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875 lines
30 KiB
Python
875 lines
30 KiB
Python
"""
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The arraypad module contains a group of functions to pad values onto the edges
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of an n-dimensional array.
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"""
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from __future__ import division, absolute_import, print_function
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import numpy as np
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from numpy.core.overrides import array_function_dispatch
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from numpy.lib.index_tricks import ndindex
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__all__ = ['pad']
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###############################################################################
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# Private utility functions.
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def _round_if_needed(arr, dtype):
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"""
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Rounds arr inplace if destination dtype is integer.
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Parameters
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----------
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arr : ndarray
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Input array.
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dtype : dtype
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The dtype of the destination array.
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"""
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if np.issubdtype(dtype, np.integer):
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arr.round(out=arr)
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def _slice_at_axis(sl, axis):
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"""
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Construct tuple of slices to slice an array in the given dimension.
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Parameters
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----------
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sl : slice
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The slice for the given dimension.
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axis : int
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The axis to which `sl` is applied. All other dimensions are left
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"unsliced".
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Returns
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-------
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sl : tuple of slices
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A tuple with slices matching `shape` in length.
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Examples
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--------
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>>> _slice_at_axis(slice(None, 3, -1), 1)
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(slice(None, None, None), slice(None, 3, -1), (...,))
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"""
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return (slice(None),) * axis + (sl,) + (...,)
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def _view_roi(array, original_area_slice, axis):
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"""
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Get a view of the current region of interest during iterative padding.
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When padding multiple dimensions iteratively corner values are
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unnecessarily overwritten multiple times. This function reduces the
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working area for the first dimensions so that corners are excluded.
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Parameters
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----------
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array : ndarray
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The array with the region of interest.
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original_area_slice : tuple of slices
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Denotes the area with original values of the unpadded array.
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axis : int
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The currently padded dimension assuming that `axis` is padded before
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`axis` + 1.
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Returns
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-------
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roi : ndarray
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The region of interest of the original `array`.
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"""
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axis += 1
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sl = (slice(None),) * axis + original_area_slice[axis:]
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return array[sl]
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def _pad_simple(array, pad_width, fill_value=None):
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"""
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Pad array on all sides with either a single value or undefined values.
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Parameters
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----------
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array : ndarray
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Array to grow.
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pad_width : sequence of tuple[int, int]
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Pad width on both sides for each dimension in `arr`.
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fill_value : scalar, optional
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If provided the padded area is filled with this value, otherwise
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the pad area left undefined.
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Returns
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-------
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padded : ndarray
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The padded array with the same dtype as`array`. Its order will default
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to C-style if `array` is not F-contiguous.
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original_area_slice : tuple
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A tuple of slices pointing to the area of the original array.
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"""
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# Allocate grown array
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new_shape = tuple(
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left + size + right
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for size, (left, right) in zip(array.shape, pad_width)
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)
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order = 'F' if array.flags.fnc else 'C' # Fortran and not also C-order
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padded = np.empty(new_shape, dtype=array.dtype, order=order)
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if fill_value is not None:
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padded.fill(fill_value)
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# Copy old array into correct space
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original_area_slice = tuple(
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slice(left, left + size)
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for size, (left, right) in zip(array.shape, pad_width)
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)
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padded[original_area_slice] = array
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return padded, original_area_slice
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def _set_pad_area(padded, axis, width_pair, value_pair):
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"""
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Set empty-padded area in given dimension.
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Parameters
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----------
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padded : ndarray
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Array with the pad area which is modified inplace.
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axis : int
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Dimension with the pad area to set.
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width_pair : (int, int)
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Pair of widths that mark the pad area on both sides in the given
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dimension.
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value_pair : tuple of scalars or ndarrays
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Values inserted into the pad area on each side. It must match or be
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broadcastable to the shape of `arr`.
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"""
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left_slice = _slice_at_axis(slice(None, width_pair[0]), axis)
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padded[left_slice] = value_pair[0]
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right_slice = _slice_at_axis(
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slice(padded.shape[axis] - width_pair[1], None), axis)
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padded[right_slice] = value_pair[1]
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def _get_edges(padded, axis, width_pair):
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"""
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Retrieve edge values from empty-padded array in given dimension.
