def get_merge_candidates(table_id,
save_dir=f"{HOME}/benchmarks/",
n_threads=1):
cg = chunkedgraph.ChunkedGraph(table_id)
bounds = np.array(cg.cv.bounds.to_list()).reshape(2, -1).T
bounds -= bounds[:, 0:1]
chunk_id_bounds = np.ceil((bounds / cg.chunk_size[:, None])).astype(np.int)
chunk_coord_gen = itertools.product(*[range(*r) for r in chunk_id_bounds])
chunk_coords = np.array(list(chunk_coord_gen), dtype=np.int)
order = np.arange(len(chunk_coords))
np.random.shuffle(order)
n_blocks = np.min([len(order), n_threads * 3])
blocks = np.array_split(order, n_blocks)
cg_serialized_info = cg.get_serialized_info()
if n_threads > 1:
del cg_serialized_info["credentials"]
multi_args = []
for block in blocks:
multi_args.append([cg_serialized_info, chunk_coords[block]])
if n_threads == 1:
results = mu.multiprocess_func(_get_merge_candidates,
multi_args,
n_threads=n_threads,
verbose=False,
debug=n_threads == 1)
else:
results = mu.multisubprocess_func(_get_merge_candidates,
multi_args,
n_threads=n_threads)
merge_edges = []
merge_edge_weights = []
for result in results:
merge_edges.extend(result[0])
merge_edge_weights.extend(result[1])
save_folder = f"{save_dir}/{table_id}/"
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with h5py.File(f"{save_folder}/merge_edge_stats.h5", "w") as f:
f.create_dataset("merge_edges", data=merge_edges, compression="gzip")
f.create_dataset("merge_edge_weights",
data=merge_edge_weights,
compression="gzip")
def mesh_lvl2_previews(cg,
lvl2_node_ids,
cv_path=None,
cv_mesh_dir=None,
mip=2,
simplification_factor=999999,
max_err=40,
parallel_download=8,
verbose=True,
cache_control="no-cache",
n_threads=1):
serialized_cg_info = cg.get_serialized_info()
del serialized_cg_info["credentials"]
if not isinstance(lvl2_node_ids, dict):
lvl2_node_ids = dict(zip(lvl2_node_ids, [None] * len(lvl2_node_ids)))
mesh_dir = cv_mesh_dir or cg._mesh_dir
multi_args = []
for lvl2_node_id in lvl2_node_ids.keys():
multi_args.append([
serialized_cg_info, lvl2_node_id, lvl2_node_ids[lvl2_node_id],
cv_path, mesh_dir, mip, simplification_factor, max_err,
parallel_download, verbose, cache_control
])
# Run parallelizing
if n_threads == 1:
mu.multiprocess_func(_mesh_lvl2_previews_threads,
multi_args,
n_threads=n_threads,
verbose=False,
debug=n_threads == 1)
else:
mu.multisubprocess_func(_mesh_lvl2_previews_threads,
multi_args,
n_threads=n_threads)
def rewrite_segmentation(dataset_name, n_threads=64, n_units_per_thread=None):
if dataset_name == "pinky":
cv_url = "gs://nkem/pinky40_v11/mst_trimmed_sem_remap/region_graph/"
from_url = "gs://neuroglancer/pinky40_v11/watershed/"
to_url = "gs://neuroglancer/svenmd/pinky40_v11/watershed/"
elif dataset_name == "basil":
cv_url = "gs://nkem/basil_4k_oldnet/region_graph/"
from_url = "gs://neuroglancer/ranl/basil_4k_oldnet/ws/"
to_url = "gs://neuroglancer/svenmd/basil_4k_oldnet_cg/watershed/"
else:
raise Exception("Dataset unknown")
file_paths = np.sort(
glob.glob(
creator_utils.dir_from_layer_name(
creator_utils.layer_name_from_cv_url(cv_url)) + "/*rg2cg*"))
if n_units_per_thread is None:
file_path_blocks = np.array_split(file_paths, n_threads * 3)
else:
n_blocks = int(np.ceil(len(file_paths) / n_units_per_thread))
file_path_blocks = np.array_split(file_paths, n_blocks)
multi_args = []
for fp_block in file_path_blocks:
multi_args.append([fp_block, from_url, to_url])
# Run parallelizing
if n_threads == 1:
