def layout_pos2temp(options: configargparse.Namespace, pos: list,
powers: list):
if options.bcs is None:
options.bcs = []
task = get_task(
geometry_board="s",
size_board=options.length,
grid_board=options.nx,
geometry=len(options.units) * "r",
size=options.units,
angle=options.angles,
intensity=options.powers,
rad=False,
method=None,
)
layout = task.layout_from_pos(pos, powers)
temp, xs, ys, zs = run_solver_c(
ndim=options.ndim,
length=options.length,
units=options.units,
bcs=options.bcs,
u0=options.u_D,
powers=powers,
nx=options.nx - 1,
F=layout,
)
return layout, temp
def generate_from_cli(options: configargparse.Namespace):
"""Generate from cli with options.
Arguments:
options (configargparse.Namespace): config options
"""
if options.bcs is None:
options.bcs = []
np.random.seed(options.seed)
seeds = np.random.randint(2**32, size=options.worker)
print(options.worker)
print(seeds)
seeds_q: Queue = Queue()
for seed in seeds:
seeds_q.put(seed)
unit_n = len(options.units)
geometry = ["r"] * unit_n
task = get_task(
geometry_board="s",
size_board=options.length,
grid_board=options.nx + 1,
geometry=geometry,
size=options.units,
angle=options.angles,
intensity=options.powers,
rad=False,
method=options.sampler,
)
task.warmup() # warm up, especially for gibbs
if options.method == "fenics":
# 创建单参数函数
method_fenics_p = partial(method_fenics,
options=options,
sampler=task.sample)
# for i in range(options.sample_n):
# method_fenics_p(i)
# multiprocess support
with Pool(options.worker, initializer=pool_init,
initargs=(seeds_q, )) as pool:
pool_it = pool.imap(method_fenics_p, range(options.sample_n))
# for i in pool_it:
# print(i)
for _ in tqdm.tqdm(
pool_it,
desc=f"{pool._processes} workers's running",
total=options.sample_n,
ncols=100,
):
pass
print(f"Generated {options.sample_n} layouts in {options.data_dir}")
def generate_from_cli(options: configargparse.Namespace):
"""Generate from cli with options.
Arguments:
options (configargparse.Namespace): config options
"""
if options.bcs is None:
options.bcs = []
options.unit_per_row = int(options.length / options.length_unit)
possible_n = options.unit_per_row**options.ndim
np.random.seed(options.seed)
if options.sampler == "uniform":
sampler = np.random.choice
powers_all = [
sampler(options.power, options.unit_n) for _ in range(options.sample_n)
] # 每个样本
layout_pos_lists = [
sampler(possible_n, options.unit_n, replace=False)
for _ in range(options.sample_n)
]
# # 叠加原理
# if options.method == 'fenics_additive':
# u_basis = []
# for layout_pos in tqdm.trange(possible_n):
# u = run_solver(options.length, options.length_unit, options.bcs,
# layout_pos,
# 0, [1.], options.nx, options.ny, False)
# u_basis.append(u)
# for i in tqdm.trange(options.sample_n):
# layout_pos_list = sorted(
# sampler(possible_n, options.unit_n, replace=False))
# u_elt = (powers[i] * u_basis[pos]
# for i, pos in enumerate(layout_pos_list))
# U = reduce(np.add, u_elt) / options.unit_n + options.u_D
# F = io.layout2matrix(options.nx, options.ny,
# unit_per_row, powers, layout_pos_list)
# xs, ys = get_mesh_grid(options.length, options.nx, options.ny)
# io.save(options, i, U, xs, ys, F, layout_pos_list)
# 无叠加原理
if options.method == "fenics":
# 创建单参数函数
method_fenics_p = partial(
method_fenics,
options=options,
sampler=sampler,
powers_all=powers_all,
layout_pos_lists=layout_pos_lists,
)
# for i in range(options.sample_n):
# method_fenics_p(i)
# multiprocess support
with Pool(options.worker) as pool:
pool_it = pool.imap_unordered(method_fenics_p,
range(options.sample_n))
for _ in tqdm.tqdm(
pool_it,
desc=f"{pool._processes} workers's running",
total=options.sample_n,
ncols=100,
):
pass
print(f"Generated {options.sample_n} layouts in {options.data_dir}")
def generate_from_cli(options: configargparse.Namespace):
"""Generate from cli with options.
Arguments:
options (configargparse.Namespace): config options
"""
if options.bcs is None:
options.bcs = []
np.random.seed(options.seed)
seeds = np.random.randint(2**32, size=options.worker)
print(options.worker)
print(seeds)
seeds_q: Queue = Queue()
for seed in seeds:
seeds_q.put(seed)
unit_n = len(options.units)
geometry = ["r"] * unit_n
positions = np.array([k for k in options.positions])
if options.positions_type == "coord":
pass
elif options.positions_type == "grid":
positions = positions / (options.nx + 1) * options.length
else:
raise LookupError(f"Type {options.positions_type} is not supported!")
task = get_task_powers_sampling(
geometry_board="s",
size_board=options.length,
grid_board=options.nx + 1,
geometry=geometry,
size=options.units,
angle=options.angles,
intensity=options.powers,
rad=False,
position=positions,
)
task.warmup() # warm up, especially for gibbs
observation = False
if options.observation_points is not None:
observation = True
# print(options)
if options.method == "fenics":
# 创建单参数函数
method_fenics_p = partial(
method_fenics,
options=options,
sampler=task.sample,
task=task,
observation=observation,
)
# for i in range(options.sample_n):
# method_fenics_p(i)
# multiprocess support
with Pool(options.worker, initializer=pool_init,
initargs=(seeds_q, )) as pool:
pool_it = pool.imap(method_fenics_p, range(options.sample_n))
# for i in pool_it:
# print(i)
for _ in tqdm.tqdm(
pool_it,
desc=f"{pool._processes} workers's running",
total=options.sample_n,
ncols=100,
):
pass
print(f"Generated {options.sample_n} layouts in {options.data_dir}")