Skip to main content

Python bindings for the Open Graph Drawing Framework (OGDF), built with nanobind.

Project description

ogdf-py

Python bindings for the Open Graph Drawing Framework (OGDF), built with nanobind.

This is a curated subset of OGDF, not a full wrapper: enough to build graphs, run layout and core graph algorithms, style and draw them, and read/write common graph file formats.

Quick Start

make bootstrap   # clone OGDF at the pinned tag and build it from source (once)
uv sync          # build the extension
uv run pytest    # run the tests
uv build         # build a wheel

make build (and make sync) run the bootstrap automatically if OGDF has not been built yet. Use make help for additional targets.

Example

import ogdf

# Build a graph.
g = ogdf.Graph()
ogdf.random_planar_connected_graph(g, 30, 45)

# Attach drawing attributes and run a force-directed layout.
ga = ogdf.GraphAttributes(g)
ogdf.FMMMLayout().call(ga)

# Export to SVG.
ogdf.draw_svg(ga, "graph.svg")

Algorithms operate on the same Graph, writing per-node/edge results into an array you pass in:

g = ogdf.Graph()
ogdf.complete_bipartite_graph(g, 3, 4)
assert ogdf.is_bipartite(g)

component = ogdf.NodeArrayInt(g)
n = ogdf.connected_components(g, component)   # -> number of components

What's included

  • Graph model: Graph, Node, Edge, node/edge iteration.

  • Attribute arrays: NodeArray / EdgeArray in int, double, and bool.

  • Attributes: GraphAttributes with coordinates, size, labels, and full styling (colors, shapes, fill patterns, stroke, edge arrows, and bends).

  • Layouts: SugiyamaLayout (layered), FMMMLayout / GEMLayout / SpringEmbedderKK / MultilevelLayout (force-directed), StressMinimization / PivotMDS (stress/MDS), PlanarizationLayout (with optional orthogonal routing), SchnyderLayout (planar straight-line), TreeLayout / RadialTreeLayout / BalloonLayout, CircularLayout, LinearLayout (arc diagram), TutteLayout (convex planar), and DominanceLayout / VisibilityLayout (upward, for DAGs).

  • Algorithms: connectivity and structure tests (is_connected, is_biconnected, is_bipartite, is_acyclic, is_planar, ...), components, cut vertices and bridges, separation pairs and SPQR-tree summary, topological numbering, shortest paths (dijkstra, bellman_ford), minimum spanning tree, maximum flow, minimum-cost flow, global minimum cut, matching (bipartite and maximum-weight), node coloring, minimum Steiner tree, maximal planar subgraph, and planar embedding.

  • Generators: classic graphs (complete, bipartite, wheel, cube, grid, Petersen, circulant, k-partite, globe, lattice), graph products (cartesian, tensor, strong, lexicographical), operations (union, complement, suspension), and random models (Erdos-Renyi, regular, planar, triconnected, Watts-Strogatz, preferential attachment, Chung-Lu, Waxman, geometric, series-parallel DAG, hierarchy).

  • File I/O: interchange formats GML, GraphML, DOT, GEXF, GDF, TLP (plus extension-based read/write), and drawing output as SVG and TikZ.

Demos

make demos   # writes SVGs and data files to build/demo-output/

The demos exercise layouts, styling, algorithm visualizations, generators, and file I/O, and assemble every drawing into build/demo-output/index.html for a side-by-side view. See demos/README.md for details.

Documentation

make docs         # build the MkDocs site into site/
make docs-serve   # serve locally with live reload

The documentation site (docs/) includes a getting-started guide, a coverage checklist of exactly what OGDF functionality is exposed, and a gallery of the demo drawings.

How it builds

scripts/bootstrap_ogdf.py shallow-clones OGDF at a pinned release tag (foxglove-202510) into thirdparty/ogdf and builds its static libraries from source. The extension then links the OGDF / COIN static libraries, so wheels are self-contained. Because OGDF is prebuilt once (a couple of minutes), rebuilding the bindings only recompiles the binding sources and is fast.

