Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 22:52 UTC

Unidata / MetPy

MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.

PythonBSD-3-Clause★ 1,442 stars⑂ 449 forkssince Feb 2011View on GitHub ↗
KindPluginLibraryhow this is determined

Unidata/MetPy holds a health index of 87 out of 100, placing it in the Excellent band. It scores highest on Community & Adoption (87/100) and lowest on Security (46/100). It was last updated 3 days ago. A single contributor accounts for most of its recent work.

87
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.

87
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 73 is calibrated to 87 on the published index scale (record calibration 2026-08-02).

Ownership

NSF UnidataOrganization · verified domain
316 followers132 public repossince Feb 2011

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIMetPy1.7.1171,62643383 days agometeorologyweather

Metrics by category

Vitality

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

80Excellent · 21% of overall
How it's scored
36/36Push recency — last push 3 days ago
30.5/36Commit cadence — 44/52 weeks with commits
18/18Commit volume — 313 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year313
human_commit_share0.54
days_since_last_push3
active_weeks_last_year44
How it's scored
27/27Ships releases — 43 releases published
7.2/36Release recency — latest release 383 days ago
19.8/27Release cadence — a release every ~108.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count43
latest_release_tagv1.7.1
releases_from_tagsno
days_since_latest_release383
mean_days_between_releases108.9

Community & Adoption

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

87Excellent · 17% of overall
How it's scored
51.2/60Stars — 1,442 stars
22.1/25Forks — 449 forks
9.8/15Watchers — 59 watchers
Inputs used
forks449
stars1,442
watchers59
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (BSD-3-Clause)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges10
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesapp.codacy.com, codecov.io, github.com, shields.io
has_pull_request_templateyes
How it's scored
69.8/80Monthly downloads — 171,626 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesMetPy
dependents—
ecosystemspypi
total_downloads—
monthly_downloads171,626
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?

69Good · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
7.8/22.5Commit distribution — top contributor authored 65% of commits
13.5/13.5Contributor breadth — 85 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled85
top_contributor_share0.653
How it's scored
31/42Issue resolution — 74% of issues closed
26.4/30PR acceptance — 2,264/2,573 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs2,264
open_issues321
closed_issues907
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.739
closed_unmerged_prs309
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
20/20Verified domain
18/25Owner reach — 316 followers of Unidata
25/25Track record — 132 public repos, account ~15 yr old
Inputs used
followers316
owner_typeOrganization
is_verifiedyes
owner_loginUnidata
public_repos132
account_age_days5,697
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
14/35Publish recency — latest publish 383 days ago
20/20Version history — 43 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesMetPy
ecosystemspypi
any_deprecatedno
min_days_since_publish383

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall
How it's scored
24/24CI workflows — 15 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff], [tool.isort]), setup.cfg ([flake8], [pycodestyle])
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://unidata.github.io/MetPy/
10/10Repository description
10/10Topics — 10 topics
0/10Wiki
Inputs used
topicspython, atmospheric-science, meteorology, weather, plotting, scientific-computations, hodograph, skew-t, weather-data, hacktoberfest
has_wikino
homepagehttps://unidata.github.io/MetPy/
docs_sitehttps://unidata.github.io/MetPy/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

46Weak · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsci/requirements.txt, pyproject.toml, setup.cfg
has_codeql_workflowno
has_security_policyno
has_dependabot_configyes
Excluded from scoring (no data or not applicable): Dependency lockfiles. 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_packages27
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:MetPy@1.7.1 runtime dependency closure — what installing the published package pulls in — 27 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.

65Good · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 47 of 54 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.87
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — docs/Makefile, tools/metar_parser/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.isort]), setup.cfg ([flake8], [pycodestyle])
0/11Static type checking
10/10Reproducible environment — devcontainer, Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 46 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesdocs/Makefile, tools/metar_parser/Makefile
has_devcontaineryes
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.46
How it's scored
0/45Type-checkable code — Python without a type-check config
51.1/55Manageable file sizes — 13/182 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes228,959
source_files_sampled182
oversized_source_files13
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

1,442GitHub stars
85contributors
313commits, last 12 months
3days since last push
43releases
1bus factor
321open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 0 ★ / 449 ⇿
0Stars
449Forks
43Releases

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.

010020030040050043092012-092019-092026-09
Major 1Minor 19Patch 21

Each point covers 13 days.

Direct dependencies 9
RegistryPackageVersion constraintManifest
PyPImatplotlib>=3.7.0pyproject.toml
PyPInumpy>=1.25.0pyproject.toml
PyPIpandas>=2.1.0pyproject.toml
PyPIpint>=0.22pyproject.toml
PyPIpooch>=1.2.0pyproject.toml
PyPIpyproj>=3.4.0pyproject.toml
PyPIscipy>=1.10.0pyproject.toml
PyPItraitlets>=5.1.0pyproject.toml
PyPIxarray>=2022.6.0pyproject.toml
All dependencies 46

Full resolved dependency set from the GitHub dependency graph: 18 direct and 28 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPImatplotlib—direct
PyPImatplotlib3.10.9direct
PyPInumpy—direct
PyPInumpy2.4.6direct
PyPIpandas—direct
PyPIpandas3.0.5direct
PyPIpint—direct
PyPIpint0.25.3direct
PyPIpooch—direct
PyPIpooch1.9.0direct
PyPIpyproj—direct
PyPIpyproj3.7.2direct
PyPIscipy—direct
PyPIscipy1.17.1direct
PyPItraitlets—direct
PyPItraitlets5.16.1direct
PyPIxarray—direct
PyPIxarray2025.10.1direct
PyPIboto31.43.14indirect
PyPIcartopy0.25.0indirect
PyPIcodespell2.4.1indirect
PyPIcoverage7.15.0indirect
PyPIdask2026.7.1indirect
PyPIdoc82.0.0indirect
PyPIflake87.3.0indirect
PyPIflake8-broken-line1.0.0indirect
PyPIflake8-isort7.0.0indirect
PyPIflake8-requirements2.3.0indirect
PyPIflake8-rst-docstrings0.4.0indirect
PyPIgeopandas1.1.2indirect
PyPIisort8.0.0indirect
PyPImyst-parser5.1.0indirect
PyPInetcdf41.7.4indirect
PyPIpackaging26.3indirect
PyPIpycodestyle2.14.0indirect
PyPIpydata-sphinx-theme0.16.1indirect
PyPIpyflakes3.4.0indirect
PyPIpytest9.1.1indirect
PyPIpytest-mpl0.19.0indirect
PyPIrestructuredtext-lint2.0.2indirect
PyPIruff0.16.0indirect
PyPIshapely2.1.2indirect
PyPIsphinx9.0.4indirect
PyPIsphinx-design0.7.0indirect
PyPIsphinx-gallery0.21.0indirect
PyPIvcrpy8.3.0indirect
Dependency advisories 0

Installing pypi:MetPy@1.7.1 pulls in 27 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.