def test_p_convergence_verifier():
from pytools.convergence import PConvergenceVerifier
pconv_verifier = PConvergenceVerifier()
for order in [2, 3, 4, 5]:
pconv_verifier.add_data_point(order, 0.1**order)
pconv_verifier()
pconv_verifier = PConvergenceVerifier()
for order in [2, 3, 4, 5]:
pconv_verifier.add_data_point(order, 0.5**order)
pconv_verifier()
pconv_verifier = PConvergenceVerifier()
for order in [2, 3, 4, 5]:
pconv_verifier.add_data_point(order, 2)
with pytest.raises(AssertionError):
pconv_verifier()
def test_p_convergence_verifier():
pytest.importorskip("numpy")
from pytools.convergence import PConvergenceVerifier
pconv_verifier = PConvergenceVerifier()
for order in [2, 3, 4, 5]:
pconv_verifier.add_data_point(order, 0.1**order)
pconv_verifier()
pconv_verifier = PConvergenceVerifier()
for order in [2, 3, 4, 5]:
pconv_verifier.add_data_point(order, 0.5**order)
pconv_verifier()
pconv_verifier = PConvergenceVerifier()
for order in [2, 3, 4, 5]:
pconv_verifier.add_data_point(order, 2)
with pytest.raises(AssertionError):
pconv_verifier()
def test_translations(ctx_factory, knl, local_expn_class, mpole_expn_class):
logging.basicConfig(level=logging.INFO)
from sympy.core.cache import clear_cache
clear_cache()
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
np.random.seed(17)
res = 20
nsources = 15
out_kernels = [knl]
extra_kwargs = {}
if isinstance(knl, HelmholtzKernel):
extra_kwargs["k"] = 0.05
if isinstance(knl, StokesletKernel):
extra_kwargs["mu"] = 0.05
# Just to make sure things also work away from the origin
origin = np.array([2, 1, 0][:knl.dim], np.float64)
sources = (0.7 *
(-0.5 + np.random.rand(knl.dim, nsources).astype(np.float64)) +
origin[:, np.newaxis])
strengths = np.ones(nsources, dtype=np.float64) * (1 / nsources)
pconv_verifier_p2m2p = PConvergenceVerifier()
pconv_verifier_p2m2m2p = PConvergenceVerifier()
pconv_verifier_p2m2m2l2p = PConvergenceVerifier()
pconv_verifier_full = PConvergenceVerifier()
from sumpy.visualization import FieldPlotter
eval_offset = np.array([5.5, 0.0, 0][:knl.dim])
centers = (
np.array(
[
# box 0: particles, first mpole here
[0, 0, 0][:knl.dim],
# box 1: second mpole here
np.array([-0.2, 0.1, 0][:knl.dim], np.float64),
# box 2: first local here
eval_offset + np.array([0.3, -0.2, 0][:knl.dim], np.float64),
# box 3: second local and eval here
eval_offset
],
dtype=np.float64) + origin).T.copy()
del eval_offset
from sumpy.expansion import VolumeTaylorExpansionBase
if isinstance(knl, HelmholtzKernel) and \
issubclass(local_expn_class, VolumeTaylorExpansionBase):
# FIXME: Embarrassing--but we run out of memory for higher orders.
orders = [2, 3]
else:
orders = [2, 3, 4]
nboxes = centers.shape[-1]
def eval_at(e2p, source_box_nr, rscale):
e2p_target_boxes = np.array([source_box_nr], dtype=np.int32)
