EoN.SIR_heterogeneous_pairwise_from_graph¶
-
EoN.
SIR_heterogeneous_pairwise_from_graph
(G, tau, gamma, initial_infecteds=None, initial_recovereds=None, rho=None, tmin=0, tmax=100, tcount=1001, return_full_data=False)[source]¶ Calls SIR_heterogeneous_pairwise after calculating Sk0, Ik0, Rk0, SkSl0, SkIl0 from a graph G and initial fraction infected rho.
Arguments: - G networkx Graph
- The contact network
- tau positive float
- transmission rate
- gamma number
- recovery rate
- initial infecteds node or iterable of nodes (default
None
) - if a single node, then this node is initially infected if an iterable, then whole set is initially infected if None, then choose randomly based on rho. If rho is also None, a random single node is chosen. If both initial_infecteds and rho are assigned, then there is an error.
- initial_recovereds iterable of nodes (default
None
) - this whole collection is made recovered. Currently there is no test for consistency with initial_infecteds. Understood that everyone who isn’t infected or recovered initially is initially susceptible.
- rho float between 0 and 1 (default
None
) - the fraction to be randomly infected at time 0 If None, then rho=1/N is used where N = G.order()
- tmin number (default 0)
- minimum report time
- tmax number (default 100)
- maximum report time
- tcount integer (default 1001)
- number of reports
- return_full_data boolean (default False)
- If True, return times, Sk, Ik, Rk, SkIl, SkSl If False, return times, S, I, R
Returns: - if return_full_data is True
- returns times, S, I, R, Sk, Ik, Rk, SkIl, SkSl
- if return_full_data is False
- return times, S, I, R
WARNING: This can have segmentation faults if there are too many degrees in the graph. This appears to happen because of trouble in numpy, and I have not been able to find a way around it.