Current File : //opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/lib/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, original_period):
"""
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.
original_period : int
Original length of data on `axis` of `arr`.
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
# Avoid wrapping with only a subset of the original area by ensuring period
# can only be a multiple of the original area's length.
period = period // original_period * original_period
# 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 left side of the non-pad area.
# Use min(period, left_pad) to ensure that chunk is not larger than
# pad area.
slice_end = left_pad + period
slice_start = slice_end - min(period, left_pad)
right_slice = _slice_at_axis(slice(slice_start, slice_end), 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 right side of the non-pad area.
# Use min(period, right_pad) to ensure that chunk is not larger than
# pad area.
slice_start = -right_pad - period
slice_end = slice_start + min(period, right_pad)
left_slice = _slice_at_axis(slice(slice_start, slice_end), 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)
original_period = padded.shape[axis] - right_index - left_index
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), original_period)
return padded
Mini Shell