def _initialize_particle_handler(self):
self._setup_data_io()
self._setup_filenames()
index_ptype = self.index_ptype
if index_ptype == "all":
self.total_particles = sum(
sum(d.total_particles.values()) for d in self.data_files)
else:
self.total_particles = sum(d.total_particles[index_ptype]
for d in self.data_files)
ds = self.dataset
self.oct_handler = ParticleOctreeContainer(
[1, 1, 1],
ds.domain_left_edge,
ds.domain_right_edge,
over_refine=ds.over_refine_factor)
self.oct_handler.n_ref = ds.n_ref
only_on_root(
mylog.info, "Allocating for %0.3e particles "
"(index particle type '%s')", self.total_particles, index_ptype)
# No more than 256^3 in the region finder.
N = min(len(self.data_files), 256)
self.regions = ParticleRegions(ds.domain_left_edge,
ds.domain_right_edge, [N, N, N],
len(self.data_files))
self._initialize_indices()
self.oct_handler.finalize()
self.max_level = self.oct_handler.max_level
self.dataset.max_level = self.max_level
tot = sum(self.oct_handler.recursively_count().values())
only_on_root(mylog.info, "Identified %0.3e octs", tot)
def test_particle_regions():
np.random.seed(int(0x4d3d3d3))
# We are going to test having 31, 127, 128 and 257 data files.
for nfiles in [2, 31, 127, 128, 129]:
# Now we create particles
# Note: we set N to nfiles here for testing purposes. Inside the code
# we set it to min(N, 256)
N = nfiles
reg = ParticleRegions([0.0, 0.0, 0.0, 0.0], [nfiles, nfiles, nfiles],
[N, N, N], nfiles)
Y, Z = np.mgrid[0.1:nfiles - 0.1:nfiles * 1j,
0.1:nfiles - 0.1:nfiles * 1j]
X = 0.5 * np.ones(Y.shape, dtype="float64")
pos = np.array([X.ravel(), Y.ravel(), Z.ravel()],
dtype="float64").transpose()
for i in range(nfiles):
reg.add_data_file(pos, i)
pos[:, 0] += 1.0
pos[:, 0] = 0.5
fr = FakeRegion(nfiles)
for i in range(nfiles):
fr.set_edges(i)
selector = RegionSelector(fr)
df = reg.identify_data_files(selector)
yield assert_equal, len(df), 1
yield assert_equal, df[0], i
pos[:, 0] += 1.0
for mask in reg.masks:
maxs = np.unique(mask.max(axis=-1).max(axis=-1))
mins = np.unique(mask.min(axis=-1).min(axis=-1))
yield assert_equal, maxs, mins
yield assert_equal, maxs, np.unique(mask)
def _initialize_particle_handler(self):
self._setup_data_io()
template = self.dataset.filename_template
ndoms = self.dataset.file_count
cls = self.dataset._file_class
self.data_files = [
cls(self.dataset, self.io, template % {'num': i}, i)
for i in range(ndoms)
]
self.total_particles = sum(
sum(d.total_particles.values()) for d in self.data_files)
ds = self.dataset
self.oct_handler = ParticleOctreeContainer(
[1, 1, 1],
ds.domain_left_edge,
ds.domain_right_edge,
over_refine=ds.over_refine_factor)
self.oct_handler.n_ref = ds.n_ref
mylog.info("Allocating for %0.3e particles", self.total_particles)
# No more than 256^3 in the region finder.
N = min(len(self.data_files), 256)
self.regions = ParticleRegions(ds.domain_left_edge,
ds.domain_right_edge, [N, N, N],
len(self.data_files))
self._initialize_indices()
self.oct_handler.finalize()
self.max_level = self.oct_handler.max_level
tot = sum(self.oct_handler.recursively_count().values())
mylog.info("Identified %0.3e octs", tot)
