def test_bitmap_no_collisions():
# Test init for slabs of points in x
left_edge = np.array([0.0, 0.0, 0.0])
right_edge = np.array([1.0, 1.0, 1.0])
periodicity = np.array([0, 0, 0], "bool")
npart = 100
nfiles = 2
file_hash = 12345
order1 = 2
order2 = 2
reg = ParticleBitmap(
left_edge, right_edge, periodicity, file_hash, nfiles, order1, order2
)
# Coarse index
posgen = yield_fake_decomp("sliced", npart, nfiles, left_edge, right_edge)
max_npart = 0
for i, (pos, hsml) in enumerate(posgen):
reg._coarse_index_data_file(pos, hsml, i)
max_npart = max(max_npart, pos.shape[0])
reg._set_coarse_index_data_file(i)
assert_equal(reg.count_total(i), np.sum(reg.masks[:, i]))
mask = reg.masks.sum(axis=1).astype("uint8")
ncoll = np.sum(mask > 1)
nc, nm = reg.find_collisions_coarse()
assert_equal(nc, 0, "%d coarse collisions" % nc)
assert_equal(ncoll, nc, "%d in mask, %d in bitmap" % (ncoll, nc))
# Refined index
sub_mi1 = np.zeros(max_npart, "uint64")
sub_mi2 = np.zeros(max_npart, "uint64")
posgen = yield_fake_decomp("sliced", npart, nfiles, left_edge, right_edge)
coll = None
for i, (pos, hsml) in enumerate(posgen):
nsub_mi, coll = reg._refined_index_data_file(
coll,
pos,
hsml,
mask,
sub_mi1,
sub_mi2,
i,
0,
count_threshold=1,
mask_threshold=2,
)
reg.bitmasks.append(i, coll)
assert_equal(reg.count_refined(i), 0)
nr, nm = reg.find_collisions_refined()
assert_equal(nr, 0, "%d collisions" % nr)
def test_bitmap_collisions():
# Test init for slabs of points in x
left_edge = np.array([0.0, 0.0, 0.0])
right_edge = np.array([1.0, 1.0, 1.0])
periodicity = np.array([0, 0, 0], "bool")
nfiles = 2
file_hash = 12345
order1 = 2
order2 = 2
reg = ParticleBitmap(
left_edge, right_edge, periodicity, file_hash, nfiles, order1, order2
)
# Use same points for all files to force collisions
pos = cell_centers(order1 + order2, left_edge, right_edge)
hsml = None
# Coarse index
max_npart = 0
for i in range(nfiles):
reg._coarse_index_data_file(pos, hsml, i)
max_npart = max(max_npart, pos.shape[0])
reg._set_coarse_index_data_file(i)
assert_equal(reg.count_total(i), np.sum(reg.masks[:, i]))
mask = reg.masks.sum(axis=1).astype("uint8")
ncoll = np.sum(mask > 1)
nc, nm = reg.find_collisions_coarse()
assert_equal(ncoll, nc, "%d in mask, %d in bitmap" % (ncoll, nc))
assert_equal(nc, 2 ** (3 * order1), "%d coarse collisions" % nc)
# Refined index
sub_mi1 = np.zeros(max_npart, "uint64")
sub_mi2 = np.zeros(max_npart, "uint64")
for i in range(nfiles):
nsub_mi, coll = reg._refined_index_data_file(
None,
pos,
hsml,
mask,
sub_mi1,
sub_mi2,
i,
0,
count_threshold=1,
mask_threshold=2,
)
reg.bitmasks.append(i, coll)
assert_equal(reg.count_refined(i), ncoll)
nr, nm = reg.find_collisions_refined()
assert_equal(nr, 2 ** (3 * (order1 + order2)), "%d collisions" % nr)
class ParticleIndex(Index):
"""The Index subclass for particle datasets"""
def __init__(self, ds, dataset_type):
self.dataset_type = dataset_type
self.dataset = weakref.proxy(ds)
self.float_type = np.float64
super(ParticleIndex, self).__init__(ds, dataset_type)
self._initialize_index()
def _setup_geometry(self):
self.regions = None
def get_smallest_dx(self):
"""
Returns (in code units) the smallest cell size in the simulation.