# These are indexed by global box numbers.
e2p_box_target_starts = np.array([0, 0, 0, 0], dtype=np.int32)
e2p_box_target_counts_nonchild = np.array([0, 0, 0, 0], dtype=np.int32)
e2p_box_target_counts_nonchild[source_box_nr] = ntargets
evt, (pot, ) = e2p(
queue,
src_expansions=mpoles,
src_base_ibox=0,
target_boxes=e2p_target_boxes,
box_target_starts=e2p_box_target_starts,
box_target_counts_nonchild=e2p_box_target_counts_nonchild,
centers=centers,
targets=targets,
rscale=rscale,
out_host=True,
**extra_kwargs)
return pot
for order in orders:
m_expn = mpole_expn_class(knl, order=order)
l_expn = local_expn_class(knl, order=order)
from sumpy import P2EFromSingleBox, E2PFromSingleBox, P2P, E2EFromCSR
p2m = P2EFromSingleBox(ctx, m_expn)
m2m = E2EFromCSR(ctx, m_expn, m_expn)
m2p = E2PFromSingleBox(ctx, m_expn, out_kernels)
m2l = E2EFromCSR(ctx, m_expn, l_expn)
l2l = E2EFromCSR(ctx, l_expn, l_expn)
l2p = E2PFromSingleBox(ctx, l_expn, out_kernels)
p2p = P2P(ctx, out_kernels, exclude_self=False)
fp = FieldPlotter(centers[:, -1], extent=0.3, npoints=res)
targets = fp.points
# {{{ compute (direct) reference solution
evt, (pot_direct, ) = p2p(queue,
targets,
sources, (strengths, ),
out_host=True,
**extra_kwargs)
# }}}
m1_rscale = 0.5
m2_rscale = 0.25
l1_rscale = 0.5
l2_rscale = 0.25
# {{{ apply P2M
p2m_source_boxes = np.array([0], dtype=np.int32)
# These are indexed by global box numbers.
p2m_box_source_starts = np.array([0, 0, 0, 0], dtype=np.int32)
p2m_box_source_counts_nonchild = np.array([nsources, 0, 0, 0],
dtype=np.int32)
evt, (mpoles, ) = p2m(
queue,
source_boxes=p2m_source_boxes,
box_source_starts=p2m_box_source_starts,
box_source_counts_nonchild=p2m_box_source_counts_nonchild,
centers=centers,
sources=sources,
strengths=(strengths, ),
nboxes=nboxes,
rscale=m1_rscale,
tgt_base_ibox=0,
#flags="print_hl_wrapper",
out_host=True,
**extra_kwargs)
# }}}
ntargets = targets.shape[-1]
pot = eval_at(m2p, 0, m1_rscale)
err = la.norm((pot - pot_direct) / res**2)
err = err / (la.norm(pot_direct) / res**2)
pconv_verifier_p2m2p.add_data_point(order, err)
# {{{ apply M2M
m2m_target_boxes = np.array([1], dtype=np.int32)
m2m_src_box_starts = np.array([0, 1], dtype=np.int32)
m2m_src_box_lists = np.array([0], dtype=np.int32)
evt, (mpoles, ) = m2m(
queue,
src_expansions=mpoles,
src_base_ibox=0,
tgt_base_ibox=0,
ntgt_level_boxes=mpoles.shape[0],
target_boxes=m2m_target_boxes,
src_box_starts=m2m_src_box_starts,
src_box_lists=m2m_src_box_lists,
centers=centers,
src_rscale=m1_rscale,
tgt_rscale=m2_rscale,
#flags="print_hl_cl",
out_host=True,
**extra_kwargs)
# }}}
pot = eval_at(m2p, 1, m2_rscale)
err = la.norm((pot - pot_direct) / res**2)
err = err / (la.norm(pot_direct) / res**2)
pconv_verifier_p2m2m2p.add_data_point(order, err)
# {{{ apply M2L
m2l_target_boxes = np.array([2], dtype=np.int32)
m2l_src_box_starts = np.array([0, 1], dtype=np.int32)
m2l_src_box_lists = np.array([1], dtype=np.int32)
evt, (mpoles, ) = m2l(
queue,
src_expansions=mpoles,
src_base_ibox=0,
tgt_base_ibox=0,
ntgt_level_boxes=mpoles.shape[0],
target_boxes=m2l_target_boxes,
src_box_starts=m2l_src_box_starts,
src_box_lists=m2l_src_box_lists,
centers=centers,
src_rscale=m2_rscale,
tgt_rscale=l1_rscale,
#flags="print_hl_cl",
out_host=True,
**extra_kwargs)
# }}}
pot = eval_at(l2p, 2, l1_rscale)
err = la.norm((pot - pot_direct) / res**2)
err = err / (la.norm(pot_direct) / res**2)
