variable_name = 'Hm0' # name of uncertain variable
dictionary_type = 'w' # dictionary where uncertain variable lives
costlist = []
dellist = []
# set output txt file
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
filename = "mc_xbeach_" + st + ".txt"
logfile = open(filename, "w+")
logfile.close()
t0 = time.time()
logfile = open(filename, "a+")#, 0)
mlmc.write(logfile, str(no_parallel_processes))
# run monte carlo algorithm
cost, del1 = mlmc.mlmc_test(no_parallel_processes, path_stem, _parallel_mc, multilevel, 'jonstable', dictionary_type, M, L, N0, Lmin, Lmax, logfile, build_output, variable_name, sample, interp_fn = interp_fn, angles_fn = angle_dict)
costlist.append(cost)
dellist.append(del1)
# record cost and expectation from this one run
logfile = open(filename, "a+")#, 0)
print('total time: ' + str(t1 - t0))
mlmc.write(logfile, "total time: %f" % (t1-t0))
mlmc.write(logfile, "\n")
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
# set up a unique file to store outputs
filename = "mlmc_xbeach_" + st + ".txt"
logfile = open(filename, "w+")
logfile.close()
# run MLMC algorithm
for i in range(no_runs):
t0 = time.time()
logfile = open(filename, "a+")#, 0)
mlmc.write(logfile, str(no_parallel_processes))
if init_test == True:
alpha_mod, beta, gamma, cost, var1, var2, del1 = mlmc.mlmc_test(no_parallel_processes, path_stem, _parallel_mc, multilevel, wavetype, dictionary_type, M, L, N0, Lmin, Lmax, logfile, build_output, variable_name, sample, interp_fn = interp_fn, normalisation_factor = init_vol)
vardifflist.append(var1)
varsing.append(var2)
costlist.append(cost)
dellist.append(del1)
expectedlist.append(sum(del1))
if eps_test == True:
# these values come from the results of init_test and thus must be replaced if a new init_test run is performed
alpha_mod = 0.921053
beta = 1.970078
gamma = 2.400694
var1 = [5.9081e-07, 6.3646e-08, 1.9078e-08, 1.0362e-08, 1.5862e-09]
del1 = [1.0026e-03, 1.2740e-04, 1.0285e-4, 9.3601e-05, 4.8057e-05]
Ns, varlevel, Plist, P_seq_list, cost_per_epsilon = mlmc.eps_only(alpha_mod, beta, gamma, var1, del1, no_parallel_processes, path_stem, _parallel_mc, multilevel, wavetype, dictionary_type, M, L, 2, epsilon, Lmin, Lmax, logfile, build_output, variable_name, sample, interp_fn = interp_fn, angles_fn = 0, normalisation_factor = init_vol)
Nslist.append(Ns)
varlist.append(varlevel)
varlist = []
# set up unique file to store outputs
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
filename = "mlmc_xbeach_" + st + ".txt"
logfile = open(filename, "w+")
logfile.close()
for i in range(no_runs):
t0 = time.time()
logfile = open(filename, "a+")#, 0)
mlmc.write(logfile, str(no_parallel_processes))
if init_test == True:
alpha, beta, gamma, cost, var1, var2, del1 = mlmc.mlmc_test(no_parallel_processes, path_stem, _parallel_mc, multilevel, 'jons', 'w', M, L, N0, Lmin, Lmax, logfile, build_output, variable_name, sample, interp_fn = interp_orig_fn)
vardifflist.append(var1)
varsing.append(var2)
costlist.append(cost)
dellist.append(del1)
expectedlist.append(sum(del1))
if eps_test == True:
# these values come from the results of init_test and thus must be replaced if a new init_test run is performed
alpha = 1.276595
beta = 1.770709
gamma = 1.432590
var1 = [0.003881187499999994, 0.000679, 0.00028910546875, 9.037011718750001e-05, 2.10205078125e-05, 7.315612792968751e-06]
del1 = [0.63725, 0.014, 0.0114375, 0.00684375, 0.00421875, 0.0029609375]
Ns, varlevel, Plist, P_seq_list, cost_per_epsilon = mlmc.eps_only(alpha, beta, gamma, var1, del1, no_parallel_processes, path_stem, _parallel_mc, multilevel, 'jons', 'w', M, L, 2, epsilon, Lmin, Lmax, logfile, build_output, variable_name, sample, interp_fn = interp_orig_fn, angles_fn = 0)
Nslist.append(Ns)
varlist.append(varlevel)