mu.multiprocess_func(_rewrite_segmentation_thread,
multi_args,
n_threads=n_threads,
verbose=True,
debug=n_threads == 1)
else:
mu.multisubprocess_func(_rewrite_segmentation_thread,
multi_args,
n_threads=n_threads)
def count_nodes_and_edges(table_id, n_threads=1):
cg = chunkedgraph.ChunkedGraph(table_id)
bounds = np.array(cg.cv.bounds.to_list()).reshape(2, -1).T
bounds -= bounds[:, 0:1]
chunk_id_bounds = np.ceil((bounds / cg.chunk_size[:, None])).astype(np.int)
chunk_coord_gen = itertools.product(*[range(*r) for r in chunk_id_bounds])
chunk_coords = np.array(list(chunk_coord_gen), dtype=np.int)
order = np.arange(len(chunk_coords))
np.random.shuffle(order)
n_blocks = np.min([len(order), n_threads * 3])
blocks = np.array_split(order, n_blocks)
cg_serialized_info = cg.get_serialized_info()
if n_threads > 1:
del cg_serialized_info["credentials"]
multi_args = []
for block in blocks:
multi_args.append([cg_serialized_info, chunk_coords[block]])
if n_threads == 1:
results = mu.multiprocess_func(_count_nodes_and_edges,
multi_args,
n_threads=n_threads,
verbose=False,
debug=n_threads == 1)
else:
results = mu.multisubprocess_func(_count_nodes_and_edges,
multi_args,
n_threads=n_threads)
n_edges_per_chunk = []
n_nodes_per_chunk = []
for result in results:
n_nodes_per_chunk.extend(result[0])
n_edges_per_chunk.extend(result[1])
return n_nodes_per_chunk, n_edges_per_chunk
def get_latest_roots(cg,
time_stamp: Optional[datetime.datetime] = None,
n_threads: int = 1) -> Sequence[np.uint64]:
# Create filters: time and id range
max_seg_id = cg.get_max_seg_id(cg.root_chunk_id) + 1
if n_threads == 1:
n_blocks = 1
else:
n_blocks = int(np.min([n_threads * 3 + 1, max_seg_id]))
seg_id_blocks = np.linspace(1, max_seg_id, n_blocks + 1, dtype=np.uint64)
cg_serialized_info = cg.get_serialized_info()
if n_threads > 1:
del cg_serialized_info["credentials"]
multi_args = []
for i_id_block in range(0, len(seg_id_blocks) - 1):
multi_args.append([
seg_id_blocks[i_id_block], seg_id_blocks[i_id_block + 1],
cg_serialized_info, time_stamp
])
if n_threads == 1:
results = mu.multiprocess_func(_read_root_rows_thread,
multi_args,
n_threads=n_threads,
verbose=False,
debug=n_threads == 1)
else:
results = mu.multisubprocess_func(_read_root_rows_thread,
multi_args,
n_threads=n_threads)
root_ids = []
for result in results:
root_ids.extend(result)
return np.array(root_ids, dtype=np.uint64)
def all_angle_weighted_distances(ds, angles, weights, rep_inds, inds):
data = []
real_inds, slice_bnds = np.unique(rep_inds, return_index=True)
ind_map = []
for ii in range(len(real_inds) - 1):
row = slice(slice_bnds[ii], slice_bnds[ii + 1])
data.append((ds[row], angles[row], weights[row]))
ind_map.append(real_inds[ii])
row = slice(slice_bnds[-1], len(ds))
data.append((ds[row], angles[row], weights[row]))
ind_map.append(real_inds[-1])
rs = mu.multiprocess_func(_multi_angle_weighted_distance, data)
rs_out = np.nan * np.zeros(len(inds))
rs_out[np.array(ind_map)] = rs
return rs_out
def oriented_vector_cones(center_vectors,
num_points,
widest_angle=np.pi / 3,
normalize=False):
"""Produces all ray cones
"""
if normalize:
cv_norm = center_vectors / \
np.linalg.norm(center_vector, axis=1)[:, np.newaxis]
else:
cv_norm = center_vectors
thetas = np.arccos(cv_norm[:, 2])
phis = np.arctan2(cv_norm[:, 1], cv_norm[:, 0])
vs_raw = unit_vector_sampler(num_points, widest_angle=widest_angle)
