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1"""
2The arraypad module contains a group of functions to pad values onto the edges
3of an n-dimensional array.
5"""
6import numpy as np
7from numpy.core.overrides import array_function_dispatch
8from numpy.lib.index_tricks import ndindex
11__all__ = ['pad']
14###############################################################################
15# Private utility functions.
18def _round_if_needed(arr, dtype):
19 """
20 Rounds arr inplace if destination dtype is integer.
22 Parameters
23 ----------
24 arr : ndarray
25 Input array.
26 dtype : dtype
27 The dtype of the destination array.
28 """
29 if np.issubdtype(dtype, np.integer):
30 arr.round(out=arr)
33def _slice_at_axis(sl, axis):
34 """
35 Construct tuple of slices to slice an array in the given dimension.
37 Parameters
38 ----------
39 sl : slice
40 The slice for the given dimension.
41 axis : int
42 The axis to which `sl` is applied. All other dimensions are left
43 "unsliced".
45 Returns
46 -------
47 sl : tuple of slices
48 A tuple with slices matching `shape` in length.
50 Examples
51 --------
52 >>> _slice_at_axis(slice(None, 3, -1), 1)
53 (slice(None, None, None), slice(None, 3, -1), (...,))
54 """
55 return (slice(None),) * axis + (sl,) + (...,)
58def _view_roi(array, original_area_slice, axis):
59 """
60 Get a view of the current region of interest during iterative padding.
62 When padding multiple dimensions iteratively corner values are
63 unnecessarily overwritten multiple times. This function reduces the
64 working area for the first dimensions so that corners are excluded.
66 Parameters
67 ----------
68 array : ndarray
69 The array with the region of interest.
70 original_area_slice : tuple of slices
71 Denotes the area with original values of the unpadded array.
72 axis : int
73 The currently padded dimension assuming that `axis` is padded before
74 `axis` + 1.
76 Returns
77 -------
78 roi : ndarray
79 The region of interest of the original `array`.
80 """
81 axis += 1
82 sl = (slice(None),) * axis + original_area_slice[axis:]
83 return array[sl]
86def _pad_simple(array, pad_width, fill_value=None):
87 """
88 Pad array on all sides with either a single value or undefined values.
90 Parameters
91 ----------
92 array : ndarray
93 Array to grow.
94 pad_width : sequence of tuple[int, int]
95 Pad width on both sides for each dimension in `arr`.
96 fill_value : scalar, optional
97 If provided the padded area is filled with this value, otherwise
98 the pad area left undefined.
100 Returns
101 -------
102 padded : ndarray
103 The padded array with the same dtype as`array`. Its order will default
104 to C-style if `array` is not F-contiguous.
105 original_area_slice : tuple
106 A tuple of slices pointing to the area of the original array.
107 """
108 # Allocate grown array
109 new_shape = tuple(
110 left + size + right
111 for size, (left, right) in zip(array.shape, pad_width)
112 )
113 order = 'F' if array.flags.fnc else 'C' # Fortran and not also C-order
114 padded = np.empty(new_shape, dtype=array.dtype, order=order)
116 if fill_value is not None:
117 padded.fill(fill_value)
119 # Copy old array into correct space
120 original_area_slice = tuple(
121 slice(left, left + size)
122 for size, (left, right) in zip(array.shape, pad_width)
123 )
124 padded[original_area_slice] = array
126 return padded, original_area_slice
129def _set_pad_area(padded, axis, width_pair, value_pair):
130 """
131 Set empty-padded area in given dimension.
133 Parameters
134 ----------
135 padded : ndarray
136 Array with the pad area which is modified inplace.
137 axis : int
138 Dimension with the pad area to set.
139 width_pair : (int, int)
140 Pair of widths that mark the pad area on both sides in the given
141 dimension.
142 value_pair : tuple of scalars or ndarrays
143 Values inserted into the pad area on each side. It must match or be
144 broadcastable to the shape of `arr`.
145 """
146 left_slice = _slice_at_axis(slice(None, width_pair[0]), axis)
147 padded[left_slice] = value_pair[0]
149 right_slice = _slice_at_axis(
150 slice(padded.shape[axis] - width_pair[1], None), axis)
151 padded[right_slice] = value_pair[1]
154def _get_edges(padded, axis, width_pair):
155 """
156 Retrieve edge values from empty-padded array in given dimension.
158 Parameters
159 ----------
160 padded : ndarray
161 Empty-padded array.
162 axis : int
163 Dimension in which the edges are considered.
164 width_pair : (int, int)
165 Pair of widths that mark the pad area on both sides in the given
166 dimension.
