def test_tree_connectivity(ctx_getter, dims, sources_are_targets):
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
dtype = np.float64
sources = make_normal_particle_array(queue, 1 * 10**5, dims, dtype)
if sources_are_targets:
targets = None
else:
targets = make_normal_particle_array(queue, 2 * 10**5, dims, dtype)
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue, sources, max_particles_in_box=30,
targets=targets, debug=True)
from boxtree.traversal import FMMTraversalBuilder
tg = FMMTraversalBuilder(ctx)
trav, _ = tg(queue, tree, debug=True)
tree = tree.get(queue=queue)
trav = trav.get(queue=queue)
levels = tree.box_levels
parents = tree.box_parent_ids.T
children = tree.box_child_ids.T
centers = tree.box_centers.T
# {{{ parent and child relations, levels match up
for ibox in range(1, tree.nboxes):
# /!\ Not testing box 0, has no parents
parent = parents[ibox]
assert levels[parent] + 1 == levels[ibox]
assert ibox in children[parent], ibox
# }}}
if 0:
import matplotlib.pyplot as pt
from boxtree.visualization import TreePlotter
plotter = TreePlotter(tree)
plotter.draw_tree(fill=False, edgecolor="black")
plotter.draw_box_numbers()
plotter.set_bounding_box()
pt.show()
# {{{ neighbor_source_boxes (list 1) consists of source boxes
for itgt_box, ibox in enumerate(trav.target_boxes):
start, end = trav.neighbor_source_boxes_starts[itgt_box:itgt_box+2]
nbl = trav.neighbor_source_boxes_lists[start:end]
if sources_are_targets:
assert ibox in nbl
for jbox in nbl:
assert (0 == children[jbox]).all(), (ibox, jbox, children[jbox])
logger.info("list 1 consists of source boxes")
# }}}
# {{{ separated siblings (list 2) are actually separated
for itgt_box, tgt_ibox in enumerate(trav.target_or_target_parent_boxes):
start, end = trav.sep_siblings_starts[itgt_box:itgt_box+2]
seps = trav.sep_siblings_lists[start:end]
assert (levels[seps] == levels[tgt_ibox]).all()
# three-ish box radii (half of size)
mindist = 2.5 * 0.5 * 2**-int(levels[tgt_ibox]) * tree.root_extent
icenter = centers[tgt_ibox]
for jbox in seps:
dist = la.norm(centers[jbox]-icenter)
assert dist > mindist, (dist, mindist)
logger.info("separated siblings (list 2) are actually separated")
# }}}
if sources_are_targets:
# {{{ sep_{smaller,bigger} are duals of each other
assert (trav.target_or_target_parent_boxes == np.arange(tree.nboxes)).all()
# {{{ list 4 <= list 3
for itarget_box, ibox in enumerate(trav.target_boxes):
for ssn in trav.sep_smaller_by_level:
start, end = ssn.starts[itarget_box:itarget_box+2]
for jbox in ssn.lists[start:end]:
rstart, rend = trav.sep_bigger_starts[jbox:jbox+2]
assert ibox in trav.sep_bigger_lists[rstart:rend], (ibox, jbox)
# }}}
# {{{ list 4 <= list 3
box_to_target_box_index = np.empty(tree.nboxes, tree.box_id_dtype)
box_to_target_box_index.fill(-1)
box_to_target_box_index[trav.target_boxes] = np.arange(
len(trav.target_boxes), dtype=tree.box_id_dtype)
assert (trav.source_boxes == trav.target_boxes).all()
assert (trav.target_or_target_parent_boxes == np.arange(
tree.nboxes, dtype=tree.box_id_dtype)).all()
for ibox in range(tree.nboxes):
start, end = trav.sep_bigger_starts[ibox:ibox+2]
for jbox in trav.sep_bigger_lists[start:end]:
# In principle, entries of sep_bigger_lists are
# source boxes. In this special case, source and target boxes
# are the same thing (i.e. leaves--see assertion above), so we
# may treat them as targets anyhow.
jtgt_box = box_to_target_box_index[jbox]
assert jtgt_box != -1
good = False
for ssn in trav.sep_smaller_by_level:
rstart, rend = ssn.starts[jtgt_box:jtgt_box+2]
good = good or ibox in ssn.lists[rstart:rend]
if not good:
from boxtree.visualization import TreePlotter
plotter = TreePlotter(tree)
plotter.draw_tree(fill=False, edgecolor="black", zorder=10)
plotter.set_bounding_box()
plotter.draw_box(ibox, facecolor='green', alpha=0.5)
plotter.draw_box(jbox, facecolor='red', alpha=0.5)
import matplotlib.pyplot as pt
pt.gca().set_aspect("equal")
pt.show()
