OFSP_obj.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
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()
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()
@param stoc_vector : numpy.ndarray.
@param model_name : str, 'MRCE' or 'HL'.
@param sink_error : float, maximum error allowed in the sink state.
@param jac : (List of Functions), The jacobian of the propensity functions.
"""
stoc_vector = np.array([True, False])
# Evolving the density a bit forward so that we have a regular marginal.
OFSP_obj = OFSP_Solver(SIR_model, "SE1", 50, 1e-6)
OFSP_obj.step(0.01)
### Hybrid MRCE Computation
MRCE_obj = Hybrid_FSP_solver(SIR_model, stoc_vector, "MRCE", 1e-7)
MRCE_obj.set_initial_values(OFSP_obj.domain_states, OFSP_obj.p, t=0.01)
for t in T:
MRCE_obj.step(t)
MRCE_obj.print_stats
#MRCE_obj.plot(inter=True)
MRCE_obj.check_point()
X = np.array([[150.0, 180.0], [1, 2]])
MRCE_obj.probe_states(X)
MRCE_obj.plot_checked()
### Hybrid HL Computation
from pyme.Hybrid_FSP import Hybrid_FSP_solver
stoc_vector = np.array([True, False])
@param stoc_vector : numpy.ndarray. @param model_name : str, 'MRCE' or 'HL'. @param sink_error : float, maximum error allowed in the sink state. @param jac : (List of Functions), The jacobian of the propensity functions. """ stoc_vector = np.array([True,False]) # Evolving the density a bit forward so that we have a regular marginal. OFSP_obj = OFSP_Solver(SIR_model,"SE1",50,1e-6) OFSP_obj.step(0.01) ### Hybrid MRCE Computation MRCE_obj = Hybrid_FSP_solver(SIR_model,stoc_vector,"MRCE",1e-7) MRCE_obj.set_initial_values(OFSP_obj.domain_states,OFSP_obj.p,t=0.01) for t in T: MRCE_obj.step(t) MRCE_obj.print_stats #MRCE_obj.plot(inter=True) MRCE_obj.check_point() X = np.array([[150.0,180.0],[1,2]]) MRCE_obj.probe_states(X) MRCE_obj.plot_checked() ### Hybrid HL Computation from pyme.Hybrid_FSP import Hybrid_FSP_solver
