Pith Number

pith:DBHPGAZE

pith:2019:DBHPGAZE6Z5X2A2HZRQXRO6B76

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SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

Andrew R. J. Nelson, Anne M. Archibald, Ant\^onio H. Ribeiro, Charles R Harris, CJ Carey, David Cournapeau, Denis Laxalde, E.A. Quintero, Eric Jones, Eric Larson, Eric W. Moore, Evgeni Burovski, Fabian Pedregosa, Ian Henriksen, \.Ilhan Polat, Jake Vanderplas, Jonathan Bright, Josef Perktold, Joshua Wilson, K. Jarrod Millman, Matt Haberland, Matthew Brett, Nikolay Mayorov, Pauli Virtanen, Paul van Mulbregt, Pearu Peterson, Ralf Gommers, Robert Cimrman, Robert Kern, SciPy 1.0 Contributors, St\'efan J. van der Walt, Travis E. Oliphant, Tyler Reddy, Warren Weckesser, Yu Feng

SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language.

arxiv:1907.10121 v1 · 2019-07-23 · cs.MS · cs.DS · cs.SE · physics.comp-ph

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Claims

C1strongest claim

SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year.

C2weakest assumption

The assumption that the usage statistics, contributor counts, and described capabilities accurately reflect the library state at the time of writing and that the overview covers the most relevant recent technical developments without significant omissions.

C3one line summary

SciPy 1.0 documents a mature open-source library that has become the de facto standard for scientific algorithms in Python with broad adoption across research projects.

References

160 extracted · 160 resolved · 2 Pith anchors

[1] Oliphant, T. E. Guide to NumPy (Trelgol Publishing USA, 2006), 1st edn. URL https://oez.es/Guide%20to% 20NumPy.pdf 2006

[2] C., & Varoquaux, G 2011 · doi:10.1109/mcse.2011.37

[3] Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011). URL http://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf 2011

[4] van der Walt, S. et al. scikit-image: image processing in Python. PeerJ 2, e453 (2014). URL https://doi.org/10. 7717/peerj.453 2014

[5] Nitz, A. et al. gwastro/pycbc: PyCBC v1.13.2 release (2018). URL https://doi.org/10.5281/zenodo. 1596771 2018 · doi:10.5281/zenodo

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Cited by

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Receipt and verification

First computed	2026-05-17T23:38:14.995959Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

184ef30324f67b7d0347cc6178bbc1ff9d55c7573589dc9b4b1fd6f61014328b

Aliases

arxiv: 1907.10121 · arxiv_version: 1907.10121v1 · doi: 10.48550/arxiv.1907.10121 · pith_short_12: DBHPGAZE6Z5X · pith_short_16: DBHPGAZE6Z5X2A2H · pith_short_8: DBHPGAZE

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DBHPGAZE6Z5X2A2HZRQXRO6B76 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 184ef30324f67b7d0347cc6178bbc1ff9d55c7573589dc9b4b1fd6f61014328b

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "4c22a7a708ede9d179b958502adfb7f41f76ee25335ed8f29464fa46ea30e589",
    "cross_cats_sorted": [
      "cs.DS",
      "cs.SE",
      "physics.comp-ph"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.MS",
    "submitted_at": "2019-07-23T20:31:36Z",
    "title_canon_sha256": "fcecfd6110b0c388f348aa810e962b943a959b587774e969a4d742a86f84543f"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}