# This assertion failing means that ibox's list 4 contains a box
# 'jbox' whose list 3 does not contain ibox.
assert good, (ibox, jbox)
# }}}
logger.info("list 3, 4 are duals")
# }}}
# {{{ sep_smaller satisfies relative level assumption
for itarget_box, ibox in enumerate(trav.target_boxes):
for ssn in trav.sep_smaller_by_level:
start, end = ssn.starts[itarget_box:itarget_box+2]
for jbox in ssn.lists[start:end]:
assert levels[ibox] < levels[jbox]
logger.info("list 3 satisfies relative level assumption")
# }}}
# {{{ sep_bigger satisfies relative level assumption
for itgt_box, tgt_ibox in enumerate(trav.target_or_target_parent_boxes):
start, end = trav.sep_bigger_starts[itgt_box:itgt_box+2]
for jbox in trav.sep_bigger_lists[start:end]:
assert levels[tgt_ibox] > levels[jbox]
logger.info("list 4 satisfies relative level assumption")
# }}}
# {{{ level_start_*_box_nrs lists make sense
for name, ref_array in [
("level_start_source_box_nrs", trav.source_boxes),
("level_start_source_parent_box_nrs", trav.source_parent_boxes),
("level_start_target_box_nrs", trav.target_boxes),
("level_start_target_or_target_parent_box_nrs",
trav.target_or_target_parent_boxes)
]:
level_starts = getattr(trav, name)
for lev in range(tree.nlevels):
start, stop = level_starts[lev:lev+2]
box_nrs = ref_array[start:stop]
assert (tree.box_levels[box_nrs] == lev).all(), name
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_tree_connectivity(ctx_getter, dims, sources_are_targets):
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
dtype = np.float64
sources = make_normal_particle_array(queue, 1 * 10**5, dims, dtype)
if sources_are_targets:
targets = None
else:
targets = make_normal_particle_array(queue, 2 * 10**5, dims, dtype)
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue,
sources,
max_particles_in_box=30,
targets=targets,
debug=True)
from boxtree.traversal import FMMTraversalBuilder
tg = FMMTraversalBuilder(ctx)
trav, _ = tg(queue, tree, debug=True)
tree = tree.get(queue=queue)
trav = trav.get(queue=queue)
levels = tree.box_levels
parents = tree.box_parent_ids.T
children = tree.box_child_ids.T
centers = tree.box_centers.T
# {{{ parent and child relations, levels match up
for ibox in range(1, tree.nboxes):
# /!\ Not testing box 0, has no parents
parent = parents[ibox]
assert levels[parent] + 1 == levels[ibox]
assert ibox in children[parent], ibox
# }}}
if 0:
import matplotlib.pyplot as pt
from boxtree.visualization import TreePlotter
plotter = TreePlotter(tree)
plotter.draw_tree(fill=False, edgecolor="black")
plotter.draw_box_numbers()
plotter.set_bounding_box()
pt.show()
# {{{ neighbor_source_boxes (list 1) consists of source boxes
for itgt_box, ibox in enumerate(trav.target_boxes):
start, end = trav.neighbor_source_boxes_starts[itgt_box:itgt_box + 2]
nbl = trav.neighbor_source_boxes_lists[start:end]
if sources_are_targets:
assert ibox in nbl
for jbox in nbl:
assert (0 == children[jbox]).all(), (ibox, jbox, children[jbox])
logger.info("list 1 consists of source boxes")
# }}}
# {{{ separated siblings (list 2) are actually separated
for itgt_box, tgt_ibox in enumerate(trav.target_or_target_parent_boxes):
start, end = trav.sep_siblings_starts[itgt_box:itgt_box + 2]
seps = trav.sep_siblings_lists[start:end]
assert (levels[seps] == levels[tgt_ibox]).all()
# three-ish box radii (half of size)
mindist = 2.5 * 0.5 * 2**-int(levels[tgt_ibox]) * tree.root_extent
icenter = centers[tgt_ibox]
for jbox in seps:
dist = la.norm(centers[jbox] - icenter)
assert dist > mindist, (dist, mindist)
logger.info("separated siblings (list 2) are actually separated")
# }}}
if sources_are_targets:
# {{{ sep_{smaller,bigger} are duals of each other
assert (trav.target_or_target_parent_boxes == np.arange(
tree.nboxes)).all()
# {{{ list 4 <= list 3
for itarget_box, ibox in enumerate(trav.target_boxes):
for ssn in trav.sep_smaller_by_level:
start, end = ssn.starts[itarget_box:itarget_box + 2]
for jbox in ssn.lists[start:end]:
rstart, rend = trav.sep_bigger_starts[jbox:jbox + 2]
assert ibox in trav.sep_bigger_lists[rstart:rend], (ibox,
jbox)
# }}}
# {{{ list 4 <= list 3
box_to_target_box_index = np.empty(tree.nboxes, tree.box_id_dtype)
box_to_target_box_index.fill(-1)
box_to_target_box_index[trav.target_boxes] = np.arange(
len(trav.target_boxes), dtype=tree.box_id_dtype)
assert (trav.source_boxes == trav.target_boxes).all()
assert (trav.target_or_target_parent_boxes == np.arange(
tree.nboxes, dtype=tree.box_id_dtype)).all()
for ibox in range(tree.nboxes):
start, end = trav.sep_bigger_starts[ibox:ibox + 2]
for jbox in trav.sep_bigger_lists[start:end]:
