with open(os.path.join(SAVE_SOLUTION, 'SIM-STATS.pkl'), 'wb') as fout:
pickle.dump(sim_stats, fout)
# ============================================================
# PART E: MC Error Sampling
# ============================================================
if MC_RUNS > 0:
ref_maxm = w_history[-1].max_order
for i, w in enumerate(w_history):
if i == 0:
continue
logger.info("MC error sampling for w[%i] (of %i)", i, len(w_history))
# memory usage info
logger.info("\n======================================\nMEMORY USED: " + str(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) + "\n======================================\n")
L2err, H1err, L2err_a0, H1err_a0 = sample_error_mc(w, pde, A, coeff_field, mesh0, ref_maxm, MC_RUNS, MC_N, MC_HMAX)
sim_stats[i - 1]["MC-L2ERR"] = L2err
sim_stats[i - 1]["MC-H1ERR"] = H1err
sim_stats[i - 1]["MC-L2ERR_a0"] = L2err_a0
sim_stats[i - 1]["MC-H1ERR_a0"] = H1err_a0
# ============================================================
# PART F: Export Updated Data and Plotting
# ============================================================
# save updated data
if SAVE_SOLUTION != "":
# save updated statistics
import pickle
with open(os.path.join(SAVE_SOLUTION, 'SIM-STATS.pkl'), 'wb') as fout:
pickle.dump(sim_stats, fout)
def run_MC(opts, conf):
# propagate config values
_G = globals()
for sec in conf.keys():
if sec == "LOGGING":
continue
secconf = conf[sec]
for key, val in secconf.iteritems():
print "CONF_" + key + "= secconf['" + key + "'] =", secconf[key]
_G["CONF_" + key] = secconf[key]
# setup logging
_G["LOG_LEVEL"] = eval("logging." + conf["LOGGING"]["level"])
print "LOG_LEVEL = logging." + conf["LOGGING"]["level"]
setup_logging(LOG_LEVEL, logfile=CONF_experiment_name + "_MC-P{0}".format(CONF_FEM_degree))
# determine path of this module
path = os.path.dirname(__file__)
# ============================================================
# PART A: Setup Problem
# ============================================================
# get boundaries
mesh0, boundaries, dim = SampleDomain.setupDomain(CONF_domain, initial_mesh_N=CONF_initial_mesh_N)
# define coefficient field
coeff_types = ("EF-square-cos", "EF-square-sin", "monomials", "constant")
from itertools import count
if CONF_mu is not None:
muparam = (CONF_mu, (0 for _ in count()))
else:
muparam = None
coeff_field = SampleProblem.setupCF(coeff_types[CONF_coeff_type], decayexp=CONF_decay_exp, gamma=CONF_gamma,
freqscale=CONF_freq_scale, freqskip=CONF_freq_skip, rvtype="uniform", scale=CONF_coeff_scale, secondparam=muparam)
# setup boundary conditions and pde
# initial_mesh_N = CONF_initial_mesh_N
pde, Dirichlet_boundary, uD, Neumann_boundary, g, f = SampleProblem.setupPDE(CONF_boundary_type, CONF_domain, CONF_problem_type, boundaries, coeff_field)
# define multioperator
A = MultiOperator(coeff_field, pde.assemble_operator, pde.assemble_operator_inner_dofs)
# ============================================================
# PART B: Import Solution
# ============================================================
import pickle
PATH_SOLUTION = os.path.join(opts.basedir, CONF_experiment_name)
FILE_SOLUTION = 'SFEM2-SOLUTIONS-P{0}.pkl'.format(CONF_FEM_degree)
FILE_STATS = 'SIM2-STATS-P{0}.pkl'.format(CONF_FEM_degree)
print "LOADING solutions from %s" % os.path.join(PATH_SOLUTION, FILE_SOLUTION)
logger.info("LOADING solutions from %s" % os.path.join(PATH_SOLUTION, FILE_SOLUTION))
# load solutions
with open(os.path.join(PATH_SOLUTION, FILE_SOLUTION), 'rb') as fin:
w_history = pickle.load(fin)
# load simulation data
logger.info("LOADING statistics from %s" % os.path.join(PATH_SOLUTION, FILE_STATS))
with open(os.path.join(PATH_SOLUTION, FILE_STATS), 'rb') as fin:
sim_stats = pickle.load(fin)
logger.info("active indices of w after initialisation: %s", w_history[-1].active_indices())
# ============================================================
# PART C: MC Error Sampling
# ============================================================
# determine reference setting
ref_mesh, ref_Lambda = generate_reference_setup(PATH_SOLUTION)
MC_N = CONF_N
MC_HMAX = CONF_maxh
if CONF_runs > 0:
# determine reference mesh
w = w_history[-1]
# ref_mesh = w.basis.basis.mesh
for _ in range(CONF_ref_mesh_refine):
ref_mesh = refine(ref_mesh)