Rtranses = []
data = []
for phi, theta in zip(phis, thetas):
data.append((phi, theta, vs_raw))
vector_cones = mu.multiprocess_func(_rotated_cone, data)
return vector_cones
def write_flat_segmentation(cg,
dataset_name,
bounding_box=None,
block_factor=2,
n_threads=1,
mip=0):
""" Applies the mapping in the chunkedgraph to the supervoxels to create
a flattened segmentation
:param cg: chunkedgraph instance
:param dataset_name: str
:param bounding_box: np.array
:param block_factor: int
:param n_threads: int
:param mip: int
:return: bool
"""
if dataset_name == "pinky":
from_url = "gs://neuroglancer/svenmd/pinky40_v11/watershed/"
to_url = "gs://neuroglancer/svenmd/pinky40_v11/segmentation/"
elif dataset_name == "basil":
from_url = "gs://neuroglancer/svenmd/basil_4k_oldnet_cg/watershed/"
to_url = "gs://neuroglancer/svenmd/basil_4k_oldnet_cg/segmentation/"
else:
raise Exception("Dataset unknown")
from_cv = cloudvolume.CloudVolume(from_url, mip=mip)
dataset_bounding_box = np.array(from_cv.bounds.to_list())
block_bounding_box_cg = \
[np.floor(dataset_bounding_box[:3] / cg.chunk_size).astype(np.int),
np.ceil(dataset_bounding_box[3:] / cg.chunk_size).astype(np.int)]
if bounding_box is not None:
bounding_box_cg = \
[np.floor(bounding_box[0] / cg.chunk_size).astype(np.int),
np.ceil(bounding_box[1] / cg.chunk_size).astype(np.int)]
m = block_bounding_box_cg[0] < bounding_box_cg[0]
block_bounding_box_cg[0][m] = bounding_box_cg[0][m]
m = block_bounding_box_cg[1] > bounding_box_cg[1]
block_bounding_box_cg[1][m] = bounding_box_cg[1][m]
block_iter = itertools.product(
np.arange(block_bounding_box_cg[0][0], block_bounding_box_cg[1][0],
block_factor),
np.arange(block_bounding_box_cg[0][1], block_bounding_box_cg[1][1],
block_factor),
np.arange(block_bounding_box_cg[0][2], block_bounding_box_cg[1][2],
block_factor))
blocks = np.array(list(block_iter))
cg_info = cg.get_serialized_info()
multi_args = []
for start_block in blocks:
end_block = start_block + block_factor
m = end_block > block_bounding_box_cg[1]
end_block[m] = block_bounding_box_cg[1][m]
multi_args.append(
[cg_info, start_block, end_block, from_url, to_url, mip])
# Run parallelizing
if n_threads == 1:
mu.multiprocess_func(_write_flat_segmentation_thread,
multi_args,
n_threads=n_threads,
verbose=True,
debug=n_threads == 1)
else:
mu.multisubprocess_func(_write_flat_segmentation_thread,
multi_args,
n_threads=n_threads)
def generate_thumbnails(
filename,
batch_name,
min_height,
width,
title_column,
abstract_column,
author_column_contains,
twitter_column_contains,
save_author_string,
thumbnail_directory,
use_oxford,
n_threads,
):
data = pd.read_csv(filename)
author_columns = []
for c in data.columns:
if re.match(author_column_contains, c) is not None:
author_columns.append(c)
twitter_columns = []
if twitter_column_contains is not False:
for c in data.columns:
if re.match(twitter_column_contains, c) is not None:
twitter_columns.append(c)
author_list = []
author_list_with_handles = []
for ii, row in data[author_columns].iterrows():
auths = row[~pd.isna(row)].tolist()
author_list.append(make_author_string(auths, use_oxford=use_oxford))
try:
twit_row = data.iloc[ii][twitter_columns]
handles = twit_row[~pd.isna(row).values].tolist()
author_list_with_handles.append(
make_author_string(auths,
twitter_list=handles,
use_oxford=use_oxford))
except:
print("Twitter handles failed!")
