JezK
Edit File: arraypad.py
""" The arraypad module contains a group of functions to pad values onto the edges of an n-dimensional array. """ import numpy as np from numpy.core.overrides import array_function_dispatch from numpy.lib.index_tricks import ndindex __all__ = ['pad'] ############################################################################### # Private utility functions. def _round_if_needed(arr, dtype): """ Rounds arr inplace if destination dtype is integer. Parameters ---------- arr : ndarray Input array. dtype : dtype The dtype of the destination array. """ if np.issubdtype(dtype, np.integer): arr.round(out=arr) def _slice_at_axis(sl, axis): """ Construct tuple of slices to slice an array in the given dimension. Parameters ---------- sl : slice The slice for the given dimension. axis : int The axis to which `sl` is applied. All other dimensions are left "unsliced". Returns ------- sl : tuple of slices A tuple with slices matching `shape` in length. Examples -------- >>> _slice_at_axis(slice(None, 3, -1), 1) (slice(None, None, None), slice(None, 3, -1), (...,)) """ return (slice(None),) * axis + (sl,) + (...,) def _view_roi(array, original_area_slice, axis): """ Get a view of the current region of interest during iterative padding. When padding multiple dimensions iteratively corner values are unnecessarily overwritten multiple times. This function reduces the working area for the first dimensions so that corners are excluded. Parameters ---------- array : ndarray The array with the region of interest. original_area_slice : tuple of slices Denotes the area with original values of the unpadded array. axis : int The currently padded dimension assuming that `axis` is padded before `axis` + 1. Returns ------- roi : ndarray The region of interest of the original `array`. """ axis += 1 sl = (slice(None),) * axis + original_area_slice[axis:] return array[sl] def _pad_simple(array, pad_width, fill_value=None): """ Pad array on all sides with either a single value or undefined values. Parameters ---------- array : ndarray Array to grow. pad_width : sequence of tuple[int, int] Pad width on both sides for each dimension in `arr`. fill_value : scalar, optional If provided the padded area is filled with this value, otherwise the pad area left undefined. Returns ------- padded : ndarray The padded array with the same dtype as`array`. Its order will default to C-style if `array` is not F-contiguous. original_area_slice : tuple A tuple of slices pointing to the area of the original array. """ # Allocate grown array new_shape = tuple( left + size + right for size, (left, right) in zip(array.shape, pad_width) ) order = 'F' if array.flags.fnc else 'C' # Fortran and not also C-order padded = np.empty(new_shape, dtype=array.dtype, order=order) if fill_value is not None: padded.fill(fill_value) # Copy old array into correct space original_area_slice = tuple( slice(left, left + size) for size, (left, right) in zip(array.shape, pad_width) ) padded[original_area_slice] = array return padded, original_area_slice def _set_pad_area(padded, axis, width_pair, value_pair): """ Set empty-padded area in given dimension. Parameters ---------- padded : ndarray Array with the pad area which is modified inplace. axis : int Dimension with the pad area to set. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. value_pair : tuple of scalars or ndarrays Values inserted into the pad area on each side. It must match or be broadcastable to the shape of `arr`. """ left_slice = _slice_at_axis(slice(None, width_pair[0]), axis) padded[left_slice] = value_pair[0] right_slice = _slice_at_axis( slice(padded.shape[axis] - width_pair[1], None), axis) padded[right_slice] = value_pair[1] def _get_edges(padded, axis, width_pair): """ Retrieve edge values from empty-padded array in given dimension. Parameters ---------- padded : ndarray Empty-padded array. axis : int Dimension in which the edges are considered. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. Returns ------- left_edge, right_edge : ndarray Edge values of the valid area in `padded` in the given dimension. Its shape will always match `padded` except for the dimension given by `axis` which will have a length of 1. """ left_index = width_pair[0] left_slice = _slice_at_axis(slice(left_index, left_index + 1), axis) left_edge = padded[left_slice] right_index = padded.shape[axis] - width_pair[1] right_slice = _slice_at_axis(slice(right_index - 1, right_index), axis) right_edge = padded[right_slice] return left_edge, right_edge def _get_linear_ramps(padded, axis, width_pair, end_value_pair): """ Construct linear ramps for empty-padded array in given dimension. Parameters ---------- padded : ndarray Empty-padded array. axis : int Dimension in which the ramps are constructed. