pad(array, pad_width, mode='constant', **kwargs)
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
vector
iaxis_pad_width
iaxis_pad_width
iaxis
iaxis
kwargs
kwargs
The array to pad.
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.
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.
<function>
Padding function, see Notes.
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.
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.
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.
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.
Padded array of rank equal to array
with shape increased according to :None:None:`pad_width`.
Pad an array.
>>> 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]])See :
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.transform._geometric._to_ndimage_mode
skimage.transform._warps.swirl
numpy.resize
numpy.lib.arraypad._as_pairs
skimage.transform._warps_cy._warp_fast
skimage.transform._warps.rotate
skimage.morphology._util._fast_pad
skimage.restoration.non_local_means.denoise_nl_means
skimage.transform._warps._clip_warp_output
skimage.transform._warps.resize
skimage.restoration._denoise.denoise_bilateral
skimage.transform._warps.warp
scipy.signal._upfirdn.upfirdn
dask.array.creation.pad
skimage.transform._warps.rescale
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them