"""
return self.ds.arr(0, "code_length")
def _get_particle_type_counts(self):
result = collections.defaultdict(lambda: 0)
for df in self.data_files:
for k in df.total_particles.keys():
result[k] += df.total_particles[k]
return dict(result)
def convert(self, unit):
return self.dataset.conversion_factors[unit]
_data_files = None
@property
def data_files(self):
if self._data_files is not None:
return self._data_files
self._setup_filenames()
return self._data_files
@data_files.setter
def data_files(self, value):
self._data_files = value
_total_particles = None
@property
def total_particles(self):
if self._total_particles is not None:
return self._total_particles
self._total_particles = sum(
sum(d.total_particles.values()) for d in self.data_files
)
return self._total_particles
def _setup_filenames(self):
template = self.dataset.filename_template
ndoms = self.dataset.file_count
cls = self.dataset._file_class
self.data_files = []
fi = 0
for i in range(int(ndoms)):
start = 0
end = start + CHUNKSIZE
while True:
df = cls(self.dataset, self.io, template % {"num": i}, fi, (start, end))
if max(df.total_particles.values()) == 0:
break
fi += 1
self.data_files.append(df)
start = end
end += CHUNKSIZE
def _initialize_index(self):
ds = self.dataset
only_on_root(
mylog.info,
"Allocating for %0.3e particles",
self.total_particles,
global_rootonly=True,
)
# if we have not yet set domain_left_edge and domain_right_edge then do
# an I/O pass over the particle coordinates to determine a bounding box
if self.ds.domain_left_edge is None:
min_ppos = np.empty(3, dtype="float64")
min_ppos[:] = np.nan
max_ppos = np.empty(3, dtype="float64")
max_ppos[:] = np.nan
only_on_root(
mylog.info,
"Bounding box cannot be inferred from metadata, reading "
"particle positions to infer bounding box",
)
for df in self.data_files:
for _, ppos in self.io._yield_coordinates(df):
min_ppos = np.nanmin(np.vstack([min_ppos, ppos]), axis=0)
max_ppos = np.nanmax(np.vstack([max_ppos, ppos]), axis=0)
only_on_root(
mylog.info,
"Load this dataset with bounding_box=[%s, %s] to avoid I/O "
"overhead from inferring bounding_box." % (min_ppos, max_ppos),
)
ds.domain_left_edge = ds.arr(1.05 * min_ppos, "code_length")
ds.domain_right_edge = ds.arr(1.05 * max_ppos, "code_length")
ds.domain_width = ds.domain_right_edge - ds.domain_left_edge
# use a trivial morton index for datasets containing a single chunk
if len(self.data_files) == 1:
order1 = 1
order2 = 1
else:
order1 = ds.index_order[0]
order2 = ds.index_order[1]
if order1 == 1 and order2 == 1:
dont_cache = True
else:
dont_cache = False
# If we have applied a bounding box then we can't cache the
# ParticleBitmap because it is doman dependent
if getattr(ds, "_domain_override", False):
dont_cache = True
if not hasattr(self.ds, "_file_hash"):
self.ds._file_hash = self._generate_hash()
self.regions = ParticleBitmap(
ds.domain_left_edge,
ds.domain_right_edge,
ds.periodicity,
self.ds._file_hash,
len(self.data_files),
index_order1=order1,
index_order2=order2,
)
# Load Morton index from file if provided
if getattr(ds, "index_filename", None) is None:
fname = ds.parameter_filename + ".index{}_{}.ewah".format(
self.regions.index_order1, self.regions.index_order2
)
else:
fname = ds.index_filename
dont_load = dont_cache and not hasattr(ds, "index_filename")
try:
if dont_load:
raise OSError
rflag = self.regions.load_bitmasks(fname)
rflag = self.regions.check_bitmasks()
self._initialize_frontend_specific()
if rflag == 0:
raise OSError
except (OSError, struct.error):
self.regions.reset_bitmasks()
self._initialize_coarse_index()
self._initialize_refined_index()
wdir = os.path.dirname(fname)
if not dont_cache and os.access(wdir, os.W_OK):
