
        'symmetric': ['reflect_type'],
    }
    try:
        unsupported_kwargs = set(kwargs) - set(allowed_kwargs[mode])
    except KeyError:
        raise ValueError("mode '{}' is not supported".format(mode))
    if unsupported_kwargs:
        raise ValueError("unsupported keyword arguments for mode '{}': {}"
                         .format(mode, unsupported_kwargs))

    stat_functions = {"maximum": np.max, "minimum": np.min,
                      "mean": np.mean, "median": np.median}

    # Create array with final shape and original values
    # (padded area is undefined)
    padded, original_area_slice = _pad_simple(array, pad_width)
    # And prepare iteration over all dimensions
    # (zipping may be more readable than using enumerate)
    axes = range(padded.ndim)

    if mode == "constant":
        values = kwargs.get("constant_values", 0)
        values = _as_pairs(values, padded.ndim)
        for axis, width_pair, value_pair in zip(axes, pad_width, values):
            roi = _view_roi(padded, original_area_slice, axis)
            _set_pad_area(roi, axis, width_pair, value_pair)

    elif mode == "empty":
        pass  # Do nothing as _pad_simple already returned the correct result

    elif array.size == 0:
        # Only modes "constant" and "empty" can extend empty axes, all other
        # modes depend on `array` not being empty
        # -> ensure every empty axis is only "padded with 0"
        for axis, width_pair in zip(axes, pad_width):
            if array.shape[axis] == 0 and any(width_pair):
                raise ValueError(
                    "can't extend empty axis {} using modes other than "
                    "'constant' or 'empty'".format(axis)
                )
        # passed, don't need to do anything more as _pad_simple already
        # returned the correct result

    elif mode == "edge":
        for axis, width_pair in zip(axes, pad_width):
            roi = _view_roi(padded, original_area_slice, axis)
            edge_pair = _get_edges(roi, axis, width_pair)
            _set_pad_area(roi, axis, width_pair, edge_pair)

    elif mode == "linear_ramp":
        end_values = kwargs.get("end_values", 0)
        end_values = _as_pairs(end_values, padded.ndim)
        for axis, width_pair, value_pair in zip(axes, pad_width, end_values):
            roi = _view_roi(padded, original_area_slice, axis)
            ramp_pair = _get_linear_ramps(roi, axis, width_pair, value_pair)
            _set_pad_area(roi, axis, width_pair, ramp_pair)

    elif mode in stat_functions:
        func = stat_functions[mode]
        length = kwargs.get("stat_length", None)
        length = _as_pairs(length, padded.ndim, as_index=True)
        for axis, width_pair, length_pair in zip(axes, pad_width, length):
            roi = _view_roi(padded, original_area_slice, axis)
            stat_pair = _get_stats(roi, axis, width_pair, length_pair, func)
            _set_pad_area(roi, axis, width_pair, stat_pair)

    elif mode in {"reflect", "symmetric"}:
        method = kwargs.get("reflect_type", "even")
        include_edge = True if mode == "symmetric" else False
        for axis, (left_index, right_index) in zip(axes, pad_width):
            if array.shape[axis] == 1 and (left_index > 0 or right_index > 0):
                # Extending singleton dimension for 'reflect' is legacy
                # behavior; it really should raise an error.
                edge_pair = _get_edges(padded, axis, (left_index, right_index))
                _set_pad_area(
                    padded, axis, (left_index, right_index), edge_pair)
                continue

            roi = _view_roi(padded, original_area_slice, axis)
            while left_index > 0 or right_index > 0:
                # Iteratively pad until dimension is filled with reflected
                # values. This is necessary if the pad area is larger than
                # the length of the original values in the current dimension.
                left_index, right_index = _set_reflect_both(
                    roi, axis, (left_index, right_index),
                    method, include_edge
                )

    elif mode == "wrap":
        for axis, (left_index, right_index) in zip(axes, pad_width):
            roi = _view_roi(padded, original_area_slice, axis)
            while left_index > 0 or right_index > 0:
                # Iteratively pad until dimension is filled with wrapped
                # values. This is necessary if the pad area is larger than
                # the length of the original values in the current dimension.
                left_index, right_index = _set_wrap_both(
                    roi, axis, (left_index, right_index))

    return padded