RandomNet

class RandomNet(pars=None, key_dict=None, **kwargs)

Bases: DynamicNetwork

Random connectivity between agents

Attributes

beta	Relative transmission on each network edge
members	Return sorted array of all members
now	Return the current time, i.e. the time vector at the current timestep.
p1	The first half of a network edge (person 1)
p2	The second half of a network edge (person 2)
states	Return a flat list of all states
statesdict	Return a flat dictionary (objdict) of all states

Methods

static get_source(inds, n_contacts)

Optimized helper function for getting contacts

get_edges(inds, n_contacts)

Efficiently find edges

Note that because of the shuffling operation, each person is assigned 2N contacts (i.e. if a person has 5 contacts, they appear 5 times in the ‘source’ array and 5 times in the ‘target’ array). Therefore, the number_of_contacts argument to this function should be HALF of the total contacts a person is expected to have, if both the source and target array outputs are used (e.g. for social contacts)

adjusted_number_of_contacts = np.round(number_of_contacts / 2).astype(ss.dtype.int)

Whereas for asymmetric contacts (e.g. staff-public interactions) it might not be necessary

Parameters:

• inds – List/array of person indices

• number_of_contacts – List/array the same length as inds with the number of unidirectional

• Therefore (contacts to assign to each person.)

• number (a person will have on average TWICE this)

• contacts. (of random)

Returns: Two arrays, for source and target

add_pairs()

Generate edges