The bootstrap script (scripts/bootstrap_ogdf.py) is cross-platform (Linux, macOS, Windows). The pin can be overridden: python scripts/bootstrap_ogdf.py --tag <tag> or OGDF_TAG=<tag> make bootstrap. In CI, run the bootstrap once per platform (e.g. in cibuildwheel's CIBW_BEFORE_ALL) so OGDF is reused across all Python versions.

Project details


Download files

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

Source Distribution

ogdf_py-0.2.0.tar.gz (127.9 kB view details)

Uploaded Source

Built Distributions

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

ogdf_py-0.2.0-cp314-cp314-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.14Windows x86-64

ogdf_py-0.2.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (3.9 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

ogdf_py-0.2.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (3.5 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

ogdf_py-0.2.0-cp314-cp314-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

ogdf_py-0.2.0-cp314-cp314-macosx_10_15_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.14macOS 10.15+ x86-64

ogdf_py-0.2.0-cp313-cp313-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.13Windows x86-64

ogdf_py-0.2.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (3.9 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

ogdf_py-0.2.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (3.5 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

ogdf_py-0.2.0-cp313-cp313-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

ogdf_py-0.2.0-cp313-cp313-macosx_10_15_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.13macOS 10.15+ x86-64

ogdf_py-0.2.0-cp312-cp312-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.12Windows x86-64

ogdf_py-0.2.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (3.9 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

ogdf_py-0.2.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (3.5 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

ogdf_py-0.2.0-cp312-cp312-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

ogdf_py-0.2.0-cp312-cp312-macosx_10_15_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.12macOS 10.15+ x86-64

ogdf_py-0.2.0-cp311-cp311-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.11Windows x86-64

ogdf_py-0.2.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (3.9 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

ogdf_py-0.2.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (3.5 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

ogdf_py-0.2.0-cp311-cp311-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

ogdf_py-0.2.0-cp311-cp311-macosx_10_15_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.11macOS 10.15+ x86-64

ogdf_py-0.2.0-cp310-cp310-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.10Windows x86-64

ogdf_py-0.2.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (3.9 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

ogdf_py-0.2.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (3.5 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

ogdf_py-0.2.0-cp310-cp310-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

ogdf_py-0.2.0-cp310-cp310-macosx_10_15_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.10macOS 10.15+ x86-64

File details

Details for the file ogdf_py-0.2.0.tar.gz.

File metadata

  • Download URL: ogdf_py-0.2.0.tar.gz
  • Upload date:
  • Size: 127.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.2

File hashes

Hashes for ogdf_py-0.2.0.tar.gz
Algorithm Hash digest
SHA256 25c043875d686e2501c68555daf57d12cb7604978cb14ffcf766593fcef6347f
MD5 8d9d2e6e37f9cc24b708e3ab7d1d6749
BLAKE2b-256 60e34e87ea51fddb7bd67b56f389e3b1770dc63d5e1f13873b527a742b10d7af

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp314-cp314-win_amd64.whl.

File metadata

  • Download URL: ogdf_py-0.2.0-cp314-cp314-win_amd64.whl
  • Upload date:
  • Size: 2.0 MB
  • Tags: CPython 3.14, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.2

File hashes

Hashes for ogdf_py-0.2.0-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 c99148863c4d32500ddf872ef5ceb8a43f22244cb563f3a1c3e202400f289755
MD5 b9b66af6ca5c3a9166d70f4c0ee62e3d
BLAKE2b-256 454b1579efdac03c0297890b054847694145d374bdf0bbdaa4a6e474b4a3bf85

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e9de53676a13bbee3deb3d5cd0c1137a2f99315391451307d983a8df8341f4ca
MD5 55e6a74c70b4eeff7062df06193211da
BLAKE2b-256 34e0a5f6482ef3b7cbc6fa5ef385803169f383c262341bf53ebfd1cc2b7ac9ff