# TODO: the following association with the sampling order does not make too much sense...
ref_maxm = CONF_sampling_order if CONF_sampling_order > 0 else max(len(mu) for mu in ref_Lambda) + CONF_sampling_order_increase
stored_rv_samples = []
for i, w in enumerate(w_history):
# if i == 0:
# continue
# memory usage info
import resource
logger.info("\n======================================\nMEMORY USED: " + str(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) + "\n======================================\n")
logger.info("================>>> MC error sampling for w[%i] (of %i) on %i cells with maxm %i <<<================" % (i, len(w_history), ref_mesh.num_cells(), ref_maxm))
MC_start = 0
old_stats = sim_stats[i]
if opts.continueMC:
try:
MC_start = sim_stats[i]["MC-N"]
logger.info("CONTINUING MC of %s for solution (iteration) %s of %s", PATH_SOLUTION, i, len(w_history))
except:
logger.info("STARTING MC of %s for solution (iteration) %s of %s", PATH_SOLUTION, i, len(w_history))
if MC_start <= 0:
sim_stats[i]["MC-N"] = 0
sim_stats[i]["MC-ERROR-L2"] = 0
sim_stats[i]["MC-ERROR-H1A"] = 0
# sim_stats[i]["MC-ERROR-L2_a0"] = 0
# sim_stats[i]["MC-ERROR-H1_a0"] = 0
MC_RUNS = max(CONF_runs - MC_start, 0)
if MC_RUNS > 0:
logger.info("STARTING %s MC RUNS", MC_RUNS)
# L2err, H1err, L2err_a0, H1err_a0, N = sample_error_mc(w, pde, A, coeff_field, mesh0, ref_maxm, MC_RUNS, MC_N, MC_HMAX)
L2err, H1err, L2err_a0, H1err_a0, N = sample_error_mc(w, pde, A, coeff_field, ref_mesh, ref_maxm, MC_RUNS, MC_N, MC_HMAX, stored_rv_samples, CONF_quadrature_degree)
# combine current and previous results
sim_stats[i]["MC-N"] = N + old_stats["MC-N"]
sim_stats[i]["MC-ERROR-L2"] = (L2err * N + old_stats["MC-ERROR-L2"]) / sim_stats[i]["MC-N"]
sim_stats[i]["MC-ERROR-H1A"] = (H1err * N + old_stats["MC-ERROR-H1A"]) / sim_stats[i]["MC-N"]
# sim_stats[i]["MC-ERROR-L2_a0"] = (L2err_a0 * N + old_stats["MC-ERRORL2_a0"]) / sim_stats[i]["MC-N"]
# sim_stats[i]["MC-ERROR-H1A_a0"] = (H1err_a0 * N + old_stats["MC-ERROR-H1A_a0"]) / sim_stats[i]["MC-N"]
print "MC-ERROR-H1A (N:%i) = %f" % (sim_stats[i]["MC-N"], sim_stats[i]["MC-ERROR-H1A"])
else:
logger.info("SKIPPING MC RUN since sufficiently many samples are available")
# ============================================================
# PART D: Export Updated Data and Plotting
# ============================================================
# save updated data
if opts.saveData:
# save updated statistics
print "SAVING statistics into %s" % os.path.join(PATH_SOLUTION, FILE_STATS)
print sim_stats[-1].keys()
logger.info("SAVING statistics into %s" % os.path.join(PATH_SOLUTION, FILE_STATS))
with open(os.path.join(PATH_SOLUTION, FILE_STATS), 'wb') as fout:
pickle.dump(sim_stats, fout)
# plot residuals
if opts.plotEstimator and len(sim_stats) > 1:
try:
from matplotlib.pyplot import figure, show, legend
X = [s["DOFS"] for s in sim_stats]
err_L2 = [s["MC-ERROR-L2"] for s in sim_stats]