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Parameters
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----------
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padded : ndarray
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Empty-padded array.
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axis : int
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Dimension in which the edges are considered.
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width_pair : (int, int)
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Pair of widths that mark the pad area on both sides in the given
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dimension.
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Returns
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-------
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left_edge, right_edge : ndarray
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Edge values of the valid area in `padded` in the given dimension. Its
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shape will always match `padded` except for the dimension given by
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`axis` which will have a length of 1.
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"""
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left_index = width_pair[0]
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left_slice = _slice_at_axis(slice(left_index, left_index + 1), axis)
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left_edge = padded[left_slice]
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right_index = padded.shape[axis] - width_pair[1]
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right_slice = _slice_at_axis(slice(right_index - 1, right_index), axis)
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right_edge = padded[right_slice]
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return left_edge, right_edge
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def _get_linear_ramps(padded, axis, width_pair, end_value_pair):
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"""
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Construct linear ramps for empty-padded array in given dimension.
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Parameters
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----------
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padded : ndarray
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Empty-padded array.
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axis : int
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Dimension in which the ramps are constructed.
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width_pair : (int, int)
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Pair of widths that mark the pad area on both sides in the given
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dimension.
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end_value_pair : (scalar, scalar)
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End values for the linear ramps which form the edge of the fully padded
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array. These values are included in the linear ramps.
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Returns
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-------
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left_ramp, right_ramp : ndarray
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Linear ramps to set on both sides of `padded`.
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"""
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edge_pair = _get_edges(padded, axis, width_pair)
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left_ramp = np.linspace(
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start=end_value_pair[0],
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stop=edge_pair[0].squeeze(axis), # Dimensions is replaced by linspace
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num=width_pair[0],
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endpoint=False,
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dtype=padded.dtype,
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axis=axis,
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)
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right_ramp = np.linspace(
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start=end_value_pair[1],
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stop=edge_pair[1].squeeze(axis), # Dimension is replaced by linspace
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num=width_pair[1],
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endpoint=False,
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dtype=padded.dtype,
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axis=axis,
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)
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# Reverse linear space in appropriate dimension
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right_ramp = right_ramp[_slice_at_axis(slice(None, None, -1), axis)]
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return left_ramp, right_ramp
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def _get_stats(padded, axis, width_pair, length_pair, stat_func):
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"""
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Calculate statistic for the empty-padded array in given dimnsion.
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Parameters
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----------
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padded : ndarray
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Empty-padded array.
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axis : int
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Dimension in which the statistic is calculated.
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width_pair : (int, int)
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Pair of widths that mark the pad area on both sides in the given
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dimension.
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length_pair : 2-element sequence of None or int
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Gives the number of values in valid area from each side that is
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taken into account when calculating the statistic. If None the entire
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valid area in `padded` is considered.
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stat_func : function
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Function to compute statistic. The expected signature is
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``stat_func(x: ndarray, axis: int, keepdims: bool) -> ndarray``.
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Returns
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-------
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left_stat, right_stat : ndarray
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Calculated statistic for both sides of `padded`.
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"""
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# Calculate indices of the edges of the area with original values
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left_index = width_pair[0]
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right_index = padded.shape[axis] - width_pair[1]
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# as well as its length
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max_length = right_index - left_index
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# Limit stat_lengths to max_length
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left_length, right_length = length_pair
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if left_length is None or max_length < left_length:
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left_length = max_length
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if right_length is None or max_length < right_length:
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right_length = max_length
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# Calculate statistic for the left side
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left_slice = _slice_at_axis(
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slice(left_index, left_index + left_length), axis)
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left_chunk = padded[left_slice]
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left_stat = stat_func(left_chunk, axis=axis, keepdims=True)
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_round_if_needed(left_stat, padded.dtype)
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if left_length == right_length == max_length:
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# return early as right_stat must be identical to left_stat
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return left_stat, left_stat
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# Calculate statistic for the right side
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right_slice = _slice_at_axis(
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slice(right_index - right_length, right_index), axis)
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right_chunk = padded[right_slice]
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right_stat = stat_func(right_chunk, axis=axis, keepdims=True)
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_round_if_needed(right_stat, padded.dtype)
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return left_stat, right_stat
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def _set_reflect_both(padded, axis, width_pair, method, include_edge=False):
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"""
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Pad `axis` of `arr` with reflection.
