Skip to main content

Fast Dijkstra using Boost Graph Library

Project description

Fast_dijkstra

wrapper of c++ Boost Dijkstra https://www.boost.org/doc/libs/latest/libs/graph/doc/dijkstra_shortest_paths.html

usage

edges must be integers

from fast_dijkstra import directed_dijkstra

edges = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)]
weights = [1.0, 4.0, 2.0, 5.0, 1.0]
sources = [0, 1]
num_threads = 4

distances, predecessor = directed_dijkstra(edges, weights, sources, num_threads)
from fast_dijkstra import limited_directed_dijkstra

edges = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)]
weights = [1.0, 4.0, 2.0, 5.0, 1.0]
sources = [0, 1]
num_threads = 4
limit=1000

distances, predecessor = limited_directed_dijkstra(edges, weights, sources, limit, num_threads)

to deploy

  1. change the version number in pyproject.toml under [project]
[project]
name = "fast_dijkstra"
version = "1.0.2"
  • the poetry config is only used for local dev. to ignore.
  1. create a new tag starting with "v"
git tag -a 'v1.0.2' -m 'description'
git push origin v1.0.2

Github action will build wheels for windows and Linux.

  1. when Done, upload to Pypi
./upload v1.0.2

local development build

sudo apt-get install -y libboost-all-dev

poetry run python setup.py bdist_wheel

or

poetry run python -m build --wheel

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

fast_dijkstra-1.1.2-cp312-cp312-win_amd64.whl (80.0 kB view details)

Uploaded CPython 3.12Windows x86-64

fast_dijkstra-1.1.2-cp312-cp312-win32.whl (71.9 kB view details)

Uploaded CPython 3.12Windows x86

fast_dijkstra-1.1.2-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl (691.1 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.26+ x86-64manylinux: glibc 2.28+ x86-64

File details

Details for the file fast_dijkstra-1.1.2-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for fast_dijkstra-1.1.2-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 497364fe0c3a351e2267e9475afb04ca044cf7358749c51190a09d170c220128
MD5 2309c64e792d1d4b5607b561d1a239a0
BLAKE2b-256 5dd2adaaf5c1e5904d301cc4ae0f16e53fb92a24e341da3779d2c9f158b285a4

See more details on using hashes here.

File details

Details for the file fast_dijkstra-1.1.2-cp312-cp312-win32.whl.

File metadata

  • Download URL: fast_dijkstra-1.1.2-cp312-cp312-win32.whl
  • Upload date:
  • Size: 71.9 kB
  • Tags: CPython 3.12, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.8.6

File hashes

Hashes for fast_dijkstra-1.1.2-cp312-cp312-win32.whl
Algorithm Hash digest
SHA256 5d0d25fa6237742d3657fb77fd60287fa3f0e3e53ec070db6a3b57afd8729979
MD5 973431b395f61170d59ebb9135d82647
BLAKE2b-256 336fc5741bfbc5f836409a8cecc9602492000231ebee09cd5b26aca191a41ade

See more details on using hashes here.

File details

Details for the file fast_dijkstra-1.1.2-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fast_dijkstra-1.1.2-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 8ad1d288296f952504fcc76d0926b651ad428fc98eaff4ef0803b7e9fda8a385
MD5 dd59370b1b31660e35b4aede5b79dd4f
BLAKE2b-256 a95552bd3b9011f36c7ccdb6d893763e5615ba4e2ce55fec9d8739a148353d82

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page