def thermalblock_demo(args):
args = parse_arguments(args)
pool = new_parallel_pool(ipython_num_engines=args['--ipython-engines'], ipython_profile=args['--ipython-profile'])
if args['--fenics']:
d, d_summary = discretize_fenics(args['XBLOCKS'], args['YBLOCKS'], args['--grid'], args['--order'])
else:
d, d_summary = discretize_pymor(args['XBLOCKS'], args['YBLOCKS'], args['--grid'], args['--list-vector-array'])
if args['--cache-region'] != 'none':
d.enable_caching(args['--cache-region'])
if args['--plot-solutions']:
print('Showing some solutions')
Us = tuple()
legend = tuple()
for mu in d.parameter_space.sample_randomly(2):
print('Solving for diffusion = \n{} ... '.format(mu['diffusion']))
sys.stdout.flush()
Us = Us + (d.solve(mu),)
legend = legend + (str(mu['diffusion']),)
d.visualize(Us, legend=legend, title='Detailed Solutions for different parameters',
separate_colorbars=False, block=True)
print('RB generation ...')
# define estimator for coercivity constant
from pymor.parameters.functionals import ExpressionParameterFunctional
coercivity_estimator = ExpressionParameterFunctional('min(diffusion)', d.parameter_type)
# inner product for computation of Riesz representatives
error_product = d.h1_0_semi_product if args['--estimator-norm'] == 'h1' else None
if args['--reductor'] == 'residual_basis':
from pymor.reductors.stationary import reduce_stationary_coercive
reductor = partial(reduce_stationary_coercive, error_product=error_product,
coercivity_estimator=coercivity_estimator)
elif args['--reductor'] == 'traditional':
from pymor.reductors.linear import reduce_stationary_affine_linear
reductor = partial(reduce_stationary_affine_linear, error_product=error_product,
coercivity_estimator=coercivity_estimator)
else:
assert False # this should never happen
if args['--pod']:
rd, rc, red_summary = reduce_pod(d=d, reductor=reductor, snapshots_per_block=args['SNAPSHOTS'],
basis_size=args['RBSIZE'], product_name=args['--pod-product'])
else:
rd, rc, red_summary = reduce_greedy(d=d, reductor=reductor, snapshots_per_block=args['SNAPSHOTS'],
extension_alg_name=args['--extension-alg'],
max_extensions=args['RBSIZE'],
use_estimator=not args['--without-estimator'], pool=pool)
if args['--pickle']:
print('\nWriting reduced discretization to file {} ...'.format(args['--pickle'] + '_reduced'))
with open(args['--pickle'] + '_reduced', 'w') as f:
dump(rd, f)
if not args['--fenics']: # FEniCS data structures do not support serialization
print('Writing detailed discretization and reconstructor to file {} ...'
.format(args['--pickle'] + '_detailed'))
with open(args['--pickle'] + '_detailed', 'w') as f:
dump((d, rc), f)
print('\nSearching for maximum error on random snapshots ...')
results = reduction_error_analysis(rd,
discretization=d,
reconstructor=rc,
estimator=True,
error_norms=(d.h1_0_semi_norm, d.l2_norm),
condition=True,
test_mus=args['--test'],
basis_sizes=0 if args['--plot-error-sequence'] else 1,
plot=args['--plot-error-sequence'],
pool=pool)
print('\n*** RESULTS ***\n')
print(d_summary)
print(red_summary)
print(results['summary'])
sys.stdout.flush()
if args['--plot-error-sequence']:
from matplotlib import pyplot as plt
plt.show(results['figure'])
if args['--plot-err']:
mumax = results['max_error_mus'][0, -1]
U = d.solve(mumax)
URB = rc.reconstruct(rd.solve(mumax))
d.visualize((U, URB, U - URB), legend=('Detailed Solution', 'Reduced Solution', 'Error'),
title='Maximum Error Solution', separate_colorbars=True, block=True)
def main(
exp_min: float = Argument(..., help='Minimal exponent'),
exp_max: float = Argument(..., help='Maximal exponent'),
ei_snapshots: int = Argument(
..., help='Number of snapshots for empirical interpolation.'),
ei_size: int = Argument(..., help='Number of interpolation DOFs.'),
snapshots: int = Argument(
..., help='Number of snapshots for basis generation.'),
rb_size: int = Argument(..., help='Size of the reduced basis.'),
cache_region: Choices('none memory disk persistent') = Option(
'disk',
help='Name of cache region to use for caching solution snapshots.'
),
ei_alg: Choices('ei_greedy deim') = Option(
'ei_greedy', help='Interpolation algorithm to use.'),
grid: int = Option(60, help='Use grid with (2*NI)*NI elements.'),
grid_type: Choices('rect tria') = Option('rect',
help='Type of grid to use.'),
initial_data: Choices('sin bump') = Option(
'sin', help='Select the initial data (sin, bump).'),
ipython_engines:
int = Option(
0,
help=
'If positive, the number of IPython cluster engines to use for parallel greedy search. '
'If zero, no parallelization is performed.'),
ipython_profile: str = Option(
None, help='IPython profile to use for parallelization.'),
lxf_lambda: float = Option(
1., help='Parameter lambda in Lax-Friedrichs flux.'),
periodic:
bool = Option(
True,
help
='If not, solve with dirichlet boundary conditions on left and bottom boundary.'
),
nt: int = Option(100, help='Number of time steps.'),
num_flux: Choices('lax_friedrichs engquist_osher') = Option(
'engquist_osher', help='Numerical flux to use.'),
plot_err: bool = Option(False, help='Plot error.'),
plot_ei_err: bool = Option(False,
help='Plot empirical interpolation error.'),
plot_error_landscape: bool = Option(
False,
help='Calculate and show plot of reduction error vs. basis sizes.'
),
plot_error_landscape_M: int = Option(
10, help='Number of collateral basis sizes to test.'),
plot_error_landscape_N: int = Option(
10, help='Number of basis sizes to test.'),
plot_solutions: bool = Option(False,
help='Plot some example solutions.'),
test: int = Option(
10,
help='Number of snapshots to use for stochastic error estimation.'
),
vx: float = Option(1., help='Speed in x-direction.'),
vy: float = Option(1., help='Speed in y-direction.'),
):
"""Model order reduction of a two-dimensional Burgers-type equation
(see pymor.analyticalproblems.burgers) using the reduced basis method
with empirical operator interpolation.
"""
print('Setup Problem ...')
problem = burgers_problem_2d(vx=vx,
vy=vy,
initial_data_type=initial_data.value,
parameter_range=(exp_min, exp_max),
torus=periodic)
print('Discretize ...')
if grid_type == 'rect':
grid *= 1. / math.sqrt(2)
fom, _ = discretize_instationary_fv(
problem,
diameter=1. / grid,
grid_type=RectGrid if grid_type == 'rect' else TriaGrid,
num_flux=num_flux.value,
lxf_lambda=lxf_lambda,
nt=nt)
if cache_region != 'none':
# building a cache_id is only needed for persistent CacheRegions
cache_id = (
f"pymordemos.burgers_ei {vx} {vy} {initial_data}"
f"{periodic} {grid} {grid_type} {num_flux} {lxf_lambda} {nt}")
fom.enable_caching(cache_region.value, cache_id)
print(fom.operator.grid)
print(f'The parameters are {fom.parameters}')
if plot_solutions:
print('Showing some solutions')
Us = ()
legend = ()
for mu in problem.parameter_space.sample_uniformly(4):
print(f"Solving for exponent = {mu['exponent']} ... ")
sys.stdout.flush()
Us = Us + (fom.solve(mu), )
legend = legend + (f"exponent: {mu['exponent']}", )
fom.visualize(Us,
legend=legend,
title='Detailed Solutions',
block=True)
pool = new_parallel_pool(ipython_num_engines=ipython_engines,
ipython_profile=ipython_profile)
eim, ei_data = interpolate_operators(
fom, ['operator'],
problem.parameter_space.sample_uniformly(ei_snapshots),
error_norm=fom.l2_norm,
product=fom.l2_product,
max_interpolation_dofs=ei_size,
alg=ei_alg.value,
pool=pool)
if plot_ei_err:
print('Showing some EI errors')
ERRs = ()
legend = ()
for mu in problem.parameter_space.sample_randomly(2):
print(f"Solving for exponent = \n{mu['exponent']} ... ")
sys.stdout.flush()
U = fom.solve(mu)
U_EI = eim.solve(mu)
ERR = U - U_EI
ERRs = ERRs + (ERR, )
legend = legend + (f"exponent: {mu['exponent']}", )
print(f'Error: {np.max(fom.l2_norm(ERR))}')
fom.visualize(ERRs,
legend=legend,
title='EI Errors',
separate_colorbars=True)
print('Showing interpolation DOFs ...')