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 9aabdcee5fe6e1dd14c20fc7a857756dc5192eee36adf46c93201cb95e969881
MD5 6111830ad8872b1971fee339406fed76
BLAKE2b-256 15bc683796ecd0ec7c6b1cb17dd84cfe84f535881075eaac1a6fccdf4d7586ec

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ae817170029bcc8bdd2941d33218c9f2b74a1603c47f3c7c931b54f4f6d6a69e
MD5 d6c10da56119f97ad45fff83c0710e12
BLAKE2b-256 5e5c1e7b347ee72e831a81d397efd79349fe2aa712154485d9675533a2de092f

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp314-cp314-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp314-cp314-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 7f42ba1dd2e9c7952507d133fe3cedfa83759942907991eef881888be7aa0054
MD5 9c7c1de1615f1f33b9a1fa980567ee20
BLAKE2b-256 9971a84c8f55977c54abdd2ea38df9f3fa8b6810709e837b4d67a7393ce35f2a

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: ogdf_py-0.2.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 2.0 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.2

File hashes

Hashes for ogdf_py-0.2.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 581d3e003f865a248bd5fe653db1ff1cb461a3ef8fe1d6f0274db4ba641fa462
MD5 34a8e3af2443b0181068eb4c5c66fe7d
BLAKE2b-256 4281bec1553ea018d2a27525343641c4adcaadf104064de805528470ad48d715

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 796e52c5ce85bcc10068b45a3f738de95621779e4b969ee01036c092ae5e4bef
MD5 79568d6f362e4c2b6ac60c1123cd2932
BLAKE2b-256 57a53bbe8304887391921a0f059b10e807f21a60d00c85135fa7d669e7529437

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 0279938120026495b05c1af5d7f87ac92b0a34fa3e490d95d6607a1ae89d12de
MD5 e1143f8cb9e93a78af80d4e75b695129
BLAKE2b-256 c6d5dc40d5b1375c8dfa909d4b9abad8759ed58e4bcee1a7448e363b8f0dbf4f

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9fce37cf9bcc913c1748f140f05d5740d20f4e3d2cf20d22f266dd166406aad9
MD5 e27335ee9454bc9018731bed2993b415
BLAKE2b-256 922d1d1cdd852879268861ccf2b554413b3d65e085610b6a69982e56551d8c09

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp313-cp313-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp313-cp313-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 5e5b31f33bab96f02d9af3465d19288dc3bb07d41eee7f07eb9908d2b300637a
MD5 88643cdf5b2af8dfdddd022a178321ea
BLAKE2b-256 f9c04ed0653ae1e9f580f66f65e2980325378c20a6528890530ac17749af99fe

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: ogdf_py-0.2.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 2.0 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.2

File hashes

Hashes for ogdf_py-0.2.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 c7e445313fa658e5362ef0b575bbdf93cf5a11e472ca82f228092688349576c8
MD5 7724675034bfa929eb485610c45deaef
BLAKE2b-256 3cc0aecc75bddb1221e797ec6b8d249d94ae636d7117e3fffb9c915aab61d606

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 2b3e56155feebca00d8d2c548d1c3b494ffde73f53227de79eef02cb4ad1eb37
MD5 dca18f194f136f1f03957d56dab1cfac
BLAKE2b-256 b5384bcc76a93e235712641cb1825a39bdc3863c1430a03120506d50f2ade21b

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 15f74223183e4a263e8f172de371ac9cdf272eeb88c863dd2deeccb79dd98002
MD5 39819dfc334260a73d9c6b529a21e4a5
BLAKE2b-256 941afa6143d70a4a9f3d675d69362b7ff4653a4f2e20258134d1de76031edef2

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6a715c982e32d5a85b60d95ec382114e98a862c37a05384e3eee317207a12b4a
MD5 101bf5638b71b13f69141bbb3261ff3b
BLAKE2b-256 798730d096d6c8940739efbcff1d4fde84d991ca4d447fc24ce9aac1a4a3477e