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Parameters
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----------
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padded : ndarray
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Input array of arbitrary shape.
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axis : int
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Axis along which to pad `arr`.
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width_pair : (int, int)
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Pair of widths that mark the pad area on both sides in the given
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dimension.
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method : str
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Controls method of reflection; options are 'even' or 'odd'.
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include_edge : bool
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If true, edge value is included in reflection, otherwise the edge
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value forms the symmetric axis to the reflection.
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Returns
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-------
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pad_amt : tuple of ints, length 2
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New index positions of padding to do along the `axis`. If these are
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both 0, padding is done in this dimension.
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"""
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left_pad, right_pad = width_pair
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old_length = padded.shape[axis] - right_pad - left_pad
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if include_edge:
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# Edge is included, we need to offset the pad amount by 1
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edge_offset = 1
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else:
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edge_offset = 0 # Edge is not included, no need to offset pad amount
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old_length -= 1 # but must be omitted from the chunk
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if left_pad > 0:
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# Pad with reflected values on left side:
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# First limit chunk size which can't be larger than pad area
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chunk_length = min(old_length, left_pad)
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# Slice right to left, stop on or next to edge, start relative to stop
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stop = left_pad - edge_offset
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start = stop + chunk_length
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left_slice = _slice_at_axis(slice(start, stop, -1), axis)
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left_chunk = padded[left_slice]
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if method == "odd":
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# Negate chunk and align with edge
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edge_slice = _slice_at_axis(slice(left_pad, left_pad + 1), axis)
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left_chunk = 2 * padded[edge_slice] - left_chunk
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# Insert chunk into padded area
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start = left_pad - chunk_length
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stop = left_pad
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pad_area = _slice_at_axis(slice(start, stop), axis)
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padded[pad_area] = left_chunk
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# Adjust pointer to left edge for next iteration
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left_pad -= chunk_length
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if right_pad > 0:
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# Pad with reflected values on right side:
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# First limit chunk size which can't be larger than pad area
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chunk_length = min(old_length, right_pad)
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# Slice right to left, start on or next to edge, stop relative to start
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start = -right_pad + edge_offset - 2
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stop = start - chunk_length
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right_slice = _slice_at_axis(slice(start, stop, -1), axis)
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right_chunk = padded[right_slice]
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if method == "odd":
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# Negate chunk and align with edge
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edge_slice = _slice_at_axis(
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slice(-right_pad - 1, -right_pad), axis)
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right_chunk = 2 * padded[edge_slice] - right_chunk
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# Insert chunk into padded area
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start = padded.shape[axis] - right_pad
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stop = start + chunk_length
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pad_area = _slice_at_axis(slice(start, stop), axis)
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padded[pad_area] = right_chunk
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# Adjust pointer to right edge for next iteration
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right_pad -= chunk_length
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return left_pad, right_pad
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def _set_wrap_both(padded, axis, width_pair):
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"""
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Pad `axis` of `arr` with wrapped values.
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Parameters
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----------
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padded : ndarray
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Input array of arbitrary shape.
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axis : int
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Axis along which to pad `arr`.
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width_pair : (int, int)
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Pair of widths that mark the pad area on both sides in the given
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dimension.
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Returns
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-------
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pad_amt : tuple of ints, length 2
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New index positions of padding to do along the `axis`. If these are
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both 0, padding is done in this dimension.
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"""
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left_pad, right_pad = width_pair
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period = padded.shape[axis] - right_pad - left_pad
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# If the current dimension of `arr` doesn't contain enough valid values
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# (not part of the undefined pad area) we need to pad multiple times.
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# Each time the pad area shrinks on both sides which is communicated with
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# these variables.
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new_left_pad = 0
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new_right_pad = 0
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if left_pad > 0:
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# Pad with wrapped values on left side
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# First slice chunk from right side of the non-pad area.