U = np.zeros(U.dim)
dofs = eim.operator.interpolation_dofs
U[dofs] = np.arange(1, len(dofs) + 1)
U[eim.operator.source_dofs] += int(len(dofs) / 2)
fom.visualize(fom.solution_space.make_array(U),
title='Interpolation DOFs')
print('RB generation ...')
reductor = InstationaryRBReductor(eim)
greedy_data = rb_greedy(
fom,
reductor,
problem.parameter_space.sample_uniformly(snapshots),
use_error_estimator=False,
error_norm=lambda U: np.max(fom.l2_norm(U)),
extension_params={'method': 'pod'},
max_extensions=rb_size,
pool=pool)
rom = greedy_data['rom']
print('\nSearching for maximum error on random snapshots ...')
tic = time.perf_counter()
mus = problem.parameter_space.sample_randomly(test)
def error_analysis(N, M):
print(f'N = {N}, M = {M}: ', end='')
rom = reductor.reduce(N)
rom = rom.with_(operator=rom.operator.with_cb_dim(M))
l2_err_max = -1
mumax = None
for mu in mus:
print('.', end='')
sys.stdout.flush()
u = rom.solve(mu)
URB = reductor.reconstruct(u)
U = fom.solve(mu)
l2_err = np.max(fom.l2_norm(U - URB))
l2_err = np.inf if not np.isfinite(l2_err) else l2_err
if l2_err > l2_err_max:
l2_err_max = l2_err
mumax = mu
print()
return l2_err_max, mumax
error_analysis = np.frompyfunc(error_analysis, 2, 2)
real_rb_size = len(reductor.bases['RB'])
real_cb_size = len(ei_data['basis'])
if plot_error_landscape:
N_count = min(real_rb_size - 1, plot_error_landscape_N)
M_count = min(real_cb_size - 1, plot_error_landscape_M)
Ns = np.linspace(1, real_rb_size, N_count).astype(np.int)
Ms = np.linspace(1, real_cb_size, M_count).astype(np.int)
else:
Ns = np.array([real_rb_size])
Ms = np.array([real_cb_size])
N_grid, M_grid = np.meshgrid(Ns, Ms)
errs, err_mus = error_analysis(N_grid, M_grid)
errs = errs.astype(np.float)
l2_err_max = errs[-1, -1]
mumax = err_mus[-1, -1]
toc = time.perf_counter()
t_est = toc - tic
print('''
*** RESULTS ***
Problem:
parameter range: ({exp_min}, {exp_max})
h: sqrt(2)/{grid}
grid-type: {grid_type}
initial-data: {initial_data}
lxf-lambda: {lxf_lambda}
nt: {nt}
not-periodic: {periodic}
num-flux: {num_flux}
(vx, vy): ({vx}, {vy})
Greedy basis generation:
number of ei-snapshots: {ei_snapshots}
prescribed collateral basis size: {ei_size}
actual collateral basis size: {real_cb_size}
number of snapshots: {snapshots}
prescribed basis size: {rb_size}
actual basis size: {real_rb_size}
elapsed time: {greedy_data[time]}
Stochastic error estimation:
number of samples: {test}
maximal L2-error: {l2_err_max} (mu = {mumax})
elapsed time: {t_est}
'''.format(**locals()))
sys.stdout.flush()
if plot_error_landscape:
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d # NOQA
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# rescale the errors since matplotlib does not support logarithmic scales on 3d plots
# https://github.com/matplotlib/matplotlib/issues/209
surf = ax.plot_surface(M_grid,
N_grid,
np.log(np.minimum(errs, 1)) / np.log(10),
rstride=1,
cstride=1,
cmap='jet')
plt.show()
if plot_err:
U = fom.solve(mumax)
URB = reductor.reconstruct(rom.solve(mumax))
fom.visualize(
(U, URB, U - URB),
legend=('Detailed Solution', 'Reduced Solution', 'Error'),
title='Maximum Error Solution',
separate_colorbars=True)
global test_results
test_results = (ei_data, greedy_data)
def main(args):
args = parse_arguments(args)
pool = new_parallel_pool(ipython_num_engines=args["--ipython-engines"], ipython_profile=args["--ipython-profile"])
if args["--fenics"]:
d, d_summary = discretize_fenics(args["XBLOCKS"], args["YBLOCKS"], args["--grid"], args["--order"])
else:
d, d_summary = discretize_pymor(args["XBLOCKS"], args["YBLOCKS"], args["--grid"], args["--list-vector-array"])
if args["--cache-region"] != "none":
d.enable_caching(args["--cache-region"])
if args["--plot-solutions"]:
print("Showing some solutions")
Us = ()
legend = ()
for mu in d.parameter_space.sample_randomly(2):
print("Solving for diffusion = \n{} ... ".format(mu["diffusion"]))
sys.stdout.flush()
Us = Us + (d.solve(mu),)
legend = legend + (str(mu["diffusion"]),)
d.visualize(
Us, legend=legend, title="Detailed Solutions for different parameters", separate_colorbars=False, block=True
)
print("RB generation ...")
# define estimator for coercivity constant
from pymor.parameters.functionals import ExpressionParameterFunctional
coercivity_estimator = ExpressionParameterFunctional("min(diffusion)", d.parameter_type)
# inner product for computation of Riesz representatives
product = d.h1_0_semi_product if args["--estimator-norm"] == "h1" else None
if args["--reductor"] == "residual_basis":
from pymor.reductors.coercive import reduce_coercive
reductor = partial(reduce_coercive, product=product, coercivity_estimator=coercivity_estimator)
elif args["--reductor"] == "traditional":
from pymor.reductors.coercive import reduce_coercive_simple
reductor = partial(reduce_coercive_simple, product=product, coercivity_estimator=coercivity_estimator)
else:
assert False # this should never happen
if args["--alg"] == "naive":
rd, rc, red_summary = reduce_naive(d=d, reductor=reductor, basis_size=args["RBSIZE"])
elif args["--alg"] == "greedy":
parallel = not (args["--fenics"] and args["--greedy-without-estimator"]) # cannot pickle FEniCS discretization
rd, rc, red_summary = reduce_greedy(
d=d,
reductor=reductor,
snapshots_per_block=args["SNAPSHOTS"],
extension_alg_name=args["--extension-alg"],
max_extensions=args["RBSIZE"],
use_estimator=not args["--greedy-without-estimator"],
pool=pool if parallel else None,
)
elif args["--alg"] == "adaptive_greedy":
parallel = not (args["--fenics"] and args["--greedy-without-estimator"]) # cannot pickle FEniCS discretization
rd, rc, red_summary = reduce_adaptive_greedy(
d=d,
reductor=reductor,
validation_mus=args["SNAPSHOTS"],
extension_alg_name=args["--extension-alg"],
max_extensions=args["RBSIZE"],
use_estimator=not args["--greedy-without-estimator"],
rho=args["--adaptive-greedy-rho"],
gamma=args["--adaptive-greedy-gamma"],
theta=args["--adaptive-greedy-theta"],
pool=pool if parallel else None,
)
elif args["--alg"] == "pod":
rd, rc, red_summary = reduce_pod(
d=d,
reductor=reductor,
snapshots_per_block=args["SNAPSHOTS"],
basis_size=args["RBSIZE"],
product_name=args["--pod-product"],
)
else:
assert False # this should never happen
if args["--pickle"]:
print("\nWriting reduced discretization to file {} ...".format(args["--pickle"] + "_reduced"))
with open(args["--pickle"] + "_reduced", "wb") as f:
dump(rd, f)
if not args["--fenics"]: # FEniCS data structures do not support serialization
print(
"Writing detailed discretization and reconstructor to file {} ...".format(
args["--pickle"] + "_detailed"
)
)
with open(args["--pickle"] + "_detailed", "wb") as f:
dump((d, rc), f)
print("\nSearching for maximum error on random snapshots ...")