pconv_verifier_p2m2m2l2p.add_data_point(order, err)
# {{{ apply L2L
l2l_target_boxes = np.array([3], dtype=np.int32)
l2l_src_box_starts = np.array([0, 1], dtype=np.int32)
l2l_src_box_lists = np.array([2], dtype=np.int32)
evt, (mpoles, ) = l2l(
queue,
src_expansions=mpoles,
src_base_ibox=0,
tgt_base_ibox=0,
ntgt_level_boxes=mpoles.shape[0],
target_boxes=l2l_target_boxes,
src_box_starts=l2l_src_box_starts,
src_box_lists=l2l_src_box_lists,
centers=centers,
src_rscale=l1_rscale,
tgt_rscale=l2_rscale,
#flags="print_hl_wrapper",
out_host=True,
**extra_kwargs)
# }}}
pot = eval_at(l2p, 3, l2_rscale)
err = la.norm((pot - pot_direct) / res**2)
err = err / (la.norm(pot_direct) / res**2)
pconv_verifier_full.add_data_point(order, err)
for name, verifier in [
("p2m2p", pconv_verifier_p2m2p),
("p2m2m2p", pconv_verifier_p2m2m2p),
("p2m2m2l2p", pconv_verifier_p2m2m2l2p),
("full", pconv_verifier_full),
]:
print(30 * "-")
print(name)
print(30 * "-")
print(verifier)
print(30 * "-")
verifier()
def test_sumpy_fmm(ctx_getter, knl, local_expn_class, mpole_expn_class):
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
nsources = 1000
ntargets = 300
dtype = np.float64
from boxtree.tools import (
make_normal_particle_array as p_normal)
sources = p_normal(queue, nsources, knl.dim, dtype, seed=15)
if 1:
offset = np.zeros(knl.dim)
offset[0] = 0.1
targets = (
p_normal(queue, ntargets, knl.dim, dtype, seed=18)
+ offset)
del offset
else:
from sumpy.visualization import FieldPlotter
fp = FieldPlotter(np.array([0.5, 0]), extent=3, npoints=200)
from pytools.obj_array import make_obj_array
targets = make_obj_array(
[fp.points[i] for i in range(knl.dim)])
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue, sources, targets=targets,
max_particles_in_box=30, debug=True)
from boxtree.traversal import FMMTraversalBuilder
tbuild = FMMTraversalBuilder(ctx)
trav, _ = tbuild(queue, tree, debug=True)
# {{{ plot tree
if 0:
host_tree = tree.get()
host_trav = trav.get()
if 1:
print("src_box", host_tree.find_box_nr_for_source(403))
print("tgt_box", host_tree.find_box_nr_for_target(28))
print(list(host_trav.target_or_target_parent_boxes).index(37))
print(host_trav.get_box_list("sep_bigger", 22))
from boxtree.visualization import TreePlotter
plotter = TreePlotter(host_tree)
plotter.draw_tree(fill=False, edgecolor="black", zorder=10)
plotter.set_bounding_box()
plotter.draw_box_numbers()
import matplotlib.pyplot as pt
pt.show()
# }}}
from pyopencl.clrandom import PhiloxGenerator
rng = PhiloxGenerator(ctx, seed=44)
weights = rng.uniform(queue, nsources, dtype=np.float64)
logger.info("computing direct (reference) result")
from pytools.convergence import PConvergenceVerifier
pconv_verifier = PConvergenceVerifier()
extra_kwargs = {}
dtype = np.float64
order_values = [1, 2, 3]
if isinstance(knl, HelmholtzKernel):
extra_kwargs["k"] = 0.05
dtype = np.complex128
if knl.dim == 3:
order_values = [1, 2]
elif knl.dim == 2 and issubclass(local_expn_class, H2DLocalExpansion):
order_values = [10, 12]
elif isinstance(knl, YukawaKernel):
extra_kwargs["lam"] = 2
dtype = np.complex128
if knl.dim == 3:
order_values = [1, 2]
elif knl.dim == 2 and issubclass(local_expn_class, Y2DLocalExpansion):
order_values = [10, 12]
from functools import partial
for order in order_values:
out_kernels = [knl]
from sumpy.fmm import SumpyExpansionWranglerCodeContainer
wcc = SumpyExpansionWranglerCodeContainer(
ctx,
