test_tree.py

from __future__ import division, absolute_import, print_function

__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 six
from six.moves import range

import numpy as np
import sys
import pytest
import logging

import pyopencl as cl
from pyopencl.tools import (  # noqa
        pytest_generate_tests_for_pyopencl as pytest_generate_tests)
from boxtree.tools import make_normal_particle_array

logger = logging.getLogger(__name__)


# {{{ bounding box test

@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("dims", [2, 3])
@pytest.mark.parametrize("nparticles", [9, 4096, 10**5])
def test_bounding_box(ctx_factory, dtype, dims, nparticles):
    logging.basicConfig(level=logging.INFO)

    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    from boxtree.tools import AXIS_NAMES
    from boxtree.bounding_box import BoundingBoxFinder

    bbf = BoundingBoxFinder(ctx)

    axis_names = AXIS_NAMES[:dims]

    logger.info("%s - %s %s" % (dtype, dims, nparticles))

    particles = make_normal_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, evt = bbf(particles, radii=None)
    bbox_cl = bbox_cl.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()

# }}}


# {{{ test basic (no source/target distinction) tree build

def run_build_test(builder, queue, dims, dtype, nparticles, do_plot,
        max_particles_in_box=None, max_leaf_refine_weight=None,
        refine_weights=None, **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)

    logger.info(75*"-")
    if max_particles_in_box is not None:
        logger.info("%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 six.iteritems(kwargs))))
    else:
        logger.info("%dD %s - %d particles - max leaf weight %d  - %s" % (
            dims, dtype.type.__name__, nparticles, max_leaf_refine_weight,
            " - ".join("%s: %s" % (k, v) for k, v in six.iteritems(kwargs))))
    logger.info(75*"-")

    particles = make_normal_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()

    tree, _ = builder(queue, particles,
                      max_particles_in_box=max_particles_in_box,
                      refine_weights=refine_weights,
                      max_leaf_refine_weight=max_leaf_refine_weight,
                      debug=True, **kwargs)
    tree = tree.get(queue=queue)

    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()

    if refine_weights is not None:
        refine_weights_reordered = refine_weights.get()[tree.user_source_ids]

    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 range(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.HAS_OWN_SRCNTGTS):
            continue

        extent_low, extent_high = tree.get_box_extent(ibox)

        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_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])

        box_particles = sorted_particles[:,
                start:start+tree.box_source_counts_cumul[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)

        if not (tree.box_flags[ibox] & bfe.HAS_CHILDREN):
            # Check that leaf particle density is as promised.
            nparticles_in_box = tree.box_source_counts_cumul[ibox]
            if max_particles_in_box is not None:
                if nparticles_in_box > max_particles_in_box:
                    print("too many particles ({0} > {1}); box {2}".format(
                        nparticles_in_box, max_particles_in_box, ibox))
                    all_good_here = False
            else:
                assert refine_weights is not None
                box_weight = np.sum(
                    refine_weights_reordered[start:start+nparticles_in_box])
                if box_weight > max_leaf_refine_weight:
                    print("refine weight exceeded ({0} > {1}); box {2}".format(
                        box_weight, max_leaf_refine_weight, ibox))
                    all_good_here = False

        all_good_so_far = all_good_so_far and all_good_here

    if do_plot: