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【python 错误解决】ImportError: cannot import name '_validate_lengths'

找到numpy包的安装目录:

E:/laidefa/Lib/site-packages/numpy/lib/arraypad.py

arraypad.py 这个文件954行以后 添加下面两个函数保存,重新加载即可消除错误


###############################################################################
# Public functions


def _pad_dispatcher(array, pad_width, mode, **kwargs):
    return (array,)


@array_function_dispatch(_pad_dispatcher, module='numpy')
def pad(array, pad_width, mode, **kwargs):
    """
    Pads an array.

    Parameters
    ----------
    array : array_like of rank N
        Input array
    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
        One of the following string values or a user supplied function.

        'constant'
            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.
        <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 int, 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 int is a shortcut for before = after = constant for
        all axes.

        Default is 0.
    end_values : sequence or int, 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 int is a shortcut for before = after = end value 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 return a rank 1 array equal in
    length to the vector argument with padded values replaced. 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[:pad_tuple[0]] and vector[-pad_tuple[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, 2, 3, 4, 5, 6, 6, 6])

    >>> np.pad(a, (2, 3), 'edge')
    array([1, 1, 1, 2, 3, 4, 5, 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
    ...     return vector
    >>> 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]])
    """
    if not np.asarray(pad_width).dtype.kind == 'i':
        raise TypeError('`pad_width` must be of integral type.')

    narray = np.array(array)
    pad_width = _as_pairs(pad_width, narray.ndim, as_index=True)

    allowedkwargs = {
        'constant': ['constant_values'],
        'edge': [],
        'linear_ramp': ['end_values'],
        'maximum': ['stat_length'],
        'mean': ['stat_length'],
        'median': ['stat_length'],
        'minimum': ['stat_length'],
        'reflect': ['reflect_type'],
        'symmetric': ['reflect_type'],
        'wrap': [],
        }

    kwdefaults = {
        'stat_length': None,
        'constant_values': 0,
        'end_values': 0,
        'reflect_type': 'even',
        }

    if isinstance(mode, np.compat.basestring):
        # Make sure have allowed kwargs appropriate for mode
        for key in kwargs:
            if key not in allowedkwargs[mode]:
                raise ValueError('%s keyword not in allowed keywords %s' %
                                 (key, allowedkwargs[mode]))

        # Set kwarg defaults
        for kw in allowedkwargs[mode]:
            kwargs.setdefault(kw, kwdefaults[kw])

        # Need to only normalize particular keywords.
        for i in kwargs:
            if i == 'stat_length':
                kwargs[i] = _as_pairs(kwargs[i], narray.ndim, as_index=True)
            if i in ['end_values', 'constant_values']:
                kwargs[i] = _as_pairs(kwargs[i], narray.ndim)
    else:
        # Drop back to old, slower np.apply_along_axis mode for user-supplied
        # vector function
        function = mode

        # Create a new padded array
        rank = list(range(narray.ndim))
        total_dim_increase = [np.sum(pad_width[i]) for i in rank]
        offset_slices = tuple(
            slice(pad_width[i][0], pad_width[i][0] + narray.shape[i])
            for i in rank)
        new_shape = np.array(narray.shape) + total_dim_increase
        newmat = np.zeros(new_shape, narray.dtype)

        # Insert the original array into the padded array
        newmat[offset_slices] = narray

        # This is the core of pad ...
        for iaxis in rank:
            np.apply_along_axis(function,
                                iaxis,
                                newmat,
                                pad_width[iaxis],
                                iaxis,
                                kwargs)
        return newmat

    # If we get here, use new padding method
    newmat = narray.copy()

    # API preserved, but completely new algorithm which pads by building the
    # entire block to pad before/after `arr` with in one step, for each axis.
    if mode == 'constant':
        for axis, ((pad_before, pad_after), (before_val, after_val)) \
                in enumerate(zip(pad_width, kwargs['constant_values'])):
            newmat = _prepend_const(newmat, pad_before, before_val, axis)
            newmat = _append_const(newmat, pad_after, after_val, axis)

    elif mode == 'edge':
        for axis, (pad_before, pad_after) in enumerate(pad_width):
            newmat = _prepend_edge(newmat, pad_before, axis)
            newmat = _append_edge(newmat, pad_after, axis)

