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...")