err_H1A = [s["MC-ERROR-H1A"] for s in sim_stats]
err_est = [s["ERROR-EST"] for s in sim_stats]
err_res = [s["ERROR-RES"] for s in sim_stats]
err_tail = [s["ERROR-TAIL"] for s in sim_stats]
mi = [s["MI"] for s in sim_stats]
num_mi = [len(m) for m in mi]
eff_H1A = [est / err for est, err in zip(err_est, err_H1A)]
# --------
# figure 1
# --------
fig1 = figure()
fig1.suptitle("residual estimator")
ax = fig1.add_subplot(111)
if REFINEMENT["TAIL"]:
ax.loglog(X, num_mi, '--y+', label='active mi')
ax.loglog(X, eff_H1A, '--yo', label='efficiency')
ax.loglog(X, err_L2, '-.b>', label='L2 error')
ax.loglog(X, err_H1A, '-.r>', label='H1A error')
ax.loglog(X, err_est, '-g<', label='error estimator')
ax.loglog(X, err_res, '-.cx', label='residual')
ax.loglog(X, err_tail, '-.m>', label='tail')
legend(loc='upper right')
print "error L2", err_L2
print "error H1A", err_H1A
print "EST", err_est
print "RES", err_res
print "TAIL", err_tail
show() # this invalidates the figure instances...
except:
import traceback
print traceback.format_exc()
logger.info("skipped plotting since matplotlib is not available...")
def run_MC(opts, conf):
# propagate config values
_G = globals()
for sec in conf.keys():
if sec == "LOGGING":
continue
secconf = conf[sec]
for key, val in secconf.iteritems():
print "CONF_" + key + "= secconf['" + key + "'] =", secconf[key]
_G["CONF_" + key] = secconf[key]
# exec "CONF_" + key + "= secconf['" + key + "']"
# setup logging
_G["LOG_LEVEL"] = eval("logging." + conf["LOGGING"]["level"])
print "LOG_LEVEL = logging." + conf["LOGGING"]["level"]
# exec "LOG_LEVEL = logging." + conf["LOGGING"]["level"]
setup_logging(LOG_LEVEL, logfile=CONF_experiment_name + "_MC")
# determine path of this module
path = os.path.dirname(__file__)
# ============================================================
# PART A: Setup Problem
# ============================================================
# get boundaries
mesh0, boundaries, dim = SampleDomain.setupDomain(CONF_domain, initial_mesh_N=CONF_initial_mesh_N)
# define coefficient field
coeff_types = ("EF-square-cos", "EF-square-sin", "monomials", "constant")
from itertools import count
if CONF_mu is not None:
muparam = (CONF_mu, (0 for _ in count()))
else:
muparam = None
coeff_field = SampleProblem.setupCF(coeff_types[CONF_coeff_type], decayexp=CONF_decay_exp, gamma=CONF_gamma,
freqscale=CONF_freq_scale, freqskip=CONF_freq_skip, rvtype="uniform", scale=CONF_coeff_scale, secondparam=muparam)
# setup boundary conditions and pde
# initial_mesh_N = CONF_initial_mesh_N
pde, Dirichlet_boundary, uD, Neumann_boundary, g, f = SampleProblem.setupPDE(CONF_boundary_type, CONF_domain, CONF_problem_type, boundaries, coeff_field)
# define multioperator
A = MultiOperator(coeff_field, pde.assemble_operator, pde.assemble_operator_inner_dofs, assembly_type=eval("ASSEMBLY_TYPE." + CONF_assembly_type))
# ============================================================
# PART B: Import Solution
# ============================================================
import pickle
