Utilities

The pygsp.utils module implements some utility functions used throughout the package.

pygsp.utils.build_logger(name)

pygsp.utils.compute_log_scales(lmin, lmax, Nscales, t1=1, t2=2)

Compute logarithm scales for wavelets.

Parameters

lminfloat

Smallest non-zero eigenvalue.

lmaxfloat

Largest eigenvalue, i.e. pygsp.graphs.Graph.lmax.

Nscalesint

Number of scales.

Returns

scalesndarray

List of scales of length Nscales.

Examples

>>> from pygsp import utils
>>> utils.compute_log_scales(1, 10, 3)
array([2.       , 0.4472136, 0.1      ])

pygsp.utils.distanz(x, y=None)

Calculate the distance between two colon vectors.

Parameters

xndarray

First colon vector

yndarray

Second colon vector

Returns

dndarray

Distance between x and y

Examples

>>> from pygsp import utils
>>> x = np.arange(3)
>>> utils.distanz(x, x)
array([[0., 1., 2.],
       [1., 0., 1.],
       [2., 1., 0.]])

pygsp.utils.filterbank_handler(func)

pygsp.utils.loadmat(path)

Load a matlab data file.

Parameters

pathstring

Path to the mat file from the data folder, without the .mat extension.

Returns

datadict

dictionary with variable names as keys, and loaded matrices as values.

Examples

>>> from pygsp import utils
>>> data = utils.loadmat('pointclouds/bunny')
>>> data['bunny'].shape
(2503, 3)

pygsp.utils.rescale_center(x)

Rescale and center data, e.g. embedding coordinates.

Parameters

xndarray

Data to be rescaled.

Returns

rndarray

Rescaled data.

Examples

>>> from pygsp import utils
>>> x = np.array([[1, 6], [2, 5], [3, 4]])
>>> utils.rescale_center(x)
array([[-1. ,  1. ],
       [-0.6,  0.6],
       [-0.2,  0.2]])

pygsp.utils.resistance_distance(G)

Compute the resistance distances of a graph.

Parameters

GGraph or sparse matrix

Graph structure or Laplacian matrix (L)

Returns

rdsparse matrix

distance matrix

References

[KRandic93]

pygsp.utils.symmetrize(W, method='average')

Symmetrize a square matrix.

Parameters

Warray_like

Square matrix to be symmetrized

methodstring

• ‘average’ : symmetrize by averaging with the transpose. Most useful when transforming a directed graph to an undirected one.

• ‘maximum’ : symmetrize by taking the maximum with the transpose. Similar to ‘fill’ except that ambiguous entries are resolved by taking the largest value.

• ‘fill’ : symmetrize by filling in the zeros in both the upper and lower triangular parts. Ambiguous entries are resolved by averaging the values.

• ‘tril’ : symmetrize by considering the lower triangular part only.

• ‘triu’ : symmetrize by considering the upper triangular part only.

Notes

You can have the sum by multiplying the average by two. It is however not a good candidate for this function as it modifies an already symmetric matrix.

Examples

>>> from pygsp import utils
>>> W = np.array([[0, 3, 0], [3, 1, 6], [4, 2, 3]], dtype=float)
>>> W
array([[0., 3., 0.],
       [3., 1., 6.],
       [4., 2., 3.]])
>>> utils.symmetrize(W, method='average')
array([[0., 3., 2.],
       [3., 1., 4.],
       [2., 4., 3.]])
>>> 2 * utils.symmetrize(W, method='average')
array([[0., 6., 4.],
       [6., 2., 8.],
       [4., 8., 6.]])
>>> utils.symmetrize(W, method='maximum')
array([[0., 3., 4.],
       [3., 1., 6.],
       [4., 6., 3.]])
>>> utils.symmetrize(W, method='fill')
array([[0., 3., 4.],
       [3., 1., 4.],
       [4., 4., 3.]])
>>> utils.symmetrize(W, method='tril')
array([[0., 3., 4.],
       [3., 1., 2.],
       [4., 2., 3.]])
>>> utils.symmetrize(W, method='triu')
array([[0., 3., 0.],
       [3., 1., 6.],
       [0., 6., 3.]])