Exemplos de Timeseries em Python

Exemplo n.º 1

Arquivo: pfcbc_anneal.py Projeto: gautelinga/surface_pfc

# solver.parameters["newton_solver"]["krylov_solver"]["maximum_iterations"] = 1000

# solver.parameters["linear_solver"] = "gmres"
# solver.parameters["preconditioner"] = "jacobi"

df.parameters["form_compiler"]["optimize"] = True
df.parameters["form_compiler"]["cpp_optimize"] = True

#
t = parameters["t_0"]
tstep = parameters["tstep"]
T = parameters["T"]

# Output file
ts = Timeseries(parameters["folder"], u_,
                ("psi", "mu", "nu", "nuhat"), geo_map, tstep,
                parameters=parameters,
                restart_folder=parameters["restart_folder"])

# Shorthand notation:
H = geo_map.H
K = geo_map.K
gab = geo_map.gab

E_0 = (2*nu_**2 - 2 * geo_map.gab[i, j]*psi_.dx(i)*psi_.dx(j) + w(psi_, tau))
E_2 = (h**2/12)*(2*(4*nuhat_**2 + 4*H*nuhat_*nu_ - 5*K*nu_**2)
                 - 2 * (2*H*nuhat_ - 2*K*gab[i, j]*psi_.dx(i)*psi_.dx(j))
                 + (tau/2)*K*psi_**2 + (1/4)*K*psi_**4)
ts.add_field(E_0, "E_0")
ts.add_field(E_2, "E_2")
ts.add_field(df.sqrt(geo_map.gab[i, j]*mu_.dx(i)*mu_.dx(j)),
             "abs_grad_mu")

Exemplo n.º 2

Arquivo: pfcbc_conserved.py Projeto: gautelinga/surface_pfc

solver = df.LinearVariationalSolver(problem)

# solver.parameters["linear_solver"] = "gmres"
# solver.parameters["preconditioner"] = "jacobi"

df.parameters["form_compiler"]["optimize"] = True
df.parameters["form_compiler"]["cpp_optimize"] = True

#
t = parameters["t_0"]
tstep = parameters["tstep"]
T = parameters["T"]

# Output file
ts = Timeseries("results_pfcbc_conserved", u_,
                ("psi", "mu", "nu", "nuhat"), geo_map, tstep,
                parameters=parameters,
                restart_folder=parameters["restart_folder"])
# Shorthand notation:
H = geo_map.H
K = geo_map.K
gab = geo_map.gab

E_0 = (2*nu_**2 - 2 * geo_map.gab[i, j]*psi_.dx(i)*psi_.dx(j) + w(psi_, tau))
ts.add_field(E_0, "E_0")
ts.add_field(df.sqrt(geo_map.gab[i, j]*mu_.dx(i)*mu_.dx(j)),
             "abs_grad_mu")

# Step in time
ts.dump(tstep)
dt_max = 5

Exemplo n.º 3

Arquivo: ns_on_torus.py Projeto: gautelinga/surface_pfc

# solver.parameters["newton_solver"]["krylov_solver"]["maximum_iterations"] = 1000

# solver.parameters["linear_solver"] = "gmres"
# solver.parameters["preconditioner"] = "jacobi"

df.parameters["form_compiler"]["optimize"] = True
df.parameters["form_compiler"]["cpp_optimize"] = True

#
t = parameters["t_0"]
tstep = parameters["tstep"]
T = parameters["T"]

# Output file
ts = Timeseries(parameters["folder"], w_,
                ("u", "p", "r"), geo_map, tstep,
                parameters=parameters,
                restart_folder=parameters["restart_folder"])

E_kin = 0.5*rho*geo_map.g_ab[i, j]*u_[i]*u_[j]
divu = geo_map.CovD10(u_)[i, i]
U = [sum([geo_map.get_function(xi + "_," + vj)*u_[dj]
          for dj, vj in enumerate(geo_map.AXIS_REF)])
     for xi in geo_map.AXIS]

ts.add_field(E_kin, "E_kin")
ts.add_field(divu, "divu")
ts.add_field(U, "U")

# Step in time
ts.dump(tstep)

Exemplo n.º 4

Arquivo: pfc_on_sphere.py Projeto: gautelinga/surface_pfc

a = df.lhs(F)
L = df.rhs(F)

# SOLVER
problem = df.LinearVariationalProblem(a, L, u_)
solver = df.LinearVariationalSolver(problem)

# solver.parameters["linear_solver"] = "gmres"
# solver.parameters["preconditioner"] = "jacobi"

df.parameters["form_compiler"]["optimize"] = True
df.parameters["form_compiler"]["cpp_optimize"] = True

# Output file
ts = Timeseries("results_pfc", u_, ("c", "mu"), geo_map, 0)

# Step in time
t = 0.0
it = 0
T = 100.0

ts.dump(it)

while t < T:
    it += 1
    t += dt

    solver.solve()

    u_1.assign(u_)