168 Returns
169 -------
170 left_edge, right_edge : ndarray
171 Edge values of the valid area in `padded` in the given dimension. Its
172 shape will always match `padded` except for the dimension given by
173 `axis` which will have a length of 1.
174 """
175 left_index = width_pair[0]
176 left_slice = _slice_at_axis(slice(left_index, left_index + 1), axis)
177 left_edge = padded[left_slice]
179 right_index = padded.shape[axis] - width_pair[1]
180 right_slice = _slice_at_axis(slice(right_index - 1, right_index), axis)
181 right_edge = padded[right_slice]
183 return left_edge, right_edge
186def _get_linear_ramps(padded, axis, width_pair, end_value_pair):
187 """
188 Construct linear ramps for empty-padded array in given dimension.
190 Parameters
191 ----------
192 padded : ndarray
193 Empty-padded array.
194 axis : int
195 Dimension in which the ramps are constructed.
196 width_pair : (int, int)
197 Pair of widths that mark the pad area on both sides in the given
198 dimension.
199 end_value_pair : (scalar, scalar)
200 End values for the linear ramps which form the edge of the fully padded
201 array. These values are included in the linear ramps.
203 Returns
204 -------
205 left_ramp, right_ramp : ndarray
206 Linear ramps to set on both sides of `padded`.
207 """
208 edge_pair = _get_edges(padded, axis, width_pair)
210 left_ramp, right_ramp = (
211 np.linspace(
212 start=end_value,
213 stop=edge.squeeze(axis), # Dimension is replaced by linspace
214 num=width,
215 endpoint=False,
216 dtype=padded.dtype,
217 axis=axis
218 )
219 for end_value, edge, width in zip(
220 end_value_pair, edge_pair, width_pair
221 )
222 )
224 # Reverse linear space in appropriate dimension
225 right_ramp = right_ramp[_slice_at_axis(slice(None, None, -1), axis)]
227 return left_ramp, right_ramp
230def _get_stats(padded, axis, width_pair, length_pair, stat_func):
231 """
232 Calculate statistic for the empty-padded array in given dimension.
234 Parameters
235 ----------
236 padded : ndarray
237 Empty-padded array.
238 axis : int
239 Dimension in which the statistic is calculated.
240 width_pair : (int, int)
241 Pair of widths that mark the pad area on both sides in the given
242 dimension.
243 length_pair : 2-element sequence of None or int
244 Gives the number of values in valid area from each side that is
245 taken into account when calculating the statistic. If None the entire
246 valid area in `padded` is considered.
247 stat_func : function
248 Function to compute statistic. The expected signature is
249 ``stat_func(x: ndarray, axis: int, keepdims: bool) -> ndarray``.
251 Returns
252 -------
253 left_stat, right_stat : ndarray
254 Calculated statistic for both sides of `padded`.
255 """
256 # Calculate indices of the edges of the area with original values
257 left_index = width_pair[0]
258 right_index = padded.shape[axis] - width_pair[1]
259 # as well as its length
260 max_length = right_index - left_index
262 # Limit stat_lengths to max_length
263 left_length, right_length = length_pair
264 if left_length is None or max_length < left_length:
265 left_length = max_length
266 if right_length is None or max_length < right_length:
267 right_length = max_length
269 if (left_length == 0 or right_length == 0) \
270 and stat_func in {np.amax, np.amin}:
271 # amax and amin can't operate on an empty array,
272 # raise a more descriptive warning here instead of the default one
273 raise ValueError("stat_length of 0 yields no value for padding")
275 # Calculate statistic for the left side
276 left_slice = _slice_at_axis(
277 slice(left_index, left_index + left_length), axis)
278 left_chunk = padded[left_slice]
279 left_stat = stat_func(left_chunk, axis=axis, keepdims=True)
280 _round_if_needed(left_stat, padded.dtype)
282 if left_length == right_length == max_length:
283 # return early as right_stat must be identical to left_stat
284 return left_stat, left_stat
286 # Calculate statistic for the right side
287 right_slice = _slice_at_axis(
288 slice(right_index - right_length, right_index), axis)
289 right_chunk = padded[right_slice]
290 right_stat = stat_func(right_chunk, axis=axis, keepdims=True)
291 _round_if_needed(right_stat, padded.dtype)
293 return left_stat, right_stat
296def _set_reflect_both(padded, axis, width_pair, method, include_edge=False):
297 """
298 Pad `axis` of `arr` with reflection.
300 Parameters
301 ----------
302 padded : ndarray
303 Input array of arbitrary shape.
304 axis : int
305 Axis along which to pad `arr`.
306 width_pair : (int, int)
307 Pair of widths that mark the pad area on both sides in the given
308 dimension.