results = reduction_error_analysis(
rd,
discretization=d,
reconstructor=rc,
estimator=True,
error_norms=(d.h1_0_semi_norm, d.l2_norm),
condition=True,
test_mus=args["--test"],
basis_sizes=0 if args["--plot-error-sequence"] else 1,
plot=args["--plot-error-sequence"],
pool=None if args["--fenics"] else pool, # cannot pickle FEniCS discretization
random_seed=999,
)
print("\n*** RESULTS ***\n")
print(d_summary)
print(red_summary)
print(results["summary"])
sys.stdout.flush()
if args["--plot-error-sequence"]:
import matplotlib.pyplot
matplotlib.pyplot.show(results["figure"])
if args["--plot-err"]:
mumax = results["max_error_mus"][0, -1]
U = d.solve(mumax)
URB = rc.reconstruct(rd.solve(mumax))
d.visualize(
(U, URB, U - URB),
legend=("Detailed Solution", "Reduced Solution", "Error"),
title="Maximum Error Solution",
separate_colorbars=True,
block=True,
)
return results
def main(args):
args = docopt(__doc__, args)
args['--cache-region'] = args['--cache-region'].lower()
args['--grid'] = int(args['--grid'])
args['--grid-type'] = args['--grid-type'].lower()
assert args['--grid-type'] in ('rect', 'tria')
args['--initial-data'] = args['--initial-data'].lower()
assert args['--initial-data'] in ('sin', 'bump')
args['--lxf-lambda'] = float(args['--lxf-lambda'])
args['--nt'] = int(args['--nt'])
args['--not-periodic'] = bool(args['--not-periodic'])
args['--num-flux'] = args['--num-flux'].lower()
assert args['--num-flux'] in ('lax_friedrichs', 'engquist_osher')
args['--plot-error-landscape-N'] = int(args['--plot-error-landscape-N'])
args['--plot-error-landscape-M'] = int(args['--plot-error-landscape-M'])
args['--test'] = int(args['--test'])
args['--vx'] = float(args['--vx'])
args['--vy'] = float(args['--vy'])
args['--ipython-engines'] = int(args['--ipython-engines'])
args['EXP_MIN'] = int(args['EXP_MIN'])
args['EXP_MAX'] = int(args['EXP_MAX'])
args['EI_SNAPSHOTS'] = int(args['EI_SNAPSHOTS'])
args['EISIZE'] = int(args['EISIZE'])
args['SNAPSHOTS'] = int(args['SNAPSHOTS'])
args['RBSIZE'] = int(args['RBSIZE'])
print('Setup Problem ...')
problem = burgers_problem_2d(vx=args['--vx'], vy=args['--vy'], initial_data_type=args['--initial-data'],
parameter_range=(args['EXP_MIN'], args['EXP_MAX']), torus=not args['--not-periodic'])
print('Discretize ...')
if args['--grid-type'] == 'rect':
args['--grid'] *= 1. / m.sqrt(2)
d, _ = discretize_instationary_fv(
problem,
diameter=1. / args['--grid'],
grid_type=RectGrid if args['--grid-type'] == 'rect' else TriaGrid,
num_flux=args['--num-flux'],
lxf_lambda=args['--lxf-lambda'],
nt=args['--nt']
)
if args['--cache-region'] != 'none':
d.enable_caching(args['--cache-region'])
print(d.operator.grid)
print('The parameter type is {}'.format(d.parameter_type))
if args['--plot-solutions']:
print('Showing some solutions')
Us = ()
legend = ()
for mu in d.parameter_space.sample_uniformly(4):
print('Solving for exponent = {} ... '.format(mu['exponent']))
sys.stdout.flush()
Us = Us + (d.solve(mu),)
legend = legend + ('exponent: {}'.format(mu['exponent']),)
d.visualize(Us, legend=legend, title='Detailed Solutions', block=True)
pool = new_parallel_pool(ipython_num_engines=args['--ipython-engines'], ipython_profile=args['--ipython-profile'])
ei_d, ei_data = interpolate_operators(d, ['operator'],
d.parameter_space.sample_uniformly(args['EI_SNAPSHOTS']), # NOQA
error_norm=d.l2_norm,
max_interpolation_dofs=args['EISIZE'],
pool=pool)
if args['--plot-ei-err']:
print('Showing some EI errors')
ERRs = ()
legend = ()
for mu in d.parameter_space.sample_randomly(2):
print('Solving for exponent = \n{} ... '.format(mu['exponent']))
sys.stdout.flush()
U = d.solve(mu)
U_EI = ei_d.solve(mu)
ERR = U - U_EI
ERRs = ERRs + (ERR,)
legend = legend + ('exponent: {}'.format(mu['exponent']),)
print('Error: {}'.format(np.max(d.l2_norm(ERR))))
d.visualize(ERRs, legend=legend, title='EI Errors', separate_colorbars=True)
print('Showing interpolation DOFs ...')
U = np.zeros(U.dim)
dofs = ei_d.operator.interpolation_dofs
U[dofs] = np.arange(1, len(dofs) + 1)
U[ei_d.operator.source_dofs] += int(len(dofs)/2)
d.visualize(d.solution_space.make_array(U),
title='Interpolation DOFs')
print('RB generation ...')
reductor = GenericRBReductor(ei_d)
greedy_data = greedy(d, reductor, d.parameter_space.sample_uniformly(args['SNAPSHOTS']),
use_estimator=False, error_norm=lambda U: np.max(d.l2_norm(U)),
extension_params={'method': 'pod'}, max_extensions=args['RBSIZE'],
pool=pool)
rd = greedy_data['rd']
print('\nSearching for maximum error on random snapshots ...')
tic = time.time()
mus = d.parameter_space.sample_randomly(args['--test'])
def error_analysis(N, M):
print('N = {}, M = {}: '.format(N, M), end='')
rd = reductor.reduce(N)
rd = rd.with_(operator=rd.operator.with_cb_dim(M))
l2_err_max = -1
mumax = None
for mu in mus:
print('.', end='')
sys.stdout.flush()
u = rd.solve(mu)
URB = reductor.reconstruct(u)
U = d.solve(mu)
l2_err = np.max(d.l2_norm(U - URB))
l2_err = np.inf if not np.isfinite(l2_err) else l2_err
if l2_err > l2_err_max:
l2_err_max = l2_err
mumax = mu
print()
return l2_err_max, mumax
error_analysis = np.frompyfunc(error_analysis, 2, 2)
real_rb_size = len(reductor.RB)
real_cb_size = len(ei_data['basis'])
if args['--plot-error-landscape']:
N_count = min(real_rb_size - 1, args['--plot-error-landscape-N'])
M_count = min(real_cb_size - 1, args['--plot-error-landscape-M'])
Ns = np.linspace(1, real_rb_size, N_count).astype(np.int)
Ms = np.linspace(1, real_cb_size, M_count).astype(np.int)
else:
Ns = np.array([real_rb_size])
Ms = np.array([real_cb_size])
N_grid, M_grid = np.meshgrid(Ns, Ms)
errs, err_mus = error_analysis(N_grid, M_grid)
errs = errs.astype(np.float)
l2_err_max = errs[-1, -1]
mumax = err_mus[-1, -1]
toc = time.time()
t_est = toc - tic
print('''
*** RESULTS ***
Problem:
parameter range: ({args[EXP_MIN]}, {args[EXP_MAX]})
h: sqrt(2)/{args[--grid]}
grid-type: {args[--grid-type]}
initial-data: {args[--initial-data]}
lxf-lambda: {args[--lxf-lambda]}
nt: {args[--nt]}
not-periodic: {args[--not-periodic]}
num-flux: {args[--num-flux]}
(vx, vy): ({args[--vx]}, {args[--vy]})
Greedy basis generation:
number of ei-snapshots: {args[EI_SNAPSHOTS]}
prescribed collateral basis size: {args[EISIZE]}
actual collateral basis size: {real_cb_size}
number of snapshots: {args[SNAPSHOTS]}
prescribed basis size: {args[RBSIZE]}
actual basis size: {real_rb_size}
elapsed time: {greedy_data[time]}
Stochastic error estimation:
number of samples: {args[--test]}
maximal L2-error: {l2_err_max} (mu = {mumax})
elapsed time: {t_est}
'''.format(**locals()))
sys.stdout.flush()
if args['--plot-error-landscape']:
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d # NOQA
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# we have to rescale the errors since matplotlib does not support logarithmic scales on 3d plots
# https://github.com/matplotlib/matplotlib/issues/209
surf = ax.plot_surface(M_grid, N_grid, np.log(np.minimum(errs, 1)) / np.log(10),
rstride=1, cstride=1, cmap='jet')
plt.show()
if args['--plot-err']:
U = d.solve(mumax)
URB = reductor.reconstruct(rd.solve(mumax))
d.visualize((U, URB, U - URB), legend=('Detailed Solution', 'Reduced Solution', 'Error'),
title='Maximum Error Solution', separate_colorbars=True)
return ei_data, greedy_data
def main(args):
args = parse_arguments(args)
pool = new_parallel_pool(ipython_num_engines=args['--ipython-engines'], ipython_profile=args['--ipython-profile'])
if args['--fenics']:
fom, fom_summary = discretize_fenics(args['XBLOCKS'], args['YBLOCKS'], args['--grid'], args['--order'])
else:
fom, fom_summary = discretize_pymor(args['XBLOCKS'], args['YBLOCKS'], args['--grid'], args['--list-vector-array'])
if args['--cache-region'] != 'none':
# building a cache_id is only needed for persistent CacheRegions
cache_id = (f"pymordemos.thermalblock {args['--fenics']} {args['XBLOCKS']} {args['YBLOCKS']}"
f"{args['--grid']} {args['--order']}")
fom.enable_caching(args['--cache-region'], cache_id)
if args['--plot-solutions']:
print('Showing some solutions')
Us = ()
legend = ()
for mu in fom.parameter_space.sample_randomly(2):
print(f"Solving for diffusion = \n{mu['diffusion']} ... ")
sys.stdout.flush()
Us = Us + (fom.solve(mu),)
legend = legend + (str(mu['diffusion']),)
fom.visualize(Us, legend=legend, title='Detailed Solutions for different parameters',
separate_colorbars=False, block=True)
print('RB generation ...')
