Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-13 00:55 UTC

tslearn-team / tslearn

The machine learning toolkit for time series analysis in Python

PythonBSD-2-Clause★ 3,168 stars⑂ 373 forkssince May 2017View on GitHub ↗

#time-series #timeseries #time-series-analysis #time-series-clustering #machine-learning #machine-learning-algorithms #machinelearning #dtw #python #time-series-classification #dynamic-time-warping

tslearn-team/tslearn holds a health index of 90 out of 100, placing it in the Excellent band. It scores highest on Community & Adoption (88/100) and lowest on AI Readiness (52/100). It was last updated today. A single contributor accounts for most of its recent work.

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.

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)

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 76 is calibrated to 90 on the published index scale (record calibration 2026-08-02).

Ownership

tslearn-teamOrganization

37 followers4 public repossince Apr 2020

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

Package ecosystems

Registry	Package	Version	Downloads / mo	Versions	Last publish
PyPI	tslearn	0.9.0	274,832	85	41 days ago

Metrics by category

Vitality

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

87Excellent · 21% of overall

Development activity

89Excellent

How it's scored

36/36	Push recency — last push 0 days ago
24.9/36	Commit cadence — 36/52 weeks with commits
18/18	Commit volume — 102 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 19 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10

Inputs used

commits_last_year	102
human_commit_share	0.96
days_since_last_push	0
active_weeks_last_year	36

Release discipline

84Excellent

How it's scored

27/27	Ships releases — 26 releases published
36/36	Release recency — latest release 43 days ago
12.6/27	Release cadence — a release every ~139.6 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	26
latest_release_tag	v0.9.0
releases_from_tags	no
days_since_latest_release	43
mean_days_between_releases	139.6

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?

88Excellent · 17% of overall

Popularity & adoption

88Excellent

How it's scored

56.8/60	Stars — 3,168 stars
21.4/25	Forks — 373 forks
9.7/15	Watchers — 57 watchers

Inputs used

forks	373
stars	3,168
watchers	57
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

85Excellent

How it's scored

22.5/22.5	README
22.5/22.5	License — recognized license (BSD-2-Clause)
18/18	CONTRIBUTING guide
13.5/13.5	Code of conduct
0/7.2	Issue template
0/6.3	PR template

Inputs used

has_readme	yes
has_license	yes
readme_badges	5
has_contributing	yes
has_issue_template	no
has_code_of_conduct	yes
readme_badge_services	badge.fury.io, codecov.io, dev.azure.com, readthedocs.org, shields.io
has_pull_request_template	no

Ecosystem adoption (downloads)

91Excellent

How it's scored

72.5/80	Monthly downloads — 274,832 downloads/month across pypi
0/20	Registry dependents — not reported by this ecosystem

Inputs used

packages	tslearn
dependents	—
ecosystems	pypi
total_downloads	—
monthly_downloads	274,832
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?

66Good · 23% of overall

Maintainer resilience (bus factor)

40Weak

How it's scored

9/54	Bus factor — 1 contributor(s) cover half of all commits
7.4/22.5	Commit distribution — top contributor authored 67% of commits
13.5/13.5	Contributor breadth — 42 contributors
10/10	OpenSSF Scorecard: Contributors — project has 20 contributing companies or organizations

Inputs used

bus_factor	1
contributors_sampled	42
top_contributor_share	0.672

Issue & PR responsiveness

65Good

How it's scored

33.5/42	Issue resolution — 80% of issues closed
23.4/30	PR acceptance — 243/312 decided PRs merged
0/13	Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15	OpenSSF Scorecard: Code-Review — Found 2/26 approved changesets -- score normalized to 0

Inputs used

merged_prs	243
open_issues	68
closed_issues	269
prs_merged_7d	0
prs_decided_7d	0
prs_merged_30d	4
prs_decided_30d	7
issue_closed_ratio	0.798
closed_unmerged_prs	69
first_time_authors_30d	0
first_time_prs_merged_30d	0
first_time_prs_decided_30d	0

Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

73Good

How it's scored

30/30	Ownership backing — organization-owned
0/20	Verified domain — verified-domain status not read for this organization
11.4/25	Owner reach — 37 followers of tslearn-team
17.1/25	Track record — 4 public repos, account ~6 yr old

Inputs used

followers	37
owner_type	Organization
is_verified	—
owner_login	tslearn-team
public_repos	4
account_age_days	2,296

Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
35/35	Publish recency — latest publish 41 days ago
20/20	Version history — 85 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	tslearn
ecosystems	pypi
any_deprecated	no
min_days_since_publish	41

Engineering Quality

Are baseline engineering and documentation practices in place?

