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from __future__ import division
import numpy as np
import sys
import pytools.test
import matplotlib.pyplot as pt
import pyopencl as cl
from pyopencl.tools import pytest_generate_tests_for_pyopencl \
as pytest_generate_tests
@pytools.test.mark_test.opencl
def test_tree(ctx_getter, do_plot=False):
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
#for dims in [2, 3]:
for dims in [2]:
nparticles = 10**5
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 htree import TreeBuilder
tb = TreeBuilder(ctx)
queue.finish()
print "building..."
tree = tb(queue, particles, max_particles_in_box=30, debug=True)
print "%d boxes, testing..." % tree.nboxes
starts = tree.box_starts.get()
pcounts = tree.box_particle_counts.get()
sorted_particles = np.array([pi.get() for pi in tree.particles])
centers = tree.box_centers.get()
levels = tree.box_levels.get()
unsorted_particles = np.array([pi.get() for pi in particles])
assert (sorted_particles
== unsorted_particles[:, tree.original_particle_ids.get()]).all()
assert np.max(levels) + 1 == tree.nlevels
root_extent = tree.root_extent
all_good_so_far = True
if do_plot:
tree.plot()
for ibox in xrange(tree.nboxes):
lev = int(levels[ibox])
box_size = root_extent / (1 << lev)
el = extent_low = centers[:, ibox] - 0.5*box_size
eh = extent_high = extent_low + box_size
box_particle_nrs = np.arange(starts[ibox], starts[ibox]+pcounts[ibox],
dtype=np.intp)
box_particles = sorted_particles[:,box_particle_nrs]
good = (
(box_particles < extent_high[:, np.newaxis])
&
(extent_low[:, np.newaxis] <= box_particles)
)
all_good_here = good.all()
if do_plot and not all_good_here and all_good_so_far:
pt.plot(
box_particles[0, np.where(~good)[1]],
box_particles[1, np.where(~good)[1]], "ro")
pt.plot([el[0], eh[0], eh[0], el[0], el[0]],
[el[1], el[1], eh[1], eh[1], el[1]], "r-", lw=1)
all_good_so_far = all_good_so_far and all_good_here
if do_plot:
pt.gca().set_aspect("equal", "datalim")
pt.show()
assert all_good_so_far
print "done"
@pytools.test.mark_test.opencl
def test_traversal(ctx_getter, do_plot=False):
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
#for dims in [2, 3]:
for dims in [2]:
nparticles = 10**5
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 htree import TreeBuilder
tb = TreeBuilder(ctx)
queue.finish()
print "building..."
tree = tb(queue, particles, max_particles_in_box=30, debug=True)
print "done"
if do_plot:
tree.plot()
from htree.traversal import FMMTraversalGenerator
tg = FMMTraversalGenerator(ctx)
tg(queue, tree)
# You can test individual routines by typing
# $ python test_kernels.py 'test_p2p(cl.create_some_context)'
if __name__ == "__main__":
# make sure that import failures get reported, instead of skipping the tests.
import pyopencl as cl
import sys
if len(sys.argv) > 1:
exec(sys.argv[1])
else:
from py.test.cmdline import main
main([__file__])
# vim: fdm=marker