Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-09 18:50 UTC

legend-exp / dspeed

Fast Digital Signal Processing for particle detector signals in Python

PythonGPL-3.0★ 6 stars⑂ 19 forkssince May 2023View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

legend-exp/dspeed holds a health index of 88 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (92/100) and lowest on Community & Adoption (43/100). It was last updated today. 2 contributors account for most of its recent work.

88
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.

88
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 74 is calibrated to 88 on the published index scale (record calibration 2026-08-02).

Ownership

LEGEND ExperimentOrganization
65 followers72 public repossince Feb 2017

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIdspeed2.4.25,154370 days ago

Metrics by category

Vitality

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

91Excellent · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
20.8/36Commit cadence — 30/52 weeks with commits
18/18Commit volume — 183 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 6 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year183
human_commit_share0.88
days_since_last_push0
active_weeks_last_year30

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 37 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~32.4 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count37
latest_release_tagv2.4.2
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases32.4
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?

43Weak · 17% of overall
How it's scored
11.3/60Stars — 6 stars
10.5/25Forks — 19 forks
3.3/15Watchers — 5 watchers
Inputs used
forks19
stars6
watchers5
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (GPL-3.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges10
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno
How it's scored
49.5/80Monthly downloads — 5,154 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesdspeed
dependents—
ecosystemspypi
total_downloads—
monthly_downloads5,154
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?

74Good · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
13.6/22.5Commit distribution — top contributor authored 39% of commits
13.5/13.5Contributor breadth — 24 contributors
10/10OpenSSF Scorecard: Contributors — project has 13 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled24
top_contributor_share0.394
How it's scored
28.3/42Issue resolution — 67% of issues closed
28/30PR acceptance — 158/169 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
6/15OpenSSF Scorecard: Code-Review — Found 2/5 approved changesets -- score normalized to 4
Inputs used
merged_prs158
open_issues18
closed_issues37
prs_merged_7d3
prs_decided_7d4
prs_merged_30d5
prs_decided_30d6
issue_closed_ratio0.673
closed_unmerged_prs11
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
13.1/25Owner reach — 65 followers of legend-exp
25/25Track record — 72 public repos, account ~9 yr old
Inputs used
followers65
owner_typeOrganization
is_verifiedno
owner_loginlegend-exp
public_repos72
account_age_days3,525

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 37 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesdspeed
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.flake8], [tool.isort])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 6 out of 6 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://dspeed.readthedocs.io
10/10Repository description
10/10Topics — 3 topics
0/10Wiki
Inputs used
topicsdigital-signal-processing, particle-detectors, vectorization
has_wikino
homepagehttps://dspeed.readthedocs.io
docs_sitehttps://dspeed.readthedocs.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

62Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2.5/2.5CI-Tests — 6 out of 6 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
3/7.5Code-Review — Found 2/5 approved changesets -- score normalized to 4
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
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 6 issue activity found in the last 90 days -- score normalized to 10
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_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6.2
Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.

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.

72Good · 4% of overall
How it's scored
45/45Agent instructions — AGENTS.md
0/15Machine-readable docs (llms.txt)
24.8/40Legible commit history — 41 of 88 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.466
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes968
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.flake8], [tool.isort])
0/11Static type checking
0/10Reproducible environment
10/10Demonstrated agent practice — 15 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 5 of the last 100 commits are automated dependency updates
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_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.15
toolchain_manifests—
dependency_bot_commit_share0.05
How it's scored
0/45Type-checkable code — Python without a type-check config
54.5/55Manageable file sizes — 1/101 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes110,755
source_files_sampled101
oversized_source_files1
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 — notebooks
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

6GitHub stars
24contributors
183commits, last 12 months
0days since last push
37releases
2bus factor
18open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:dspeed@2.4.2; advisories assessed against the repository dependency graph instead
  • No resolved dependencies carried a version and a supported ecosystem

More detail

Star and fork history 0 ★ / 19 ⇿
0Stars
19Forks
34Releases

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.

0481216201922023-062025-022026-10
Major 1Minor 12Patch 12

Each point covers 3 days.

OpenSSF Scorecard 6.2 / 10
6.2aggregate

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 · 2026-10-09 18:50 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
10CI-Tests6 out of 6 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
4Code-ReviewFound 2/5 approved changesets -- score normalized to 4
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
10Maintained30 commit(s) and 6 issue activity found in the last 90 days -- score normalized to 10
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 16
RegistryPackageVersion constraintManifest
PyPIPyWavelets—pyproject.toml
PyPIcolorlog—pyproject.toml
PyPIdbetto—pyproject.toml
PyPIh5py>=3.2pyproject.toml
PyPIiminuit—pyproject.toml
PyPIlegend-pydataobj>=2pyproject.toml
PyPIlegend-lh5io>=0.2.3pyproject.toml
PyPImatplotlib—pyproject.toml
PyPInumba!=0.53.*,!=0.54.*pyproject.toml
PyPInumpy>2pyproject.toml
PyPIparse—pyproject.toml
PyPIpint—pyproject.toml
PyPIpyarrow—pyproject.toml
PyPIpyyaml—pyproject.toml
PyPIscipy—pyproject.toml
PyPItqdm>=4.27pyproject.toml
All dependencies 9

Full resolved dependency set from the GitHub dependency graph: 6 direct and 3 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIh5py—direct
PyPIlegend-lh5io—direct
PyPIlegend-pydataobj—direct
PyPInumba—direct
PyPInumpy—direct
PyPItqdm—direct
PyPIpytest—indirect
PyPIsetuptools—indirect
PyPIsetuptools-scm—indirect
Dependency advisories not assessed

Advisory matching could not run for this report: No resolved dependencies carried a version and a supported ecosystem

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.