# define estimator for coercivity constant
from pymor.parameters.functionals import ExpressionParameterFunctional
coercivity_estimator = ExpressionParameterFunctional('min(diffusion)', fom.parameter_type)
# inner product for computation of Riesz representatives
product = fom.h1_0_semi_product if args['--product'] == 'h1' else None
if args['--reductor'] == 'residual_basis':
from pymor.reductors.coercive import CoerciveRBReductor
reductor = CoerciveRBReductor(fom, product=product, coercivity_estimator=coercivity_estimator,
check_orthonormality=False)
elif args['--reductor'] == 'traditional':
from pymor.reductors.coercive import SimpleCoerciveRBReductor
reductor = SimpleCoerciveRBReductor(fom, product=product, coercivity_estimator=coercivity_estimator,
check_orthonormality=False)
else:
assert False # this should never happen
if args['--alg'] == 'naive':
rom, red_summary = reduce_naive(fom=fom, reductor=reductor, basis_size=args['RBSIZE'])
elif args['--alg'] == 'greedy':
parallel = not (args['--fenics'] and args['--greedy-without-estimator']) # cannot pickle FEniCS model
rom, red_summary = reduce_greedy(fom=fom, reductor=reductor, snapshots_per_block=args['SNAPSHOTS'],
extension_alg_name=args['--extension-alg'],
max_extensions=args['RBSIZE'],
use_estimator=not args['--greedy-without-estimator'],
pool=pool if parallel else None)
elif args['--alg'] == 'adaptive_greedy':
parallel = not (args['--fenics'] and args['--greedy-without-estimator']) # cannot pickle FEniCS model
rom, red_summary = reduce_adaptive_greedy(fom=fom, reductor=reductor, validation_mus=args['SNAPSHOTS'],
extension_alg_name=args['--extension-alg'],
max_extensions=args['RBSIZE'],
use_estimator=not args['--greedy-without-estimator'],
rho=args['--adaptive-greedy-rho'],
gamma=args['--adaptive-greedy-gamma'],
theta=args['--adaptive-greedy-theta'],
pool=pool if parallel else None)
elif args['--alg'] == 'pod':
rom, red_summary = reduce_pod(fom=fom, reductor=reductor, snapshots_per_block=args['SNAPSHOTS'],
basis_size=args['RBSIZE'])
else:
assert False # this should never happen
if args['--pickle']:
print(f"\nWriting reduced model to file {args['--pickle']}_reduced ...")
with open(args['--pickle'] + '_reduced', 'wb') as f:
dump(rom, f)
if not args['--fenics']: # FEniCS data structures do not support serialization
print(f"Writing detailed model and reductor to file {args['--pickle']}_detailed ...")
with open(args['--pickle'] + '_detailed', 'wb') as f:
dump((fom, reductor), f)
print('\nSearching for maximum error on random snapshots ...')
results = reduction_error_analysis(rom,
fom=fom,
reductor=reductor,
estimator=True,
error_norms=(fom.h1_0_semi_norm, fom.l2_norm),
condition=True,
test_mus=args['--test'],
basis_sizes=0 if args['--plot-error-sequence'] else 1,
plot=args['--plot-error-sequence'],
pool=None if args['--fenics'] else pool, # cannot pickle FEniCS model
random_seed=999)
print('\n*** RESULTS ***\n')
print(fom_summary)
print(red_summary)
print(results['summary'])
sys.stdout.flush()
if args['--plot-error-sequence']:
import matplotlib.pyplot
matplotlib.pyplot.show(results['figure'])
if args['--plot-err']:
mumax = results['max_error_mus'][0, -1]
U = fom.solve(mumax)
URB = reductor.reconstruct(rom.solve(mumax))
fom.visualize((U, URB, U - URB), legend=('Detailed Solution', 'Reduced Solution', 'Error'),
title='Maximum Error Solution', separate_colorbars=True, block=True)
return results
def thermalblock_demo(args):
args['--grid'] = int(args['--grid'])
args['RBSIZE'] = int(args['RBSIZE'])
args['--test'] = int(args['--test'])
args['--ipython-engines'] = int(args['--ipython-engines'])
args['--extension-alg'] = args['--extension-alg'].lower()
assert args['--extension-alg'] in {'trivial', 'gram_schmidt'}
args['--product'] = args['--product'].lower()
assert args['--product'] in {'trivial', 'h1'}
args['--reductor'] = args['--reductor'].lower()
assert args['--reductor'] in {'traditional', 'residual_basis'}
args['--cache-region'] = args['--cache-region'].lower()
args['--validation-mus'] = int(args['--validation-mus'])
args['--rho'] = float(args['--rho'])
args['--gamma'] = float(args['--gamma'])
args['--theta'] = float(args['--theta'])
problem = thermal_block_problem(num_blocks=(2, 2))
functionals = [
ExpressionParameterFunctional('diffusion[0]', {'diffusion': 2}),
ExpressionParameterFunctional('diffusion[1]**2', {'diffusion': 2}),
ExpressionParameterFunctional('diffusion[0]', {'diffusion': 2}),
ExpressionParameterFunctional('diffusion[1]', {'diffusion': 2})
]
problem = problem.with_(
diffusion=problem.diffusion.with_(coefficients=functionals), )
print('Discretize ...')
fom, _ = discretize_stationary_cg(problem, diameter=1. / args['--grid'])
if args['--list-vector-array']:
from pymor.discretizers.builtin.list import convert_to_numpy_list_vector_array
fom = convert_to_numpy_list_vector_array(fom)
if args['--cache-region'] != 'none':
# building a cache_id is only needed for persistent CacheRegions
cache_id = f"pymordemos.thermalblock_adaptive {args['--grid']}"
fom.enable_caching(args['--cache-region'], cache_id)
if args['--plot-solutions']:
print('Showing some solutions')
Us = ()
legend = ()
for mu in problem.parameter_space.sample_randomly(2):
print(f"Solving for diffusion = \n{mu['diffusion']} ... ")
sys.stdout.flush()
Us = Us + (fom.solve(mu), )
legend = legend + (str(mu['diffusion']), )
fom.visualize(Us,
legend=legend,
title='Detailed Solutions for different parameters',
block=True)
print('RB generation ...')
product = fom.h1_0_semi_product if args['--product'] == 'h1' else None
coercivity_estimator = ExpressionParameterFunctional(
'min([diffusion[0], diffusion[1]**2])', fom.parameters)
reductors = {
'residual_basis':
CoerciveRBReductor(fom,
product=product,
coercivity_estimator=coercivity_estimator),
'traditional':
SimpleCoerciveRBReductor(fom,
product=product,
coercivity_estimator=coercivity_estimator)
}
reductor = reductors[args['--reductor']]
pool = new_parallel_pool(ipython_num_engines=args['--ipython-engines'],
ipython_profile=args['--ipython-profile'])
greedy_data = rb_adaptive_greedy(
fom,
reductor,
problem.parameter_space,
validation_mus=args['--validation-mus'],
rho=args['--rho'],
gamma=args['--gamma'],
theta=args['--theta'],
use_estimator=not args['--without-estimator'],
error_norm=fom.h1_0_semi_norm,
max_extensions=args['RBSIZE'],
visualize=not args['--no-visualize-refinement'])
rom = greedy_data['rom']
if args['--pickle']:
print(
f"\nWriting reduced model to file {args['--pickle']}_reduced ...")
with open(args['--pickle'] + '_reduced', 'wb') as f:
dump(rom, f)
print(
f"Writing detailed model and reductor to file {args['--pickle']}_detailed ..."