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# Use min(period, left_pad) to ensure that chunk is not larger than
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# pad area
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right_slice = _slice_at_axis(
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slice(-right_pad - min(period, left_pad),
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-right_pad if right_pad != 0 else None),
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axis
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)
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right_chunk = padded[right_slice]
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if left_pad > period:
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# Chunk is smaller than pad area
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pad_area = _slice_at_axis(slice(left_pad - period, left_pad), axis)
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new_left_pad = left_pad - period
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else:
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# Chunk matches pad area
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pad_area = _slice_at_axis(slice(None, left_pad), axis)
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padded[pad_area] = right_chunk
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if right_pad > 0:
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# Pad with wrapped values on right side
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# First slice chunk from left side of the non-pad area.
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# Use min(period, right_pad) to ensure that chunk is not larger than
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# pad area
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left_slice = _slice_at_axis(
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slice(left_pad, left_pad + min(period, right_pad),), axis)
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left_chunk = padded[left_slice]
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if right_pad > period:
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# Chunk is smaller than pad area
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pad_area = _slice_at_axis(
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slice(-right_pad, -right_pad + period), axis)
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new_right_pad = right_pad - period
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else:
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# Chunk matches pad area
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pad_area = _slice_at_axis(slice(-right_pad, None), axis)
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padded[pad_area] = left_chunk
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return new_left_pad, new_right_pad
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def _as_pairs(x, ndim, as_index=False):
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"""
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Broadcast `x` to an array with the shape (`ndim`, 2).
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A helper function for `pad` that prepares and validates arguments like
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`pad_width` for iteration in pairs.
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Parameters
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----------
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x : {None, scalar, array-like}
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The object to broadcast to the shape (`ndim`, 2).
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ndim : int
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Number of pairs the broadcasted `x` will have.
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as_index : bool, optional
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If `x` is not None, try to round each element of `x` to an integer
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(dtype `np.intp`) and ensure every element is positive.
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Returns
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-------
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pairs : nested iterables, shape (`ndim`, 2)
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The broadcasted version of `x`.
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Raises
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------
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ValueError
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If `as_index` is True and `x` contains negative elements.
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Or if `x` is not broadcastable to the shape (`ndim`, 2).
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"""
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if x is None:
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# Pass through None as a special case, otherwise np.round(x) fails
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# with an AttributeError
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return ((None, None),) * ndim
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x = np.array(x)
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if as_index:
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x = np.round(x).astype(np.intp, copy=False)
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if x.ndim < 3:
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# Optimization: Possibly use faster paths for cases where `x` has
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# only 1 or 2 elements. `np.broadcast_to` could handle these as well
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# but is currently slower
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if x.size == 1:
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# x was supplied as a single value
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x = x.ravel() # Ensure x[0] works for x.ndim == 0, 1, 2
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if as_index and x < 0:
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raise ValueError("index can't contain negative values")
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return ((x[0], x[0]),) * ndim
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if x.size == 2 and x.shape != (2, 1):
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# x was supplied with a single value for each side
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# but except case when each dimension has a single value
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# which should be broadcasted to a pair,
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# e.g. [[1], [2]] -> [[1, 1], [2, 2]] not [[1, 2], [1, 2]]
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x = x.ravel() # Ensure x[0], x[1] works
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if as_index and (x[0] < 0 or x[1] < 0):
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raise ValueError("index can't contain negative values")
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return ((x[0], x[1]),) * ndim
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if as_index and x.min() < 0:
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raise ValueError("index can't contain negative values")
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# Converting the array with `tolist` seems to improve performance
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# when iterating and indexing the result (see usage in `pad`)
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return np.broadcast_to(x, (ndim, 2)).tolist()
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def _pad_dispatcher(array, pad_width, mode=None, **kwargs):
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return (array,)
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###############################################################################
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# Public functions
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@array_function_dispatch(_pad_dispatcher, module='numpy')
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def pad(array, pad_width, mode='constant', **kwargs):
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"""
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Pad an array.
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Parameters
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----------
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array : array_like of rank N
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The array to pad.
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pad_width : {sequence, array_like, int}
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Number of values padded to the edges of each axis.