author_list_with_handles = author_list
title_list = data[title_column].tolist()
abstract_list = data[abstract_column].tolist()
if not os.path.exists(thumbnail_directory):
os.mkdir(thumbnail_directory)
if batch_name is None:
batch_dir = f"batch_{str(datetime.date.today()).replace('-', '_')}"
else:
batch_dir = batch_name
if not os.path.exists(f"{thumbnail_directory}/{batch_dir}"):
os.mkdir(f"{thumbnail_directory}/{batch_dir}")
if n_threads > 1:
print(f"Making all images with {n_threads} processes...")
all_args = []
t0 = time.time()
for title, authors, abstract in zip(title_list, author_list,
abstract_list):
all_args.append([
title,
authors,
abstract,
width,
min_height,
thumbnail_directory,
batch_dir,
])
mu.multiprocess_func(_save_data_multithreaded,
all_args,
n_threads=n_threads)
print(f"\tImages produced in {time.time()-t0:.2f} s.")
else:
for title, authors, abstract in tqdm.tqdm(zip(title_list, author_list,
abstract_list),
total=len(title_list)):
img = thumbnail_image(title,
authors,
abstract,
image_width=width,
min_height=min_height)
fname = simple_filename(title,
f"{thumbnail_directory}/{batch_dir}",
max_words=8)
img.save(
fname,
dpi=(150, 150),
)
if save_author_string:
data["authors_with_handles"] = author_list_with_handles
pure_filename = os.path.split(filename)[-1]
fn = pure_filename.split(".")
out_name = f"{thumbnail_directory}/{batch_dir}/{fn[-2].replace('/','')}_with_tweets.csv"
data.to_csv(out_name)
print(f"Data saved to {out_name}")
return
def get_root_ids_and_sv_chunks(table_id,
save_dir=f"{HOME}/benchmarks/",
n_threads=1):
cg = chunkedgraph.ChunkedGraph(table_id)
save_folder = f"{save_dir}/{table_id}/"
if not os.path.exists(save_folder):
os.makedirs(save_folder)
if not os.path.exists(f"{save_folder}/root_ids.h5"):
root_ids = chunkedgraph_comp.get_latest_roots(cg, n_threads=n_threads)
with h5py.File(f"{save_folder}/root_ids.h5", "w") as f:
f.create_dataset("root_ids", data=root_ids)
else:
with h5py.File(f"{save_folder}/root_ids.h5", "r") as f:
root_ids = f["root_ids"].value
cg_serialized_info = cg.get_serialized_info()
if n_threads > 1:
del cg_serialized_info["credentials"]
order = np.arange(len(root_ids))
np.random.shuffle(order)
order = order
n_blocks = np.min([len(order), n_threads * 3])
blocks = np.array_split(order, n_blocks)
multi_args = []
for block in blocks:
multi_args.append([cg_serialized_info, root_ids[block]])
if n_threads == 1:
results = mu.multiprocess_func(_get_root_ids_and_sv_chunks,
multi_args,
n_threads=n_threads,
verbose=False,
debug=n_threads == 1)
else:
results = mu.multisubprocess_func(_get_root_ids_and_sv_chunks,
multi_args,
n_threads=n_threads)
root_ids = []
n_l1_nodes_per_root = []
rep_l1_nodes = []
rep_l1_chunk_ids = []
for result in results:
root_ids.extend(result[0])
n_l1_nodes_per_root.extend(result[1])
rep_l1_nodes.extend(result[2])
rep_l1_chunk_ids.extend(result[3])
save_folder = f"{save_dir}/{table_id}/"
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with h5py.File(f"{save_folder}/root_stats.h5", "w") as f:
f.create_dataset("root_ids", data=root_ids, compression="gzip")
f.create_dataset("n_l1_nodes_per_root",
data=n_l1_nodes_per_root,
compression="gzip")
f.create_dataset("rep_l1_nodes", data=rep_l1_nodes, compression="gzip")
f.create_dataset("rep_l1_chunk_ids",
data=rep_l1_chunk_ids,
compression="gzip")
def get_merge_split_timings(table_id,
save_dir=f"{HOME}/benchmarks/",
job_size=500,
n_threads=1):
save_folder = f"{save_dir}/{table_id}/"
merge_edges, merge_edge_weights = load_merge_stats(save_folder)