partial(mpole_expn_class, knl),
partial(local_expn_class, knl),
out_kernels)
wrangler = wcc.get_wrangler(queue, tree, dtype,
fmm_level_to_order=lambda kernel, kernel_args, tree, lev: order,
kernel_extra_kwargs=extra_kwargs)
from boxtree.fmm import drive_fmm
pot, = drive_fmm(trav, wrangler, weights)
from sumpy import P2P
p2p = P2P(ctx, out_kernels, exclude_self=False)
evt, (ref_pot,) = p2p(queue, targets, sources, (weights,),
**extra_kwargs)
pot = pot.get()
ref_pot = ref_pot.get()
rel_err = la.norm(pot - ref_pot, np.inf) / la.norm(ref_pot, np.inf)
logger.info("order %d -> relative l2 error: %g" % (order, rel_err))
pconv_verifier.add_data_point(order, rel_err)
print(pconv_verifier)
pconv_verifier()
def test_sumpy_fmm(ctx_getter, knl, local_expn_class, mpole_expn_class):
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
nsources = 1000
ntargets = 300
dtype = np.float64
from boxtree.tools import (make_normal_particle_array as p_normal)
sources = p_normal(queue, nsources, knl.dim, dtype, seed=15)
if 1:
offset = np.zeros(knl.dim)
offset[0] = 0.1
targets = (p_normal(queue, ntargets, knl.dim, dtype, seed=18) + offset)
del offset
else:
from sumpy.visualization import FieldPlotter
fp = FieldPlotter(np.array([0.5, 0]), extent=3, npoints=200)
from pytools.obj_array import make_obj_array
targets = make_obj_array([fp.points[i] for i in range(knl.dim)])
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue,
sources,
targets=targets,
max_particles_in_box=30,
debug=True)
from boxtree.traversal import FMMTraversalBuilder
tbuild = FMMTraversalBuilder(ctx)
trav, _ = tbuild(queue, tree, debug=True)
# {{{ plot tree
if 0:
host_tree = tree.get()
host_trav = trav.get()
if 1:
print("src_box", host_tree.find_box_nr_for_source(403))
print("tgt_box", host_tree.find_box_nr_for_target(28))
print(list(host_trav.target_or_target_parent_boxes).index(37))
print(host_trav.get_box_list("sep_bigger", 22))
from boxtree.visualization import TreePlotter
plotter = TreePlotter(host_tree)
plotter.draw_tree(fill=False, edgecolor="black", zorder=10)
plotter.set_bounding_box()
plotter.draw_box_numbers()
import matplotlib.pyplot as pt
pt.show()
# }}}
from pyopencl.clrandom import PhiloxGenerator
rng = PhiloxGenerator(ctx, seed=44)
weights = rng.uniform(queue, nsources, dtype=np.float64)
logger.info("computing direct (reference) result")
from pytools.convergence import PConvergenceVerifier
pconv_verifier = PConvergenceVerifier()
extra_kwargs = {}
dtype = np.float64
order_values = [1, 2, 3]
if isinstance(knl, HelmholtzKernel):
extra_kwargs["k"] = 0.05
dtype = np.complex128
if knl.dim == 3:
order_values = [1, 2]
elif knl.dim == 2 and issubclass(local_expn_class, H2DLocalExpansion):
order_values = [10, 12]
elif isinstance(knl, YukawaKernel):
extra_kwargs["lam"] = 2
dtype = np.complex128
if knl.dim == 3:
order_values = [1, 2]
elif knl.dim == 2 and issubclass(local_expn_class, Y2DLocalExpansion):
order_values = [10, 12]
from functools import partial
for order in order_values:
out_kernels = [knl]
from sumpy.fmm import SumpyExpansionWranglerCodeContainer
wcc = SumpyExpansionWranglerCodeContainer(
ctx, partial(mpole_expn_class, knl),
partial(local_expn_class, knl), out_kernels)
wrangler = wcc.get_wrangler(
queue,
tree,
dtype,
fmm_level_to_order=lambda kernel, kernel_args, tree, lev: order,
kernel_extra_kwargs=extra_kwargs)
from boxtree.fmm import drive_fmm
