T = np.arange(0.0, 0.5, 0.01)
output_data = []
for isp_position in T:
OFSP_catalytic.step(isp_position)
OFSP_catalytic.print_stats # Prints some information of where the solver is.
output_data.append(OFSP_catalytic.print_stats)
""" Runtime plotting"""
#OFSP_ABC.plot(inter=True) # For interactive plotting
""" Check Point"""
OFSP_catalytic.check_point()
""" Probing """
#X = np.zeros((3,2))
#X[:,0] = [8,2,0]
#X[:,1] = [7,2,1]
X = np.zeros((5, 2))
X[:, 0] = [48, 2, 0, 80, 0]
X[:, 1] = [47, 2, 1, 80, 0]
#X[:,2] = [48,1,0,79,1]
OFSP_catalytic.probe_states(X)
elapsed_time = time.time() - start_time
print "Time elapsed:" + ' ' + str(elapsed_time) + ' ' + "seconds"
OFSP_catalytic.plot()
#OFSP_catalytic.plot_contour()
OFSP_catalytic.plot_checked()
np.savetxt('ISPData_CatalyticOFSP.csv',
np.column_stack(output_data),
delimiter=',')
"SE1" Simple N-step expander.
validity_test : func ,
Validity function is by default looking for non negative states
"""
OFSP_obj = OFSP_Solver(SIR_model, 50, 1e-6, expander_name="SE1")
for t in T:
OFSP_obj.step(t)
OFSP_obj.plot(
inter=True) # Interactive mode graphs the marginal each time called.
OFSP_obj.print_stats
OFSP_obj.check_point()
X = np.array([[150, 180], [50, 20]
]) # We can probe various states for their probabilities.
OFSP_obj.probe_states(
X) # Probes and stores the states of the respective time step.
OFSP_obj.plot_checked()
from pyme.Hybrid_FSP import Hybrid_FSP_solver
"""
Initialising a Hybrid solver class. Where we want to consider S to be stochastic and I to be deterministic
def __init__(self,model,stoc_vector,model_name,sink_error,jac=None):
@param model : model.Model.
@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.
global error allowed in the sink state per step. expander_name : str , "SE1" Simple N-step expander. validity_test : func , Validity function is by default looking for non negative states """ OFSP_obj = OFSP_Solver(SIR_model,50,1e-6,expander_name="SE1") for t in T: OFSP_obj.step(t) OFSP_obj.plot(inter=True) # Interactive mode graphs the marginal each time called. OFSP_obj.print_stats OFSP_obj.check_point() X = np.array([[150,180],[50,20]]) # We can probe various states for their probabilities. OFSP_obj.probe_states(X) # Probes and stores the states of the respective time step. OFSP_obj.plot_checked() from pyme.Hybrid_FSP import Hybrid_FSP_solver """ Initialising a Hybrid solver class. Where we want to consider S to be stochastic and I to be deterministic def __init__(self,model,stoc_vector,model_name,sink_error,jac=None): @param model : model.Model. @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.
from pyme.OFSP import OFSP_Solver
# OFSP
start_time = time.time()
OFSP_dual_enzy_model = OFSP_Solver(dual_enzy_model,1,1e-6)
T = np.arange(0.0,2.0,0.01)
output_data = []
for isp_position in T:
OFSP_dual_enzy_model.step(isp_position)
OFSP_dual_enzy_model.print_stats
output_data.append(OFSP_dual_enzy_model.print_stats)
""" Check Point"""
OFSP_dual_enzy_model.check_point()
""" Probing """
X = np.zeros((6,2))
X[:,0] = [48,18,2,0,10,0]
X[:,1] = [48,19,1,1,10,0]
OFSP_dual_enzy_model.probe_states(X)
elapsed_time = time.time() - start_time
print "Time elapsed:" +' '+ str( elapsed_time) +' '+ "seconds"
OFSP_dual_enzy_model.plot()
#OFSP_dual_enzy_model.plot_contour()
OFSP_dual_enzy_model.plot_checked()
np.savetxt('OFSPData_dual_enzyOFSP.csv', np.column_stack(output_data), delimiter=',')
# Example with Validity function """ def validity_func(X): return np.sum(np.abs(X),axis=0) == 10 # since we started with 10 states. # The solver initialisation would look like OFSP_ABC = OFSP_Solver(ABC_model,10,1e-6,validity_test = validity_func) """ T = np.arange(0.01,1.0,0.01) for t in T: OFSP_ABC.step(t) OFSP_ABC.print_stats # Prints some information of where the solver is. """ Runtime plotting""" #OFSP_ABC.plot(inter=True) # For interactive plotting """ Check Point""" OFSP_ABC.check_point() """ Probing """ X = np.zeros((3,2)) X[:,0] = [8,2,0] X[:,1] = [7,2,1] OFSP_ABC.probe_states(X) OFSP_ABC.plot() OFSP_ABC.plot_checked()
