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configurable parallelism #64

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Sep 15, 2023
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44 changes: 35 additions & 9 deletions src/snkit/network.py
Original file line number Diff line number Diff line change
@@ -1,6 +1,8 @@
"""Network representation and utilities
"""
import logging
import os
from typing import Optional
import warnings

import geopandas
Expand Down Expand Up @@ -38,11 +40,20 @@
from snkit.utils import tqdm_standin as tqdm

# optional parallel processing
if "SNKIT_PARALLEL" in os.environ and os.environ["SNKIT_PARALLEL"] in ("1", "TRUE"):
PARALLEL = True
if "SNKIT_PROCESSES" in os.environ:
processes_env_var = os.environ["SNKIT_PROCESSES"]
try:
requested_processes = int(processes_env_var)
except TypeError:
raise RuntimeError(
"SNKIT_PROCESSES env var must be a non-negative integer. "
"Use 0 or unset for serial operation."
)
PARALLEL_PROCESS_COUNT = min([os.cpu_count(), requested_processes])
import multiprocessing
logging.info(f"SNKIT_PROCESSES={processes_env_var}, using {PARALLEL_PROCESS_COUNT} processes")
else:
PARALLEL = False
PARALLEL_PROCESS_COUNT = 0


class Network:
Expand Down Expand Up @@ -316,18 +327,33 @@ def _split_edges_at_nodes(
return split_edges


def split_edges_at_nodes(network, tolerance=1e-9):
"""Split network edges where they intersect node geometries"""
def split_edges_at_nodes(network: Network, tolerance: float = 1e-9, chunk_size: Optional[int] = None):
"""
Split network edges where they intersect node geometries.

N.B. Can operate in parallel if SNKIT_PROCESSES is in the environment and a
positive integer.

Args:
network: Network object to split edges for.
tolerance: Proximity within which nodes are said to intersect an edge.
chunk_size: When splitting in parallel, set the number of edges per
unit of work.

Returns:
Network with edges split at nodes (within proximity tolerance).
"""
split_edges = []

n = len(network.edges)
if PARALLEL and (n > 10_000):
chunk_size = int(n / os.cpu_count())
if PARALLEL_PROCESS_COUNT > 1:
if chunk_size is None:
chunk_size = max([1, int(n / PARALLEL_PROCESS_COUNT)])
args = [
(network.edges.iloc[i : i + chunk_size, :], network.nodes, tolerance)
(network.edges.iloc[i: i + chunk_size, :], network.nodes, tolerance)
for i in range(0, n, chunk_size)
]
with multiprocessing.Pool() as pool:
with multiprocessing.Pool(PARALLEL_PROCESS_COUNT) as pool:
results = pool.starmap(_split_edges_at_nodes, args)

# flatten return list
Expand Down
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