Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-05 15:43 UTC

MIT-LCP / wfdb-python

Native Python WFDB package

Jupyter Notebook · PythonMIT★ 852 stars⑂ 324 forkssince Jun 2016View on GitHub ↗

MIT-LCP/wfdb-python holds a health index of 80 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (88/100) and lowest on AI Readiness (48/100). It was last updated 66 days ago. 2 contributors account for most of its recent work.

80
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.

80
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 68 is calibrated to 80 on the published index scale (record calibration 2026-08-02).

Ownership

529 followers87 public repossince Jul 2014

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIwfdb4.3.1183,54150213 days ago

Metrics by category

Vitality

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

48Weak · 21% of overall
How it's scored
18/36Push recencylast push 66 days ago
7.6/36Commit cadence11/52 weeks with commits
13.5/18Commit volume31 commits in the last year
0/10OpenSSF Scorecard: Maintained1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year31
human_commit_share1
days_since_last_push66
active_weeks_last_year11
How it's scored
27/27Ships releases21 releases published
16.2/36Release recencylatest release 213 days ago
12.6/27Release cadencea release every ~200 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count21
latest_release_tagv4.3.1
releases_from_tagsno
days_since_latest_release213
mean_days_between_releases200
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

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

71Good · 17% of overall
How it's scored
47.5/60Stars852 stars
20.9/25Forks324 forks
9.2/15Watchers46 watchers
Inputs used
forks324
stars852
watchers46
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno
How it's scored
70.2/80Monthly downloads183,541 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packageswfdb
dependents
ecosystemspypi
total_downloads
monthly_downloads183,541
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?

73Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
13.7/22.5Commit distributiontop contributor authored 39% of commits
13.5/13.5Contributor breadth28 contributors
10/10OpenSSF Scorecard: Contributorsproject has 13 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled28
top_contributor_share0.392
How it's scored
30.9/42Issue resolution74% of issues closed
25.5/30PR acceptance195/229 decided PRs merged
0/13Newcomer PR acceptance0/1 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Reviewall changesets reviewed
Inputs used
merged_prs195
open_issues86
closed_issues239
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d1
issue_closed_ratio0.735
closed_unmerged_prs34
first_time_authors_30d1
first_time_prs_merged_30d0
first_time_prs_decided_30d1
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domain
19.6/25Owner reach529 followers of MIT-LCP
25/25Track record87 public repos, account ~12 yr old
Inputs used
followers529
owner_typeOrganization
is_verifiedno
owner_loginMIT-LCP
public_repos87
account_age_days4,437
How it's scored
25/25Published & resolvable1 package(s) on pypi
26/35Publish recencylatest publish 213 days ago
20/20Version history50 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packageswfdb
ecosystemspypi
any_deprecatedno
min_days_since_publish213

Engineering Quality

Are baseline engineering and documentation practices in place?

88Excellent · 19% of overall
How it's scored
24/24CI workflows3 workflow(s)
24/24Tests present
16/16Linter configpyproject.toml ([tool.black])
0/9.6Pre-commit hooks
0/6.4.editorconfig
16/20OpenSSF Scorecard: CI-Tests10 out of 12 merged PRs checked by a CI test -- score normalized to 8
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://wfdb.io/
10/10Repository description
10/10Topics5 topics
10/10Wiki
Inputs used
topicsphysionet, ecg, wfdb, python, ekg
has_wikiyes
homepagehttps://wfdb.io/
docs_sitehttps://wfdb.io/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

64Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
2/2.5CI-Tests10 out of 12 merged PRs checked by a CI test -- score normalized to 8
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Reviewall changesets reviewed
2.5/2.5Contributorsproject has 13 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0/7.5Maintained1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate5.5
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages31
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:wfdb@4.3.1 runtime dependency closure — what installing the published package pulls in — 31 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.

48Weak · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
15.5/40Legible commit history29 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.29
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapdocs/Makefile
22/22Automated tests
11/11Lint / format configpyproject.toml ([tool.black])
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codeJupyter Notebook without a type-check config
49.2/55Manageable file sizes4/38 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes119,924
source_files_sampled38
oversized_source_files4
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesnotebooks
Inputs used
example_dirsnotebooks
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

852GitHub stars
28contributors
31commits, last 12 months
66days since last push
21releases
2bus factor
86open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 324 ⇿
0Stars
324Forks
19Releases

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.

012525037532452016-112021-102026-09
Major 1Minor 8Patch 10

Each point covers 9 days.

OpenSSF Scorecard 5.5 / 10
5.5aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-09-05 15:42 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
8CI-Tests10 out of 12 merged PRs checked by a CI test -- score normalized to 8
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 13 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained1 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 8
RegistryPackageVersion constraintManifest
PyPInumpy>= 1.26.4pyproject.toml
PyPIscipy>= 1.13.0pyproject.toml
PyPIpandas>= 2.2.3pyproject.toml
PyPIsoundfile>= 0.10.0pyproject.toml
PyPImatplotlib>= 3.2.2pyproject.toml
PyPIrequests>= 2.8.1pyproject.toml
PyPIfsspec>= 2023.10.0pyproject.toml
PyPIaiohttp>= 3.10.11pyproject.toml
All dependencies 12

Full resolved dependency set from the GitHub dependency graph: 8 direct and 4 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaiohttpdirect
PyPIfsspecdirect
PyPImatplotlibdirect
PyPInumpydirect
PyPIpandasdirect
PyPIrequestsdirect
PyPIscipydirect
PyPIsoundfiledirect
PyPInumpydoc1.7.0indirect
PyPIreadthedocs-sphinx-search0.3.2indirect
PyPIsphinx7.0.0indirect
PyPIsphinx-rtd-theme3.0.0indirect
Dependency advisories 0

Installing pypi:wfdb@4.3.1 pulls in 31 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 v0.34.0 — full methodology · metrics wiki.

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