probs = merge_edge_weights / np.sum(merge_edge_weights)
if n_threads == 1:
n_jobs = n_threads * 3
else:
n_jobs = n_threads * 3
cg = chunkedgraph.ChunkedGraph(table_id)
cg_serialized_info = cg.get_serialized_info()
if n_threads > 0:
del cg_serialized_info["credentials"]
time_start = time.time()
order = np.arange(len(merge_edges))
np.random.seed(np.int(time.time()))
replace = False
blocks = np.random.choice(order,
job_size * n_jobs,
p=probs,
replace=replace).reshape(n_jobs, job_size)
multi_args = []
for block in blocks:
multi_args.append([cg_serialized_info, merge_edges[block]])
print(f"Building jobs took {time.time()-time_start}s")
time_start = time.time()
if n_threads == 1:
results = mu.multiprocess_func(_get_merge_timings,
multi_args,
n_threads=n_threads,
verbose=False,
debug=n_threads == 1)
else:
results = mu.multisubprocess_func(_get_merge_timings,
multi_args,
n_threads=n_threads)
dt = time.time() - time_start
timings = []
for result in results:
timings.extend(result[0])
percentiles = [np.percentile(timings, k) for k in range(1, 100, 1)]
mean = np.mean(timings)
std = np.std(timings)
median = np.median(timings)
merge_results = {
"percentiles": percentiles,
"p01": percentiles[0],
"p05": percentiles[4],
"p95": percentiles[94],
"p99": percentiles[98],
"mean": mean,
"std": std,
"median": median,
"total_time_s": dt,
"job_size": job_size,
"n_jobs": n_jobs,
"n_threads": n_threads,
"replace": replace,
"requests_per_s": job_size * n_jobs / dt
}
timings = []
for result in results:
timings.extend(result[1])
percentiles = [np.percentile(timings, k) for k in range(1, 100, 1)]
mean = np.mean(timings)
std = np.std(timings)
median = np.median(timings)
split_results = {
"percentiles": percentiles,
"p01": percentiles[0],
"p05": percentiles[4],
"p95": percentiles[94],
"p99": percentiles[98],
"mean": mean,
"std": std,
"median": median,
"total_time_s": dt,
"job_size": job_size,
"n_jobs": n_jobs,
"n_threads": n_threads,
"replace": replace,
"requests_per_s": job_size * n_jobs / dt
}
return merge_results, split_results
def mesh_single_layer(self, layer, bounding_box=None, block_factor=2,
n_threads=128):
assert layer <= self.highest_mesh_layer
dataset_bounding_box = np.array(self.cv.bounds.to_list())
block_bounding_box_cg = \
[np.floor(dataset_bounding_box[:3] /
self.cg.chunk_size).astype(np.int),
np.ceil(dataset_bounding_box[3:] /
self.cg.chunk_size).astype(np.int)]
if bounding_box is not None:
bounding_box_cg = \
[np.floor(bounding_box[0] /
self.cg.chunk_size).astype(np.int),
np.ceil(bounding_box[1] /
self.cg.chunk_size).astype(np.int)]
m = block_bounding_box_cg[0] < bounding_box_cg[0]
block_bounding_box_cg[0][m] = bounding_box_cg[0][m]
m = block_bounding_box_cg[1] > bounding_box_cg[1]
block_bounding_box_cg[1][m] = bounding_box_cg[1][m]
block_bounding_box_cg /= 2 ** np.max([0, layer - 2])
block_bounding_box_cg = np.ceil(block_bounding_box_cg)
n_jobs = np.product(block_bounding_box_cg[1] -
block_bounding_box_cg[0]) / \
block_factor ** 2 < n_threads
while n_jobs < n_threads and block_factor > 1:
block_factor -= 1
n_jobs = np.product(block_bounding_box_cg[1] -
block_bounding_box_cg[0]) / \
block_factor ** 2 < n_threads
block_iter = itertools.product(np.arange(block_bounding_box_cg[0][0],
block_bounding_box_cg[1][0],
block_factor),
np.arange(block_bounding_box_cg[0][1],
block_bounding_box_cg[1][1],
block_factor),
np.arange(block_bounding_box_cg[0][2],
block_bounding_box_cg[1][2],
block_factor))
blocks = np.array(list(block_iter), dtype=np.int)
cg_info = self.cg.get_serialized_info()