# In principle, entries of sep_bigger_lists are
# source boxes. In this special case, source and target boxes
# are the same thing (i.e. leaves--see assertion above), so we
# may treat them as targets anyhow.
jtgt_box = box_to_target_box_index[jbox]
assert jtgt_box != -1
good = False
for ssn in trav.sep_smaller_by_level:
rstart, rend = ssn.starts[jtgt_box:jtgt_box + 2]
good = good or ibox in ssn.lists[rstart:rend]
if not good:
from boxtree.visualization import TreePlotter
plotter = TreePlotter(tree)
plotter.draw_tree(fill=False, edgecolor="black", zorder=10)
plotter.set_bounding_box()
plotter.draw_box(ibox, facecolor='green', alpha=0.5)
plotter.draw_box(jbox, facecolor='red', alpha=0.5)
import matplotlib.pyplot as pt
pt.gca().set_aspect("equal")
pt.show()
# This assertion failing means that ibox's list 4 contains a box
# 'jbox' whose list 3 does not contain ibox.
assert good, (ibox, jbox)
# }}}
logger.info("list 3, 4 are duals")
# }}}
# {{{ sep_smaller satisfies relative level assumption
for itarget_box, ibox in enumerate(trav.target_boxes):
for ssn in trav.sep_smaller_by_level:
start, end = ssn.starts[itarget_box:itarget_box + 2]
for jbox in ssn.lists[start:end]:
assert levels[ibox] < levels[jbox]
logger.info("list 3 satisfies relative level assumption")
# }}}
# {{{ sep_bigger satisfies relative level assumption
for itgt_box, tgt_ibox in enumerate(trav.target_or_target_parent_boxes):
start, end = trav.sep_bigger_starts[itgt_box:itgt_box + 2]
for jbox in trav.sep_bigger_lists[start:end]:
assert levels[tgt_ibox] > levels[jbox]
logger.info("list 4 satisfies relative level assumption")
# }}}
# {{{ level_start_*_box_nrs lists make sense
for name, ref_array in [("level_start_source_box_nrs", trav.source_boxes),
("level_start_source_parent_box_nrs",
trav.source_parent_boxes),
("level_start_target_box_nrs", trav.target_boxes),
("level_start_target_or_target_parent_box_nrs",
trav.target_or_target_parent_boxes)]:
level_starts = getattr(trav, name)
for lev in range(tree.nlevels):
start, stop = level_starts[lev:lev + 2]
box_nrs = ref_array[start:stop]
assert (tree.box_levels[box_nrs] == lev).all(), name
def test_fmm_completeness(ctx_getter, dims, nsources_req, ntargets_req,
who_has_extent, source_gen, target_gen, filter_kind,
well_sep_is_n_away, extent_norm,
from_sep_smaller_crit):
"""Tests whether the built FMM traversal structures and driver completely
capture all interactions.
"""
sources_have_extent = "s" in who_has_extent
targets_have_extent = "t" in who_has_extent
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
dtype = np.float64
try:
sources = source_gen(queue, nsources_req, dims, dtype, seed=15)
nsources = len(sources[0])
if ntargets_req is None:
# This says "same as sources" to the tree builder.
targets = None
ntargets = ntargets_req
else:
targets = target_gen(queue, ntargets_req, dims, dtype, seed=16)
ntargets = len(targets[0])
except ImportError:
pytest.skip("loo.py not available, but needed for particle array "
"generation")
from pyopencl.clrandom import PhiloxGenerator
rng = PhiloxGenerator(queue.context, seed=12)
if sources_have_extent:
source_radii = 2**rng.uniform(queue, nsources, dtype=dtype, a=-10, b=0)
else:
source_radii = None
if targets_have_extent:
target_radii = 2**rng.uniform(queue, ntargets, dtype=dtype, a=-10, b=0)
else:
target_radii = None
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue,
sources,
targets=targets,
max_particles_in_box=30,
source_radii=source_radii,
target_radii=target_radii,
debug=True,
stick_out_factor=0.25,
extent_norm=extent_norm)
if 0:
tree.get().plot()
import matplotlib.pyplot as pt
pt.show()
from boxtree.traversal import FMMTraversalBuilder
tbuild = FMMTraversalBuilder(ctx,
well_sep_is_n_away=well_sep_is_n_away,
from_sep_smaller_crit=from_sep_smaller_crit)
trav, _ = tbuild(queue, tree, debug=True)
if who_has_extent:
pre_merge_trav = trav
trav = trav.merge_close_lists(queue)
#weights = np.random.randn(nsources)
weights = np.ones(nsources)
weights_sum = np.sum(weights)
host_trav = trav.get(queue=queue)
host_tree = host_trav.tree
if who_has_extent:
pre_merge_host_trav = pre_merge_trav.get(queue=queue)
from boxtree.tree import ParticleListFilter
plfilt = ParticleListFilter(ctx)
if filter_kind:
flags = rng.uniform(queue, ntargets or nsources, np.int32, a=0, b=2) \
.astype(np.int8)
if filter_kind == "user":
filtered_targets = plfilt.filter_target_lists_in_user_order(
queue, tree, flags)
wrangler = ConstantOneExpansionWranglerWithFilteredTargetsInUserOrder(
host_tree, filtered_targets.get(queue=queue))
elif filter_kind == "tree":
filtered_targets = plfilt.filter_target_lists_in_tree_order(
queue, tree, flags)
wrangler = ConstantOneExpansionWranglerWithFilteredTargetsInTreeOrder(
host_tree, filtered_targets.get(queue=queue))
else:
raise ValueError("unsupported value of 'filter_kind'")
else:
wrangler = ConstantOneExpansionWrangler(host_tree)
flags = cl.array.empty(queue, ntargets or nsources, dtype=np.int8)
flags.fill(1)
if ntargets is None and not filter_kind:
# This check only works for targets == sources.
assert (wrangler.reorder_potentials(
wrangler.reorder_sources(weights)) == weights).all()
from boxtree.fmm import drive_fmm
pot = drive_fmm(host_trav, wrangler, weights)
if filter_kind:
pot = pot[flags.get() > 0]
rel_err = la.norm((pot - weights_sum) / nsources)
good = rel_err < 1e-8
# {{{ build, evaluate matrix (and identify incorrect interactions)
if 0 and not good:
mat = np.zeros((ntargets, nsources), dtype)
from pytools import ProgressBar
logging.getLogger().setLevel(logging.WARNING)
pb = ProgressBar("matrix", nsources)
for i in range(nsources):
unit_vec = np.zeros(nsources, dtype=dtype)
unit_vec[i] = 1
mat[:, i] = drive_fmm(host_trav, wrangler, unit_vec)
pb.progress()
pb.finished()
logging.getLogger().setLevel(logging.INFO)
import matplotlib.pyplot as pt
if 0:
pt.imshow(mat)
pt.colorbar()
pt.show()
incorrect_tgts, incorrect_srcs = np.where(mat != 1)
if 1 and len(incorrect_tgts):
from boxtree.visualization import TreePlotter
plotter = TreePlotter(host_tree)
plotter.draw_tree(fill=False, edgecolor="black")
plotter.draw_box_numbers()
plotter.set_bounding_box()
tree_order_incorrect_tgts = \
host_tree.indices_to_tree_target_order(incorrect_tgts)
tree_order_incorrect_srcs = \
host_tree.indices_to_tree_source_order(incorrect_srcs)
src_boxes = [
host_tree.find_box_nr_for_source(i)
for i in tree_order_incorrect_srcs
]
tgt_boxes = [
host_tree.find_box_nr_for_target(i)
for i in tree_order_incorrect_tgts
]
print(src_boxes)
print(tgt_boxes)
# plot all sources/targets
if 0:
pt.plot(host_tree.targets[0],
host_tree.targets[1],
"v",
alpha=0.9)
pt.plot(host_tree.sources[0],
host_tree.sources[1],
"gx",
alpha=0.9)
# plot offending sources/targets
if 0:
pt.plot(host_tree.targets[0][tree_order_incorrect_tgts],
host_tree.targets[1][tree_order_incorrect_tgts], "rv")
pt.plot(host_tree.sources[0][tree_order_incorrect_srcs],
host_tree.sources[1][tree_order_incorrect_srcs], "go")
pt.gca().set_aspect("equal")
from boxtree.visualization import draw_box_lists
draw_box_lists(
plotter, pre_merge_host_trav if who_has_extent else host_trav,
22)
# from boxtree.visualization import draw_same_level_non_well_sep_boxes
# draw_same_level_non_well_sep_boxes(plotter, host_trav, 2)
pt.show()
# }}}
if 0 and not good:
import matplotlib.pyplot as pt
pt.plot(pot - weights_sum)
pt.show()
if 0 and not good:
import matplotlib.pyplot as pt
filt_targets = [
host_tree.targets[0][flags.get() > 0],
host_tree.targets[1][flags.get() > 0],
]
host_tree.plot()
bad = np.abs(pot - weights_sum) >= 1e-3
bad_targets = [
filt_targets[0][bad],
filt_targets[1][bad],
]
print(bad_targets[0].shape)
pt.plot(filt_targets[0], filt_targets[1], "x")
pt.plot(bad_targets[0], bad_targets[1], "v")
pt.show()
assert good
[rng.normal(queue, nparticles, dtype=np.float64) for i in range(dims)])
# -----------------------------------------------------------------------------
# build tree and traversals (lists)
# -----------------------------------------------------------------------------
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue, particles, max_particles_in_box=30)
from boxtree.traversal import FMMTraversalBuilder
tg = FMMTraversalBuilder(ctx)
trav, _ = tg(queue, tree)
# ENDEXAMPLE
# -----------------------------------------------------------------------------
# plot the tree
# -----------------------------------------------------------------------------
import matplotlib.pyplot as pt
pt.plot(particles[0].get(), particles[1].get(), "x")
from boxtree.visualization import TreePlotter
plotter = TreePlotter(tree.get(queue=queue))
plotter.draw_tree(fill=False, edgecolor="black")
plotter.draw_box_numbers()
plotter.set_bounding_box()
pt.gca().set_aspect("equal")
pt.savefig("tree.png")
def test_fmm_completeness(
ctx_getter, dims, nsources_req, ntargets_req, who_has_extent, source_gen, target_gen, filter_kind
):
"""Tests whether the built FMM traversal structures and driver completely
capture all interactions.