)
with open(args['--pickle'] + '_detailed', 'wb') as f:
dump((fom, reductor), f)
print('\nSearching for maximum error on random snapshots ...')
results = reduction_error_analysis(
rom,
fom=fom,
reductor=reductor,
estimator=True,
error_norms=(fom.h1_0_semi_norm, ),
condition=True,
test_mus=problem.parameter_space.sample_randomly(args['--test']),
basis_sizes=25 if args['--plot-error-sequence'] else 1,
plot=True,
pool=pool)
real_rb_size = rom.solution_space.dim
print('''
*** RESULTS ***
Problem:
number of blocks: 2x2
h: sqrt(2)/{args[--grid]}
Greedy basis generation:
estimator disabled: {args[--without-estimator]}
extension method: {args[--extension-alg]}
product: {args[--product]}
prescribed basis size: {args[RBSIZE]}
actual basis size: {real_rb_size}
elapsed time: {greedy_data[time]}
'''.format(**locals()))
print(results['summary'])
sys.stdout.flush()
if args['--plot-error-sequence']:
from matplotlib import pyplot as plt
plt.show(results['figure'])
if args['--plot-err']:
mumax = results['max_error_mus'][0, -1]
U = fom.solve(mumax)
URB = reductor.reconstruct(rom.solve(mumax))
fom.visualize(
(U, URB, U - URB),
legend=('Detailed Solution', 'Reduced Solution', 'Error'),
title='Maximum Error Solution',
separate_colorbars=True,
block=True)
def main(args):
args = docopt(__doc__, args)
args['--cache-region'] = args['--cache-region'].lower()
args['--ei-alg'] = args['--ei-alg'].lower()
assert args['--ei-alg'] in ('ei_greedy', 'deim')
args['--grid'] = int(args['--grid'])
args['--grid-type'] = args['--grid-type'].lower()
assert args['--grid-type'] in ('rect', 'tria')
args['--initial-data'] = args['--initial-data'].lower()
assert args['--initial-data'] in ('sin', 'bump')
args['--lxf-lambda'] = float(args['--lxf-lambda'])
args['--nt'] = int(args['--nt'])
args['--not-periodic'] = bool(args['--not-periodic'])
args['--num-flux'] = args['--num-flux'].lower()
assert args['--num-flux'] in ('lax_friedrichs', 'engquist_osher')
args['--plot-error-landscape-N'] = int(args['--plot-error-landscape-N'])
args['--plot-error-landscape-M'] = int(args['--plot-error-landscape-M'])
args['--test'] = int(args['--test'])
args['--vx'] = float(args['--vx'])
args['--vy'] = float(args['--vy'])
args['--ipython-engines'] = int(args['--ipython-engines'])
args['EXP_MIN'] = int(args['EXP_MIN'])
args['EXP_MAX'] = int(args['EXP_MAX'])
args['EI_SNAPSHOTS'] = int(args['EI_SNAPSHOTS'])
args['EISIZE'] = int(args['EISIZE'])
args['SNAPSHOTS'] = int(args['SNAPSHOTS'])
args['RBSIZE'] = int(args['RBSIZE'])
print('Setup Problem ...')
problem = burgers_problem_2d(vx=args['--vx'],
vy=args['--vy'],
initial_data_type=args['--initial-data'],
parameter_range=(args['EXP_MIN'],
args['EXP_MAX']),
torus=not args['--not-periodic'])
print('Discretize ...')
if args['--grid-type'] == 'rect':
args['--grid'] *= 1. / math.sqrt(2)
fom, _ = discretize_instationary_fv(
problem,
diameter=1. / args['--grid'],
grid_type=RectGrid if args['--grid-type'] == 'rect' else TriaGrid,
num_flux=args['--num-flux'],
lxf_lambda=args['--lxf-lambda'],
nt=args['--nt'])
if args['--cache-region'] != 'none':
fom.enable_caching(args['--cache-region'])
print(fom.operator.grid)
print(f'The parameter type is {fom.parameter_type}')
if args['--plot-solutions']:
print('Showing some solutions')
Us = ()
legend = ()
for mu in fom.parameter_space.sample_uniformly(4):
print(f"Solving for exponent = {mu['exponent']} ... ")
sys.stdout.flush()
Us = Us + (fom.solve(mu), )
legend = legend + (f"exponent: {mu['exponent']}", )
fom.visualize(Us,
legend=legend,
title='Detailed Solutions',
block=True)
pool = new_parallel_pool(ipython_num_engines=args['--ipython-engines'],
ipython_profile=args['--ipython-profile'])
eim, ei_data = interpolate_operators(
fom,
['operator'],
fom.parameter_space.sample_uniformly(args['EI_SNAPSHOTS']), # NOQA
error_norm=fom.l2_norm,
product=fom.l2_product,
max_interpolation_dofs=args['EISIZE'],
alg=args['--ei-alg'],
pool=pool)
if args['--plot-ei-err']:
print('Showing some EI errors')
ERRs = ()
legend = ()
for mu in fom.parameter_space.sample_randomly(2):
print(f"Solving for exponent = \n{mu['exponent']} ... ")
sys.stdout.flush()
U = fom.solve(mu)
U_EI = eim.solve(mu)
ERR = U - U_EI
ERRs = ERRs + (ERR, )
legend = legend + (f"exponent: {mu['exponent']}", )
print(f'Error: {np.max(fom.l2_norm(ERR))}')
fom.visualize(ERRs,
legend=legend,
title='EI Errors',
separate_colorbars=True)
print('Showing interpolation DOFs ...')
U = np.zeros(U.dim)
dofs = eim.operator.interpolation_dofs
U[dofs] = np.arange(1, len(dofs) + 1)
U[eim.operator.source_dofs] += int(len(dofs) / 2)
fom.visualize(fom.solution_space.make_array(U),
title='Interpolation DOFs')
print('RB generation ...')
reductor = InstationaryRBReductor(eim)
greedy_data = rb_greedy(fom,
reductor,
fom.parameter_space.sample_uniformly(
args['SNAPSHOTS']),
use_estimator=False,
error_norm=lambda U: np.max(fom.l2_norm(U)),
extension_params={'method': 'pod'},
max_extensions=args['RBSIZE'],
pool=pool)
rom = greedy_data['rom']
print('\nSearching for maximum error on random snapshots ...')
