numpy.lib.arraypad

module documentation

The arraypad module contains a group of functions to pad values onto the edges of an n-dimensional array.

Function	`_as_pairs`	Broadcast `x` to an array with the shape (`ndim`, 2).
Function	`_get_edges`	Retrieve edge values from empty-padded array in given dimension.
Function	`_get_linear_ramps`	Construct linear ramps for empty-padded array in given dimension.
Function	`_get_stats`	Calculate statistic for the empty-padded array in given dimension.
Function	`_pad_dispatcher`	Undocumented
Function	`_pad_simple`	Pad array on all sides with either a single value or undefined values.
Function	`_round_if_needed`	Rounds arr inplace if destination dtype is integer.
Function	`_set_pad_area`	Set empty-padded area in given dimension.
Function	`_set_reflect_both`	Pad `axis` of `arr` with reflection.
Function	`_set_wrap_both`	Pad `axis` of `arr` with wrapped values.
Function	`_slice_at_axis`	Construct tuple of slices to slice an array in the given dimension.
Function	`_view_roi`	Get a view of the current region of interest during iterative padding.
Function	`pad`	Pad an array.

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).

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.

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.

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.

def _pad_dispatcher(array, pad_width, mode=None, **kwargs):

Undocumented

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.

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.

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.

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.

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.

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), (...,))

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.

@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),) 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.

New in version 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),) 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),) 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),) 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

New in version 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]])