LOAD_SOLUTION = os.path.join(opts.basedir, CONF_experiment_name)
logger.info("loading solutions from %s" % os.path.join(LOAD_SOLUTION, 'SFEM-SOLUTIONS.pkl'))
# load solutions
with open(os.path.join(LOAD_SOLUTION, 'SFEM-SOLUTIONS.pkl'), 'rb') as fin:
w_history = pickle.load(fin)
# load simulation data
logger.info("loading statistics from %s" % os.path.join(LOAD_SOLUTION, 'SIM-STATS.pkl'))
with open(os.path.join(LOAD_SOLUTION, 'SIM-STATS.pkl'), 'rb') as fin:
sim_stats = pickle.load(fin)
logger.info("active indices of w after initialisation: %s", w_history[-1].active_indices())
# ============================================================
# PART C: MC Error Sampling
# ============================================================
MC_N = CONF_N
MC_HMAX = CONF_max_h
if CONF_runs > 0:
# determine reference mesh
w = w_history[-1]
ref_mesh, _ = create_joint_mesh([w[mu].mesh for mu in w.active_indices()])
for _ in range(CONF_ref_mesh_refine):
ref_mesh = refine(ref_mesh)
ref_maxm = CONF_sampling_order if CONF_sampling_order > 0 else w.max_order + CONF_sampling_order_increase
for i, w in enumerate(w_history):
# if i == 0:
# continue
logger.info("MC error sampling for w[%i] (of %i)", i, len(w_history))
# memory usage info
import resource
logger.info("\n======================================\nMEMORY USED: " + str(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) + "\n======================================\n")
MC_start = 0
old_stats = sim_stats[i]
if opts.continueMC:
try:
MC_start = sim_stats[i]["MC-N"]
logger.info("CONTINUING MC of %s for solution (iteration) %s of %s", LOAD_SOLUTION, i, len(w_history))
except:
logger.info("STARTING MC of %s for solution (iteration) %s of %s", LOAD_SOLUTION, i, len(w_history))
if MC_start <= 0:
sim_stats[i]["MC-N"] = 0
sim_stats[i]["MC-L2ERR"] = 0
sim_stats[i]["MC-H1ERR"] = 0
sim_stats[i]["MC-L2ERR_a0"] = 0
sim_stats[i]["MC-H1ERR_a0"] = 0
MC_RUNS = max(CONF_runs - MC_start, 0)
if MC_RUNS > 0:
logger.info("STARTING %s MC RUNS", MC_RUNS)
# L2err, H1err, L2err_a0, H1err_a0, N = sample_error_mc(w, pde, A, coeff_field, mesh0, ref_maxm, MC_RUNS, MC_N, MC_HMAX)
L2err, H1err, L2err_a0, H1err_a0, N = sample_error_mc(w, pde, A, coeff_field, ref_mesh, ref_maxm, MC_RUNS, MC_N, MC_HMAX)
# combine current and previous results
sim_stats[i]["MC-N"] = N + old_stats["MC-N"]
sim_stats[i]["MC-L2ERR"] = (L2err * N + old_stats["MC-L2ERR"]) / sim_stats[i]["MC-N"]
sim_stats[i]["MC-H1ERR"] = (H1err * N + old_stats["MC-H1ERR"]) / sim_stats[i]["MC-N"]
sim_stats[i]["MC-L2ERR_a0"] = (L2err_a0 * N + old_stats["MC-L2ERR_a0"]) / sim_stats[i]["MC-N"]
sim_stats[i]["MC-H1ERR_a0"] = (H1err_a0 * N + old_stats["MC-H1ERR_a0"]) / sim_stats[i]["MC-N"]
print "MC-H1ERR (N:%i) = %f" % (sim_stats[i]["MC-N"], sim_stats[i]["MC-H1ERR"])
else:
logger.info("SKIPPING MC RUN since sufficiently many samples are available")