81Excellent · 19% of overall

Engineering practices

68Good

How it's scored

24/24	CI workflows — 3 workflow(s)
24/24	Tests present
0/16	Linter config
0/9.6	Pre-commit hooks
0/6.4	.editorconfig
20/20	OpenSSF Scorecard: CI-Tests — 24 out of 24 merged PRs checked by a CI test -- score normalized to 10

Inputs used

has_ci	yes
has_tests	yes
has_editorconfig	no
has_linter_config	no
has_precommit_config	no

Documentation

100Exceptional

How it's scored

30/30	README
25/25	Documentation directory
15/15	Documentation / homepage site — https://tslearn.readthedocs.io
10/10	Repository description
10/10	Topics — 11 topics
10/10	Wiki

Inputs used

topics	time-series, timeseries, time-series-analysis, time-series-clustering, machine-learning, machine-learning-algorithms, machinelearning, dtw, python, time-series-classification, dynamic-time-warping
has_wiki	yes
homepage	https://tslearn.readthedocs.io
docs_site	https://tslearn.readthedocs.io
has_readme	yes
has_docs_dir	yes
has_description	yes

Security

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

61Moderate · 16% of overall

Security posture

51Moderate

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
0/7.5	Branch-Protection — branch protection not enabled on development/release branches
2.5/2.5	CI-Tests — 24 out of 24 merged PRs checked by a CI test -- score normalized to 10
0/2.5	CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5	Code-Review — Found 2/26 approved changesets -- score normalized to 0
2.5/2.5	Contributors — project has 20 contributing companies or organizations
10/10	Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5	Dependency-Update-Tool — update tool detected
0/5	Fuzzing — project is not fuzzed
2.5/2.5	License — license file detected
7.5/7.5	Maintained — 19 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
0/5	Packaging — no data
0/5	Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5	SAST — SAST tool is not run on all commits -- score normalized to 0
0/5	Security-Policy — security policy file not detected
0/7.5	Signed-Releases — no data
0/7.5	Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5	Vulnerabilities — 0 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	16
scorecard_version	v5.5.0
checks_inconclusive	2
scorecard_aggregate	5.1

Excluded from scoring (no data or not applicable): Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional

How it's scored

35/35	Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25	Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40	No advisories left outstanding — no advisory carries a publication date

Inputs used

source	osv
advisories	0
affected_packages	0
assessed_packages	14
unassessed_packages	0
affected_by_severity	none
direct_affected_packages	0

Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:tslearn@0.9.0 runtime dependency closure — what installing the published package pulls in — 14 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.

52Moderate · 4% of overall

Agent context & guidance

38Weak

How it's scored

0/45	Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15	Machine-readable docs (llms.txt)
37.8/40	Legible commit history — 68 of 96 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	0.708
agent_instruction_files	—
agent_instruction_max_bytes	—

Verify loop (build / test / typecheck)

48Weak

How it's scored

18/18	One-command bootstrap — docs/Makefile
22/22	Automated tests
0/11	Lint / format config
0/11	Static type checking
0/10	Reproducible environment
0/10	Demonstrated agent practice — no agent-authored commits among the last 100
8/8	Automated maintenance — 4 of the last 100 commits are automated dependency updates
0/10	OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0

Inputs used

has_nix	no
has_tests	yes
lockfiles	—
has_dockerfile	no
typed_language	no
bootstrap_files	docs/Makefile
has_devcontainer	no
has_linter_config	no
typecheck_configs	—
agent_commit_share	0
toolchain_manifests	—
dependency_bot_commit_share	0.04

Code legibility for models

46Weak

How it's scored

0/45	Type-checkable code — Python without a type-check config
45.9/55	Manageable file sizes — 23/139 source files over 60KB

Inputs used

primary_language	Python
largest_source_bytes	453,560
source_files_sampled	139
oversized_source_files	23

Machine-readable interfaces

100Exceptional

How it's scored

0/40	API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20	MCP server — not applicable to this kind of software
40/40	Runnable examples — examples

Inputs used

example_dirs	examples
has_mcp_signal	no
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

3,168GitHub stars

42contributors

102commits, last 12 months

0days since last push

26releases

1bus factor

68open 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 ★ / 373 ⇿

0Stars

373Forks

26Releases

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.

Major 0Minor 6Patch 12

Each point covers 9 days.