309 method : str
310 Controls method of reflection; options are 'even' or 'odd'.
311 include_edge : bool
312 If true, edge value is included in reflection, otherwise the edge
313 value forms the symmetric axis to the reflection.
315 Returns
316 -------
317 pad_amt : tuple of ints, length 2
318 New index positions of padding to do along the `axis`. If these are
319 both 0, padding is done in this dimension.
320 """
321 left_pad, right_pad = width_pair
322 old_length = padded.shape[axis] - right_pad - left_pad
324 if include_edge:
325 # Edge is included, we need to offset the pad amount by 1
326 edge_offset = 1
327 else:
328 edge_offset = 0 # Edge is not included, no need to offset pad amount
329 old_length -= 1 # but must be omitted from the chunk
331 if left_pad > 0:
332 # Pad with reflected values on left side:
333 # First limit chunk size which can't be larger than pad area
334 chunk_length = min(old_length, left_pad)
335 # Slice right to left, stop on or next to edge, start relative to stop
336 stop = left_pad - edge_offset
337 start = stop + chunk_length
338 left_slice = _slice_at_axis(slice(start, stop, -1), axis)
339 left_chunk = padded[left_slice]
341 if method == "odd":
342 # Negate chunk and align with edge
343 edge_slice = _slice_at_axis(slice(left_pad, left_pad + 1), axis)
344 left_chunk = 2 * padded[edge_slice] - left_chunk
346 # Insert chunk into padded area
347 start = left_pad - chunk_length
348 stop = left_pad
349 pad_area = _slice_at_axis(slice(start, stop), axis)
350 padded[pad_area] = left_chunk
351 # Adjust pointer to left edge for next iteration
352 left_pad -= chunk_length
354 if right_pad > 0:
355 # Pad with reflected values on right side:
356 # First limit chunk size which can't be larger than pad area
357 chunk_length = min(old_length, right_pad)
358 # Slice right to left, start on or next to edge, stop relative to start
359 start = -right_pad + edge_offset - 2
360 stop = start - chunk_length
361 right_slice = _slice_at_axis(slice(start, stop, -1), axis)
362 right_chunk = padded[right_slice]
364 if method == "odd":
365 # Negate chunk and align with edge
366 edge_slice = _slice_at_axis(
367 slice(-right_pad - 1, -right_pad), axis)
368 right_chunk = 2 * padded[edge_slice] - right_chunk
370 # Insert chunk into padded area
371 start = padded.shape[axis] - right_pad
372 stop = start + chunk_length
373 pad_area = _slice_at_axis(slice(start, stop), axis)
374 padded[pad_area] = right_chunk
375 # Adjust pointer to right edge for next iteration
376 right_pad -= chunk_length
378 return left_pad, right_pad
381def _set_wrap_both(padded, axis, width_pair):
382 """
383 Pad `axis` of `arr` with wrapped values.
385 Parameters
386 ----------
387 padded : ndarray
388 Input array of arbitrary shape.
389 axis : int
390 Axis along which to pad `arr`.
391 width_pair : (int, int)
392 Pair of widths that mark the pad area on both sides in the given
393 dimension.
395 Returns
396 -------
397 pad_amt : tuple of ints, length 2
398 New index positions of padding to do along the `axis`. If these are
399 both 0, padding is done in this dimension.
400 """
401 left_pad, right_pad = width_pair
402 period = padded.shape[axis] - right_pad - left_pad
404 # If the current dimension of `arr` doesn't contain enough valid values
405 # (not part of the undefined pad area) we need to pad multiple times.
406 # Each time the pad area shrinks on both sides which is communicated with
407 # these variables.
408 new_left_pad = 0
409 new_right_pad = 0
411 if left_pad > 0:
412 # Pad with wrapped values on left side
413 # First slice chunk from right side of the non-pad area.
414 # Use min(period, left_pad) to ensure that chunk is not larger than
415 # pad area
416 right_slice = _slice_at_axis(
417 slice(-right_pad - min(period, left_pad),
418 -right_pad if right_pad != 0 else None),
419 axis
420 )
421 right_chunk = padded[right_slice]
423 if left_pad > period:
424 # Chunk is smaller than pad area
425 pad_area = _slice_at_axis(slice(left_pad - period, left_pad), axis)
426 new_left_pad = left_pad - period
427 else:
428 # Chunk matches pad area
429 pad_area = _slice_at_axis(slice(None, left_pad), axis)
430 padded[pad_area] = right_chunk
432 if right_pad > 0:
433 # Pad with wrapped values on right side
434 # First slice chunk from left side of the non-pad area.
435 # Use min(period, right_pad) to ensure that chunk is not larger than
436 # pad area
437 left_slice = _slice_at_axis(
438 slice(left_pad, left_pad + min(period, right_pad),), axis)
439 left_chunk = padded[left_slice]
441 if right_pad > period:
442 # Chunk is smaller than pad area
443 pad_area = _slice_at_axis(
444 slice(-right_pad, -right_pad + period), axis)
445 new_right_pad = right_pad - period
446 else:
447 # Chunk matches pad area
448 pad_area = _slice_at_axis(slice(-right_pad, None), axis)
449 padded[pad_area] = left_chunk
451 return new_left_pad, new_right_pad
454def _as_pairs(x, ndim, as_index=False):
455 """
456 Broadcast `x` to an array with the shape (`ndim`, 2).
458 A helper function for `pad` that prepares and validates arguments like
459 `pad_width` for iteration in pairs.
461 Parameters
462 ----------
463 x : {None, scalar, array-like}
464 The object to broadcast to the shape (`ndim`, 2).
465 ndim : int
466 Number of pairs the broadcasted `x` will have.
467 as_index : bool, optional
468 If `x` is not None, try to round each element of `x` to an integer
469 (dtype `np.intp`) and ensure every element is positive.
471 Returns
472 -------
473 pairs : nested iterables, shape (`ndim`, 2)
474 The broadcasted version of `x`.
476 Raises
477 ------
478 ValueError
479 If `as_index` is True and `x` contains negative elements.
480 Or if `x` is not broadcastable to the shape (`ndim`, 2).
481 """
482 if x is None:
483 # Pass through None as a special case, otherwise np.round(x) fails
484 # with an AttributeError
485 return ((None, None),) * ndim
487 x = np.array(x)
488 if as_index:
489 x = np.round(x).astype(np.intp, copy=False)
491 if x.ndim < 3:
492 # Optimization: Possibly use faster paths for cases where `x` has
493 # only 1 or 2 elements. `np.broadcast_to` could handle these as well
494 # but is currently slower
496 if x.size == 1:
497 # x was supplied as a single value
498 x = x.ravel() # Ensure x[0] works for x.ndim == 0, 1, 2
499 if as_index and x < 0:
500 raise ValueError("index can't contain negative values")
501 return ((x[0], x[0]),) * ndim
503 if x.size == 2 and x.shape != (2, 1):
504 # x was supplied with a single value for each side
505 # but except case when each dimension has a single value
506 # which should be broadcasted to a pair,
507 # e.g. [[1], [2]] -> [[1, 1], [2, 2]] not [[1, 2], [1, 2]]
508 x = x.ravel() # Ensure x[0], x[1] works
509 if as_index and (x[0] < 0 or x[1] < 0):
510 raise ValueError("index can't contain negative values")
511 return ((x[0], x[1]),) * ndim
513 if as_index and x.min() < 0:
514 raise ValueError("index can't contain negative values")
516 # Converting the array with `tolist` seems to improve performance
517 # when iterating and indexing the result (see usage in `pad`)
518 return np.broadcast_to(x, (ndim, 2)).tolist()
521def _pad_dispatcher(array, pad_width, mode=None, **kwargs):
522 return (array,)
525###############################################################################
526# Public functions
529@array_function_dispatch(_pad_dispatcher, module='numpy')
530def pad(array, pad_width, mode='constant', **kwargs):
531 """
532 Pad an array.
534 Parameters
535 ----------
536 array : array_like of rank N
537 The array to pad.
538 pad_width : {sequence, array_like, int}
539 Number of values padded to the edges of each axis.
540 ``((before_1, after_1), ... (before_N, after_N))`` unique pad widths
541 for each axis.
542 ``(before, after)`` or ``((before, after),)`` yields same before
543 and after pad for each axis.