"""
sources_have_extent = "s" in who_has_extent
targets_have_extent = "t" in who_has_extent
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
dtype = np.float64
try:
sources = source_gen(queue, nsources_req, dims, dtype, seed=15)
nsources = len(sources[0])
if ntargets_req is None:
# This says "same as sources" to the tree builder.
targets = None
ntargets = ntargets_req
else:
targets = target_gen(queue, ntargets_req, dims, dtype, seed=16)
ntargets = len(targets[0])
except ImportError:
pytest.skip("loo.py not available, but needed for particle array " "generation")
from pyopencl.clrandom import RanluxGenerator
rng = RanluxGenerator(queue, seed=13)
if sources_have_extent:
source_radii = 2 ** rng.uniform(queue, nsources, dtype=dtype, a=-10, b=0)
else:
source_radii = None
if targets_have_extent:
target_radii = 2 ** rng.uniform(queue, ntargets, dtype=dtype, a=-10, b=0)
else:
target_radii = None
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(
queue,
sources,
targets=targets,
max_particles_in_box=30,
source_radii=source_radii,
target_radii=target_radii,
debug=True,
)
if 0:
tree.get().plot()
import matplotlib.pyplot as pt
pt.show()
from boxtree.traversal import FMMTraversalBuilder
tbuild = FMMTraversalBuilder(ctx)
trav, _ = tbuild(queue, tree, debug=True)
if trav.sep_close_smaller_starts is not None:
trav = trav.merge_close_lists(queue)
weights = np.random.randn(nsources)
# weights = np.ones(nsources)
weights_sum = np.sum(weights)
host_trav = trav.get(queue=queue)
host_tree = host_trav.tree
if filter_kind:
flags = rng.uniform(queue, ntargets or nsources, np.int32, a=0, b=2).astype(np.int8)
if filter_kind == "user":
from boxtree.tree import filter_target_lists_in_user_order
filtered_targets = filter_target_lists_in_user_order(queue, tree, flags)
wrangler = ConstantOneExpansionWranglerWithFilteredTargetsInUserOrder(
host_tree, filtered_targets.get(queue=queue)
)
elif filter_kind == "tree":
from boxtree.tree import filter_target_lists_in_tree_order
filtered_targets = filter_target_lists_in_tree_order(queue, tree, flags)
wrangler = ConstantOneExpansionWranglerWithFilteredTargetsInTreeOrder(
host_tree, filtered_targets.get(queue=queue)
)
else:
raise ValueError("unsupported value of 'filter_kind'")
else:
wrangler = ConstantOneExpansionWrangler(host_tree)
if ntargets is None and not filter_kind:
# This check only works for targets == sources.
assert (wrangler.reorder_potentials(wrangler.reorder_sources(weights)) == weights).all()
from boxtree.fmm import drive_fmm
pot = drive_fmm(host_trav, wrangler, weights)
# {{{ build, evaluate matrix (and identify missing interactions)
if 0:
mat = np.zeros((ntargets, nsources), dtype)
from pytools import ProgressBar
logging.getLogger().setLevel(logging.WARNING)
pb = ProgressBar("matrix", nsources)
for i in range(nsources):
unit_vec = np.zeros(nsources, dtype=dtype)
unit_vec[i] = 1
mat[:, i] = drive_fmm(host_trav, wrangler, unit_vec)
pb.progress()
pb.finished()
logging.getLogger().setLevel(logging.INFO)
import matplotlib.pyplot as pt
if 1:
pt.spy(mat)
pt.show()
missing_tgts, missing_srcs = np.where(mat == 0)
if 1 and len(missing_tgts):
from boxtree.visualization import TreePlotter
plotter = TreePlotter(host_tree)
plotter.draw_tree(fill=False, edgecolor="black")
plotter.draw_box_numbers()
plotter.set_bounding_box()
tree_order_missing_tgts = host_tree.indices_to_tree_target_order(missing_tgts)
tree_order_missing_srcs = host_tree.indices_to_tree_source_order(missing_srcs)
src_boxes = [host_tree.find_box_nr_for_source(i) for i in tree_order_missing_srcs]
tgt_boxes = [host_tree.find_box_nr_for_target(i) for i in tree_order_missing_tgts]
print(src_boxes)
print(tgt_boxes)