tic = time.time()
mus = fom.parameter_space.sample_randomly(args['--test'])
def error_analysis(N, M):
print(f'N = {N}, M = {M}: ', end='')
rom = reductor.reduce(N)
rom = rom.with_(operator=rom.operator.with_cb_dim(M))
l2_err_max = -1
mumax = None
for mu in mus:
print('.', end='')
sys.stdout.flush()
u = rom.solve(mu)
URB = reductor.reconstruct(u)
U = fom.solve(mu)
l2_err = np.max(fom.l2_norm(U - URB))
l2_err = np.inf if not np.isfinite(l2_err) else l2_err
if l2_err > l2_err_max:
l2_err_max = l2_err
mumax = mu
print()
return l2_err_max, mumax
error_analysis = np.frompyfunc(error_analysis, 2, 2)
real_rb_size = len(reductor.bases['RB'])
real_cb_size = len(ei_data['basis'])
if args['--plot-error-landscape']:
N_count = min(real_rb_size - 1, args['--plot-error-landscape-N'])
M_count = min(real_cb_size - 1, args['--plot-error-landscape-M'])
Ns = np.linspace(1, real_rb_size, N_count).astype(np.int)
Ms = np.linspace(1, real_cb_size, M_count).astype(np.int)
else:
Ns = np.array([real_rb_size])
Ms = np.array([real_cb_size])
N_grid, M_grid = np.meshgrid(Ns, Ms)
errs, err_mus = error_analysis(N_grid, M_grid)
errs = errs.astype(np.float)
l2_err_max = errs[-1, -1]
mumax = err_mus[-1, -1]
toc = time.time()
t_est = toc - tic
print('''
*** RESULTS ***
Problem:
parameter range: ({args[EXP_MIN]}, {args[EXP_MAX]})
h: sqrt(2)/{args[--grid]}
grid-type: {args[--grid-type]}
initial-data: {args[--initial-data]}
lxf-lambda: {args[--lxf-lambda]}
nt: {args[--nt]}
not-periodic: {args[--not-periodic]}
num-flux: {args[--num-flux]}
(vx, vy): ({args[--vx]}, {args[--vy]})
Greedy basis generation:
number of ei-snapshots: {args[EI_SNAPSHOTS]}
prescribed collateral basis size: {args[EISIZE]}
actual collateral basis size: {real_cb_size}
number of snapshots: {args[SNAPSHOTS]}
prescribed basis size: {args[RBSIZE]}
actual basis size: {real_rb_size}
elapsed time: {greedy_data[time]}
Stochastic error estimation:
number of samples: {args[--test]}
maximal L2-error: {l2_err_max} (mu = {mumax})
elapsed time: {t_est}
'''.format(**locals()))
sys.stdout.flush()
if args['--plot-error-landscape']:
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d # NOQA
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# we have to rescale the errors since matplotlib does not support logarithmic scales on 3d plots
# https://github.com/matplotlib/matplotlib/issues/209
surf = ax.plot_surface(M_grid,
N_grid,
np.log(np.minimum(errs, 1)) / np.log(10),
rstride=1,
cstride=1,
cmap='jet')
plt.show()
if args['--plot-err']:
U = fom.solve(mumax)
URB = reductor.reconstruct(rom.solve(mumax))
fom.visualize(
(U, URB, U - URB),
legend=('Detailed Solution', 'Reduced Solution', 'Error'),
title='Maximum Error Solution',
separate_colorbars=True)
return ei_data, greedy_data
def thermalblock_demo(args):
args['--grid'] = int(args['--grid'])
args['RBSIZE'] = int(args['RBSIZE'])
args['--test'] = int(args['--test'])
args['--ipython-engines'] = int(args['--ipython-engines'])
args['--estimator-norm'] = args['--estimator-norm'].lower()
assert args['--estimator-norm'] in {'trivial', 'h1'}
args['--extension-alg'] = args['--extension-alg'].lower()
assert args['--extension-alg'] in {'trivial', 'gram_schmidt', 'h1_gram_schmidt'}
args['--reductor'] = args['--reductor'].lower()
assert args['--reductor'] in {'traditional', 'residual_basis'}
args['--cache-region'] = args['--cache-region'].lower()
args['--validation-mus'] = int(args['--validation-mus'])
args['--rho'] = float(args['--rho'])
args['--gamma'] = float(args['--gamma'])
args['--theta'] = float(args['--theta'])
print('Solving on TriaGrid(({0},{0}))'.format(args['--grid']))
print('Setup Problem ...')
problem = ThermalBlockProblem(num_blocks=(2, 2))
functionals = [ExpressionParameterFunctional('diffusion[0]', {'diffusion': (2,)}),
ExpressionParameterFunctional('diffusion[1]**2', {'diffusion': (2,)}),
ExpressionParameterFunctional('diffusion[0]', {'diffusion': (2,)}),
ExpressionParameterFunctional('diffusion[1]', {'diffusion': (2,)})]
problem = EllipticProblem(domain=problem.domain,
diffusion_functions=problem.diffusion_functions,
diffusion_functionals=functionals,
rhs=problem.rhs,
parameter_space=CubicParameterSpace({'diffusion': (2,)}, 0.1, 1.))
print('Discretize ...')
discretization, _ = discretize_elliptic_cg(problem, diameter=1. / args['--grid'])
if args['--list-vector-array']:
from pymor.playground.discretizers.numpylistvectorarray import convert_to_numpy_list_vector_array
discretization = convert_to_numpy_list_vector_array(discretization)
if args['--cache-region'] != 'none':
discretization.enable_caching(args['--cache-region'])
print('The parameter type is {}'.format(discretization.parameter_type))
if args['--plot-solutions']:
print('Showing some solutions')
Us = ()
legend = ()
for mu in discretization.parameter_space.sample_randomly(2):
print('Solving for diffusion = \n{} ... '.format(mu['diffusion']))
sys.stdout.flush()
Us = Us + (discretization.solve(mu),)
legend = legend + (str(mu['diffusion']),)
discretization.visualize(Us, legend=legend, title='Detailed Solutions for different parameters', block=True)
print('RB generation ...')
product = discretization.h1_0_semi_product if args['--estimator-norm'] == 'h1' else None
coercivity_estimator=ExpressionParameterFunctional('min([diffusion[0], diffusion[1]**2])', discretization.parameter_type)
reductors = {'residual_basis': partial(reduce_coercive, product=product,
coercivity_estimator=coercivity_estimator),
'traditional': partial(reduce_coercive_simple, product=product,
coercivity_estimator=coercivity_estimator)}
reductor = reductors[args['--reductor']]
extension_algorithms = {'trivial': trivial_basis_extension,
'gram_schmidt': gram_schmidt_basis_extension,
'h1_gram_schmidt': partial(gram_schmidt_basis_extension, product=discretization.h1_0_semi_product)}
extension_algorithm = extension_algorithms[args['--extension-alg']]
pool = new_parallel_pool(ipython_num_engines=args['--ipython-engines'], ipython_profile=args['--ipython-profile'])
greedy_data = adaptive_greedy(discretization, reductor,
validation_mus=args['--validation-mus'], rho=args['--rho'], gamma=args['--gamma'],
theta=args['--theta'],
use_estimator=not args['--without-estimator'], error_norm=discretization.h1_0_semi_norm,
extension_algorithm=extension_algorithm, max_extensions=args['RBSIZE'],
visualize=args['--visualize-refinement'])
rb_discretization, reconstructor = greedy_data['reduced_discretization'], greedy_data['reconstructor']
if args['--pickle']:
print('\nWriting reduced discretization to file {} ...'.format(args['--pickle'] + '_reduced'))
with open(args['--pickle'] + '_reduced', 'wb') as f:
dump(rb_discretization, f)
print('Writing detailed discretization and reconstructor to file {} ...'.format(args['--pickle'] + '_detailed'))
with open(args['--pickle'] + '_detailed', 'wb') as f:
dump((discretization, reconstructor), f)
print('\nSearching for maximum error on random snapshots ...')
results = reduction_error_analysis(rb_discretization,
discretization=discretization,
reconstructor=reconstructor,
estimator=True,
error_norms=(discretization.h1_0_semi_norm,),
condition=True,
test_mus=args['--test'],
basis_sizes=25 if args['--plot-error-sequence'] else 1,
plot=True,
pool=pool)
real_rb_size = rb_discretization.solution_space.dim
print('''
*** RESULTS ***
Problem:
number of blocks: 2x2
h: sqrt(2)/{args[--grid]}
Greedy basis generation:
estimator disabled: {args[--without-estimator]}
estimator norm: {args[--estimator-norm]}
extension method: {args[--extension-alg]}
prescribed basis size: {args[RBSIZE]}
actual basis size: {real_rb_size}
elapsed time: {greedy_data[time]}
'''.format(**locals()))
print(results['summary'])
sys.stdout.flush()
if args['--plot-error-sequence']:
from matplotlib import pyplot as plt
plt.show(results['figure'])
if args['--plot-err']:
mumax = results['max_error_mus'][0, -1]
U = discretization.solve(mumax)
URB = reconstructor.reconstruct(rb_discretization.solve(mumax))
discretization.visualize((U, URB, U - URB), legend=('Detailed Solution', 'Reduced Solution', 'Error'),
title='Maximum Error Solution', separate_colorbars=True, block=True)
def main(
xblocks: int = Argument(..., help='Number of blocks in x direction.'),
yblocks: int = Argument(..., help='Number of blocks in y direction.'),
snapshots: int = Argument(
...,
help='naive: ignored\n\n'
'greedy/pod: Number of training_set parameters per block '
'(in total SNAPSHOTS^(XBLOCKS * YBLOCKS) parameters).\n\n'
'adaptive_greedy: size of validation set.\n\n'),
rbsize: int = Argument(..., help='Size of the reduced basis.'),
adaptive_greedy_gamma: float = Option(
0.2, help='See pymor.algorithms.adaptivegreedy.'),
adaptive_greedy_rho: float = Option(
1.1, help='See pymor.algorithms.adaptivegreedy.'),
adaptive_greedy_theta: float = Option(
0., help='See pymor.algorithms.adaptivegreedy.'),
alg: Choices('naive greedy adaptive_greedy pod') = Option(
'greedy', help='The model reduction algorithm to use.'),
cache_region: Choices('none memory disk persistent') = Option(
'none',
help='Name of cache region to use for caching solution snapshots.'),
extension_alg: Choices('trivial gram_schmidt') = Option(
'gram_schmidt', help='Basis extension algorithm to be used.'),
fenics: bool = Option(False, help='Use FEniCS model.'),
greedy_with_error_estimator: bool = Option(
True, help='Use error estimator for basis generation.'),
grid: int = Option(100, help='Use grid with 4*NI*NI elements'),
ipython_engines: int = Option(
None,
help='If positive, the number of IPython cluster engines to use for '
'parallel greedy search. If zero, no parallelization is performed.'),
ipython_profile: str = Option(
None, help='IPython profile to use for parallelization.'),
list_vector_array: bool = Option(
False,
help=
'Solve using ListVectorArray[NumpyVector] instead of NumpyVectorArray.'
