Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-13 15:48 UTC

dynamicslab / pysindy

A package for the sparse identification of nonlinear dynamical systems from data

PythonCustom license★ 1,899 stars⑂ 387 forkssince May 2019View on GitHub ↗

dynamicslab/pysindy holds a health index of 83 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (92/100) and lowest on Vitality (50/100). It was last updated 94 days ago. 2 contributors account for most of its recent work.

83
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.

83
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 70 is calibrated to 83 on the published index scale (record calibration 2026-08-02).

Ownership

dynamicslabOrganization
1,481 followers20 public repossince Jan 2020

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIpysindy2.1.027,12438247 days ago

Metrics by category

Vitality

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

50Moderate · 21% of overall
How it's scored
9.9/36Push recency — last push 94 days ago
13.8/36Commit cadence — 20/52 weeks with commits
18/18Commit volume — 120 commits in the last year
0/10OpenSSF Scorecard: Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year120
human_commit_share1
days_since_last_push94
active_weeks_last_year20
How it's scored
27/27Ships releases — 39 releases published
16.2/36Release recency — latest release 247 days ago
12.6/27Release cadence — a release every ~141.6 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count39
latest_release_tagv2.1.0
releases_from_tagsno
days_since_latest_release247
mean_days_between_releases141.6
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?

74Good · 17% of overall
How it's scored
53.2/60Stars — 1,899 stars
21.6/25Forks — 387 forks
8.3/15Watchers — 32 watchers
Inputs used
forks387
stars1,899
watchers32
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno

Sustainability & Governance

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

78Good · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
15.1/22.5Commit distribution — top contributor authored 33% of commits
13.5/13.5Contributor breadth — 26 contributors
10/10OpenSSF Scorecard: Contributors — project has 13 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled26
top_contributor_share0.329
How it's scored
34.1/42Issue resolution — 81% of issues closed
26.6/30PR acceptance — 172/194 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs172
open_issues78
closed_issues336
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.812
closed_unmerged_prs22
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
22.8/25Owner reach — 1,481 followers of dynamicslab
21.6/25Track record — 20 public repos, account ~6 yr old
Inputs used
followers1,481
owner_typeOrganization
is_verifiedno
owner_logindynamicslab
public_repos20
account_age_days2,435
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
26/35Publish recency — latest publish 247 days ago
20/20Version history — 38 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagespysindy
ecosystemspypi
any_deprecatedno
min_days_since_publish247

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall
How it's scored
24/24CI workflows — 6 workflow(s)
24/24Tests present
16/16Linter config — .flake8, pyproject.toml ([tool.black], [tool.autopep8])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 2 out of 2 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://pysindy.readthedocs.io/en/latest/
10/10Repository description
10/10Topics — 6 topics
0/10Wiki
Inputs used
topicsdynamical-systems, machine-learning, sparse-regression, model-discovery, system-identification, nonlinear-dynamics
has_wikino
homepagehttps://pysindy.readthedocs.io/en/latest/
docs_sitehttps://pysindy.readthedocs.io/en/latest/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

58Moderate · 16% of overall
How it's scored
0/7.5Binary-Artifacts — binaries present in source code
2.2/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 2 out of 2 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 13 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.2/2.5License — license file detected
0/7.5Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate4.8
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

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_packages11
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:pysindy@2.1.0 runtime dependency closure — what installing the published package pulls in — 11 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.

53Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
17.1/40Legible commit history — 32 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.32
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — examples/data/vonKarman_pod/pod_modes/makefile
22/22Automated tests
11/11Lint / format config — .flake8, pyproject.toml ([tool.black], [tool.autopep8])
0/11Static type checking
0/10Reproducible environment
10/10Demonstrated agent practice — 6 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesexamples/data/vonKarman_pod/pod_modes/makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.06
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/84 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes55,722
source_files_sampled84
oversized_source_files0
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, notebooks
Inputs used
example_dirsexamples, notebooks
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

1,899GitHub stars
26contributors
120commits, last 12 months
94days since last push
39releases
2bus factor
78open 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 ★ / 387 ⇿
0Stars
387Forks
36Releases

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.

010020030040038472020-032023-062026-09
Major 2Minor 7Patch 21

Each point covers 6 days.

OpenSSF Scorecard 4.8 / 10
4.8aggregate

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 · 2026-09-13 15:47 UTC

0Binary-Artifactsbinaries present in source code
3Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests2 out of 2 merged PRs checked by a CI test -- score normalized to 10
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
9Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
10Packagingpackaging workflow 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 5
RegistryPackageVersion constraintManifest
PyPIscikit-learn>=1.1, !=1.5.0, !=1.6.0, !=1.7.1pyproject.toml
PyPInumpy>=2.0pyproject.toml
PyPIderivative>=0.6.2pyproject.toml
PyPIscipy—pyproject.toml
PyPItyping_extensions—pyproject.toml
All dependencies 5

Full resolved dependency set from the GitHub dependency graph: 5 direct and 0 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIderivative—direct
PyPInumpy—direct
PyPIscikit-learn—direct
PyPIscipy—direct
PyPItyping-extensions—direct
Dependency advisories 0

Installing pypi:pysindy@2.1.0 pulls in 11 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 statistics — PyPI.