def __init__(self, args):
self.args = args
self.first = True
self.problem = thermal_block_problem(num_blocks=(args['XBLOCKS'], args['YBLOCKS']),
parameter_range=(PARAM_MIN, PARAM_MAX))
self.m, pack = discretize_stationary_cg(self.problem, diameter=1. / args['--grid'])
self.grid = pack['grid']
def __init__(self, args):
self.args = args
self.first = True
self.problem = thermal_block_problem(num_blocks=(args['XBLOCKS'], args['YBLOCKS']),
parameter_range=(PARAM_MIN, PARAM_MAX))
self.discretization, pack = discretize_stationary_cg(self.problem, diameter=1. / args['--grid'])
self.grid = pack['grid']
def __init__(self, xblocks, yblocks, snapshots, rbsize, grid, product):
self.snapshots, self.rbsize, self.product = snapshots, rbsize, product
self.xblocks, self.yblocks = xblocks, yblocks
self.first = True
self.problem = thermal_block_problem(num_blocks=(xblocks, yblocks),
parameter_range=(PARAM_MIN,
PARAM_MAX))
self.m, pack = discretize_stationary_cg(self.problem,
diameter=1. / grid)
self.grid = pack['grid']
def thermalblock_factory(xblocks, yblocks, diameter, seed):
from pymor.analyticalproblems.thermalblock import thermal_block_problem
from pymor.discretizers.cg import discretize_stationary_cg
from pymor.functions.basic import GenericFunction
from pymor.operators.cg import InterpolationOperator
p = thermal_block_problem((xblocks, yblocks))
d, d_data = discretize_stationary_cg(p, diameter)
f = GenericFunction(lambda X, mu: X[..., 0]**mu['exp'] + X[..., 1],
dim_domain=2, parameter_type={'exp': ()})
iop = InterpolationOperator(d_data['grid'], f)
U = d.operator.source.empty()
V = d.operator.range.empty()
np.random.seed(seed)
for exp in np.random.random(5):
U.append(iop.as_vector(exp))
for exp in np.random.random(6):
V.append(iop.as_vector(exp))
return d.operator, d.parameter_space.sample_randomly(1, seed=seed)[0], U, V, d.h1_product, d.l2_product
def thermalblock_factory(xblocks, yblocks, diameter, seed):
from pymor.analyticalproblems.thermalblock import thermal_block_problem
from pymor.discretizers.builtin import discretize_stationary_cg
from pymor.functions.basic import GenericFunction
from pymor.discretizers.builtin.cg import InterpolationOperator
p = thermal_block_problem((xblocks, yblocks))
m, m_data = discretize_stationary_cg(p, diameter)
f = GenericFunction(lambda X, mu: X[..., 0]**mu['exp'] + X[..., 1],
dim_domain=2, parameter_type={'exp': ()})
iop = InterpolationOperator(m_data['grid'], f)
U = m.operator.source.empty()
V = m.operator.range.empty()
np.random.seed(seed)
for exp in np.random.random(5):
U.append(iop.as_vector(exp))
for exp in np.random.random(6):
V.append(iop.as_vector(exp))
return m.operator, m.parameter_space.sample_randomly(1, seed=seed)[0], U, V, m.h1_product, m.l2_product
def discretize_pymor(xblocks, yblocks, grid_num_intervals, use_list_vector_array):
from pymor.analyticalproblems.thermalblock import thermal_block_problem
from pymor.discretizers.builtin import discretize_stationary_cg
from pymor.playground.discretizers.numpylistvectorarray import convert_to_numpy_list_vector_array
print('Discretize ...')