# Sometimes os mis-reports whether a directory is writable,
# So pass if writing the bitmask file fails.
try:
self.regions.save_bitmasks(fname)
except OSError:
pass
rflag = self.regions.check_bitmasks()
def _initialize_coarse_index(self):
pb = get_pbar("Initializing coarse index ", len(self.data_files))
for i, data_file in enumerate(self.data_files):
pb.update(i)
for ptype, pos in self.io._yield_coordinates(data_file):
ds = self.ds
if hasattr(ds, "_sph_ptypes") and ptype == ds._sph_ptypes[0]:
hsml = self.io._get_smoothing_length(
data_file, pos.dtype, pos.shape
)
else:
hsml = None
self.regions._coarse_index_data_file(pos, hsml, data_file.file_id)
self.regions._set_coarse_index_data_file(data_file.file_id)
pb.finish()
self.regions.find_collisions_coarse()
def _initialize_refined_index(self):
mask = self.regions.masks.sum(axis=1).astype("uint8")
max_npart = max(sum(d.total_particles.values()) for d in self.data_files) * 28
sub_mi1 = np.zeros(max_npart, "uint64")
sub_mi2 = np.zeros(max_npart, "uint64")
pb = get_pbar("Initializing refined index", len(self.data_files))
mask_threshold = getattr(self, "_index_mask_threshold", 2)
count_threshold = getattr(self, "_index_count_threshold", 256)
mylog.debug(
"Using estimated thresholds of %s and %s for refinement",
mask_threshold,
count_threshold,
)
total_refined = 0
total_coarse_refined = (
(mask >= 2) & (self.regions.particle_counts > count_threshold)
).sum()
mylog.debug(
"This should produce roughly %s zones, for %s of the domain",
total_coarse_refined,
100 * total_coarse_refined / mask.size,
)
for i, data_file in enumerate(self.data_files):
coll = None
pb.update(i)
nsub_mi = 0
for ptype, pos in self.io._yield_coordinates(data_file):
if pos.size == 0:
continue
if hasattr(self.ds, "_sph_ptypes") and ptype == self.ds._sph_ptypes[0]:
hsml = self.io._get_smoothing_length(
data_file, pos.dtype, pos.shape
)
else:
hsml = None
nsub_mi, coll = self.regions._refined_index_data_file(
coll,
pos,
hsml,
mask,
sub_mi1,
sub_mi2,
data_file.file_id,
nsub_mi,
count_threshold=count_threshold,
mask_threshold=mask_threshold,
)
total_refined += nsub_mi
self.regions.bitmasks.append(data_file.file_id, coll)
pb.finish()
self.regions.find_collisions_refined()
def _detect_output_fields(self):
# TODO: Add additional fields
dsl = []
units = {}
pcounts = self._get_particle_type_counts()
field_cache = {}
for dom in self.data_files:
if dom.filename in field_cache:
fl, _units = field_cache[dom.filename]
else:
fl, _units = self.io._identify_fields(dom)
field_cache[dom.filename] = fl, _units
units.update(_units)
dom._calculate_offsets(fl, pcounts)
for f in fl:
if f not in dsl:
dsl.append(f)
self.field_list = dsl
ds = self.dataset
ds.particle_types = tuple(set(pt for pt, ds in dsl))
# This is an attribute that means these particle types *actually*
# exist. As in, they are real, in the dataset.
ds.field_units.update(units)
ds.particle_types_raw = ds.particle_types
def _identify_base_chunk(self, dobj):
# Must check that chunk_info contains the right number of ghost zones
if getattr(dobj, "_chunk_info", None) is None:
if isinstance(dobj, ParticleContainer):
dobj._chunk_info = [dobj]
else:
# TODO: only return files
if getattr(dobj.selector, "is_all_data", False):
nfiles = self.regions.nfiles
dfi = np.arange(nfiles)
else:
dfi, file_masks, addfi = self.regions.identify_file_masks(
dobj.selector
)
nfiles = len(file_masks)
dobj._chunk_info = [None for _ in range(nfiles)]
for i in range(nfiles):
domain_id = i + 1
dobj._chunk_info[i] = ParticleContainer(
dobj, [self.data_files[dfi[i]]], domain_id=domain_id
)