    elif mode == 'linear_ramp':
        for axis, ((pad_before, pad_after), (before_val, after_val)) \
                in enumerate(zip(pad_width, kwargs['end_values'])):
            newmat = _prepend_ramp(newmat, pad_before, before_val, axis)
            newmat = _append_ramp(newmat, pad_after, after_val, axis)

    elif mode == 'maximum':
        for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \
                in enumerate(zip(pad_width, kwargs['stat_length'])):
            newmat = _prepend_max(newmat, pad_before, chunk_before, axis)
            newmat = _append_max(newmat, pad_after, chunk_after, axis)

    elif mode == 'mean':
        for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \
                in enumerate(zip(pad_width, kwargs['stat_length'])):
            newmat = _prepend_mean(newmat, pad_before, chunk_before, axis)
            newmat = _append_mean(newmat, pad_after, chunk_after, axis)

    elif mode == 'median':
        for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \
                in enumerate(zip(pad_width, kwargs['stat_length'])):
            newmat = _prepend_med(newmat, pad_before, chunk_before, axis)
            newmat = _append_med(newmat, pad_after, chunk_after, axis)

    elif mode == 'minimum':
        for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \
                in enumerate(zip(pad_width, kwargs['stat_length'])):
            newmat = _prepend_min(newmat, pad_before, chunk_before, axis)
            newmat = _append_min(newmat, pad_after, chunk_after, axis)

    elif mode == 'reflect':
        for axis, (pad_before, pad_after) in enumerate(pad_width):
            if narray.shape[axis] == 0:
                # Axes with non-zero padding cannot be empty.
                if pad_before > 0 or pad_after > 0:
                    raise ValueError("There aren't any elements to reflect"
                                     " in axis {} of `array`".format(axis))
                # Skip zero padding on empty axes.
                continue

            # Recursive padding along any axis where `pad_amt` is too large
            # for indexing tricks. We can only safely pad the original axis
            # length, to keep the period of the reflections consistent.
            if ((pad_before > 0) or
                    (pad_after > 0)) and newmat.shape[axis] == 1:
                # Extending singleton dimension for 'reflect' is legacy
                # behavior; it really should raise an error.
                newmat = _prepend_edge(newmat, pad_before, axis)
                newmat = _append_edge(newmat, pad_after, axis)
                continue

            method = kwargs['reflect_type']
            safe_pad = newmat.shape[axis] - 1
            while ((pad_before > safe_pad) or (pad_after > safe_pad)):
                pad_iter_b = min(safe_pad,
                                 safe_pad * (pad_before // safe_pad))
                pad_iter_a = min(safe_pad, safe_pad * (pad_after // safe_pad))
                newmat = _pad_ref(newmat, (pad_iter_b,
                                           pad_iter_a), method, axis)
                pad_before -= pad_iter_b
                pad_after -= pad_iter_a
                safe_pad += pad_iter_b + pad_iter_a
            newmat = _pad_ref(newmat, (pad_before, pad_after), method, axis)

    elif mode == 'symmetric':
        for axis, (pad_before, pad_after) in enumerate(pad_width):
            # Recursive padding along any axis where `pad_amt` is too large
            # for indexing tricks. We can only safely pad the original axis
            # length, to keep the period of the reflections consistent.
            method = kwargs['reflect_type']
            safe_pad = newmat.shape[axis]
            while ((pad_before > safe_pad) or
                   (pad_after > safe_pad)):
                pad_iter_b = min(safe_pad,
                                 safe_pad * (pad_before // safe_pad))
                pad_iter_a = min(safe_pad, safe_pad * (pad_after // safe_pad))
                newmat = _pad_sym(newmat, (pad_iter_b,
                                           pad_iter_a), method, axis)
                pad_before -= pad_iter_b
                pad_after -= pad_iter_a
                safe_pad += pad_iter_b + pad_iter_a
            newmat = _pad_sym(newmat, (pad_before, pad_after), method, axis)

    elif mode == 'wrap':
        for axis, (pad_before, pad_after) in enumerate(pad_width):
            # Recursive padding along any axis where `pad_amt` is too large
            # for indexing tricks. We can only safely pad the original axis
            # length, to keep the period of the reflections consistent.
            safe_pad = newmat.shape[axis]
            while ((pad_before > safe_pad) or
                   (pad_after > safe_pad)):
                pad_iter_b = min(safe_pad,
                                 safe_pad * (pad_before // safe_pad))
                pad_iter_a = min(safe_pad, safe_pad * (pad_after // safe_pad))
                newmat = _pad_wrap(newmat, (pad_iter_b, pad_iter_a), axis)

                pad_before -= pad_iter_b
                pad_after -= pad_iter_a
                safe_pad += pad_iter_b + pad_iter_a
            newmat = _pad_wrap(newmat, (pad_before, pad_after), axis)

    return newmat




def _normalize_shape(ndarray, shape, cast_to_int=True):
    """
    Private function which does some checks and normalizes the possibly
    much simpler representations of ‘pad_width‘, ‘stat_length‘,
    ‘constant_values‘, ‘end_values‘.
    Parameters
    ----------
    narray : ndarray
        Input ndarray
    shape : {sequence, array_like, float, int}, optional
        The width of padding (pad_width), the number of elements on the
        edge of the narray used for statistics (stat_length), the constant
        value(s) to use when filling padded regions (constant_values), or the
        endpoint target(s) for linear ramps (end_values).
        ((before_1, after_1), ... (before_N, after_N)) unique number of
        elements for each axis where `N` is rank of `narray`.
        ((before, after),) yields same before and after constants for each
        axis.
        (constant,) or val is a shortcut for before = after = constant for
        all axes.
    cast_to_int : bool, optional
        Controls if values in ``shape`` will be rounded and cast to int
        before being returned.
    Returns
    -------
    normalized_shape : tuple of tuples
        val                               => ((val, val), (val, val), ...)
        [[val1, val2], [val3, val4], ...] => ((val1, val2), (val3, val4), ...)
        ((val1, val2), (val3, val4), ...) => no change
        [[val1, val2], ]                  => ((val1, val2), (val1, val2), ...)
        ((val1, val2), )                  => ((val1, val2), (val1, val2), ...)
        [[val ,     ], ]                  => ((val, val), (val, val), ...)
        ((val ,     ), )                  => ((val, val), (val, val), ...)
    """
    ndims = ndarray.ndim
    # Shortcut shape=None
    if shape is None:
        return ((None, None), ) * ndims
    # Convert any input `info` to a NumPy array
    shape_arr = np.asarray(shape)
    try:
        shape_arr = np.broadcast_to(shape_arr, (ndims, 2))
    except ValueError:
        fmt = "Unable to create correctly shaped tuple from %s"
        raise ValueError(fmt % (shape,))
    # Cast if necessary
    if cast_to_int is True:
        shape_arr = np.round(shape_arr).astype(int)
    # Convert list of lists to tuple of tuples
    return tuple(tuple(axis) for axis in shape_arr.tolist())

def _validate_lengths(narray, number_elements):
    """
    Private function which does some checks and reformats pad_width and
    stat_length using _normalize_shape.
    Parameters
    ----------
    narray : ndarray
        Input ndarray
    number_elements : {sequence, int}, optional
        The width of padding (pad_width) or the number of elements on the edge
        of the narray used for statistics (stat_length).
        ((before_1, after_1), ... (before_N, after_N)) unique number of
        elements for each axis.
        ((before, after),) yields same before and after constants for each
        axis.
        (constant,) or int is a shortcut for before = after = constant for all
        axes.
    Returns
    -------
    _validate_lengths : tuple of tuples
        int                               => ((int, int), (int, int), ...)
        [[int1, int2], [int3, int4], ...] => ((int1, int2), (int3, int4), ...)
        ((int1, int2), (int3, int4), ...) => no change
        [[int1, int2], ]                  => ((int1, int2), (int1, int2), ...)
        ((int1, int2), )                  => ((int1, int2), (int1, int2), ...)
        [[int ,     ], ]                  => ((int, int), (int, int), ...)
        ((int ,     ), )                  => ((int, int), (int, int), ...)
    """
    normshp = _normalize_shape(narray, number_elements)
    for i in normshp:
        chk = [1 if x is None else x for x in i]
        chk = [1 if x >= 0 else -1 for x in chk]
        if (chk[0] < 0) or (chk[1] < 0):
            fmt = "%s cannot contain negative values."
            raise ValueError(fmt % (number_elements,))
    return normshp
###############################################################################
# Public functions

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