# setup analytical problem
problem = thermal_block_problem(num_blocks=(xblocks, yblocks))
# discretize using continuous finite elements
fom, _ = discretize_stationary_cg(problem, diameter=1. / grid_num_intervals)
if use_list_vector_array:
fom = convert_to_numpy_list_vector_array(fom)
summary = f'''pyMOR model:
number of blocks: {xblocks}x{yblocks}
grid intervals: {grid_num_intervals}
ListVectorArray: {use_list_vector_array}
'''
return fom, summary
def discretize_pymor(xblocks, yblocks, grid_num_intervals, use_list_vector_array):
from pymor.analyticalproblems.thermalblock import thermal_block_problem
from pymor.discretizers.cg import discretize_stationary_cg
from pymor.playground.discretizers.numpylistvectorarray import convert_to_numpy_list_vector_array
print('Discretize ...')
# setup analytical problem
problem = thermal_block_problem(num_blocks=(xblocks, yblocks))
# discretize using continuous finite elements
d, _ = discretize_stationary_cg(problem, diameter=1. / grid_num_intervals)
if use_list_vector_array:
d = convert_to_numpy_list_vector_array(d)
summary = '''pyMOR discretization:
number of blocks: {xblocks}x{yblocks}
grid intervals: {grid_num_intervals}
ListVectorArray: {use_list_vector_array}
'''.format(**locals())
return d, summary
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)
# Copyright 2013-2017 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
import pytest
from pymor.domaindescriptions.basic import RectDomain
from pymor.analyticalproblems.burgers import burgers_problem, burgers_problem_2d
from pymor.analyticalproblems.elliptic import StationaryProblem
from pymor.analyticalproblems.helmholtz import helmholtz_problem
from pymor.analyticalproblems.thermalblock import thermal_block_problem
from pymor.functions.basic import GenericFunction, ConstantFunction, LincombFunction
from pymor.parameters.functionals import ExpressionParameterFunctional
picklable_thermalblock_problems = \
[thermal_block_problem(),
thermal_block_problem(num_blocks=(3, 2)),
thermal_block_problem(num_blocks=(1, 1)),
thermal_block_problem(num_blocks=(2, 2), parameter_range=(1., 100.))]
non_picklable_thermalblock_problems = \
[thermal_block_problem(num_blocks=(1, 3), parameter_range=(0.4, 0.5)).with_(
rhs=GenericFunction(dim_domain=2, mapping=lambda X: X[..., 0] + X[..., 1]))]
thermalblock_problems = picklable_thermalblock_problems + non_picklable_thermalblock_problems
burgers_problems = \
[burgers_problem(),
burgers_problem(v=0.2, circle=False),
# Copyright 2013-2018 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
import pytest
from pymor.domaindescriptions.basic import RectDomain
from pymor.analyticalproblems.burgers import burgers_problem, burgers_problem_2d
from pymor.analyticalproblems.elliptic import StationaryProblem
from pymor.analyticalproblems.helmholtz import helmholtz_problem
from pymor.analyticalproblems.thermalblock import thermal_block_problem
from pymor.functions.basic import GenericFunction, ConstantFunction, LincombFunction
from pymor.parameters.functionals import ExpressionParameterFunctional
picklable_thermalblock_problems = \
[thermal_block_problem(),
thermal_block_problem(num_blocks=(3, 2)),
thermal_block_problem(num_blocks=(1, 1)),
thermal_block_problem(num_blocks=(2, 2), parameter_range=(1., 100.))]
non_picklable_thermalblock_problems = \
[thermal_block_problem(num_blocks=(1, 3), parameter_range=(0.4, 0.5)).with_(
rhs=GenericFunction(dim_domain=2, mapping=lambda X: X[..., 0] + X[..., 1]))]
thermalblock_problems = picklable_thermalblock_problems + non_picklable_thermalblock_problems
burgers_problems = \
[burgers_problem(),
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 = 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 = StationaryProblem(domain=problem.domain,
diffusion=problem.diffusion.with_(coefficients=functionals),
rhs=problem.rhs,
parameter_space=CubicParameterSpace({'diffusion': (2,)}, 0.1, 1.))
print('Discretize ...')
discretization, _ = discretize_stationary_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(
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)