The arraypad module contains a group of functions to pad values onto the edges of an n-dimensional array.
Function | pad |
Pad an array. |
Function | _as |
Broadcast x to an array with the shape (ndim , 2). |
Function | _get |
Retrieve edge values from empty-padded array in given dimension. |
Function | _get |
Construct linear ramps for empty-padded array in given dimension. |
Function | _get |
Calculate statistic for the empty-padded array in given dimension. |
Function | _pad |
Undocumented |
Function | _pad |
Pad array on all sides with either a single value or undefined values. |
Function | _round |
Rounds arr inplace if destination dtype is integer. |
Function | _set |
Set empty-padded area in given dimension. |
Function | _set |
Pad axis of arr with reflection. |
Function | _set |
Pad axis of arr with wrapped values. |
Function | _slice |
Construct tuple of slices to slice an array in the given dimension. |
Function | _view |
Get a view of the current region of interest during iterative padding. |
def pad(array, pad_width, mode='constant', **kwargs): (source) ¶
Pad an array.
Notes
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]])
Parameters | |
array:array_like of rank N | The array to pad. |
padarray_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.
|
**kwargs | Undocumented |
statsequence 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. |
constantsequence 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. |
endsequence 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 | 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 | |
ndarray | pad - Padded array of rank equal to array with shape increased
according to pad_width . |
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. |
asbool , 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 | |
nested iterables, shape(ndim , 2) | pairs - 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). |
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. |
widthint , int) | Pair of widths that mark the pad area on both sides in the given dimension. |
Returns | |
ndarray | left_edge, right_edge - 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. |
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. |
widthint , int) | Pair of widths that mark the pad area on both sides in the given dimension. |
endscalar , 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 | |
ndarray | left_ramp, right_ramp - Linear ramps to set on both sides of padded . |
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. |
widthint , int) | Pair of widths that mark the pad area on both sides in the given dimension. |
lengthNone 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. |
statfunction | Function to compute statistic. The expected signature is stat_func(x: ndarray, axis: int, keepdims: bool) -> ndarray. |
Returns | |
ndarray | left_stat, right_stat - Calculated statistic for both sides of padded . |
Pad array on all sides with either a single value or undefined values.
Parameters | |
array:ndarray | Array to grow. |
padsequence of tuple[int , int] | Pad width on both sides for each dimension in arr . |
fillscalar , optional | If provided the padded area is filled with this value, otherwise the pad area left undefined. |
Returns | |
Rounds arr inplace if destination dtype is integer.
Parameters | |
arr:ndarray | Input array. |
dtype:dtype | The dtype of the destination array. |
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. |
widthint , int) | Pair of widths that mark the pad area on both sides in the given dimension. |
valuetuple of scalars or ndarrays | Values inserted into the pad area on each side. It must match or be
broadcastable to the shape of arr . |
Pad axis
of arr
with reflection.
Parameters | |
padded:ndarray | Input array of arbitrary shape. |
axis:int | Axis along which to pad arr . |
widthint , 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'. |
includebool | If true, edge value is included in reflection, otherwise the edge value forms the symmetric axis to the reflection. |
Returns | |
tuple of ints , length 2 | pad_amt - New index positions of padding to do along the axis . If these are
both 0, padding is done in this dimension. |
Pad axis
of arr
with wrapped values.
Parameters | |
padded:ndarray | Input array of arbitrary shape. |
axis:int | Axis along which to pad arr . |
widthint , int) | Pair of widths that mark the pad area on both sides in the given dimension. |
Returns | |
tuple of ints , length 2 | pad_amt - New index positions of padding to do along the axis . If these are
both 0, padding is done in this dimension. |
Construct tuple of slices to slice an array in the given dimension.
Examples
>>> _slice_at_axis(slice(None, 3, -1), 1) (slice(None, None, None), slice(None, 3, -1), (...,))
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 | |
tuple of slices | sl - A tuple with slices matching shape in length. |
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. |
originaltuple 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 | |
ndarray | roi - The region of interest of the original array . |