pot, = drive_fmm(trav, wrangler, weights)
from sumpy import P2P
p2p = P2P(ctx, out_kernels, exclude_self=False)
evt, (ref_pot, ) = p2p(queue, targets, sources, (weights, ),
**extra_kwargs)
pot = pot.get()
ref_pot = ref_pot.get()
rel_err = la.norm(pot - ref_pot, np.inf) / la.norm(ref_pot, np.inf)
logger.info("order %d -> relative l2 error: %g" % (order, rel_err))
pconv_verifier.add_data_point(order, rel_err)
print(pconv_verifier)
pconv_verifier()
def test_translations(ctx_getter, knl, local_expn_class, mpole_expn_class):
logging.basicConfig(level=logging.INFO)
from sympy.core.cache import clear_cache
clear_cache()
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
np.random.seed(17)
res = 20
nsources = 15
out_kernels = [knl]
extra_kwargs = {}
if isinstance(knl, HelmholtzKernel):
extra_kwargs["k"] = 0.05
# Just to make sure things also work away from the origin
origin = np.array([2, 1], np.float64)
sources = (0.7*(-0.5+np.random.rand(knl.dim, nsources).astype(np.float64))
+ origin[:, np.newaxis])
strengths = np.ones(nsources, dtype=np.float64) * (1/nsources)
pconv_verifier_p2m2p = PConvergenceVerifier()
pconv_verifier_p2m2m2p = PConvergenceVerifier()
pconv_verifier_p2m2m2l2p = PConvergenceVerifier()
pconv_verifier_full = PConvergenceVerifier()
from sumpy.visualization import FieldPlotter
eval_offset = np.array([5.5, 0.0])
centers = (np.array(
[
# box 0: particles, first mpole here
[0, 0],
# box 1: second mpole here
np.array([-0.2, 0.1], np.float64),
# box 2: first local here
eval_offset + np.array([0.3, -0.2], np.float64),
# box 3: second local and eval here
eval_offset
],
dtype=np.float64) + origin).T.copy()
del eval_offset
from sumpy.expansion import VolumeTaylorExpansionBase
if isinstance(knl, HelmholtzKernel) and \
issubclass(local_expn_class, VolumeTaylorExpansionBase):
# FIXME: Embarrassing--but we run out of memory for higher orders.
orders = [2, 3]
else:
orders = [2, 3, 4]
nboxes = centers.shape[-1]
def eval_at(e2p, source_box_nr, rscale):
e2p_target_boxes = np.array([source_box_nr], dtype=np.int32)
# These are indexed by global box numbers.
e2p_box_target_starts = np.array([0, 0, 0, 0], dtype=np.int32)
e2p_box_target_counts_nonchild = np.array([0, 0, 0, 0],
dtype=np.int32)
e2p_box_target_counts_nonchild[source_box_nr] = ntargets
evt, (pot,) = e2p(
queue,
src_expansions=mpoles,
src_base_ibox=0,
target_boxes=e2p_target_boxes,
box_target_starts=e2p_box_target_starts,
box_target_counts_nonchild=e2p_box_target_counts_nonchild,
centers=centers,
targets=targets,
rscale=rscale,
out_host=True, **extra_kwargs
)
return pot
for order in orders:
m_expn = mpole_expn_class(knl, order=order)
l_expn = local_expn_class(knl, order=order)
from sumpy import P2EFromSingleBox, E2PFromSingleBox, P2P, E2EFromCSR
p2m = P2EFromSingleBox(ctx, m_expn)
m2m = E2EFromCSR(ctx, m_expn, m_expn)
m2p = E2PFromSingleBox(ctx, m_expn, out_kernels)
m2l = E2EFromCSR(ctx, m_expn, l_expn)
l2l = E2EFromCSR(ctx, l_expn, l_expn)
l2p = E2PFromSingleBox(ctx, l_expn, out_kernels)
p2p = P2P(ctx, out_kernels, exclude_self=False)
fp = FieldPlotter(centers[:, -1], extent=0.3, npoints=res)
targets = fp.points
# {{{ compute (direct) reference solution
evt, (pot_direct,) = p2p(
queue,
targets, sources, (strengths,),
out_host=True, **extra_kwargs)
# }}}
m1_rscale = 0.5
m2_rscale = 0.25
l1_rscale = 0.5
l2_rscale = 0.25
# {{{ apply P2M
p2m_source_boxes = np.array([0], dtype=np.int32)