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp312-cp312-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp312-cp312-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 0fdecf6d468938f390c93284214f930c47eb9c01a89ba33deabb1f10aa79c569
MD5 8184fe8dce5bb6ba69a21983a0bec6b3
BLAKE2b-256 c333a11607807830533190d54a1336f05ee8c7f51e359bb1a9f393e11ca3c90b

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: ogdf_py-0.2.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 2.0 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.2

File hashes

Hashes for ogdf_py-0.2.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 61269ee6d96b6a9da5127f399cb477b9b2262c173a9d6a3af3f308da356e6e05
MD5 fb69216f49b3a09cc5c81030a8af3c9e
BLAKE2b-256 23d687c8a92df6602b72708fec8a5608e7dd4ab22017b7f1b5c5b3c6d3dfe013

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 faddd2c683983014206ef5339d3a53c2b4c6087fa390cc323f931b4819edc70b
MD5 b6685faef6ecedd849d991510653c64c
BLAKE2b-256 4d63b3647ccddd02681c2729df290efcd87eff25611a93db273a78c78b96c915

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 a50b03fe3ff63dce4ad5da841677e6c9929f6da6b970b8c258f4d88c647f6177
MD5 7cca6d7f1396440e7a2c49f909b45c21
BLAKE2b-256 1ef81152c3e7f2bda32c00d930a037b40b840d327c24a97198aeab3b672284bc

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ac3265cd3d185369950ea23efecd77c5261544525a8240a117726068c5c14a58
MD5 88494edd8f4c117daab65e9ca1561e44
BLAKE2b-256 44d75ad3c8aef06ec7c0e2dd1b9924b95cbe59bb9b49fff32c33fc1b8e552341

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp311-cp311-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp311-cp311-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 4a8b7b61e844d415ad5f98d53170e6d1303112600cf29e95cd613dd2300a900c
MD5 e01e8acec23e34428981a9016d6069b1
BLAKE2b-256 3ff5075ec957f58858fcba789fa509d16431ae9f56ef1d0bfe1b607745eeec8c

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: ogdf_py-0.2.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 2.0 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.2

File hashes

Hashes for ogdf_py-0.2.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 e6bce28710e9c45e589cea6b326b40325fed7cf04f3e2323a93454310ea64a06
MD5 93480dee78a70902bcdea248ddbe1201
BLAKE2b-256 da6fa8e2a0277d839f696d5e0f09b831164dd8091779a928b130e7d58b6d4dba

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 cd8d66a551dcddd8f952a17551921f7957c712e90ecc3ddb5b46224d4bcd1e3c
MD5 d586110695d8eccf286c41e072e65c5a
BLAKE2b-256 6a9723ff3d98f9432d64affba86bf6516c22131ba7f117f4bff5313e7bc119ae

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 bb11538d32f7ea0dcc80aa8307e170c5b56532bee77dee124919c5f1cb496082
MD5 3e4bf7bb0467abd6cd592da8de8b6232
BLAKE2b-256 91f2de0ffdd7ded0db6b7fa894421eb0ff9379c09e1a45c5e9bc950c1863086b

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 85d9fbb7f3683edcc494699811f974657f7a0174b4e10a12827f5f8819ecca2d
MD5 698fb5d605c6bd6e11a33301cecd0f66
BLAKE2b-256 1a27e422d83dfdec5f36a05696f0f39d5c7d395e9c8f621d926b31780ee773cd

See more details on using hashes here.

File details

Details for the file ogdf_py-0.2.0-cp310-cp310-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for ogdf_py-0.2.0-cp310-cp310-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 ddfb2614d23a84a15a57f538e5926f1b89590ba06c88392f5bc95417d48d2401
MD5 38e9f923b52931511ae4566c2b7798a9
BLAKE2b-256 dfc31bb3dde7d5363384ad1256f37ba9937e6c01439691224f02c414cf5fe98f

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