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 tree(self):
"""Build and return a :class:`boxtree.tree.TreeWithLinkedPointSources`
for this source with these targets.
|cached|
"""
code_getter = self.code_getter
lpot_src = self.lpot_source
with cl.CommandQueue(self.cl_context) as queue:
nelements = sum(grp.nelements
for grp in lpot_src.fine_density_discr.groups)
el_centers = cl.array.empty(
self.cl_context, (lpot_src.ambient_dim, nelements),
self.coord_dtype)
el_radii = cl.array.empty(self.cl_context, nelements, self.coord_dtype)
# {{{ find sources and radii (=element 'centroids')
# FIXME: Should probably use quad weights to find 'centroids' to deal
# with non-symmetric elements.
i_el_base = 0
for grp in lpot_src.fine_density_discr.groups:
el_centers_view = el_centers[:, i_el_base:i_el_base+grp.nelements]
el_radii_view = el_radii[i_el_base:i_el_base+grp.nelements]
nodes_view = grp.view(lpot_src.fine_density_discr.nodes())
code_getter.find_element_centers(
queue, el_centers=el_centers_view, nodes=nodes_view)
code_getter.find_element_radii(
queue, el_centers=el_centers_view, nodes=nodes_view,
el_radii=el_radii_view)
i_el_base += grp.nelements
# }}}
target_info = self.target_info()
tree, _ = code_getter.build_tree(queue,
particles=el_centers, source_radii=el_radii,
max_particles_in_box=30,
targets=target_info.targets,
target_radii=self.target_radii(),
debug=self.debug)
# {{{ link point sources
point_source_starts = cl.array.empty(self.cl_context,
nelements+1, tree.particle_id_dtype)
i_el_base = 0
for grp in lpot_src.fine_density_discr.groups:
point_source_starts.setitem(
slice(i_el_base, i_el_base+grp.nelements),
cl.array.arange(queue,
grp.node_nr_base, grp.node_nr_base + grp.nnodes,
grp.nunit_nodes,
dtype=point_source_starts.dtype),
queue=queue)
i_el_base += grp.nelements
point_source_starts.setitem(
-1, self.lpot_source.fine_density_discr.nnodes, queue=queue)
from boxtree.tree import link_point_sources
tree = link_point_sources(queue, tree,
point_source_starts,
self.lpot_source.fine_density_discr.nodes())
# }}}
return tree
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)
def test_extent_tree(ctx_getter, dims, 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)
from pyopencl.clrandom import RanluxGenerator
rng = RanluxGenerator(queue, 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,
max_particles_in_box=10, debug=True)
logger.info("transfer tree, check orderings")
tree = dev_tree.get(queue=queue)
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
for ibox in range(tree.nboxes):
extent_low, extent_high = tree.get_box_extent(ibox)
box_radius = np.max(extent_high-extent_low) * 0.5
stick_out_dist = tree.stick_out_factor * box_radius
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])
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]
good = (
(check_particles + check_radii
< extent_high[:, np.newaxis] + stick_out_dist)
&
(extent_low[:, np.newaxis] - stick_out_dist
<= check_particles - check_radii)
).all(axis=0)
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)