Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-19 14:23 UTC

fraunhoferportugal / tsfel

An intuitive library to extract features from time series.

PythonBSD-3-Clause★ 1,101 stars⑂ 157 forkssince Jan 2019View on GitHub ↗

fraunhoferportugal/tsfel holds a health index of 75 out of 100, placing it in the Good band. It scores highest on Engineering Quality (89/100) and lowest on AI Readiness (48/100). It was last updated 4 days ago. 2 contributors account for most of its recent work.

75
overall / 100
Good

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.

75
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 65 is calibrated to 75 on the published index scale (record calibration 2026-08-02).

Ownership

31 followers19 public repossince Jan 2015

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPItsfel0.2.0-13395 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
36/36Push recencylast push 4 days ago
1.4/36Commit cadence2/52 weeks with commits
6.3/18Commit volume4 commits in the last year
2/10OpenSSF Scorecard: Maintained3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
Inputs used
commits_last_year4
human_commit_share
days_since_last_push4
active_weeks_last_year2
How it's scored
27/27Ships releases12 releases published
7.2/36Release recencylatest release 395 days ago
12.6/27Release cadencea release every ~226.4 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count12
latest_release_tagv0.2.0
releases_from_tagsno
days_since_latest_release395
mean_days_between_releases226.4
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?

63Moderate · 17% of overall
How it's scored
49.3/60Stars1,101 stars
18.3/25Forks157 forks
6.8/15Watchers18 watchers
Inputs used
forks157
stars1,101
watchers18
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (BSD-3-Clause)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingno
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?

68Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
13.1/22.5Commit distributiontop contributor authored 42% of commits
13.5/13.5Contributor breadth14 contributors
10/10OpenSSF Scorecard: Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
Inputs used
bus_factor2
contributors_sampled14
top_contributor_share0.419
How it's scored
39.6/42Issue resolution94% of issues closed
24.7/30PR acceptance74/90 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 2/24 approved changesets -- score normalized to 0
Inputs used
merged_prs74
open_issues5
closed_issues84
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.944
closed_unmerged_prs16
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domain
10.8/25Owner reach31 followers of fraunhoferportugal
21.5/25Track record19 public repos, account ~11 yr old
Inputs used
followers31
owner_typeOrganization
is_verifiedno
owner_loginfraunhoferportugal
public_repos19
account_age_days4,259
How it's scored
25/25Published & resolvable1 package(s) on pypi
14/35Publish recencylatest publish 395 days ago
20/20Version history13 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagestsfel
ecosystemspypi
any_deprecatedno
min_days_since_publish395

Engineering Quality

Are baseline engineering and documentation practices in place?

89Excellent · 19% of overall
How it's scored
24/24CI workflows1 workflow(s)
24/24Tests present
16/16Linter config.flake8, pyproject.toml ([tool.black], [tool.isort])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
8/20OpenSSF Scorecard: CI-Tests2 out of 5 merged PRs checked by a CI test -- score normalized to 4
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 sitehttps://tsfel.readthedocs.io
10/10Repository description
10/10Topics6 topics
10/10Wiki
Inputs used
topicsclassification, colab-notebook, data-science, feature-engineering, feature-extraction, time-series
has_wikiyes
homepagehttps://tsfel.readthedocs.io
docs_sitehttps://tsfel.readthedocs.io
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
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
1/2.5CI-Tests2 out of 5 merged PRs checked by a CI test -- score normalized to 4
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 2/24 approved changesets -- score normalized to 0
2.5/2.5Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
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
1.5/7.5Maintained3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
1.5/5SASTSAST tool is not run on all commits -- score normalized to 3
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
6/7.5Vulnerabilities2 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate4.7
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_packages40
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:tsfel@0.2.0 runtime dependency closure — what installing the published package pulls in — 40 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)
0/40Legible commit historyno data
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
18/18One-command bootstrapdocs/Makefile
22/22Automated tests
11/11Lint / format config.flake8, pyproject.toml ([tool.black], [tool.isort])
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno data
0/8Automated maintenanceno data
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_share
toolchain_manifests
dependency_bot_commit_share
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.
How it's scored
0/45Type-checkable codePython without a type-check config
53.2/55Manageable file sizes1/31 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes83,107
source_files_sampled31
oversized_source_files1
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

1,101GitHub stars
14contributors
4commits, last 12 months
4days since last push
12releases
2bus factor
5open issues
PyPIpackage ecosystems

Data collection warnings

  • GraphQL snapshot failed, fell back to REST: GitHub GraphQL error: Something went wrong while executing your query on 2026-09-19T14:21:03Z. Please include `B702:115324:8030482:7C95739:6AAE9A4F` when reporting this issue.
  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'tsfel' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced

More detail

Star and fork history 0 ★ / 157 ⇿
0Stars
157Forks
12Releases

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.

025507510012515012942019-022022-122026-09
Major 0Minor 2Patch 9

Each point covers 7 days.

OpenSSF Scorecard 4.7 / 10
4.7aggregate

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 · 2026-09-19 14:22 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
4CI-Tests2 out of 5 merged PRs checked by a CI test -- score normalized to 4
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 2/24 approved changesets -- score normalized to 0
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
2Maintained3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
3SASTSAST tool is not run on all commits -- score normalized to 3
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
8Vulnerabilities2 existing vulnerabilities detected
All dependencies 18

Full resolved dependency set from the GitHub dependency graph: 0 direct and 18 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIcolorednoise2.2.0indirect
PyPIipythonindirect
PyPIipython8.27.0indirect
PyPImatplotlib3.9.2indirect
PyPInumpyindirect
PyPInumpydocindirect
PyPIpandasindirect
PyPIpre-commit3.8.0indirect
PyPIpydata-sphinx-themeindirect
PyPIpywaveletsindirect
PyPIrequestsindirect
PyPIscikit-learnindirect
PyPIscipyindirect
PyPIsetuptoolsindirect
PyPIsphinxindirect
PyPIsphinx-copybuttonindirect
PyPIsphinx-designindirect
PyPIstatsmodelsindirect
Dependency advisories 0

Installing pypi:tsfel@0.2.0 pulls in 40 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.