# ============================================================
# PART D: Export Updated Data and Plotting
# ============================================================
# save updated data
if opts.saveData:
# save updated statistics
import pickle
SAVE_SOLUTION = os.path.join(opts.basedir, CONF_experiment_name)
try:
os.makedirs(SAVE_SOLUTION)
except:
pass
logger.info("saving statistics into %s" % os.path.join(SAVE_SOLUTION, 'SIM-STATS.pkl'))
with open(os.path.join(SAVE_SOLUTION, 'SIM-STATS.pkl'), 'wb') as fout:
pickle.dump(sim_stats, fout)
# plot residuals
if opts.plotEstimator and len(sim_stats) > 1:
try:
from matplotlib.pyplot import figure, show, legend
x = [s["DOFS"] for s in sim_stats]
L2 = [s["L2"] for s in sim_stats]
H1 = [s["H1"] for s in sim_stats]
errest = [sqrt(s["EST"]) for s in sim_stats]
res_part = [s["RES-PART"] for s in sim_stats]
proj_part = [s["PROJ-PART"] for s in sim_stats]
pcg_part = [s["PCG-PART"] for s in sim_stats]
_reserrmu = [s["RES-mu"] for s in sim_stats]
_projerrmu = [s["PROJ-mu"] for s in sim_stats]
if CONF_runs > 0:
mcL2 = [s["MC-L2ERR"] for s in sim_stats]
mcH1 = [s["MC-H1ERR"] for s in sim_stats]
mcL2_a0 = [s["MC-L2ERR_a0"] for s in sim_stats]
mcH1_a0 = [s["MC-H1ERR_a0"] for s in sim_stats]
effest = [est / err for est, err in zip(errest, mcH1)]
mi = [s["MI"] for s in sim_stats]
num_mi = [len(m) for m in mi]
reserrmu = defaultdict(list)
for rem in _reserrmu:
for mu, v in rem:
reserrmu[mu].append(v)
print "errest", errest
if CONF_runs > 0:
print "mcH1", mcH1
print "efficiency", [est / err for est, err in zip(errest, mcH1)]
# --------
# figure 2
# --------
fig2 = figure()
fig2.suptitle("residual estimator")
ax = fig2.add_subplot(111)
if CONF_refine_Lambda:
ax.loglog(x, num_mi, '--y+', label='active mi')
ax.loglog(x, errest, '-g<', label='error estimator')
ax.loglog(x, res_part, '-.cx', label='residual part')
ax.loglog(x[1:], proj_part[1:], '-.m>', label='projection part')
ax.loglog(x, pcg_part, '-.b>', label='pcg part')
if MC_RUNS > 0:
ax.loglog(x, mcH1, '-b^', label='MC H1 error')
ax.loglog(x, mcL2, '-ro', label='MC L2 error')
# ax.loglog(x, H1, '-b^', label='H1 residual')
# ax.loglog(x, L2, '-ro', label='L2 residual')
legend(loc='upper right')
# --------
# figure 3
# --------
fig3 = figure()
fig3.suptitle("efficiency residual estimator")
ax = fig3.add_subplot(111)
ax.loglog(x, errest, '-g<', label='error estimator')
if MC_RUNS > 0:
ax.loglog(x, mcH1, '-b^', label='MC H1 error')
ax.loglog(x, effest, '-ro', label='efficiency')
legend(loc='upper right')
# # --------
# # figure 4
# # --------
# fig4 = figure()
# fig4.suptitle("residual contributions")
# ax = fig4.add_subplot(111)
# for mu, v in reserrmu.iteritems():
# ms = str(mu)
# ms = ms[ms.find('=') + 1:-1]
# ax.loglog(x[-len(v):], v, '-g<', label=ms)
# legend(loc='upper right')
show() # this invalidates the figure instances...
except:
import traceback
print traceback.format_exc()
logger.info("skipped plotting since matplotlib is not available...")