544 ``(pad,)`` or ``int`` is a shortcut for before = after = pad width
545 for all axes.
546 mode : str or function, optional
547 One of the following string values or a user supplied function.
549 'constant' (default)
550 Pads with a constant value.
551 'edge'
552 Pads with the edge values of array.
553 'linear_ramp'
554 Pads with the linear ramp between end_value and the
555 array edge value.
556 'maximum'
557 Pads with the maximum value of all or part of the
558 vector along each axis.
559 'mean'
560 Pads with the mean value of all or part of the
561 vector along each axis.
562 'median'
563 Pads with the median value of all or part of the
564 vector along each axis.
565 'minimum'
566 Pads with the minimum value of all or part of the
567 vector along each axis.
568 'reflect'
569 Pads with the reflection of the vector mirrored on
570 the first and last values of the vector along each
571 axis.
572 'symmetric'
573 Pads with the reflection of the vector mirrored
574 along the edge of the array.
575 'wrap'
576 Pads with the wrap of the vector along the axis.
577 The first values are used to pad the end and the
578 end values are used to pad the beginning.
579 'empty'
580 Pads with undefined values.
582 .. versionadded:: 1.17
584 <function>
585 Padding function, see Notes.
586 stat_length : sequence or int, optional
587 Used in 'maximum', 'mean', 'median', and 'minimum'. Number of
588 values at edge of each axis used to calculate the statistic value.
590 ``((before_1, after_1), ... (before_N, after_N))`` unique statistic
591 lengths for each axis.
593 ``(before, after)`` or ``((before, after),)`` yields same before
594 and after statistic lengths for each axis.
596 ``(stat_length,)`` or ``int`` is a shortcut for
597 ``before = after = statistic`` length for all axes.
599 Default is ``None``, to use the entire axis.
600 constant_values : sequence or scalar, optional
601 Used in 'constant'. The values to set the padded values for each
602 axis.
604 ``((before_1, after_1), ... (before_N, after_N))`` unique pad constants
605 for each axis.
607 ``(before, after)`` or ``((before, after),)`` yields same before
608 and after constants for each axis.
610 ``(constant,)`` or ``constant`` is a shortcut for
611 ``before = after = constant`` for all axes.
613 Default is 0.
614 end_values : sequence or scalar, optional
615 Used in 'linear_ramp'. The values used for the ending value of the
616 linear_ramp and that will form the edge of the padded array.
618 ``((before_1, after_1), ... (before_N, after_N))`` unique end values
619 for each axis.
621 ``(before, after)`` or ``((before, after),)`` yields same before
622 and after end values for each axis.
624 ``(constant,)`` or ``constant`` is a shortcut for
625 ``before = after = constant`` for all axes.
627 Default is 0.
628 reflect_type : {'even', 'odd'}, optional
629 Used in 'reflect', and 'symmetric'. The 'even' style is the
630 default with an unaltered reflection around the edge value. For
631 the 'odd' style, the extended part of the array is created by
632 subtracting the reflected values from two times the edge value.
634 Returns
635 -------
636 pad : ndarray
637 Padded array of rank equal to `array` with shape increased
638 according to `pad_width`.
640 Notes
641 -----
642 .. versionadded:: 1.7.0
644 For an array with rank greater than 1, some of the padding of later
645 axes is calculated from padding of previous axes. This is easiest to
646 think about with a rank 2 array where the corners of the padded array
647 are calculated by using padded values from the first axis.
649 The padding function, if used, should modify a rank 1 array in-place. It
650 has the following signature::
652 padding_func(vector, iaxis_pad_width, iaxis, kwargs)
654 where
656 vector : ndarray
657 A rank 1 array already padded with zeros. Padded values are
658 vector[:iaxis_pad_width[0]] and vector[-iaxis_pad_width[1]:].
659 iaxis_pad_width : tuple
660 A 2-tuple of ints, iaxis_pad_width[0] represents the number of
661 values padded at the beginning of vector where
662 iaxis_pad_width[1] represents the number of values padded at
663 the end of vector.