# NOTE: One fun thing about the way IO works is that it
# consolidates things quite nicely. So we should feel free to
# create as many objects as part of the chunk as we want, since
# it'll take the set() of them. So if we break stuff up like
# this here, we end up in a situation where we have the ability
# to break things down further later on for buffer zones and the
# like.
(dobj._current_chunk,) = self._chunk_all(dobj)
def _chunk_all(self, dobj):
oobjs = getattr(dobj._current_chunk, "objs", dobj._chunk_info)
yield YTDataChunk(dobj, "all", oobjs, None)
def _chunk_spatial(self, dobj, ngz, sort=None, preload_fields=None):
sobjs = getattr(dobj._current_chunk, "objs", dobj._chunk_info)
for og in sobjs:
with og._expand_data_files():
if ngz > 0:
g = og.retrieve_ghost_zones(ngz, [], smoothed=True)
else:
g = og
yield YTDataChunk(dobj, "spatial", [g])
def _chunk_io(self, dobj, cache=True, local_only=False):
oobjs = getattr(dobj._current_chunk, "objs", dobj._chunk_info)
for container in oobjs:
yield YTDataChunk(dobj, "io", [container], None, cache=cache)
def _generate_hash(self):
# Generate an FNV hash by creating a byte array containing the
# modification time of as well as the first and last 1 MB of data in
# every output file
ret = bytearray()
for pfile in self.data_files:
# only look at "real" files, not "fake" files generated by the
# chunking system
if pfile.start not in (0, None):
continue
try:
mtime = os.path.getmtime(pfile.filename)
except OSError as e:
if e.errno == errno.ENOENT:
# this is an in-memory file so we return with a dummy
# value
return -1
else:
raise
ret.extend(str(mtime).encode("utf-8"))
size = os.path.getsize(pfile.filename)
if size > 1e6:
size = int(1e6)
with open(pfile.filename, "rb") as fh:
# read in first and last 1 MB of data
data = fh.read(size)
fh.seek(-size, os.SEEK_END)
data = fh.read(size)
ret.extend(data)
return fnv_hash(ret)
def _initialize_frontend_specific(self):
"""This is for frontend-specific initialization code
If there are frontend-specific things that need to be set while
creating the index, this function forces these operations to happen
in cases where we are reloading the index from a sidecar file.
"""
pass
def FakeBitmap(
npart,
nfiles,
order1,
order2,
left_edge=None,
right_edge=None,
periodicity=None,
decomp="sliced",
buff=0.1,
distrib="uniform",
fname=None,
):
if left_edge is None:
left_edge = np.array([0.0, 0.0, 0.0])
if right_edge is None:
right_edge = np.array([1.0, 1.0, 1.0])
if periodicity is None:
periodicity = np.array([0, 0, 0], "bool")
reg = ParticleBitmap(
left_edge, right_edge, periodicity, 12345, nfiles, order1, order2
)
# Load from file if it exists
if isinstance(fname, str) and os.path.isfile(fname):
reg.load_bitmasks(fname)
else:
# Create positions for each file
posgen = yield_fake_decomp(
decomp, npart, nfiles, left_edge, right_edge, buff=buff, distrib=distrib
)
# Coarse index
max_npart = 0
for i, (pos, hsml) in enumerate(posgen):
max_npart = max(max_npart, pos.shape[0])
reg._coarse_index_data_file(pos, hsml, i)
reg._set_coarse_index_data_file(i)
if i != (nfiles - 1):
raise RuntimeError(
f"There are positions for {i + 1} files, but there should be {nfiles}."
)
# Refined index
mask = reg.masks.sum(axis=1).astype("uint8")
sub_mi1 = np.zeros(max_npart, "uint64")
sub_mi2 = np.zeros(max_npart, "uint64")
posgen = yield_fake_decomp(
decomp, npart, nfiles, left_edge, right_edge, buff=buff, distrib=distrib
)
coll = None
for i, (pos, hsml) in enumerate(posgen):
nsub_mi, coll = reg._refined_index_data_file(
coll,
pos,
hsml,
mask,
sub_mi1,
sub_mi2,
i,
0,
count_threshold=1,
mask_threshold=2,
)
reg.bitmasks.append(i, coll)
# Save if file name provided
if isinstance(fname, str):
reg.save_bitmasks(fname)
return reg
