from __future__ import division __copyright__ = "Copyright (C) 2012 Andreas Kloeckner" __license__ = """ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import numpy as np import numpy.linalg as la import sys import pytools.test import pyopencl as cl from pyopencl.tools import pytest_generate_tests_for_pyopencl \ as pytest_generate_tests def make_particle_array(queue, nparticles, dims, dtype, seed=15): from pyopencl.clrandom import RanluxGenerator rng = RanluxGenerator(queue, seed=seed) from pytools.obj_array import make_obj_array return make_obj_array([ rng.normal(queue, nparticles, dtype=dtype) for i in range(dims)]) def particle_array_to_host(parray): return np.array([x.get() for x in parray], order="F").T # {{{ bounding box test def test_bounding_box(ctx_getter): ctx = ctx_getter() queue = cl.CommandQueue(ctx) from boxtree import AXIS_NAMES from boxtree.bounding_box import BoundingBoxFinder bbf = BoundingBoxFinder(ctx) #for dtype in [np.float32, np.float64]: for dtype in [np.float64, np.float32]: for dims in [2, 3]: axis_names = AXIS_NAMES[:dims] for nparticles in [9, 4096, 10**5]: print dtype, dims, nparticles particles = make_particle_array(queue, nparticles, dims, dtype) bbox_min = [np.min(x.get()) for x in particles] bbox_max = [np.max(x.get()) for x in particles] bbox_cl = bbf(particles).get() bbox_min_cl = np.empty(dims, dtype) bbox_max_cl = np.empty(dims, dtype) for i, ax in enumerate(axis_names): bbox_min_cl[i] = bbox_cl["min_"+ax] bbox_max_cl[i] = bbox_cl["max_"+ax] assert (bbox_min == bbox_min_cl).all() assert (bbox_max == bbox_max_cl).all() # }}} # {{{ basic tree build test def run_build_test(builder, queue, dims, dtype, nparticles, do_plot, max_particles_in_box=30, **kwargs): dtype = np.dtype(dtype) if dtype == np.float32: tol = 1e-4 elif dtype == np.float64: tol = 1e-12 else: raise RuntimeError("unsupported dtype: %s" % dtype) print 75*"-" print "%dD %s - %d particles - max %d per box - %s" % ( dims, dtype.type.__name__, nparticles, max_particles_in_box, " - ".join("%s: %s" % (k, v) for k, v in kwargs.iteritems())) print 75*"-" particles = make_particle_array(queue, nparticles, dims, dtype) if do_plot: import matplotlib.pyplot as pt pt.plot(particles[0].get(), particles[1].get(), "x") queue.finish() print "building..." tree = builder(queue, particles, max_particles_in_box=max_particles_in_box, debug=True, **kwargs).get() print "%d boxes, testing..." % tree.nboxes sorted_particles = np.array(list(tree.sources)) unsorted_particles = np.array([pi.get() for pi in particles]) assert (sorted_particles == unsorted_particles[:, tree.user_source_ids]).all() all_good_so_far = True if do_plot: from boxtree.visualization import TreePlotter plotter = TreePlotter(tree) plotter.draw_tree(fill=False, edgecolor="black", zorder=10) plotter.set_bounding_box() from boxtree import box_flags_enum as bfe scaled_tol = tol*tree.root_extent for ibox in xrange(tree.nboxes): # Empty boxes exist in non-pruned trees--which themselves are undocumented. # These boxes will fail these tests. if not (tree.box_flags[ibox] & bfe.IS_NONEMPTY): continue extent_low, extent_high = tree.get_box_extent(ibox) if extent_low[0] == extent_low[1]: print "ZERO", ibox, tree.box_centers[:, ibox] 1/0 assert (extent_low >= tree.bounding_box[0] - scaled_tol).all(), ( ibox, extent_low, tree.bounding_box[0]) assert (extent_high <= tree.bounding_box[1] + scaled_tol).all(), ( ibox, extent_high, tree.bounding_box[1]) start = tree.box_source_starts[ibox] box_particles = sorted_particles[:,start:start+tree.box_source_counts[ibox]] good = ( (box_particles < extent_high[:, np.newaxis] + scaled_tol) & (extent_low[:, np.newaxis] - scaled_tol <= 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") plotter.draw_box(ibox, edgecolor="red") if not all_good_here: print "BAD BOX", ibox 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_particle_tree(ctx_getter, do_plot=False): ctx = ctx_getter() queue = cl.CommandQueue(ctx) from boxtree import TreeBuilder builder = TreeBuilder(ctx) for dtype in [ #np.float64, np.float32, ]: for dims in [2, 3]: # test single-box corner case run_build_test(builder, queue, dims, dtype, 4, do_plot=False) # test bi-level corner case run_build_test(builder, queue, dims, dtype, 50, do_plot=False) # test unpruned tree build run_build_test(builder, queue, dims, dtype, 10**5, do_plot=False, skip_prune=True) # exercise reallocation code run_build_test(builder, queue, dims, dtype, 10**5, do_plot=False, nboxes_guess=5) # test many empty leaves corner case run_build_test(builder, queue, dims, dtype, 10**5, do_plot=False, max_particles_in_box=5) # test vanilla tree build run_build_test(builder, queue, dims, dtype, 10**5, do_plot=do_plot) @pytools.test.mark_test.opencl def test_source_target_tree(ctx_getter, do_plot=False): ctx = ctx_getter() queue = cl.CommandQueue(ctx) for dims in [2, 3]: nsources = 2 * 10**5 ntargets = 3 * 10**5 dtype = np.float64 sources = make_particle_array(queue, nsources, dims, dtype, seed=12) targets = make_particle_array(queue, ntargets, dims, dtype, seed=19) 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 import TreeBuilder tb = TreeBuilder(ctx) queue.finish() print "building..." tree = tb(queue, sources, targets=targets, max_particles_in_box=10, debug=True).get() print "%d boxes, testing..." % tree.nboxes sorted_sources = np.array(list(tree.sources)) sorted_targets = np.array(list(tree.targets)) unsorted_sources = np.array([pi.get() for pi in sources]) unsorted_targets = np.array([pi.get() for pi in targets]) assert (sorted_sources == unsorted_sources[:, tree.user_source_ids]).all() user_target_ids = np.empty(tree.ntargets, dtype=np.intp) user_target_ids[tree.sorted_target_ids] = np.arange(tree.ntargets, dtype=np.intp) assert (sorted_targets == unsorted_targets[:, user_target_ids]).all() all_good_so_far = True if do_plot: from boxtree.visualization import TreePlotter plotter = TreePlotter(tree) plotter.draw_tree(fill=False, edgecolor="black", zorder=10) plotter.set_bounding_box() for ibox in xrange(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 src_start = tree.box_source_starts[ibox] tgt_start = tree.box_target_starts[ibox] for what, particles in [ ("sources", sorted_sources[:,src_start:src_start+tree.box_source_counts[ibox]]), ("targets", sorted_targets[:,tgt_start:tgt_start+tree.box_target_counts[ibox]]), ]: good = ( (particles < extent_high[:, np.newaxis]) & (extent_low[:, np.newaxis] <= particles) ).all(axis=0) all_good_here = good.all() if do_plot and not all_good_here: pt.plot( particles[0, np.where(~good)[0]], particles[1, np.where(~good)[0]], "ro") plotter.draw_box(ibox, edgecolor="red") pt.show() if not all_good_here: print "BAD BOX %s %d" % (what, ibox) 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" # }}} # {{{ connectivity test @pytools.test.mark_test.opencl def test_tree_connectivity(ctx_getter): ctx = ctx_getter() queue = cl.CommandQueue(ctx) 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)]) from boxtree import TreeBuilder tb = TreeBuilder(ctx) tree = tb(queue, particles, max_particles_in_box=30, debug=True) print "tree built" from boxtree.traversal import FMMTraversalBuilder tg = FMMTraversalBuilder(ctx) trav = tg(queue, tree).get() tree = tree.get() print "traversal built" 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 xrange(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_leaves (list 1) consists of leaves for ileaf, ibox in enumerate(trav.leaf_boxes): start, end = trav.neighbor_leaves_starts[ileaf:ileaf+2] nbl = trav.neighbor_leaves_lists[start:end] assert ibox in nbl for jbox in nbl: assert (0 == children[jbox]).all(), (ibox, jbox, children[jbox]) print "list 1 tested" # }}} # {{{ separated siblings (list 2) are actually separated for ibox in xrange(tree.nboxes): start, end = trav.sep_siblings_starts[ibox:ibox+2] seps = trav.sep_siblings_lists[start:end] assert (levels[seps] == levels[ibox]).all() # three-ish box radii (half of size) mindist = 2.5 * 0.5 * 2**-int(levels[ibox]) * tree.root_extent icenter = centers[ibox] for jbox in seps: dist = la.norm(centers[jbox]-icenter) assert dist > mindist, (dist, mindist) # }}} # {{{ sep_{smaller,bigger}_nonsiblings are duals of each other # (technically, we only test one half of that) for ileaf, ibox in enumerate(trav.leaf_boxes): start, end = trav.sep_smaller_nonsiblings_starts[ileaf:ileaf+2] for jbox in trav.sep_smaller_nonsiblings_lists[start:end]: rstart, rend = trav.sep_bigger_nonsiblings_starts[jbox:jbox+2] assert ibox in trav.sep_bigger_nonsiblings_lists[rstart:rend], (ibox, jbox) print "list 3, 4 are duals" # }}} # {{{ sep_smaller_nonsiblings satisfies size assumption for ileaf, ibox in enumerate(trav.leaf_boxes): start, end = trav.sep_smaller_nonsiblings_starts[ileaf:ileaf+2] for jbox in trav.sep_smaller_nonsiblings_lists[start:end]: assert levels[ibox] < levels[jbox] print "list 3 satisfies size assumption" # }}} # {{{ sep_smaller_nonsiblings satisfies size assumption for ibox in xrange(tree.nboxes): start, end = trav.sep_bigger_nonsiblings_starts[ibox:ibox+2] for jbox in trav.sep_bigger_nonsiblings_lists[start:end]: assert levels[ibox] > levels[jbox] print "list 4 satisfies size assumption" # }}} # }}} # {{{ fmm interaction completeness test class ConstantOneExpansionWrangler: """This implements the 'analytical routines' for a Green's function that is constant 1 everywhere. For 'charges' of 'ones', this should get every particle a copy of the particle count. """ def __init__(self, tree): self.tree = tree def expansion_zeros(self): return np.zeros(self.tree.nboxes, dtype=np.float64) def potential_zeros(self): return np.zeros(self.tree.ntargets, dtype=np.float64) def _get_source_slice(self, ibox): pstart = self.tree.box_source_starts[ibox] return slice( pstart, pstart + self.tree.box_source_counts[ibox]) def _get_target_slice(self, ibox): pstart = self.tree.box_target_starts[ibox] return slice( pstart, pstart + self.tree.box_target_counts[ibox]) def reorder_src_weights(self, src_weights): return src_weights[self.tree.user_source_ids] def reorder_potentials(self, potentials): return potentials[self.tree.sorted_target_ids] def form_multipoles(self, leaf_boxes, src_weights): mpoles = self.expansion_zeros() for ibox in leaf_boxes: pslice = self._get_source_slice(ibox) mpoles[ibox] += np.sum(src_weights[pslice]) return mpoles def coarsen_multipoles(self, parent_boxes, start_parent_box, end_parent_box, mpoles): tree = self.tree for ibox in parent_boxes[start_parent_box:end_parent_box]: for child in tree.box_child_ids[:, ibox]: if child: mpoles[ibox] += mpoles[child] def eval_direct(self, leaf_boxes, neighbor_leaves_starts, neighbor_leaves_lists, src_weights): pot = self.potential_zeros() for itgt_leaf, itgt_box in enumerate(leaf_boxes): tgt_pslice = self._get_target_slice(itgt_box) src_sum = 0 start, end = neighbor_leaves_starts[itgt_leaf:itgt_leaf+2] for isrc_box in neighbor_leaves_lists[start:end]: src_pslice = self._get_source_slice(isrc_box) src_sum += np.sum(src_weights[src_pslice]) pot[tgt_pslice] = src_sum return pot def multipole_to_local(self, starts, lists, mpole_exps): local_exps = self.expansion_zeros() for itgt_box in xrange(self.tree.nboxes): start, end = starts[itgt_box:itgt_box+2] contrib = 0 #print itgt_box, "<-", lists[start:end] for isrc_box in lists[start:end]: contrib += mpole_exps[isrc_box] local_exps[itgt_box] += contrib return local_exps def eval_multipoles(self, leaf_boxes, sep_smaller_nonsiblings_starts, sep_smaller_nonsiblings_lists, mpole_exps): pot = self.potential_zeros() for itgt_leaf, itgt_box in enumerate(leaf_boxes): tgt_pslice = self._get_target_slice(itgt_box) contrib = 0 start, end = sep_smaller_nonsiblings_starts[itgt_leaf:itgt_leaf+2] for isrc_box in sep_smaller_nonsiblings_lists[start:end]: contrib += mpole_exps[isrc_box] pot[tgt_pslice] += contrib return pot def refine_locals(self, start_box, end_box, local_exps): for ibox in xrange(start_box, end_box): local_exps[ibox] += local_exps[self.tree.box_parent_ids[ibox]] return local_exps def eval_locals(self, leaf_boxes, local_exps): pot = self.potential_zeros() for ibox in leaf_boxes: tgt_pslice = self._get_target_slice(ibox) pot[tgt_pslice] += local_exps[ibox] return pot @pytools.test.mark_test.opencl def test_fmm_completeness(ctx_getter): """Tests whether the built FMM traversal structures and driver completely capture all interactions. """ ctx = ctx_getter() queue = cl.CommandQueue(ctx) for dims in [ 2, 3 ]: for nsources, ntargets in [ (10**6, None), (10**5, 3 * 10**5), ]: dtype = np.float64 sources = make_particle_array(queue, nsources, dims, dtype, seed=15) if ntargets is None: # This says "same as sources" to the tree builder. targets = None else: targets = make_particle_array( queue, ntargets, dims, dtype, seed=18) from boxtree import TreeBuilder tb = TreeBuilder(ctx) tree = tb(queue, sources, targets=targets, max_particles_in_box=30, debug=True) print "tree built" from boxtree.traversal import FMMTraversalBuilder tg = FMMTraversalBuilder(ctx) trav = tg(queue, tree).get() print "traversal built" weights = np.random.randn(nsources) #weights = np.ones(nparticles) weights_sum = np.sum(weights) from boxtree.fmm import drive_fmm wrangler = ConstantOneExpansionWrangler(trav.tree) if ntargets is None: # This check only works for targets == sources. assert (wrangler.reorder_potentials( wrangler.reorder_src_weights(weights)) == weights).all() pot = drive_fmm(trav, wrangler, weights) # {{{ build, evaluate matrix (and identify missing interactions) if 0: mat = np.zeros((ntargets, nsources), dtype) from pytools import ProgressBar pb = ProgressBar("matrix", nsources) for i in xrange(nsources): unit_vec = np.zeros(nsources, dtype=dtype) unit_vec[i] = 1 mat[:,i] = drive_fmm(trav, wrangler, unit_vec) pb.progress() pb.finished() missing_tgts, missing_srcs = np.where(mat == 0) if len(missing_tgts): 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() for tgt, src in zip(missing_tgts, missing_srcs): pt.plot( trav.tree.particles[0][tgt], trav.tree.particles[1][tgt], "ro") pt.plot( trav.tree.particles[0][src], trav.tree.particles[1][src], "go") pt.show() #pt.spy(mat) #pt.show() # }}} assert la.norm((pot - weights_sum) / nsources) < 1e-8 # }}} # {{{ test Helmholtz fmm with pyfmmlib @pytools.test.mark_test.opencl def test_pyfmmlib_fmm(ctx_getter): from pytest import importorskip importorskip("pyfmmlib") ctx = ctx_getter() queue = cl.CommandQueue(ctx) nsources = 10**3 ntargets = 10**3 dims = 2 dtype = np.float64 