pt.plot(host_tree.targets[0][tree_order_missing_tgts], host_tree.targets[1][tree_order_missing_tgts], "rv")
pt.plot(host_tree.sources[0][tree_order_missing_srcs], host_tree.sources[1][tree_order_missing_srcs], "go")
pt.gca().set_aspect("equal")
pt.show()
# }}}
if filter_kind:
pot = pot[flags.get() > 0]
rel_err = la.norm((pot - weights_sum) / nsources)
good = rel_err < 1e-8
if 0 and not good:
import matplotlib.pyplot as pt
pt.plot(pot - weights_sum)
pt.show()
if 0 and not good:
import matplotlib.pyplot as pt
filt_targets = [host_tree.targets[0][flags.get() > 0], host_tree.targets[1][flags.get() > 0]]
host_tree.plot()
bad = np.abs(pot - weights_sum) >= 1e-3
bad_targets = [filt_targets[0][bad], filt_targets[1][bad]]
print(bad_targets[0].shape)
pt.plot(filt_targets[0], filt_targets[1], "x")
pt.plot(bad_targets[0], bad_targets[1], "v")
pt.show()
assert good
def test_extent_tree(ctx_getter, dims, extent_norm, do_plot=False):
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
nsources = 100000
ntargets = 200000
dtype = np.float64
npoint_sources_per_source = 16
sources = make_normal_particle_array(queue, nsources, dims, dtype,
seed=12)
targets = make_normal_particle_array(queue, ntargets, dims, dtype,
seed=19)
refine_weights = cl.array.zeros(queue, nsources+ntargets, np.int32)
refine_weights[:nsources] = 1
from pyopencl.clrandom import PhiloxGenerator
rng = PhiloxGenerator(queue.context, seed=13)
source_radii = 2**rng.uniform(queue, nsources, dtype=dtype,
a=-10, b=0)
target_radii = 2**rng.uniform(queue, ntargets, dtype=dtype,
a=-10, b=0)
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
queue.finish()
dev_tree, _ = tb(queue, sources, targets=targets,
source_radii=source_radii,
target_radii=target_radii,
extent_norm=extent_norm,
refine_weights=refine_weights,
max_leaf_refine_weight=20,
#max_particles_in_box=10,
# Set artificially small, to exercise the reallocation code.
nboxes_guess=10,
debug=True,
stick_out_factor=0)
logger.info("transfer tree, check orderings")
tree = dev_tree.get(queue=queue)
if do_plot:
import matplotlib.pyplot as pt
pt.plot(sources[0].get(), sources[1].get(), "rx")
pt.plot(targets[0].get(), targets[1].get(), "g+")
from boxtree.visualization import TreePlotter
plotter = TreePlotter(tree)
plotter.draw_tree(fill=False, edgecolor="black", zorder=10)
plotter.draw_box_numbers()
plotter.set_bounding_box()
pt.gca().set_aspect("equal", "datalim")
pt.show()
sorted_sources = np.array(list(tree.sources))
sorted_targets = np.array(list(tree.targets))
sorted_source_radii = tree.source_radii
sorted_target_radii = tree.target_radii
unsorted_sources = np.array([pi.get() for pi in sources])
unsorted_targets = np.array([pi.get() for pi in targets])
unsorted_source_radii = source_radii.get()
unsorted_target_radii = target_radii.get()
assert (sorted_sources
== unsorted_sources[:, tree.user_source_ids]).all()
assert (sorted_source_radii
== unsorted_source_radii[tree.user_source_ids]).all()
# {{{ test box structure, stick-out criterion
logger.info("test box structure, stick-out criterion")
user_target_ids = np.empty(tree.ntargets, dtype=np.intp)
user_target_ids[tree.sorted_target_ids] = np.arange(tree.ntargets, dtype=np.intp)
if ntargets:
assert (sorted_targets
== unsorted_targets[:, user_target_ids]).all()
assert (sorted_target_radii
== unsorted_target_radii[user_target_ids]).all()
all_good_so_far = True
# {{{ check sources, targets
assert np.sum(tree.box_source_counts_nonchild) == nsources
assert np.sum(tree.box_target_counts_nonchild) == ntargets
for ibox in range(tree.nboxes):
kid_sum = sum(
tree.box_target_counts_cumul[ichild_box]
for ichild_box in tree.box_child_ids[:, ibox]
if ichild_box != 0)