),
order: int = Option(
1,
help=
'Polynomial order of the Lagrange finite elements to use in FEniCS.'),
pickle: str = Option(
None,
help=
'Pickle reduced model, as well as reductor and high-dimensional model '
'to files with this prefix.'),
product: Choices('euclidean h1') = Option(
'h1',
help=
'Product w.r.t. which to orthonormalize and calculate Riesz representatives.'
),
plot_err: bool = Option(False, help='Plot error'),
plot_error_sequence: bool = Option(
False, help='Plot reduction error vs. basis size.'),
plot_solutions: bool = Option(False, help='Plot some example solutions.'),
reductor: Choices('traditional residual_basis') = Option(
'residual_basis', help='Reductor (error estimator) to choose.'),
test: int = Option(
10, help='Use COUNT snapshots for stochastic error estimation.'),
):
"""Thermalblock demo."""
if fenics and cache_region != 'none':
raise ValueError(
'Caching of high-dimensional solutions is not supported for FEniCS model.'
)
if not fenics and order != 1:
raise ValueError(
'Higher-order finite elements only supported for FEniCS model.')
pool = new_parallel_pool(ipython_num_engines=ipython_engines,
ipython_profile=ipython_profile)
if fenics:
fom, fom_summary = discretize_fenics(xblocks, yblocks, grid, order)
else:
fom, fom_summary = discretize_pymor(xblocks, yblocks, grid,
list_vector_array)
parameter_space = fom.parameters.space(0.1, 1.)
if cache_region != 'none':
# building a cache_id is only needed for persistent CacheRegions
cache_id = (f"pymordemos.thermalblock {fenics} {xblocks} {yblocks}"
f"{grid} {order}")
fom.enable_caching(cache_region.value, cache_id)
if plot_solutions:
print('Showing some solutions')
Us = ()
legend = ()
for mu in parameter_space.sample_randomly(2):
print(f"Solving for diffusion = \n{mu['diffusion']} ... ")
sys.stdout.flush()
Us = Us + (fom.solve(mu), )
legend = legend + (str(mu['diffusion']), )
fom.visualize(Us,
legend=legend,
title='Detailed Solutions for different parameters',
separate_colorbars=False,
block=True)
print('RB generation ...')
# define estimator for coercivity constant
from pymor.parameters.functionals import ExpressionParameterFunctional
coercivity_estimator = ExpressionParameterFunctional(
'min(diffusion)', fom.parameters)
# inner product for computation of Riesz representatives
product = fom.h1_0_semi_product if product == 'h1' else None
if reductor == 'residual_basis':
from pymor.reductors.coercive import CoerciveRBReductor
reductor = CoerciveRBReductor(
fom,
product=product,
coercivity_estimator=coercivity_estimator,
check_orthonormality=False)
elif reductor == 'traditional':
from pymor.reductors.coercive import SimpleCoerciveRBReductor
reductor = SimpleCoerciveRBReductor(
fom,
product=product,
coercivity_estimator=coercivity_estimator,
check_orthonormality=False)
else:
assert False # this should never happen
if alg == 'naive':
rom, red_summary = reduce_naive(fom=fom,
reductor=reductor,
parameter_space=parameter_space,
basis_size=rbsize)
elif alg == 'greedy':
parallel = greedy_with_error_estimator or not fenics # cannot pickle FEniCS model
rom, red_summary = reduce_greedy(
fom=fom,
reductor=reductor,
parameter_space=parameter_space,
snapshots_per_block=snapshots,
extension_alg_name=extension_alg.value,
max_extensions=rbsize,
use_error_estimator=greedy_with_error_estimator,
pool=pool if parallel else None)
elif alg == 'adaptive_greedy':
parallel = greedy_with_error_estimator or not fenics # cannot pickle FEniCS model
rom, red_summary = reduce_adaptive_greedy(
fom=fom,
reductor=reductor,
parameter_space=parameter_space,
validation_mus=snapshots,
extension_alg_name=extension_alg.value,
max_extensions=rbsize,
use_error_estimator=greedy_with_error_estimator,
rho=adaptive_greedy_rho,
gamma=adaptive_greedy_gamma,
theta=adaptive_greedy_theta,
pool=pool if parallel else None)
elif alg == 'pod':
rom, red_summary = reduce_pod(fom=fom,
reductor=reductor,
parameter_space=parameter_space,
snapshots_per_block=snapshots,
basis_size=rbsize)
else:
assert False # this should never happen
if pickle:
print(f"\nWriting reduced model to file {pickle}_reduced ...")
with open(pickle + '_reduced', 'wb') as f:
dump((rom, parameter_space), f)
if not fenics: # FEniCS data structures do not support serialization
print(
f"Writing detailed model and reductor to file {pickle}_detailed ..."
)
with open(pickle + '_detailed', 'wb') as f:
dump((fom, reductor), f)
print('\nSearching for maximum error on random snapshots ...')
results = reduction_error_analysis(
rom,
fom=fom,
reductor=reductor,
error_estimator=True,
error_norms=(fom.h1_0_semi_norm, fom.l2_norm),
condition=True,
test_mus=parameter_space.sample_randomly(test, seed=999),
basis_sizes=0 if plot_error_sequence else 1,
plot=plot_error_sequence,
pool=None if fenics else pool # cannot pickle FEniCS model
)
print('\n*** RESULTS ***\n')
print(fom_summary)
print(red_summary)
print(results['summary'])
sys.stdout.flush()
if plot_error_sequence:
import matplotlib.pyplot
matplotlib.pyplot.show()
if plot_err:
mumax = results['max_error_mus'][0, -1]
U = fom.solve(mumax)
URB = reductor.reconstruct(rom.solve(mumax))
fom.visualize(
(U, URB, U - URB),
legend=('Detailed Solution', 'Reduced Solution', 'Error'),
title='Maximum Error Solution',
separate_colorbars=True,
block=True)
global test_results
test_results = results
def main(args):
args = parse_arguments(args)
pool = new_parallel_pool(ipython_num_engines=args['--ipython-engines'], ipython_profile=args['--ipython-profile'])
if args['--fenics']:
fom, fom_summary = discretize_fenics(args['XBLOCKS'], args['YBLOCKS'], args['--grid'], args['--order'])
else:
fom, fom_summary = discretize_pymor(args['XBLOCKS'], args['YBLOCKS'], args['--grid'], args['--list-vector-array'])
if args['--cache-region'] != 'none':
fom.enable_caching(args['--cache-region'])
if args['--plot-solutions']:
print('Showing some solutions')
Us = ()
legend = ()
for mu in fom.parameter_space.sample_randomly(2):
print(f"Solving for diffusion = \n{mu['diffusion']} ... ")
sys.stdout.flush()
Us = Us + (fom.solve(mu),)
legend = legend + (str(mu['diffusion']),)
fom.visualize(Us, legend=legend, title='Detailed Solutions for different parameters',
separate_colorbars=False, block=True)
print('RB generation ...')
