EoN.EBCM_pref_mix

EoN.EBCM_pref_mix(N, Pk, Pnk, tau, gamma, rho=None, tmin=0, tmax=100, tcount=1001, return_full_data=False)

Encodes the system derived in exercise 6.21 of Kiss, Miller, & Simon. Please cite the book if using this algorithm.

I anticipate eventually adding an option so that the initial condition is not uniformly distributed. So could give rho_k

Arguments:

N positive integer

number of nodes.

Pk dict (could also be an array or a list)

Pk[k] is the probability a random node has degree k.

Pnk dict of dicts (possibly array/list)

Pnk[k1][k2] is the probability a neighbor of a degree k1 node has degree k2.

tau positive float

transmission rate

gamma number

recovery rate

rho number (optional)

initial proportion infected. Defaults to 1/N.

tmin number (default 0)

minimum time

tmax number (default 100)

maximum time

tcount integer (default 1001)

number of time points for data (including end points)

return_full_data boolean (default False)

whether to return theta or not

Returns:

if return_full_data == False:

returns t, S, I, R, all numpy arrays

if …== True

returns t, S, I, R and theta where theta[k] is a numpy array giving theta for degree k