assert (
tree.box_target_counts_cumul[ibox]
== (
tree.box_target_counts_nonchild[ibox]
+ kid_sum)), ibox
for ibox in range(tree.nboxes):
extent_low, extent_high = tree.get_box_extent(ibox)
assert (extent_low
>= tree.bounding_box[0] - 1e-12*tree.root_extent).all(), ibox
assert (extent_high
<= tree.bounding_box[1] + 1e-12*tree.root_extent).all(), ibox
box_children = tree.box_child_ids[:, ibox]
existing_children = box_children[box_children != 0]
assert (tree.box_source_counts_nonchild[ibox]
+ np.sum(tree.box_source_counts_cumul[existing_children])
== tree.box_source_counts_cumul[ibox])
assert (tree.box_target_counts_nonchild[ibox]
+ np.sum(tree.box_target_counts_cumul[existing_children])
== tree.box_target_counts_cumul[ibox])
del existing_children
del box_children
for ibox in range(tree.nboxes):
lev = int(tree.box_levels[ibox])
box_radius = 0.5 * tree.root_extent / (1 << lev)
box_center = tree.box_centers[:, ibox]
extent_low = box_center - box_radius
extent_high = box_center + box_radius
stick_out_dist = tree.stick_out_factor * box_radius
radius_with_stickout = (1 + tree.stick_out_factor) * box_radius
for what, starts, counts, points, radii in [
("source", tree.box_source_starts, tree.box_source_counts_cumul,
sorted_sources, sorted_source_radii),
("target", tree.box_target_starts, tree.box_target_counts_cumul,
sorted_targets, sorted_target_radii),
]:
bstart = starts[ibox]
bslice = slice(bstart, bstart+counts[ibox])
check_particles = points[:, bslice]
check_radii = radii[bslice]
if extent_norm == "linf":
good = (
(check_particles + check_radii
< extent_high[:, np.newaxis] + stick_out_dist)
& # noqa: W504
(extent_low[:, np.newaxis] - stick_out_dist
<= check_particles - check_radii)
).all(axis=0)
elif extent_norm == "l2":
center_dists = np.sqrt(
np.sum(
(check_particles - box_center.reshape(-1, 1))**2,
axis=0))
good = (
(center_dists + check_radii)**2
< dims * radius_with_stickout**2)
else:
raise ValueError("unexpected value of extent_norm")
all_good_here = good.all()
if not all_good_here:
print("BAD BOX %s %d level %d"
% (what, ibox, tree.box_levels[ibox]))
all_good_so_far = all_good_so_far and all_good_here
assert all_good_here
# }}}
assert all_good_so_far
# }}}
# {{{ create, link point sources
logger.info("creating point sources")
np.random.seed(20)
from pytools.obj_array import make_obj_array
point_sources = make_obj_array([
cl.array.to_device(queue,
unsorted_sources[i][:, np.newaxis]
+ unsorted_source_radii[:, np.newaxis]
* np.random.uniform(
-1, 1, size=(nsources, npoint_sources_per_source))
)
for i in range(dims)])
point_source_starts = cl.array.arange(queue,
0, (nsources+1)*npoint_sources_per_source, npoint_sources_per_source,
dtype=tree.particle_id_dtype)
from boxtree.tree import link_point_sources
dev_tree = link_point_sources(queue, dev_tree,
point_source_starts, point_sources,
debug=True)
def test_extent_tree(ctx_factory, dims, extent_norm, do_plot=False):
logging.basicConfig(level=logging.INFO)
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
nsources = 100000
ntargets = 200000
dtype = np.float64
npoint_sources_per_source = 16
sources = make_normal_particle_array(queue, nsources, dims, dtype, seed=12)
targets = make_normal_particle_array(queue, ntargets, dims, dtype, seed=19)
refine_weights = cl.array.zeros(queue, nsources + ntargets, np.int32)
refine_weights[:nsources] = 1
from pyopencl.clrandom import PhiloxGenerator
rng = PhiloxGenerator(queue.context, seed=13)
source_radii = 2**rng.uniform(queue, nsources, dtype=dtype, a=-10, b=0)
target_radii = 2**rng.uniform(queue, ntargets, dtype=dtype, a=-10, b=0)
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
queue.finish()