del (cg_info['credentials'])
multi_args = []
for start_block in blocks:
end_block = start_block + block_factor
m = end_block > block_bounding_box_cg[1]
end_block[m] = block_bounding_box_cg[1][m]
multi_args.append([cg_info, start_block, end_block, self.cg.cv_path,
self.cv_mesh_dir, self.mesh_mip, layer])
random.shuffle(multi_args)
random.shuffle(multi_args)
# Run parallelizing
if n_threads == 1:
mu.multiprocess_func(meshgen._mesh_layer_thread, multi_args,
n_threads=n_threads, verbose=True,
debug=n_threads == 1)
else:
mu.multisubprocess_func(meshgen._mesh_layer_thread, multi_args,
n_threads=n_threads,
suffix="%s_%d" % (self.table_id, layer))
def create_chunked_graph(table_id=None,
cv_url=None,
ws_url=None,
fan_out=2,
bbox=None,
chunk_size=(512, 512, 128),
verbose=False,
n_threads=1):
""" Creates chunked graph from downloaded files
:param table_id: str
:param cv_url: str
:param ws_url: str
:param fan_out: int
:param bbox: [[x_, y_, z_], [_x, _y, _z]]
:param chunk_size: tuple
:param verbose: bool
:param n_threads: int
"""
if cv_url is None or ws_url is None:
if "basil" in table_id:
cv_url = "gs://nkem/basil_4k_oldnet/region_graph/"
ws_url = "gs://neuroglancer/svenmd/basil_4k_oldnet_cg/watershed/"
elif "pinky40" in table_id:
cv_url = "gs://nkem/pinky40_v11/mst_trimmed_sem_remap/region_graph/"
ws_url = "gs://neuroglancer/svenmd/pinky40_v11/watershed/"
elif "pinky100" in table_id:
cv_url = "gs://nkem/pinky100_v0/region_graph/"
ws_url = "gs://neuroglancer/nkem/pinky100_v0/ws/lost_no-random/bbox1_0/"
else:
raise Exception("Could not identify region graph ressource")
times = []
time_start = time.time()
chunk_size = np.array(list(chunk_size))
file_paths = np.sort(
glob.glob(
creator_utils.dir_from_layer_name(
creator_utils.layer_name_from_cv_url(cv_url)) + "/*"))
file_path_blocks = np.array_split(file_paths, n_threads * 3)
multi_args = []
for fp_block in file_path_blocks:
multi_args.append([fp_block, table_id, chunk_size, bbox])
if n_threads == 1:
results = mu.multiprocess_func(_preprocess_chunkedgraph_data_thread,
multi_args,
n_threads=n_threads,
verbose=True,
debug=n_threads == 1)
else:
results = mu.multisubprocess_func(_preprocess_chunkedgraph_data_thread,
multi_args,
n_threads=n_threads)
in_chunk_connected_paths = np.array([])
in_chunk_connected_ids = np.array([], dtype=np.uint64).reshape(-1, 3)
in_chunk_disconnected_paths = np.array([])
in_chunk_disconnected_ids = np.array([], dtype=np.uint64).reshape(-1, 3)
between_chunk_paths = np.array([])
between_chunk_ids = np.array([], dtype=np.uint64).reshape(-1, 2, 3)
isolated_paths = np.array([])
isolated_ids = np.array([], dtype=np.uint64).reshape(-1, 3)
for result in results:
in_chunk_connected_paths = np.concatenate(
[in_chunk_connected_paths, result[0]])
in_chunk_connected_ids = np.concatenate(
[in_chunk_connected_ids, result[1]])
in_chunk_disconnected_paths = np.concatenate(
[in_chunk_disconnected_paths, result[2]])
in_chunk_disconnected_ids = np.concatenate(
[in_chunk_disconnected_ids, result[3]])
between_chunk_paths = np.concatenate([between_chunk_paths, result[4]])
between_chunk_ids = np.concatenate([between_chunk_ids, result[5]])
isolated_paths = np.concatenate([isolated_paths, result[6]])
isolated_ids = np.concatenate([isolated_ids, result[7]])
assert len(in_chunk_connected_ids) == len(in_chunk_connected_paths) == \
len(in_chunk_disconnected_ids) == len(in_chunk_disconnected_paths) == \
len(isolated_ids) == len(isolated_paths)
in_chunk_connected_ids, in_chunk_connected_paths = \
