Lib module¶

Tools for the other modules.

Warning

These tools have been designed primarily for internal use throughout the library and often work only in very specific situations (e.g. find_idx_nearest() works only on sorted arrays), so make sure you read their doc carefully before using them.

Content¶

`nngt.lib.InvalidArgument`	Error raised when an argument is invalid.
`nngt.lib.delta_distrib`([graph, elist, num, ...])	Delta distribution for edge attributes.
`nngt.lib.find_idx_nearest`(array, values)	Find the indices of the nearest elements of values in a sorted array.
`nngt.lib.gaussian_distrib`(graph[, elist, ...])	Gaussian distribution for edge attributes.
`nngt.lib.lin_correlated_distrib`(graph[, ...])
`nngt.lib.log_correlated_distrib`(graph[, ...])
`nngt.lib.lognormal_distrib`(graph[, elist, ...])
`nngt.lib.nonstring_container`(obj)	Returns true for any iterable which is not a string or byte sequence.
`nngt.lib.not_implemented`(args, *kwargs)
`nngt.lib.seed`([seed])	Seed the random generator used by NNGT (i.e.
`nngt.lib.uniform_distrib`(graph[, elist, ...])	Uniform distribution for edge attributes.
`nngt.lib.valid_gen_arguments`(func)

Details¶

Various tools for random number generation, array searching and type testing.

nngt.lib.delta_distrib(graph=None, elist=None, num=None, value=1.0, **kwargs)[source]¶

Delta distribution for edge attributes.

Parameters:	graph (`Graph` or subclass) – Graph for which an edge attribute will be generated. elist (@todo) value (float, optional (default: 1.)) – Value of the delta distribution. Returns (`numpy.ndarray`) – Attribute value for each edge in graph.

nngt.lib.find_idx_nearest(array, values)[source]¶

Find the indices of the nearest elements of values in a sorted array.

Warning

Both array and values should be numpy.array objects and array MUST be sorted in increasing order.

Parameters:	array (reference list or np.ndarray) values (double, list or array of values to find in array)
Returns:	idx (int or array representing the index of the closest value in array)

nngt.lib.gaussian_distrib(graph, elist=None, num=None, avg=1.0, std=0.2, **kwargs)[source]¶

Gaussian distribution for edge attributes.

Parameters:	graph (`Graph` or subclass) – Graph for which an edge attribute will be generated. elist (@todo) avg (float, optional (default: 0.)) – Average of the Gaussian distribution. std (float, optional (default: 1.5)) – Standard deviation of the Gaussian distribution. Returns (`numpy.ndarray`) – Attribute value for each edge in graph.

exception nngt.lib.InvalidArgument[source]¶: Error raised when an argument is invalid.

nngt.lib.lin_correlated_distrib(graph, elist=None, correl_attribute='betweenness', noise_scale=None, lower=0.0, upper=2.0, **kwargs)[source]¶

nngt.lib.log_correlated_distrib(graph, elist=None, correl_attribute='betweenness', noise_scale=None, lower=0.0, upper=2.0, **kwargs)[source]¶

nngt.lib.lognormal_distrib(graph, elist=None, num=None, position=1.0, scale=0.2, **kwargs)[source]¶

nngt.lib.nonstring_container(obj)[source]¶: Returns true for any iterable which is not a string or byte sequence.

nngt.lib.uniform_distrib(graph, elist=None, num=None, lower=0.0, upper=1.5, **kwargs)[source]¶

Uniform distribution for edge attributes.

Parameters:	graph (`Graph` or subclass) – Graph for which an edge attribute will be generated. elist (@todo) lower (float, optional (default: 0.)) – Min value of the uniform distribution. upper (float, optional (default: 1.5)) – Max value of the uniform distribution. Returns (`numpy.ndarray`) – Attribute value for each edge in graph.