# import Hybrid Solver
from pyme.Hybrid_FSP import Hybrid_FSP_solver
"""
("S_1 = Zombies","S_2 = People")
* S_1 is considered stochastic
* S_2 will be approximated by marginal distributions
"""
stoc_vector = np.array([True,False])
# initialising the Hybrid Solver
Zombie_solver = Hybrid_FSP_solver(Zombie_model,stoc_vector,"MRCE",1e-7)
Zombie_solver.set_initial_values(Zombie_OFSP.domain_states,Zombie_OFSP.p,t=Zombie_OFSP.t)
T = np.arange(Zombie_OFSP.t,2.0,0.1)
for t in T:
Zombie_solver.step(t)
Zombie_solver.print_stats
Zombie_solver.plot()
####### Hybrid Solver Class ######## from pyme.Hybrid_FSP import Hybrid_FSP_solver ### Hybrid MRCE Computation stoc_vector = np.array([True,False,False,True]) """ Templetes and Virons are stochastic. Genome and Structures are modelled by conditional / marginal expectations. """ MRCE_obj = Hybrid_FSP_solver(Viral_D_model,stoc_vector,"MRCE",1e-7,jac=Jac_Mat) MRCE_obj.set_initial_values(OFSP_obj.domain_states,OFSP_obj.p,t=0.005) for t in T: MRCE_obj.step(t) MRCE_obj.print_stats MRCE_obj.plot(inter=True) ### Hybrid HL Computation HL_obj = Hybrid_FSP_solver(Viral_D_model,stoc_vector,"HL",1e-7) HL_obj.set_initial_values(OFSP_obj.domain_states,OFSP_obj.p,t=0.005) for t in T: HL_obj.step(t) HL_obj.print_stats
Author Vikram Sunkara
"""
import numpy as np
#import model
from Zombie_model import *
# import Hybrid Solver
from pyme.Hybrid_FSP import Hybrid_FSP_solver
"""
("S_1 = Zombies","S_2 = People")
* S_1 is considered stochastic
* S_2 will be approximated by marginal distributions
"""
stoc_vector = np.array([True, False])
# initialising the Hybrid Solver
Zombie_solver = Hybrid_FSP_solver(Zombie_model, stoc_vector, "MRCE", 1e-7)
Zombie_solver.set_initial_values(Zombie_OFSP.domain_states,
Zombie_OFSP.p,
t=Zombie_OFSP.t)
T = np.arange(Zombie_OFSP.t, 2.0, 0.1)
for t in T:
Zombie_solver.step(t)
Zombie_solver.print_stats
Zombie_solver.plot()