def test_random(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import RanluxGenerator
if has_double_support(context.devices[0]):
dtypes = [np.float32, np.float64]
else:
dtypes = [np.float32]
gen = RanluxGenerator(queue, 5120)
for ary_size in [300, 301, 302, 303, 10007]:
for dtype in dtypes:
ran = cl_array.zeros(queue, ary_size, dtype)
gen.fill_uniform(ran)
assert (0 < ran.get()).all()
assert (ran.get() < 1).all()
gen.synchronize(queue)
ran = cl_array.zeros(queue, ary_size, dtype)
gen.fill_uniform(ran, a=4, b=7)
assert (4 < ran.get()).all()
assert (ran.get() < 7).all()
ran = gen.normal(queue, (10007,), dtype, mu=4, sigma=3)
dtypes = [np.int32]
for dtype in dtypes:
ran = gen.uniform(queue, (10000007,), dtype, a=200, b=300)
assert (200 <= ran.get()).all()
assert (ran.get() < 300).all()
def make_normal_particle_array(queue, nparticles, dims, dtype, seed=15):
from pyopencl.clrandom import RanluxGenerator
rng = RanluxGenerator(queue, seed=seed)
return make_obj_array([
rng.normal(queue, nparticles, dtype=dtype)
for i in range(dims)])
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
dims = 2
nparticles = 500
# -----------------------------------------------------------------------------
# generate some random particle positions
# -----------------------------------------------------------------------------
from pyopencl.clrandom import RanluxGenerator
rng = RanluxGenerator(queue, seed=15)
from pytools.obj_array import make_obj_array
particles = make_obj_array([
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=5)
from boxtree.traversal import FMMTraversalBuilder
tg = FMMTraversalBuilder(ctx)
trav, _ = tg(queue, tree)
# ENDEXAMPLE
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
dims = 2
nparticles = 10**4
# -----------------------------------------------------------------------------
# generate some random particle positions
# -----------------------------------------------------------------------------
from pyopencl.clrandom import RanluxGenerator
rng = RanluxGenerator(queue, seed=15)
from pytools.obj_array import make_obj_array
particles = make_obj_array(
[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
# -----------------------------------------------------------------------------
def plot_traversal(ctx_getter, do_plot=False):
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
#for dims in [2, 3]:
for dims in [2]:
nparticles = 10**4
dtype = np.float64
from pyopencl.clrandom import RanluxGenerator
rng = RanluxGenerator(queue, seed=15)
from pytools.obj_array import make_obj_array
particles = make_obj_array(
[rng.normal(queue, nparticles, dtype=dtype) for i in range(dims)])
#if do_plot:
#pt.plot(particles[0].get(), particles[1].get(), "x")
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
queue.finish()
tree = tb(queue, particles, max_particles_in_box=30, debug=True)
from boxtree.traversal import FMMTraversalBuilder
tg = FMMTraversalBuilder(ctx)
trav = tg(queue, tree).get()
from boxtree.visualization import TreePlotter
plotter = TreePlotter(tree)
plotter.draw_tree(fill=False, edgecolor="black")
#plotter.draw_box_numbers()
plotter.set_bounding_box()
from random import randrange, seed
seed(7)
# {{{ generic box drawing helper
def draw_some_box_lists(starts, lists, key_to_box=None, count=5):
actual_count = 0
while actual_count < count:
if key_to_box is not None:
key = randrange(len(key_to_box))
ibox = key_to_box[key]
else:
key = ibox = randrange(tree.nboxes)
start, end = starts[key:key + 2]
if start == end:
continue
#print ibox, start, end, lists[start:end]
for jbox in lists[start:end]:
plotter.draw_box(jbox, facecolor='yellow')
plotter.draw_box(ibox, facecolor='red')
actual_count += 1
# }}}
if 0:
# colleagues
draw_some_box_lists(trav.colleagues_starts, trav.colleagues_lists)
elif 0:
# near neighbors ("list 1")
draw_some_box_lists(trav.neighbor_leaves_starts,
trav.neighbor_leaves_lists,
key_to_box=trav.source_boxes)
elif 0:
# well-separated siblings (list 2)
draw_some_box_lists(trav.sep_siblings_starts,
trav.sep_siblings_lists)
elif 1:
# separated smaller (list 3)
draw_some_box_lists(trav.sep_smaller_starts,
trav.sep_smaller_lists,
key_to_box=trav.source_boxes)
elif 1:
# separated bigger (list 4)
draw_some_box_lists(trav.sep_bigger_starts, trav.sep_bigger_lists)
import matplotlib.pyplot as pt
pt.show()
def plot_traversal(ctx_getter, do_plot=False):
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
#for dims in [2, 3]:
for dims in [2]:
nparticles = 10**4
dtype = np.float64
from pyopencl.clrandom import RanluxGenerator
rng = RanluxGenerator(queue, seed=15)
from pytools.obj_array import make_obj_array
particles = make_obj_array([
rng.normal(queue, nparticles, dtype=dtype)
for i in range(dims)])
#if do_plot:
#pt.plot(particles[0].get(), particles[1].get(), "x")
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
queue.finish()
tree = tb(queue, particles, max_particles_in_box=30, debug=True)
from boxtree.traversal import FMMTraversalBuilder
tg = FMMTraversalBuilder(ctx)
trav = tg(queue, tree).get()
from boxtree.visualization import TreePlotter
plotter = TreePlotter(tree)
plotter.draw_tree(fill=False, edgecolor="black")
#plotter.draw_box_numbers()
plotter.set_bounding_box()
from random import randrange, seed
seed(7)
# {{{ generic box drawing helper
def draw_some_box_lists(starts, lists, key_to_box=None,
count=5):
actual_count = 0
while actual_count < count:
if key_to_box is not None:
key = randrange(len(key_to_box))
ibox = key_to_box[key]
else:
key = ibox = randrange(tree.nboxes)
start, end = starts[key:key+2]
if start == end:
continue
#print ibox, start, end, lists[start:end]
for jbox in lists[start:end]:
plotter.draw_box(jbox, facecolor='yellow')
plotter.draw_box(ibox, facecolor='red')
actual_count += 1
# }}}
if 0:
# colleagues
draw_some_box_lists(
trav.colleagues_starts,
trav.colleagues_lists)
elif 0:
# near neighbors ("list 1")
draw_some_box_lists(
trav.neighbor_leaves_starts,
trav.neighbor_leaves_lists,
key_to_box=trav.source_boxes)
elif 0:
# well-separated siblings (list 2)
draw_some_box_lists(
trav.sep_siblings_starts,
trav.sep_siblings_lists)
elif 1:
# separated smaller (list 3)
draw_some_box_lists(
trav.sep_smaller_starts,
trav.sep_smaller_lists,
key_to_box=trav.source_boxes)
elif 1:
# separated bigger (list 4)
draw_some_box_lists(
trav.sep_bigger_starts,
trav.sep_bigger_lists)
import matplotlib.pyplot as pt
pt.show()
def make_normal_particle_array(queue, nparticles, dims, dtype, seed=15):
from pyopencl.clrandom import RanluxGenerator
rng = RanluxGenerator(queue, seed=seed)
return make_obj_array(
[rng.normal(queue, nparticles, dtype=dtype) for i in range(dims)])