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((before_1, after_1), ... (before_N, after_N)) unique pad widths
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for each axis.
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((before, after),) yields same before and after pad for each axis.
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(pad,) or int is a shortcut for before = after = pad width for all
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axes.
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mode : str or function, optional
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One of the following string values or a user supplied function.
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'constant' (default)
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Pads with a constant value.
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'edge'
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Pads with the edge values of array.
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'linear_ramp'
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Pads with the linear ramp between end_value and the
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array edge value.
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'maximum'
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Pads with the maximum value of all or part of the
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vector along each axis.
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'mean'
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Pads with the mean value of all or part of the
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vector along each axis.
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'median'
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Pads with the median value of all or part of the
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vector along each axis.
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'minimum'
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Pads with the minimum value of all or part of the
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vector along each axis.
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'reflect'
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Pads with the reflection of the vector mirrored on
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the first and last values of the vector along each
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axis.
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'symmetric'
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Pads with the reflection of the vector mirrored
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along the edge of the array.
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'wrap'
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Pads with the wrap of the vector along the axis.
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The first values are used to pad the end and the
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end values are used to pad the beginning.
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'empty'
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Pads with undefined values.
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.. versionadded:: 1.17
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<function>
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Padding function, see Notes.
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stat_length : sequence or int, optional
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Used in 'maximum', 'mean', 'median', and 'minimum'. Number of
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values at edge of each axis used to calculate the statistic value.
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((before_1, after_1), ... (before_N, after_N)) unique statistic
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lengths for each axis.
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((before, after),) yields same before and after statistic lengths
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for each axis.
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(stat_length,) or int is a shortcut for before = after = statistic
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length for all axes.
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Default is ``None``, to use the entire axis.
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constant_values : sequence or scalar, optional
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Used in 'constant'. The values to set the padded values for each
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axis.
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``((before_1, after_1), ... (before_N, after_N))`` unique pad constants
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for each axis.
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``((before, after),)`` yields same before and after constants for each
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axis.
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``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
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all axes.
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Default is 0.
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end_values : sequence or scalar, optional
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Used in 'linear_ramp'. The values used for the ending value of the
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linear_ramp and that will form the edge of the padded array.
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``((before_1, after_1), ... (before_N, after_N))`` unique end values
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for each axis.
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``((before, after),)`` yields same before and after end values for each
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axis.
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``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
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all axes.
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Default is 0.
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reflect_type : {'even', 'odd'}, optional
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Used in 'reflect', and 'symmetric'. The 'even' style is the
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default with an unaltered reflection around the edge value. For
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the 'odd' style, the extended part of the array is created by
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subtracting the reflected values from two times the edge value.
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Returns
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-------
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pad : ndarray
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Padded array of rank equal to `array` with shape increased
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according to `pad_width`.
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Notes
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-----
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.. versionadded:: 1.7.0
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For an array with rank greater than 1, some of the padding of later
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axes is calculated from padding of previous axes. This is easiest to
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think about with a rank 2 array where the corners of the padded array
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are calculated by using padded values from the first axis.
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The padding function, if used, should modify a rank 1 array in-place. It
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has the following signature::
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padding_func(vector, iaxis_pad_width, iaxis, kwargs)
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where
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vector : ndarray
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A rank 1 array already padded with zeros. Padded values are
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vector[:iaxis_pad_width[0]] and vector[-iaxis_pad_width[1]:].
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iaxis_pad_width : tuple
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A 2-tuple of ints, iaxis_pad_width[0] represents the number of
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values padded at the beginning of vector where
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iaxis_pad_width[1] represents the number of values padded at
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the end of vector.
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iaxis : int
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The axis currently being calculated.
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kwargs : dict
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Any keyword arguments the function requires.