664 iaxis : int
665 The axis currently being calculated.
666 kwargs : dict
667 Any keyword arguments the function requires.
669 Examples
670 --------
671 >>> a = [1, 2, 3, 4, 5]
672 >>> np.pad(a, (2, 3), 'constant', constant_values=(4, 6))
673 array([4, 4, 1, ..., 6, 6, 6])
675 >>> np.pad(a, (2, 3), 'edge')
676 array([1, 1, 1, ..., 5, 5, 5])
678 >>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))
679 array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4])
681 >>> np.pad(a, (2,), 'maximum')
682 array([5, 5, 1, 2, 3, 4, 5, 5, 5])
684 >>> np.pad(a, (2,), 'mean')
685 array([3, 3, 1, 2, 3, 4, 5, 3, 3])
687 >>> np.pad(a, (2,), 'median')
688 array([3, 3, 1, 2, 3, 4, 5, 3, 3])
690 >>> a = [[1, 2], [3, 4]]
691 >>> np.pad(a, ((3, 2), (2, 3)), 'minimum')
692 array([[1, 1, 1, 2, 1, 1, 1],
693 [1, 1, 1, 2, 1, 1, 1],
694 [1, 1, 1, 2, 1, 1, 1],
695 [1, 1, 1, 2, 1, 1, 1],
696 [3, 3, 3, 4, 3, 3, 3],
697 [1, 1, 1, 2, 1, 1, 1],
698 [1, 1, 1, 2, 1, 1, 1]])
700 >>> a = [1, 2, 3, 4, 5]
701 >>> np.pad(a, (2, 3), 'reflect')
702 array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
704 >>> np.pad(a, (2, 3), 'reflect', reflect_type='odd')
705 array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8])
707 >>> np.pad(a, (2, 3), 'symmetric')
708 array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])
710 >>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd')
711 array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])
713 >>> np.pad(a, (2, 3), 'wrap')
714 array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])
716 >>> def pad_with(vector, pad_width, iaxis, kwargs):
717 ... pad_value = kwargs.get('padder', 10)
718 ... vector[:pad_width[0]] = pad_value
719 ... vector[-pad_width[1]:] = pad_value
720 >>> a = np.arange(6)
721 >>> a = a.reshape((2, 3))
722 >>> np.pad(a, 2, pad_with)
723 array([[10, 10, 10, 10, 10, 10, 10],
724 [10, 10, 10, 10, 10, 10, 10],
725 [10, 10, 0, 1, 2, 10, 10],
726 [10, 10, 3, 4, 5, 10, 10],
727 [10, 10, 10, 10, 10, 10, 10],
728 [10, 10, 10, 10, 10, 10, 10]])
729 >>> np.pad(a, 2, pad_with, padder=100)
730 array([[100, 100, 100, 100, 100, 100, 100],
731 [100, 100, 100, 100, 100, 100, 100],
732 [100, 100, 0, 1, 2, 100, 100],
733 [100, 100, 3, 4, 5, 100, 100],
734 [100, 100, 100, 100, 100, 100, 100],
735 [100, 100, 100, 100, 100, 100, 100]])
736 """
737 array = np.asarray(array)
738 pad_width = np.asarray(pad_width)
740 if not pad_width.dtype.kind == 'i':
741 raise TypeError('`pad_width` must be of integral type.')
743 # Broadcast to shape (array.ndim, 2)
744 pad_width = _as_pairs(pad_width, array.ndim, as_index=True)
746 if callable(mode):
747 # Old behavior: Use user-supplied function with np.apply_along_axis
748 function = mode
749 # Create a new zero padded array
750 padded, _ = _pad_simple(array, pad_width, fill_value=0)
751 # And apply along each axis
753 for axis in range(padded.ndim):
754 # Iterate using ndindex as in apply_along_axis, but assuming that
755 # function operates inplace on the padded array.