# define estimator for coercivity constant
from pymor.parameters.functionals import ExpressionParameterFunctional
coercivity_estimator = ExpressionParameterFunctional('min(diffusion)', fom.parameter_type)
# inner product for computation of Riesz representatives
product = fom.h1_0_semi_product if args['--product'] == 'h1' else None
if args['--reductor'] == 'residual_basis':
from pymor.reductors.coercive import CoerciveRBReductor
reductor = CoerciveRBReductor(fom, product=product, coercivity_estimator=coercivity_estimator,
check_orthonormality=False)
elif args['--reductor'] == 'traditional':
from pymor.reductors.coercive import SimpleCoerciveRBReductor
reductor = SimpleCoerciveRBReductor(fom, product=product, coercivity_estimator=coercivity_estimator,
check_orthonormality=False)
else:
assert False # this should never happen
if args['--alg'] == 'naive':
rom, red_summary = reduce_naive(fom=fom, reductor=reductor, basis_size=args['RBSIZE'])
elif args['--alg'] == 'greedy':
parallel = not (args['--fenics'] and args['--greedy-without-estimator']) # cannot pickle FEniCS model
rom, red_summary = reduce_greedy(fom=fom, reductor=reductor, snapshots_per_block=args['SNAPSHOTS'],
extension_alg_name=args['--extension-alg'],
max_extensions=args['RBSIZE'],
use_estimator=not args['--greedy-without-estimator'],
pool=pool if parallel else None)
elif args['--alg'] == 'adaptive_greedy':
parallel = not (args['--fenics'] and args['--greedy-without-estimator']) # cannot pickle FEniCS model
rom, red_summary = reduce_adaptive_greedy(fom=fom, reductor=reductor, validation_mus=args['SNAPSHOTS'],
extension_alg_name=args['--extension-alg'],
max_extensions=args['RBSIZE'],
use_estimator=not args['--greedy-without-estimator'],
rho=args['--adaptive-greedy-rho'],
gamma=args['--adaptive-greedy-gamma'],
theta=args['--adaptive-greedy-theta'],
pool=pool if parallel else None)
elif args['--alg'] == 'pod':
rom, red_summary = reduce_pod(fom=fom, reductor=reductor, snapshots_per_block=args['SNAPSHOTS'],
basis_size=args['RBSIZE'])
else:
assert False # this should never happen
if args['--pickle']:
print(f"\nWriting reduced model to file {args['--pickle']}_reduced ...")
with open(args['--pickle'] + '_reduced', 'wb') as f:
dump(rom, f)
if not args['--fenics']: # FEniCS data structures do not support serialization
print(f"Writing detailed model and reductor to file {args['--pickle']}_detailed ...")
with open(args['--pickle'] + '_detailed', 'wb') as f:
dump((fom, reductor), f)
print('\nSearching for maximum error on random snapshots ...')
results = reduction_error_analysis(rom,
fom=fom,
reductor=reductor,
estimator=True,
error_norms=(fom.h1_0_semi_norm, fom.l2_norm),
condition=True,
test_mus=args['--test'],
basis_sizes=0 if args['--plot-error-sequence'] else 1,
plot=args['--plot-error-sequence'],
pool=None if args['--fenics'] else pool, # cannot pickle FEniCS model
random_seed=999)
print('\n*** RESULTS ***\n')
print(fom_summary)
print(red_summary)
print(results['summary'])
sys.stdout.flush()
if args['--plot-error-sequence']:
import matplotlib.pyplot
matplotlib.pyplot.show(results['figure'])
if args['--plot-err']:
mumax = results['max_error_mus'][0, -1]
U = fom.solve(mumax)
URB = reductor.reconstruct(rom.solve(mumax))
fom.visualize((U, URB, U - URB), legend=('Detailed Solution', 'Reduced Solution', 'Error'),
title='Maximum Error Solution', separate_colorbars=True, block=True)
return results
def main(
rbsize: int = Argument(..., help='Size of the reduced basis.'),
cache_region: Choices('none memory disk persistent') = Option(
'none',
help='Name of cache region to use for caching solution snapshots.'
),
error_estimator: bool = Option(True, help='Use error estimator for basis generation.'),
gamma: float = Option(0.2, help='Weight factor for age penalty term in refinement indicators.'),
grid: int = Option(100, help='Use grid with 2*NI*NI elements.'),
ipython_engines: int = Option(
0,
help='If positive, the number of IPython cluster engines to use for parallel greedy search. '
'If zero, no parallelization is performed.'
),
ipython_profile: str = Option(None, help='IPython profile to use for parallelization.'),
list_vector_array: bool = Option(
False,
help='Solve using ListVectorArray[NumpyVector] instead of NumpyVectorArray.'
),
pickle: str = Option(
None,
help='Pickle reduced discretization, as well as reductor and high-dimensional model to files with this prefix.'
),
plot_err: bool = Option(False, help='Plot error.'),
plot_solutions: bool = Option(False, help='Plot some example solutions.'),
plot_error_sequence: bool = Option(False, help='Plot reduction error vs. basis size.'),
product: Choices('euclidean h1') = Option(
'h1',
help='Product w.r.t. which to orthonormalize and calculate Riesz representatives.'
),
reductor: Choices('traditional residual_basis') = Option(
'residual_basis',
help='Reductor (error estimator) to choose (traditional, residual_basis).'
),
rho: float = Option(1.1, help='Maximum allowed ratio between error on validation set and on training set.'),
test: int = Option(10, help='Use COUNT snapshots for stochastic error estimation.'),
theta: float = Option(0., help='Ratio of elements to refine.'),
validation_mus: int = Option(0, help='Size of validation set.'),
visualize_refinement: bool = Option(True, help='Visualize the training set refinement indicators.'),
):
"""Modified thermalblock demo using adaptive greedy basis generation algorithm."""
problem = thermal_block_problem(num_blocks=(2, 2))
functionals = [ExpressionParameterFunctional('diffusion[0]', {'diffusion': 2}),
ExpressionParameterFunctional('diffusion[1]**2', {'diffusion': 2}),
ExpressionParameterFunctional('diffusion[0]', {'diffusion': 2}),
ExpressionParameterFunctional('diffusion[1]', {'diffusion': 2})]
problem = problem.with_(
diffusion=problem.diffusion.with_(coefficients=functionals),
)
print('Discretize ...')
fom, _ = discretize_stationary_cg(problem, diameter=1. / grid)
if list_vector_array:
from pymor.discretizers.builtin.list import convert_to_numpy_list_vector_array
fom = convert_to_numpy_list_vector_array(fom)
if cache_region != 'none':
# building a cache_id is only needed for persistent CacheRegions
cache_id = f"pymordemos.thermalblock_adaptive {grid}"
fom.enable_caching(cache_region.value, cache_id)
if plot_solutions:
print('Showing some solutions')
Us = ()
legend = ()
for mu in problem.parameter_space.sample_randomly(2):
print(f"Solving for diffusion = \n{mu['diffusion']} ... ")
sys.stdout.flush()
Us = Us + (fom.solve(mu),)
legend = legend + (str(mu['diffusion']),)
fom.visualize(Us, legend=legend, title='Detailed Solutions for different parameters', block=True)
print('RB generation ...')
product_op = fom.h1_0_semi_product if product == 'h1' else None
coercivity_estimator = ExpressionParameterFunctional('min([diffusion[0], diffusion[1]**2])',
fom.parameters)
reductors = {'residual_basis': CoerciveRBReductor(fom, product=product_op,
coercivity_estimator=coercivity_estimator),
'traditional': SimpleCoerciveRBReductor(fom, product=product_op,
coercivity_estimator=coercivity_estimator)}
reductor = reductors[reductor]
pool = new_parallel_pool(ipython_num_engines=ipython_engines, ipython_profile=ipython_profile)
greedy_data = rb_adaptive_greedy(
fom, reductor, problem.parameter_space,
validation_mus=validation_mus,
rho=rho,
gamma=gamma,
theta=theta,
use_error_estimator=error_estimator,
error_norm=fom.h1_0_semi_norm,
max_extensions=rbsize,
visualize=visualize_refinement
)
rom = greedy_data['rom']
if pickle:
print(f"\nWriting reduced model to file {pickle}_reduced ...")
with open(pickle + '_reduced', 'wb') as f:
dump(rom, f)
print(f"Writing detailed model and reductor to file {pickle}_detailed ...")
with open(pickle + '_detailed', 'wb') as f:
dump((fom, reductor), f)
print('\nSearching for maximum error on random snapshots ...')
results = reduction_error_analysis(rom,
fom=fom,
reductor=reductor,
error_estimator=True,
error_norms=(fom.h1_0_semi_norm,),
condition=True,
test_mus=problem.parameter_space.sample_randomly(test),
basis_sizes=25 if plot_error_sequence else 1,
plot=True,
pool=pool)
real_rb_size = rom.solution_space.dim
print('''
*** RESULTS ***
Problem:
number of blocks: 2x2
h: sqrt(2)/{grid}
Greedy basis generation:
error estimator enalbed: {error_estimator}
product: {product}
prescribed basis size: {rbsize}
actual basis size: {real_rb_size}
elapsed time: {greedy_data[time]}
'''.format(**locals()))
print(results['summary'])
sys.stdout.flush()
if plot_error_sequence:
from matplotlib import pyplot as plt
plt.show()
if plot_err:
mumax = results['max_error_mus'][0, -1]
U = fom.solve(mumax)
URB = reductor.reconstruct(rom.solve(mumax))
fom.visualize((U, URB, U - URB), legend=('Detailed Solution', 'Reduced Solution', 'Error'),
title='Maximum Error Solution', separate_colorbars=True, block=True)