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Examples
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--------
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>>> a = [1, 2, 3, 4, 5]
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>>> np.pad(a, (2, 3), 'constant', constant_values=(4, 6))
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array([4, 4, 1, ..., 6, 6, 6])
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>>> np.pad(a, (2, 3), 'edge')
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array([1, 1, 1, ..., 5, 5, 5])
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>>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))
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array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4])
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>>> np.pad(a, (2,), 'maximum')
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array([5, 5, 1, 2, 3, 4, 5, 5, 5])
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>>> np.pad(a, (2,), 'mean')
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array([3, 3, 1, 2, 3, 4, 5, 3, 3])
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>>> np.pad(a, (2,), 'median')
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array([3, 3, 1, 2, 3, 4, 5, 3, 3])
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>>> a = [[1, 2], [3, 4]]
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>>> np.pad(a, ((3, 2), (2, 3)), 'minimum')
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array([[1, 1, 1, 2, 1, 1, 1],
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[1, 1, 1, 2, 1, 1, 1],
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[1, 1, 1, 2, 1, 1, 1],
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[1, 1, 1, 2, 1, 1, 1],
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[3, 3, 3, 4, 3, 3, 3],
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[1, 1, 1, 2, 1, 1, 1],
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[1, 1, 1, 2, 1, 1, 1]])
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>>> a = [1, 2, 3, 4, 5]
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>>> np.pad(a, (2, 3), 'reflect')
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array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
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>>> np.pad(a, (2, 3), 'reflect', reflect_type='odd')
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array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8])
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>>> np.pad(a, (2, 3), 'symmetric')
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array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])
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>>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd')
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array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])
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>>> np.pad(a, (2, 3), 'wrap')
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array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])
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>>> def pad_with(vector, pad_width, iaxis, kwargs):
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... pad_value = kwargs.get('padder', 10)
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... vector[:pad_width[0]] = pad_value
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... vector[-pad_width[1]:] = pad_value
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>>> a = np.arange(6)
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>>> a = a.reshape((2, 3))
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>>> np.pad(a, 2, pad_with)
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array([[10, 10, 10, 10, 10, 10, 10],
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[10, 10, 10, 10, 10, 10, 10],
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[10, 10, 0, 1, 2, 10, 10],
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[10, 10, 3, 4, 5, 10, 10],
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[10, 10, 10, 10, 10, 10, 10],
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[10, 10, 10, 10, 10, 10, 10]])
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>>> np.pad(a, 2, pad_with, padder=100)
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array([[100, 100, 100, 100, 100, 100, 100],
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[100, 100, 100, 100, 100, 100, 100],
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[100, 100, 0, 1, 2, 100, 100],
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[100, 100, 3, 4, 5, 100, 100],
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[100, 100, 100, 100, 100, 100, 100],
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[100, 100, 100, 100, 100, 100, 100]])
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"""
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array = np.asarray(array)
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pad_width = np.asarray(pad_width)
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if not pad_width.dtype.kind == 'i':
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raise TypeError('`pad_width` must be of integral type.')
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# Broadcast to shape (array.ndim, 2)
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pad_width = _as_pairs(pad_width, array.ndim, as_index=True)
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if callable(mode):
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# Old behavior: Use user-supplied function with np.apply_along_axis
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function = mode
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# Create a new zero padded array
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padded, _ = _pad_simple(array, pad_width, fill_value=0)
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# And apply along each axis
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for axis in range(padded.ndim):
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# Iterate using ndindex as in apply_along_axis, but assuming that
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# function operates inplace on the padded array.
