Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-08 18:52 UTC

PythonOT / POT

POT : Python Optimal Transport

PythonMIT★ 2,834 stars⑂ 553 forkssince Oct 2016View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

PythonOT/POT holds a health index of 86 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (95/100) and lowest on Security (21/100). It was last updated 10 days ago. A single contributor accounts for most of its recent work.

86
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

86
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 72 is calibrated to 86 on the published index scale (record calibration 2026-08-02).

Ownership

61 followers8 public repossince Apr 2020

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIPOT0.9.7.post1541,9333310 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

76Good · 21% of overall
How it's scored
28.8/36Push recency — last push 10 days ago
18/36Commit cadence — 26/52 weeks with commits
16/18Commit volume — 59 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year59
human_commit_share1
days_since_last_push10
active_weeks_last_year26
How it's scored
27/27Ships releases — 33 releases published
36/36Release recency — latest release 10 days ago
12.6/27Release cadence — a release every ~134.3 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count33
latest_release_tag0.9.7.post1
releases_from_tagsno
days_since_latest_release10
mean_days_between_releases134.3

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

91Excellent · 17% of overall
How it's scored
56/60Stars — 2,834 stars
22.9/25Forks — 553 forks
9/15Watchers — 42 watchers
Inputs used
forks553
stars2,834
watchers42
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesbadge.fury.io, codecov.io, github.com
has_pull_request_templateyes
How it's scored
76.5/80Monthly downloads — 541,933 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesPOT
dependents
ecosystemspypi
total_downloads
monthly_downloads541,933
unverified_packages_excluded
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

72Good · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
8.8/22.5Commit distribution — top contributor authored 61% of commits
13.5/13.5Contributor breadth — 86 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled86
top_contributor_share0.607
How it's scored
37/42Issue resolution — 88% of issues closed
26.6/30PR acceptance — 436/492 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs436
open_issues34
closed_issues252
prs_merged_7d0
prs_decided_7d0
prs_merged_30d1
prs_decided_30d1
issue_closed_ratio0.881
closed_unmerged_prs56
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain — verified-domain status not read for this organization
12.9/25Owner reach — 61 followers of PythonOT
18.9/25Track record — 8 public repos, account ~6 yr old
Inputs used
followers61
owner_typeOrganization
is_verified
owner_loginPythonOT
public_repos8
account_age_days2,304
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 10 days ago
20/20Version history — 33 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesPOT
ecosystemspypi
any_deprecatedno
min_days_since_publish10

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall
How it's scored
24/24CI workflows — 7 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://PythonOT.github.io/
10/10Repository description
10/10Topics — 16 topics
10/10Wiki
Inputs used
topicsoptimal-transport, numerical-optimization, machine-learning, emd, ot-mapping-estimation, wasserstein-barycenter, ot-solver, python, wasserstein, wasserstein-discriminant-analysis, gromov-wasserstein, wasserstein-barycenters, sinkhorn-divergences, sinkhorn-knopp, pot, domain-adaptation
has_wikiyes
homepagehttps://PythonOT.github.io/
docs_sitehttps://PythonOT.github.io/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

21At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifests.github/requirements_doctests.txt, .github/requirements_no_backend.txt, .github/requirements_strict.txt, .github/requirements_test_windows.txt, docs/requirements.txt, docs/requirements_rtd.txt, pyproject.toml, setup.cfg, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages2
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:POT@0.9.7.post1 runtime dependency closure — what installing the published package pulls in — 2 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

58Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 96 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.96
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrap — Makefile, docs/Makefile
22/22Automated tests
11/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, docs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
53.1/55Manageable file sizes — 8/230 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes109,626
source_files_sampled230
oversized_source_files8
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

2,834GitHub stars
86contributors
59commits, last 12 months
10days since last push
33releases
1bus factor
34open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard did not return a usable result (exit code -9); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 553 ⇿
0Stars
553Forks
33Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0100200300400500600545672016-102021-092026-08
Major 0Minor 6Patch 10

Each point covers 9 days.

All dependencies 38

Full resolved dependency set from the GitHub dependency graph: 0 direct and 38 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIautogradindirect
PyPIccompilerindirect
PyPIcooindirect
PyPIcvxoptindirect
PyPIcvxpyindirect
PyPIcythonindirect
PyPIembeddindirect
PyPIgeomlossindirect
PyPIgesindirect
PyPIistindirect
PyPIjaxindirect
PyPIjaxlibindirect
PyPIlikindirect
PyPImappindirect
PyPImatplotlibindirect
PyPImemory-profilerindirect
PyPImyst-parserindirect
PyPInetworkxindirect
PyPInumpyindirect
PyPInumpydocindirect
PyPIotindirect
PyPIpillowindirect
PyPIpykeopsindirect
PyPIpymanoptindirect
PyPIpymanopt0.2.6rc1indirect
PyPIpytestindirect
PyPIpytest-covindirect
PyPIscikit-learnindirect
PyPIscipyindirect
PyPIsetuptoolsindirect
PyPIsphinxindirect
PyPIsphinx-galleryindirect
PyPIsphinx-rtd-themeindirect
PyPIsphinxcontrib-jqueryindirect
PyPItensorflowindirect
PyPItorchindirect
PyPItorch-geometricindirect
PyPIwassindirect
Dependency advisories 0

Installing pypi:POT@0.9.7.post1 pulls in 2 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.