# These are indexed by global box numbers.
p2m_box_source_starts = np.array([0, 0, 0, 0], dtype=np.int32)
p2m_box_source_counts_nonchild = np.array([nsources, 0, 0, 0],
dtype=np.int32)
evt, (mpoles,) = p2m(queue,
source_boxes=p2m_source_boxes,
box_source_starts=p2m_box_source_starts,
box_source_counts_nonchild=p2m_box_source_counts_nonchild,
centers=centers,
sources=sources,
strengths=strengths,
nboxes=nboxes,
rscale=m1_rscale,
tgt_base_ibox=0,
#flags="print_hl_wrapper",
out_host=True, **extra_kwargs)
# }}}
ntargets = targets.shape[-1]
pot = eval_at(m2p, 0, m1_rscale)
err = la.norm((pot - pot_direct)/res**2)
err = err / (la.norm(pot_direct) / res**2)
pconv_verifier_p2m2p.add_data_point(order, err)
# {{{ apply M2M
m2m_target_boxes = np.array([1], dtype=np.int32)
m2m_src_box_starts = np.array([0, 1], dtype=np.int32)
m2m_src_box_lists = np.array([0], dtype=np.int32)
evt, (mpoles,) = m2m(queue,
src_expansions=mpoles,
src_base_ibox=0,
tgt_base_ibox=0,
ntgt_level_boxes=mpoles.shape[0],
target_boxes=m2m_target_boxes,
src_box_starts=m2m_src_box_starts,
src_box_lists=m2m_src_box_lists,
centers=centers,
src_rscale=m1_rscale,
tgt_rscale=m2_rscale,
#flags="print_hl_cl",
out_host=True, **extra_kwargs)
# }}}
pot = eval_at(m2p, 1, m2_rscale)
err = la.norm((pot - pot_direct)/res**2)
err = err / (la.norm(pot_direct) / res**2)
pconv_verifier_p2m2m2p.add_data_point(order, err)
# {{{ apply M2L
m2l_target_boxes = np.array([2], dtype=np.int32)
m2l_src_box_starts = np.array([0, 1], dtype=np.int32)
m2l_src_box_lists = np.array([1], dtype=np.int32)
evt, (mpoles,) = m2l(queue,
src_expansions=mpoles,
src_base_ibox=0,
tgt_base_ibox=0,
ntgt_level_boxes=mpoles.shape[0],
target_boxes=m2l_target_boxes,
src_box_starts=m2l_src_box_starts,
src_box_lists=m2l_src_box_lists,
centers=centers,
src_rscale=m2_rscale,
tgt_rscale=l1_rscale,
#flags="print_hl_cl",
out_host=True, **extra_kwargs)
# }}}
pot = eval_at(l2p, 2, l1_rscale)
err = la.norm((pot - pot_direct)/res**2)
err = err / (la.norm(pot_direct) / res**2)
pconv_verifier_p2m2m2l2p.add_data_point(order, err)
# {{{ apply L2L
l2l_target_boxes = np.array([3], dtype=np.int32)
l2l_src_box_starts = np.array([0, 1], dtype=np.int32)
l2l_src_box_lists = np.array([2], dtype=np.int32)
evt, (mpoles,) = l2l(queue,
src_expansions=mpoles,
src_base_ibox=0,
tgt_base_ibox=0,
ntgt_level_boxes=mpoles.shape[0],
target_boxes=l2l_target_boxes,
src_box_starts=l2l_src_box_starts,
src_box_lists=l2l_src_box_lists,
centers=centers,
src_rscale=l1_rscale,
tgt_rscale=l2_rscale,
#flags="print_hl_wrapper",
out_host=True, **extra_kwargs)
# }}}
pot = eval_at(l2p, 3, l2_rscale)
err = la.norm((pot - pot_direct)/res**2)
err = err / (la.norm(pot_direct) / res**2)
pconv_verifier_full.add_data_point(order, err)
for name, verifier in [
("p2m2p", pconv_verifier_p2m2p),
("p2m2m2p", pconv_verifier_p2m2m2p),
("p2m2m2l2p", pconv_verifier_p2m2m2l2p),
("full", pconv_verifier_full),
]:
print(30*"-")
print(name)
print(30*"-")
print(verifier)
print(30*"-")
verifier()