helmholtz_k = 2 sources = make_particle_array(queue, nsources, dims, dtype, seed=15) targets = ( make_particle_array(queue, ntargets, dims, dtype, seed=18) + np.array([5, 0])) sources_host = particle_array_to_host(sources) targets_host = particle_array_to_host(targets) from boxtree import TreeBuilder tb = TreeBuilder(ctx) tree = tb(queue, sources, targets=targets, max_particles_in_box=30, debug=True) print "tree built" from boxtree.traversal import FMMTraversalBuilder tg = FMMTraversalBuilder(ctx) trav = tg(queue, tree).get() print "traversal built" weights = np.random.randn(nsources) #weights = np.ones(nsources) from pyfmmlib import hpotgrad2dall_vec ref_pot, _, _ = hpotgrad2dall_vec(ifgrad=False, ifhess=False, sources=sources_host.T, charge=weights, targets=targets_host.T, zk=helmholtz_k) from boxtree.pyfmmlib_integration import Helmholtz2DExpansionWrangler wrangler = Helmholtz2DExpansionWrangler(trav.tree, helmholtz_k, nterms=15) from boxtree.fmm import drive_fmm pot = drive_fmm(trav, wrangler, weights) rel_err = la.norm(pot - ref_pot) / la.norm(ref_pot) print rel_err #assert < 1e-8 #assert la.norm((pot - weights_sum) / nparticles) < 1e-8 # }}} # {{{ geometry query test def test_geometry_query(ctx_getter, do_plot=False): ctx = ctx_getter() queue = cl.CommandQueue(ctx) dims = 2 nparticles = 10**5 dtype = np.float64 particles = make_particle_array(queue, nparticles, dims, dtype) if do_plot: import matplotlib.pyplot as pt pt.plot(particles[0].get(), particles[1].get(), "x") from boxtree 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 nballs = 10**4 ball_centers = make_particle_array(queue, nballs, dims, dtype) ball_radii = cl.array.empty(queue, nballs, dtype).fill(0.1) from boxtree.geo_lookup import LeavesToBallsLookupBuilder lblb = LeavesToBallsLookupBuilder(ctx) lbl = lblb(queue, tree, ball_centers, ball_radii) # get data to host for test tree = tree.get() lbl = lbl.get() ball_centers = np.array([x.get() for x in ball_centers]).T ball_radii = ball_radii.get() from boxtree import box_flags_enum for ibox in xrange(tree.nboxes): # We only want leaves here. if tree.box_flags[ibox] & box_flags_enum.HAS_CHILDREN: continue box_center = tree.box_centers[:, ibox] ext_l, ext_h = tree.get_box_extent(ibox) box_rad = 0.5*(ext_h-ext_l)[0] linf_circle_dists = np.max(np.abs(ball_centers-box_center), axis=-1) near_circles, = np.where(linf_circle_dists - ball_radii < box_rad) start, end = lbl.balls_near_box_starts[ibox:ibox+2] #print sorted(lbl.balls_near_box_lists[start:end]) #print sorted(near_circles) assert sorted(lbl.balls_near_box_lists[start:end]) == sorted(near_circles) # }}} # {{{ visualization helper (not a test) 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() print "building..." tree = tb(queue, particles, max_particles_in_box=30, debug=True) print "done" 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.leaf_boxes) elif 0: # well-separated siblings (list 2) draw_some_box_lists( trav.sep_siblings_starts, trav.sep_siblings_lists) elif 1: # separated smaller non-siblings (list 3) draw_some_box_lists( trav.sep_smaller_nonsiblings_starts, trav.sep_smaller_nonsiblings_lists, key_to_box=trav.leaf_boxes) elif 1: # separated bigger non-siblings (list 4) draw_some_box_lists( trav.sep_bigger_nonsiblings_starts, trav.sep_bigger_nonsiblings_lists) import matplotlib.pyplot as pt pt.show() # }}} # You can test individual routines by typing # $ python test_kernels.py 'test_p2p(cl.create_some_context)' if __name__ == "__main__": import sys if len(sys.argv) > 1: exec(sys.argv[1]) else: from py.test.cmdline import main main([__file__]) # vim: fdm=marker