def solve_model(self, log10_theta, modelid):
def fsp_constr(X, out):
out[:, 0] = X[:, 0]
out[:, 1] = X[:, 1]
out[:, 2] = X[:, 2]
out[:, 3] = X[:, 3]
out[:, 4] = X[:, 3]
theta = np.power(10.0, log10_theta)
copymax = self.copymaxs[modelid]
constr_init = np.array([2, 2, 2, 5, copymax])
propensity_t, propensity_x = self.propensity_factory(theta, modelid)
fsp_solver = FspSolverMultiSinks(mpi.COMM_SELF)
fsp_solver.SetModel(self.STOICH_MATRIX, propensity_t, propensity_x,
[1])
fsp_solver.SetFspShape(
constr_fun=fsp_constr,
constr_bound=constr_init,
exp_factors=np.array([0.0, 0.0, 0.0, 0.2, 0.0]),
)
fsp_solver.SetInitialDist(self.X0, self.P0)
fsp_solver.SetOdeSolver(self.ODE_METHOD)
fsp_solver.SetOdeTolerances(1.0e-4, 1.0e-10)
solutions = fsp_solver.SolveTspan(self.t_meas + theta[-1], 1.0e-8)
return solutions
def loglike(self, log10_thetas, dataz, surrogate_id):
thetas = np.power(10.0, log10_thetas)
nsamp = thetas.shape[0]
loglike_values = np.zeros((nsamp, 1))
data = dataz[surrogate_id]
for jnc in range(0, nsamp):
prop = self.prop_factory(thetas[jnc, :], surrogate_id)
solver = FspSolverMultiSinks(mpi.COMM_SELF)
solver.SetModel(self.stoich_mat, None, prop)
solver.SetFspShape(None, self.constr_init)
solver.SetInitialDist(self.x0, self.p0)
solver.SetOdeSolver("KRYLOV")
solver.SetKrylovOrthLength(2)
solver.SetKrylovDimRange(30, 30)
solutions = solver.SolveTspan(self.t_meas, 1.0e-8)
ll = 0.0
for i in range(0, len(self.t_meas)):
ll = ll + data[i].LogLikelihood(
solutions[i],
np.array([self.num_gene_states, self.num_gene_states + 1]),
)
loglike_values[jnc, 0] = ll
return loglike_values
out[:] = x[:,1]
if reaction is 2:
out[:] = x[:,1]
if reaction is 3:
out[:] = x[:,2]
def t_fun(t, out):
out[0] = 0.01
out[1] = 0.001
out[2] = 1
out[3] = 0.1
n_t = 100
tspan = np.linspace(0, 1000, n_t)
solver = FspSolverMultiSinks(mpi.COMM_WORLD)
solver.SetModel(stoich_mat, t_fun, propensity)
solver.SetFspShape(None, constr_init)
solver.SetInitialDist(x0, p0)
solver.SetVerbosity(2)
solver.SetUp()
solutions = solver.SolveTspan(tspan, 1.0e-4)
marginals = []
for i in range(0, 3):
for j in range(0, n_t):
marginals.append(solutions[j].Marginal(i))
def weightfun(x, fout):
out[:, 5] = np.multiply(X[:, 0], X[:, 2])
init_bounds = np.array([22, 2, 2, 44, 4, 44])
exp_factors = np.array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
rank = MPI.COMM_WORLD.rank
x0 = np.array([[21, 0, 0]])
p0 = np.array([1.0])
sm = np.array([[1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1],
[0, 0, -1]])
tspan = np.linspace(0, 1, 2)
# Create FSP solver object
solver = FspSolverMultiSinks(MPI.COMM_WORLD)
solver.SetModel(sm, None, propensity)
solver.SetFspShape(constr_fun=rep_constr, constr_bound=init_bounds)
solver.SetInitialDist(x0, p0)
solver.SetOdeSolver("CVODE")
solver.SetVerbosity(2)
t1 = MPI.Wtime()
solution_cvode = solver.SolveTspan(tspan, 1.0e-4)
t_cvode = MPI.Wtime() - t1
solver.ClearState()
solver.SetFspShape(constr_fun=rep_constr, constr_bound=init_bounds)
solver.SetOdeSolver("KRYLOV")
solver.SetKrylovOrthLength(-1)
solver.SetVerbosity(2)