OpenSSF Scorecard 5.1 / 10

5.1aggregate

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 · 2026-08-13 00:54 UTC

10	Binary-Artifacts	no binaries found in the repo
0	Branch-Protection	branch protection not enabled on development/release branches
10	CI-Tests	24 out of 24 merged PRs checked by a CI test -- score normalized to 10
0	CII-Best-Practices	no effort to earn an OpenSSF best practices badge detected
0	Code-Review	Found 2/26 approved changesets -- score normalized to 0
10	Contributors	project has 20 contributing companies or organizations
10	Dangerous-Workflow	no dangerous workflow patterns detected
10	Dependency-Update-Tool	update tool detected
0	Fuzzing	project is not fuzzed
10	License	license file detected
10	Maintained	19 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
n/a	Packaging	packaging workflow not detected
0	Pinned-Dependencies	dependency not pinned by hash detected -- score normalized to 0
0	SAST	SAST tool is not run on all commits -- score normalized to 0
0	Security-Policy	security policy file not detected
n/a	Signed-Releases	no releases found
0	Token-Permissions	detected GitHub workflow tokens with excessive permissions
10	Vulnerabilities	0 existing vulnerabilities detected

Direct dependencies 6

Registry	Package	Version constraint	Manifest
PyPI	scikit-learn	`>=1.5`	pyproject.toml
PyPI	numpy	`>=1.24.3`	pyproject.toml
PyPI	scipy	`>=1.10.1`	pyproject.toml
PyPI	numba	`>=0.61`	pyproject.toml
PyPI	joblib	`>=1.2`	pyproject.toml
PyPI	statsmodels	`>=0.14`	pyproject.toml

All dependencies 56

Full resolved dependency set from the GitHub dependency graph: 11 direct and 45 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	joblib	`—`	direct
PyPI	joblib	`1.5.3`	direct
PyPI	numba	`—`	direct
PyPI	numba	`0.65.0`	direct
PyPI	numpy	`—`	direct
PyPI	numpy	`2.4.4`	direct
PyPI	scikit-learn	`—`	direct
PyPI	scikit-learn	`1.8.0`	direct
PyPI	scipy	`—`	direct
PyPI	scipy	`1.17.1`	direct
PyPI	statsmodels	`—`	direct
PyPI	accessible-pygments	`0.0.5`	indirect
PyPI	alabaster	`1.0.0`	indirect
PyPI	babel	`2.18.0`	indirect
PyPI	beautifulsoup4	`4.14.3`	indirect
PyPI	certifi	`2026.2.25`	indirect
PyPI	charset-normalizer	`3.4.7`	indirect
PyPI	contourpy	`1.3.3`	indirect
PyPI	cycler	`0.12.1`	indirect
PyPI	docutils	`0.22.4`	indirect
PyPI	fonttools	`4.62.1`	indirect
PyPI	idna	`3.15`	indirect
PyPI	imagesize	`2.0.0`	indirect
PyPI	jinja2	`3.1.6`	indirect
PyPI	keras	`—`	indirect
PyPI	kiwisolver	`1.5.0`	indirect
PyPI	llvmlite	`0.47.0`	indirect
PyPI	markupsafe	`3.0.3`	indirect
PyPI	matplotlib	`3.10.8`	indirect
PyPI	numpydoc	`1.10.0`	indirect
PyPI	packaging	`26.1`	indirect
PyPI	pillow	`12.3.0`	indirect
PyPI	pydata-sphinx-theme	`0.17.0`	indirect
PyPI	pygments	`2.20.0`	indirect
PyPI	pypandoc	`1.17`	indirect
PyPI	pyparsing	`3.3.2`	indirect
PyPI	python-dateutil	`2.9.0.post0`	indirect
PyPI	requests	`2.33.1`	indirect
PyPI	roman-numerals	`4.1.0`	indirect
PyPI	setuptools	`—`	indirect
PyPI	setuptools-scm	`—`	indirect
PyPI	six	`1.17.0`	indirect
PyPI	snowballstemmer	`3.0.1`	indirect
PyPI	soupsieve	`2.8.4`	indirect
PyPI	sphinx	`9.1.0`	indirect
PyPI	sphinx-copybutton	`0.5.2`	indirect
PyPI	sphinx-gallery	`0.20.0`	indirect
PyPI	sphinxcontrib-applehelp	`2.0.0`	indirect
PyPI	sphinxcontrib-devhelp	`2.0.0`	indirect
PyPI	sphinxcontrib-htmlhelp	`2.1.0`	indirect
PyPI	sphinxcontrib-jsmath	`1.0.1`	indirect
PyPI	sphinxcontrib-qthelp	`2.0.0`	indirect
PyPI	sphinxcontrib-serializinghtml	`2.0.0`	indirect
PyPI	threadpoolctl	`3.6.0`	indirect
PyPI	typing-extensions	`4.15.0`	indirect
PyPI	urllib3	`2.7.0`	indirect

Dependency advisories 0

Installing pypi:tslearn@0.9.0 pulls in 14 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.

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 statistics — PyPI.

Software health index

Score profile

Ownership

Package ecosystems

Metrics by category

Key facts

Data collection warnings

More detail

Feedback

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