757 # view with the iteration axis at the end
758 view = np.moveaxis(padded, axis, -1)
760 # compute indices for the iteration axes, and append a trailing
761 # ellipsis to prevent 0d arrays decaying to scalars (gh-8642)
762 inds = ndindex(view.shape[:-1])
763 inds = (ind + (Ellipsis,) for ind in inds)
764 for ind in inds:
765 function(view[ind], pad_width[axis], axis, kwargs)
767 return padded
769 # Make sure that no unsupported keywords were passed for the current mode
770 allowed_kwargs = {
771 'empty': [], 'edge': [], 'wrap': [],
772 'constant': ['constant_values'],
773 'linear_ramp': ['end_values'],
774 'maximum': ['stat_length'],
775 'mean': ['stat_length'],
776 'median': ['stat_length'],
777 'minimum': ['stat_length'],
778 'reflect': ['reflect_type'],
779 'symmetric': ['reflect_type'],
780 }
781 try:
782 unsupported_kwargs = set(kwargs) - set(allowed_kwargs[mode])
783 except KeyError:
784 raise ValueError("mode '{}' is not supported".format(mode)) from None
785 if unsupported_kwargs:
786 raise ValueError("unsupported keyword arguments for mode '{}': {}"
787 .format(mode, unsupported_kwargs))
789 stat_functions = {"maximum": np.amax, "minimum": np.amin,
790 "mean": np.mean, "median": np.median}
792 # Create array with final shape and original values
793 # (padded area is undefined)
794 padded, original_area_slice = _pad_simple(array, pad_width)
795 # And prepare iteration over all dimensions
796 # (zipping may be more readable than using enumerate)
797 axes = range(padded.ndim)
799 if mode == "constant":
800 values = kwargs.get("constant_values", 0)
801 values = _as_pairs(values, padded.ndim)
802 for axis, width_pair, value_pair in zip(axes, pad_width, values):
803 roi = _view_roi(padded, original_area_slice, axis)
804 _set_pad_area(roi, axis, width_pair, value_pair)
806 elif mode == "empty":
807 pass # Do nothing as _pad_simple already returned the correct result
809 elif array.size == 0:
810 # Only modes "constant" and "empty" can extend empty axes, all other
811 # modes depend on `array` not being empty
812 # -> ensure every empty axis is only "padded with 0"
813 for axis, width_pair in zip(axes, pad_width):
814 if array.shape[axis] == 0 and any(width_pair):
815 raise ValueError(
816 "can't extend empty axis {} using modes other than "
817 "'constant' or 'empty'".format(axis)
818 )
819 # passed, don't need to do anything more as _pad_simple already
820 # returned the correct result
822 elif mode == "edge":
823 for axis, width_pair in zip(axes, pad_width):
824 roi = _view_roi(padded, original_area_slice, axis)
825 edge_pair = _get_edges(roi, axis, width_pair)
826 _set_pad_area(roi, axis, width_pair, edge_pair)
828 elif mode == "linear_ramp":
829 end_values = kwargs.get("end_values", 0)
830 end_values = _as_pairs(end_values, padded.ndim)
831 for axis, width_pair, value_pair in zip(axes, pad_width, end_values):
832 roi = _view_roi(padded, original_area_slice, axis)
833 ramp_pair = _get_linear_ramps(roi, axis, width_pair, value_pair)
834 _set_pad_area(roi, axis, width_pair, ramp_pair)
836 elif mode in stat_functions:
837 func = stat_functions[mode]
838 length = kwargs.get("stat_length", None)
839 length = _as_pairs(length, padded.ndim, as_index=True)
840 for axis, width_pair, length_pair in zip(axes, pad_width, length):
841 roi = _view_roi(padded, original_area_slice, axis)
842 stat_pair = _get_stats(roi, axis, width_pair, length_pair, func)
843 _set_pad_area(roi, axis, width_pair, stat_pair)
845 elif mode in {"reflect", "symmetric"}:
846 method = kwargs.get("reflect_type", "even")
847 include_edge = True if mode == "symmetric" else False
848 for axis, (left_index, right_index) in zip(axes, pad_width):
849 if array.shape[axis] == 1 and (left_index > 0 or right_index > 0):
850 # Extending singleton dimension for 'reflect' is legacy
851 # behavior; it really should raise an error.
852 edge_pair = _get_edges(padded, axis, (left_index, right_index))
853 _set_pad_area(
854 padded, axis, (left_index, right_index), edge_pair)
855 continue
857 roi = _view_roi(padded, original_area_slice, axis)
858 while left_index > 0 or right_index > 0:
859 # Iteratively pad until dimension is filled with reflected
860 # values. This is necessary if the pad area is larger than
861 # the length of the original values in the current dimension.
862 left_index, right_index = _set_reflect_both(
863 roi, axis, (left_index, right_index),
864 method, include_edge
865 )
867 elif mode == "wrap":
868 for axis, (left_index, right_index) in zip(axes, pad_width):
869 roi = _view_roi(padded, original_area_slice, axis)
870 while left_index > 0 or right_index > 0:
871 # Iteratively pad until dimension is filled with wrapped
872 # values. This is necessary if the pad area is larger than
873 # the length of the original values in the current dimension.
874 left_index, right_index = _set_wrap_both(
875 roi, axis, (left_index, right_index))
877 return padded