dev_tree, _ = tb(
queue,
sources,
targets=targets,
source_radii=source_radii,
target_radii=target_radii,
extent_norm=extent_norm,
refine_weights=refine_weights,
max_leaf_refine_weight=20,
#max_particles_in_box=10,
# Set artificially small, to exercise the reallocation code.
nboxes_guess=10,
debug=True,
stick_out_factor=0)
logger.info("transfer tree, check orderings")
tree = dev_tree.get(queue=queue)
if do_plot:
import matplotlib.pyplot as pt
pt.plot(sources[0].get(), sources[1].get(), "rx")
pt.plot(targets[0].get(), targets[1].get(), "g+")
from boxtree.visualization import TreePlotter
plotter = TreePlotter(tree)
plotter.draw_tree(fill=False, edgecolor="black", zorder=10)
plotter.draw_box_numbers()
plotter.set_bounding_box()
pt.gca().set_aspect("equal", "datalim")
pt.show()
sorted_sources = np.array(list(tree.sources))
sorted_targets = np.array(list(tree.targets))
sorted_source_radii = tree.source_radii
sorted_target_radii = tree.target_radii
unsorted_sources = np.array([pi.get() for pi in sources])
unsorted_targets = np.array([pi.get() for pi in targets])
unsorted_source_radii = source_radii.get()
unsorted_target_radii = target_radii.get()
assert (sorted_sources == unsorted_sources[:, tree.user_source_ids]).all()
assert (sorted_source_radii == unsorted_source_radii[tree.user_source_ids]
).all()
# {{{ test box structure, stick-out criterion
logger.info("test box structure, stick-out criterion")
user_target_ids = np.empty(tree.ntargets, dtype=np.intp)
user_target_ids[tree.sorted_target_ids] = np.arange(tree.ntargets,
dtype=np.intp)
if ntargets:
assert (sorted_targets == unsorted_targets[:, user_target_ids]).all()
assert (sorted_target_radii == unsorted_target_radii[user_target_ids]
).all()
all_good_so_far = True
# {{{ check sources, targets
assert np.sum(tree.box_source_counts_nonchild) == nsources
assert np.sum(tree.box_target_counts_nonchild) == ntargets
for ibox in range(tree.nboxes):
kid_sum = sum(tree.box_target_counts_cumul[ichild_box]
for ichild_box in tree.box_child_ids[:, ibox]
if ichild_box != 0)
assert (tree.box_target_counts_cumul[ibox] == (
tree.box_target_counts_nonchild[ibox] + kid_sum)), ibox
for ibox in range(tree.nboxes):
extent_low, extent_high = tree.get_box_extent(ibox)
assert (extent_low >=
tree.bounding_box[0] - 1e-12 * tree.root_extent).all(), ibox
assert (extent_high <=
tree.bounding_box[1] + 1e-12 * tree.root_extent).all(), ibox
box_children = tree.box_child_ids[:, ibox]
existing_children = box_children[box_children != 0]
assert (tree.box_source_counts_nonchild[ibox] +
np.sum(tree.box_source_counts_cumul[existing_children]) ==
tree.box_source_counts_cumul[ibox])
assert (tree.box_target_counts_nonchild[ibox] +
np.sum(tree.box_target_counts_cumul[existing_children]) ==
tree.box_target_counts_cumul[ibox])
del existing_children
del box_children
for ibox in range(tree.nboxes):
lev = int(tree.box_levels[ibox])
box_radius = 0.5 * tree.root_extent / (1 << lev)
box_center = tree.box_centers[:, ibox]
extent_low = box_center - box_radius
extent_high = box_center + box_radius
stick_out_dist = tree.stick_out_factor * box_radius
radius_with_stickout = (1 + tree.stick_out_factor) * box_radius
for what, starts, counts, points, radii in [
("source", tree.box_source_starts, tree.box_source_counts_cumul,
sorted_sources, sorted_source_radii),
("target", tree.box_target_starts, tree.box_target_counts_cumul,
sorted_targets, sorted_target_radii),
]:
bstart = starts[ibox]
bslice = slice(bstart, bstart + counts[ibox])
check_particles = points[:, bslice]
check_radii = radii[bslice]
if extent_norm == "linf":
good = ((check_particles + check_radii <
extent_high[:, np.newaxis] + stick_out_dist)
& # noqa: W504
(extent_low[:, np.newaxis] - stick_out_dist <=
check_particles - check_radii)).all(axis=0)
elif extent_norm == "l2":
center_dists = np.sqrt(
np.sum((check_particles - box_center.reshape(-1, 1))**2,
axis=0))
good = ((center_dists + check_radii)**2 <
dims * radius_with_stickout**2)
else:
raise ValueError("unexpected value of extent_norm")
all_good_here = good.all()
if not all_good_here:
print("BAD BOX %s %d level %d" %
(what, ibox, tree.box_levels[ibox]))
all_good_so_far = all_good_so_far and all_good_here
assert all_good_here
# }}}
assert all_good_so_far
# }}}
# {{{ create, link point sources
logger.info("creating point sources")
np.random.seed(20)
from pytools.obj_array import make_obj_array
point_sources = make_obj_array([
cl.array.to_device(
queue, unsorted_sources[i][:, np.newaxis] +
unsorted_source_radii[:, np.newaxis] * np.random.uniform(
-1, 1, size=(nsources, npoint_sources_per_source)))
for i in range(dims)
])
point_source_starts = cl.array.arange(queue,
0, (nsources + 1) *
npoint_sources_per_source,
npoint_sources_per_source,
dtype=tree.particle_id_dtype)
from boxtree.tree import link_point_sources
dev_tree = link_point_sources(queue,
dev_tree,
point_source_starts,
point_sources,
debug=True)
for i in range(dims)])
# -----------------------------------------------------------------------------
# build tree and traversals (lists)
# -----------------------------------------------------------------------------
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue, particles, max_particles_in_box=30)
from boxtree.traversal import FMMTraversalBuilder
tg = FMMTraversalBuilder(ctx)
trav, _ = tg(queue, tree)
# ENDEXAMPLE
# -----------------------------------------------------------------------------
# plot the tree
# -----------------------------------------------------------------------------
import matplotlib.pyplot as pt
pt.plot(particles[0].get(), particles[1].get(), "x")
from boxtree.visualization import TreePlotter
plotter = TreePlotter(tree.get(queue=queue))
plotter.draw_tree(fill=False, edgecolor="black")
plotter.draw_box_numbers()
plotter.set_bounding_box()
pt.gca().set_aspect("equal")
pt.savefig("tree.png")