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. end_value_pair : (scalar, scalar) End values for the linear ramps which form the edge of the fully padded array. These values are included in the linear ramps. Returns ------- left_ramp, right_ramp : ndarray Linear ramps to set on both sides of `padded`. """ edge_pair = _get_edges(padded, axis, width_pair) left_ramp, right_ramp = ( np.linspace( start=end_value, stop=edge.squeeze(axis), # Dimension is replaced by linspace num=width, endpoint=False, dtype=padded.dtype, axis=axis ) for end_value, edge, width in zip( end_value_pair, edge_pair, width_pair ) ) # Reverse linear space in appropriate dimension right_ramp = right_ramp[_slice_at_axis(slice(None, None, -1), axis)] return left_ramp, right_ramp def _get_stats(padded, axis, width_pair, length_pair, stat_func): """ Calculate statistic for the empty-padded array in given dimension. Parameters ---------- padded : ndarray Empty-padded array. axis : int Dimension in which the statistic is calculated. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. length_pair : 2-element sequence of None or int Gives the number of values in valid area from each side that is taken into account when calculating the statistic. If None the entire valid area in `padded` is considered. stat_func : function Function to compute statistic. The expected signature is ``stat_func(x: ndarray, axis: int, keepdims: bool) -> ndarray``. Returns ------- left_stat, right_stat : ndarray Calculated statistic for both sides of `padded`. """ # Calculate indices of the edges of the area with original values left_index = width_pair[0] right_index = padded.shape[axis] - width_pair[1] # as well as its length max_length = right_index - left_index # Limit stat_lengths to max_length left_length, right_length = length_pair if left_length is None or max_length < left_length: left_length = max_length if right_length is None or max_length < right_length: right_length = max_length if (left_length == 0 or right_length == 0) \ and stat_func in {np.amax, np.amin}: # amax and amin can't operate on an empty array, # raise a more descriptive warning here instead of the default one raise ValueError("stat_length of 0 yields no value for padding") # Calculate statistic for the left side left_slice = _slice_at_axis( slice(left_index, left_index + left_length), axis) left_chunk = padded[left_slice] left_stat = stat_func(left_chunk, axis=axis, keepdims=True) _round_if_needed(left_stat, padded.dtype) if left_length == right_length == max_length: # return early as right_stat must be identical to left_stat return left_stat, left_stat # Calculate statistic for the right side right_slice = _slice_at_axis( slice(right_index - right_length, right_index), axis) right_chunk = padded[right_slice] right_stat = stat_func(right_chunk, axis=axis, keepdims=True) _round_if_needed(right_stat, padded.dtype) return left_stat, right_stat def _set_reflect_both(padded, axis, width_pair, method, include_edge=False): """ Pad `axis` of `arr` with reflection. Parameters ---------- padded : ndarray Input array of arbitrary shape. axis : int Axis along which to pad `arr`. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. method : str Controls method of reflection; options are 'even' or 'odd'. include_edge : bool If true, edge value is included in reflection, otherwise the edge value forms the symmetric axis to the reflection. Returns ------- pad_amt : tuple of ints, length 2 New index positions of padding to do along the `axis`. If these are both 0, padding is done in this dimension. """ left_pad, right_pad = width_pair old_length = padded.shape[axis] - right_pad - left_pad if include_edge: # Edge is included, we need to offset the pad amount by 1 edge_offset = 1 else: edge_offset = 0 # Edge is not included, no need to offset pad amount old_length -= 1 # but must be omitted from the chunk if left_pad > 0: # Pad with reflected values on left side: # First limit chunk size which can't be larger than pad area chunk_length = min(old_length, left_pad) # Slice right to left, stop on or next to edge, start relative to stop stop = left_pad - edge_offset start = stop + chunk_length left_slice = _slice_at_axis(slice(start, stop, -1), axis) left_chunk = padded[left_slice] if method == "odd": # Negate chunk and align with edge edge_slice = _slice_at_axis(slice(left_pad, left_pad + 1), axis) left_chunk = 2 * padded[edge_slice] - left_chunk # Insert chunk into padded area start = left_pad - chunk_length stop = left_pad pad_area = _slice_at_axis(slice(start, stop), axis) padded[pad_area] = left_chunk # Adjust pointer to left edge for next iteration left_pad -= chunk_length if right_pad > 0: # Pad with reflected values on right side: # First limit chunk size which can't be larger than pad area chunk_length = min(old_length, right_pad) # Slice right to left, start on or next to edge, stop relative to start start = -right_pad + edge_offset - 2 stop = start - chunk_length right_slice = _slice_at_axis(slice(start, stop, -1), axis) right_chunk = padded[right_slice] if method == "odd": # Negate chunk and align with edge edge_slice = _slice_at_axis( slice(-right_pad - 1, -right_pad), axis) right_chunk = 2 * padded[edge_slice] - right_chunk # Insert chunk into padded area start = padded.shape[axis] - right_pad stop = start + chunk_length pad_area = _slice_at_axis(slice(start, stop), axis) padded[pad_area] = right_chunk # Adjust pointer to right edge for next iteration right_pad -= chunk_length return left_pad, right_pad def _set_wrap_both(padded, axis, width_pair): """ Pad `axis` of `arr` with wrapped values. Parameters ---------- padded : ndarray Input array of arbitrary shape. axis : int Axis along which to pad `arr`. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. Returns ------- pad_amt : tuple of ints, length 2 New index positions of padding to do along the `axis`. If these are both 0, padding is done in this dimension. """ left_pad, right_pad = width_pair period = padded.shape[axis] - right_pad - left_pad # If the current dimension of `arr` doesn't contain enough valid values # (not part of the undefined pad area) we need to pad multiple times. # Each time the pad area shrinks on both sides which is communicated with # these variables. new_left_pad = 0 new_right_pad = 0 if left_pad > 0: # Pad with wrapped values on left side # First slice chunk from right side of the non-pad area. # Use min(period, left_pad) to ensure that chunk is not larger than # pad area right_slice = _slice_at_axis( slice(-right_pad - min(period, left_pad), -right_pad if right_pad != 0 else None), axis ) right_chunk = padded[right_slice] if left_pad > period: # Chunk is smaller than pad area pad_area = _slice_at_axis(slice(left_pad - period, left_pad), axis) new_left_pad = left_pad - period else: # Chunk matches pad area pad_area = _slice_at_axis(slice(None, left_pad), axis) padded[pad_area] = right_chunk if right_pad > 0: # Pad with wrapped values on right side # First slice chunk from left side of the non-pad area. # Use min(period, right_pad) to ensure that chunk is not larger than # pad area left_slice = _slice_at_axis( slice(left_pad, left_pad + min(period, right_pad),), axis) left_chunk = padded[left_slice] if right_pad > period: # Chunk is smaller than pad area pad_area = _slice_at_axis( slice(-right_pad, -right_pad + period), axis) new_right_pad = right_pad - period else: # Chunk matches pad area pad_area = _slice_at_axis(slice(-right_pad, None), axis) padded[pad_area] = left_chunk return new_left_pad, new_right_pad def _as_pairs(x, ndim, as_index=False): """ Broadcast `x` to an array with the shape (`ndim`, 2). A helper function for `pad` that prepares and validates arguments like `pad_width` for iteration in pairs. Parameters ---------- x : {None, scalar, array-like} The object to broadcast to the shape (`ndim`, 2). ndim : int Number of pairs the broadcasted `x` will have. as_index : bool, optional If `x` is not None, try to round each element of `x` to an integer (dtype `np.intp`) and ensure every element is positive. Returns ------- pairs : nested iterables, shape (`ndim`, 2) The broadcasted version of `x`. Raises ------ ValueError If `as_index` is True and `x` contains negative elements. Or if `x` is not broadcastable to the shape (`ndim`, 2). """ if x is None: # Pass through None as a special case, otherwise np.round(x) fails # with an AttributeError return ((None, None),) * ndim x = np.array(x) if as_index: x = np.round(x).astype(np.intp, copy=False) if x.ndim < 3: # Optimization: Possibly use faster paths for cases where `x` has # only 1 or 2 elements. `np.broadcast_to` could handle these as well # but is currently slower if x.size == 1: # x was supplied as a single value x = x.ravel() # Ensure x[0] works for x.ndim == 0, 1, 2 if as_index and x < 0: raise ValueError("index can't contain negative values") return ((x[0], x[0]),) * ndim if x.size == 2 and x.shape != (2, 1): # x was supplied with a single value for each side # but except case when each dimension has a single value # which should be broadcasted to a pair, # e.g. [[1], [2]] -> [[1, 1], [2, 2]] not [[1, 2], [1, 2]] x = x.ravel() # Ensure x[0], x[1] works if as_index and (x[0] < 0 or x[1] < 0): raise ValueError("index can't contain negative values") return ((x[0], x[1]),) * ndim if as_index and x.min() < 0: raise ValueError("index can't contain negative values") # Converting the array with `tolist` seems to improve performance # when iterating and indexing the result (see usage in `pad`) return np.broadcast_to(x, (ndim, 2)).tolist() def _pad_dispatcher(array, pad_width, mode=None, **kwargs): return (array,) ############################################################################### # Public functions @array_function_dispatch(_pad_dispatcher, module='numpy') 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)`` or ``((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)`` or ``((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)`` or ``((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)`` or ``((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)) from None 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