_sort_arrays(in_chunk_connected_ids, in_chunk_connected_paths)
in_chunk_disconnected_ids, in_chunk_disconnected_paths = \
_sort_arrays(in_chunk_disconnected_ids, in_chunk_disconnected_paths)
isolated_ids, isolated_paths = \
_sort_arrays(isolated_ids, isolated_paths)
times.append(["Preprocessing", time.time() - time_start])
print("Preprocessing took %.3fs = %.2fh" %
(times[-1][1], times[-1][1] / 3600))
time_start = time.time()
multi_args = []
in_chunk_id_blocks = np.array_split(in_chunk_connected_ids,
max(1, n_threads))
cumsum = 0
for in_chunk_id_block in in_chunk_id_blocks:
multi_args.append([
between_chunk_ids, between_chunk_paths, in_chunk_id_block, cumsum
])
cumsum += len(in_chunk_id_block)
# Run parallelizing
if n_threads == 1:
results = mu.multiprocess_func(_between_chunk_masks_thread,
multi_args,
n_threads=n_threads,
verbose=True,
debug=n_threads == 1)
else:
results = mu.multisubprocess_func(_between_chunk_masks_thread,
multi_args,
n_threads=n_threads)
times.append(["Data sorting", time.time() - time_start])
print("Data sorting took %.3fs = %.2fh" %
(times[-1][1], times[-1][1] / 3600))
time_start = time.time()
n_layers = int(
np.ceil(
pychunkedgraph.backend.chunkedgraph_utils.log_n(
np.max(in_chunk_connected_ids) + 1, fan_out))) + 2
print("N layers: %d" % n_layers)
cg = chunkedgraph.ChunkedGraph(table_id=table_id,
n_layers=np.uint64(n_layers),
fan_out=np.uint64(fan_out),
chunk_size=np.array(chunk_size,
dtype=np.uint64),
cv_path=ws_url,
is_new=True)
# Fill lowest layer and create first abstraction layer
# Create arguments for parallelizing
multi_args = []
for result in results:
offset, between_chunk_paths_out_masked, between_chunk_paths_in_masked = result
for i_chunk in range(len(between_chunk_paths_out_masked)):
multi_args.append([
table_id, in_chunk_connected_paths[offset + i_chunk],
in_chunk_disconnected_paths[offset + i_chunk],
isolated_paths[offset + i_chunk],
between_chunk_paths_in_masked[i_chunk],
between_chunk_paths_out_masked[i_chunk], verbose
])
random.shuffle(multi_args)
print("%d jobs for creating layer 1 + 2" % len(multi_args))
# Run parallelizing
if n_threads == 1:
mu.multiprocess_func(_create_atomic_layer_thread,
multi_args,
n_threads=n_threads,
verbose=True,
debug=n_threads == 1)
else:
mu.multisubprocess_func(_create_atomic_layer_thread,
multi_args,
n_threads=n_threads)
times.append(["Layers 1 + 2", time.time() - time_start])
# Fill higher abstraction layers
child_chunk_ids = in_chunk_connected_ids.copy()
for layer_id in range(3, n_layers + 1):
time_start = time.time()
print("\n\n\n --- LAYER %d --- \n\n\n" % layer_id)
parent_chunk_ids = child_chunk_ids // cg.fan_out
parent_chunk_ids = parent_chunk_ids.astype(np.int)
u_pcids, inds = np.unique(parent_chunk_ids,
axis=0,
return_inverse=True)
if len(u_pcids) > n_threads:
n_threads_per_process = 1
else:
n_threads_per_process = int(np.ceil(n_threads / len(u_pcids)))
multi_args = []
for ind in range(len(u_pcids)):
multi_args.append([
table_id, layer_id,
child_chunk_ids[inds == ind].astype(np.int),
n_threads_per_process
])
child_chunk_ids = u_pcids
# Run parallelizing
if n_threads == 1:
mu.multiprocess_func(_add_layer_thread,
multi_args,
n_threads=n_threads,
verbose=True,
debug=n_threads == 1)
else:
mu.multisubprocess_func(_add_layer_thread,
multi_args,
n_threads=n_threads,
suffix=str(layer_id))
times.append(["Layer %d" % layer_id, time.time() - time_start])
for time_entry in times:
print("%s: %.2fs = %.2fmin = %.2fh" %
(time_entry[0], time_entry[1], time_entry[1] / 60,
time_entry[1] / 3600))
