def test_set_initial_distribution(self):
solver = sensfsp.SensFspSolverMultiSinks(mpi.COMM_WORLD)
solver.SetModel(self.stoich_mat, tcoeff, propensity, dtcoeff,
dpropensity, dprop_sparsity)
X0 = np.array([[0, 0]])
p0 = np.array([1.0])
s0 = np.array([0.0])
solver.SetInitialDist(X0, p0, [s0] * 4)
def test_set_model(self):
solver = sensfsp.SensFspSolverMultiSinks(mpi.COMM_WORLD)
solver.SetModel(num_parameters=4,
stoich_matrix=self.stoich_mat,
propensity_t=tcoeff,
propensity_x=propensity,
tv_reactions=list(range(4)),
d_propensity_t=dtcoeff,
d_propensity_t_sp=[[i] for i in range(4)],
d_propensity_x=None)
def test_set_initial_distribution(self):
solver = sensfsp.SensFspSolverMultiSinks(mpi.COMM_WORLD)
solver.SetModel(num_parameters=4,
stoich_matrix=self.stoich_mat,
propensity_t=tcoeff,
propensity_x=propensity,
tv_reactions=list(range(4)),
d_propensity_t=dtcoeff,
d_propensity_t_sp=[[i] for i in range(4)],
d_propensity_x=None)
X0 = np.array([[0, 0]])
p0 = np.array([1.0])
s0 = np.array([0.0])
solver.SetInitialDist(X0, p0, [s0] * 4)
def test_solve_serial(self):
solver = sensfsp.SensFspSolverMultiSinks(mpi.COMM_SELF)
solver.SetModel(self.stoich_mat, tcoeff, propensity, dtcoeff,
dpropensity, dprop_sparsity)
solver.SetFspShape(simple_constr, init_bounds)
X0 = np.array([[0, 0]])
p0 = np.array([1.0])
s0 = np.array([0.0])
solver.SetInitialDist(X0, p0, [s0] * 4)
solution = solver.Solve(10.0, 1.0e-4)
prob = np.asarray(solution.GetProbViewer())
self.assertAlmostEqual(prob.sum(), 1.0, 4)
for i in range(0, 4):
svec = np.asarray(solution.GetSensViewer(i))
self.assertAlmostEqual(sum(svec), 0.0, 2)
solution.RestoreSensViewer(i, svec)
def test_solve_serial(self):
solver = sensfsp.SensFspSolverMultiSinks(mpi.COMM_SELF)
solver.SetModel(num_parameters=4,
stoich_matrix=self.stoich_mat,
propensity_t=tcoeff,
propensity_x=propensity,
tv_reactions=list(range(4)),
d_propensity_t=dtcoeff,
d_propensity_t_sp=[[i] for i in range(4)],
d_propensity_x=None)
solver.SetFspShape(simple_constr, init_bounds)
X0 = np.array([[0, 0]])
p0 = np.array([1.0])
s0 = np.array([0.0])
solver.SetInitialDist(X0, p0, [s0] * 4)
solution = solver.Solve(10.0, 1.0E-4)
prob = np.asarray(solution.GetProbViewer())
self.assertAlmostEqual(prob.sum(), 1.0, 4)
for i in range(0, 4):
svec = np.asarray(solution.GetSensViewer(i))
self.assertAlmostEqual(sum(svec), 0.0, 2)
solution.RestoreSensViewer(i, svec)
def test_set_shape(self):
solver = sensfsp.SensFspSolverMultiSinks(mpi.COMM_WORLD)
solver.SetModel(self.stoich_mat, tcoeff, propensity, dtcoeff,
dpropensity)
solver.SetFspShape(simple_constr, init_bounds)
def test_set_model(self):
solver = sensfsp.SensFspSolverMultiSinks(mpi.COMM_WORLD)
solver.SetModel(self.stoich_mat, tcoeff, propensity, dtcoeff,
dpropensity, dprop_sparsity)
def test_serial_constructor(self):
solver = sensfsp.SensFspSolverMultiSinks(mpi.COMM_SELF)
for i in range(0, 4):
dprop_sparsity[i][i] = 1
X0 = np.array([[0, 0]])
p0 = np.array([1.0])
s0 = np.array([0.0])
n_cells = 1000
t_meas = np.linspace(0, 10)
# %%
comm = mpi.COMM_WORLD
my_rank = comm.rank
# %%
my_solver = sensfsp.SensFspSolverMultiSinks(comm)
my_solver.SetModel(stoich_matrix, tcoeff, propensity, dtcoeff, dpropensity,
dprop_sparsity)
my_solver.SetFspShape(constr_fun=simple_constr, constr_bound=init_bounds)
my_solver.SetInitialDist(X0, p0, [s0] * 4)
my_solver.SetVerbosity(2)
solutions = my_solver.SolveTspan(t_meas, 1.0e-6)
# %%
# FIMs for observing both species
FIMatrices = []
for v in solutions:
FIMatrices.append(v.ComputeFIM())
# %%
# FIMs for observing species 0
FIMatrices0 = []