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# view with the iteration axis at the end
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view = np.moveaxis(padded, axis, -1)
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# compute indices for the iteration axes, and append a trailing
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# ellipsis to prevent 0d arrays decaying to scalars (gh-8642)
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inds = ndindex(view.shape[:-1])
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inds = (ind + (Ellipsis,) for ind in inds)
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for ind in inds:
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function(view[ind], pad_width[axis], axis, kwargs)
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return padded
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# Make sure that no unsupported keywords were passed for the current mode
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allowed_kwargs = {
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'empty': [], 'edge': [], 'wrap': [],
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'constant': ['constant_values'],
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'linear_ramp': ['end_values'],
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'maximum': ['stat_length'],
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'mean': ['stat_length'],
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'median': ['stat_length'],
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'minimum': ['stat_length'],
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'reflect': ['reflect_type'],
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'symmetric': ['reflect_type'],
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}
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try:
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unsupported_kwargs = set(kwargs) - set(allowed_kwargs[mode])
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except KeyError:
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raise ValueError("mode '{}' is not supported".format(mode))
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if unsupported_kwargs:
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raise ValueError("unsupported keyword arguments for mode '{}': {}"
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.format(mode, unsupported_kwargs))
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stat_functions = {"maximum": np.max, "minimum": np.min,
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"mean": np.mean, "median": np.median}
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# Create array with final shape and original values
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# (padded area is undefined)
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padded, original_area_slice = _pad_simple(array, pad_width)
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# And prepare iteration over all dimensions
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# (zipping may be more readable than using enumerate)
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axes = range(padded.ndim)
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if mode == "constant":
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values = kwargs.get("constant_values", 0)
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values = _as_pairs(values, padded.ndim)
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for axis, width_pair, value_pair in zip(axes, pad_width, values):
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roi = _view_roi(padded, original_area_slice, axis)
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_set_pad_area(roi, axis, width_pair, value_pair)
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elif mode == "empty":
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pass # Do nothing as _pad_simple already returned the correct result
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elif array.size == 0:
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# Only modes "constant" and "empty" can extend empty axes, all other
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# modes depend on `array` not being empty
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# -> ensure every empty axis is only "padded with 0"
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for axis, width_pair in zip(axes, pad_width):
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if array.shape[axis] == 0 and any(width_pair):
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raise ValueError(
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"can't extend empty axis {} using modes other than "
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"'constant' or 'empty'".format(axis)
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)
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# passed, don't need to do anything more as _pad_simple already
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# returned the correct result
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elif mode == "edge":
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for axis, width_pair in zip(axes, pad_width):
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roi = _view_roi(padded, original_area_slice, axis)
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edge_pair = _get_edges(roi, axis, width_pair)
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_set_pad_area(roi, axis, width_pair, edge_pair)
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elif mode == "linear_ramp":
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end_values = kwargs.get("end_values", 0)
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end_values = _as_pairs(end_values, padded.ndim)
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for axis, width_pair, value_pair in zip(axes, pad_width, end_values):
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roi = _view_roi(padded, original_area_slice, axis)
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ramp_pair = _get_linear_ramps(roi, axis, width_pair, value_pair)
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_set_pad_area(roi, axis, width_pair, ramp_pair)
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elif mode in stat_functions:
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func = stat_functions[mode]
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length = kwargs.get("stat_length", None)
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length = _as_pairs(length, padded.ndim, as_index=True)
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for axis, width_pair, length_pair in zip(axes, pad_width, length):
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roi = _view_roi(padded, original_area_slice, axis)
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stat_pair = _get_stats(roi, axis, width_pair, length_pair, func)
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_set_pad_area(roi, axis, width_pair, stat_pair)
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elif mode in {"reflect", "symmetric"}:
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method = kwargs.get("reflect_type", "even")
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include_edge = True if mode == "symmetric" else False
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for axis, (left_index, right_index) in zip(axes, pad_width):
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if array.shape[axis] == 1 and (left_index > 0 or right_index > 0):
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# Extending singleton dimension for 'reflect' is legacy
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# behavior; it really should raise an error.
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edge_pair = _get_edges(padded, axis, (left_index, right_index))
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_set_pad_area(
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padded, axis, (left_index, right_index), edge_pair)
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continue
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roi = _view_roi(padded, original_area_slice, axis)
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while left_index > 0 or right_index > 0:
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# Iteratively pad until dimension is filled with reflected
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# values. This is necessary if the pad area is larger than
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# the length of the original values in the current dimension.
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left_index, right_index = _set_reflect_both(
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roi, axis, (left_index, right_index),
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method, include_edge
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)
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elif mode == "wrap":
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for axis, (left_index, right_index) in zip(axes, pad_width):
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roi = _view_roi(padded, original_area_slice, axis)
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while left_index > 0 or right_index > 0:
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# Iteratively pad until dimension is filled with wrapped
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# values. This is necessary if the pad area is larger than
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# the length of the original values in the current dimension.
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left_index, right_index = _set_wrap_both(
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roi, axis, (left_index, right_index))
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return padded
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