Public record
Software health reportschema 0.27.0 · metrics 1.13.0 · 2026-07-24 21:12 UTC

MTgeophysics / mtpy-v2

Tools to work with magnetotelluric transfer functions, based on MTH5 and mt-metadata. This is an update to mtpy.

PythonMIT★ 61 stars⑂ 24 forkssince Oct 2023View on GitHub ↗

MTgeophysics/mtpy-v2 holds a health index of 66 out of 100, placing it in the Moderate band. It scores highest on Vitality (88/100) and lowest on Security (36/100). It was last updated today. A single contributor accounts for most of its recent work.

66
overall / 100
Moderate

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At risk ceiling of 49. AI Readiness sits outside the overall score.

66
Excellent85-100Exemplary; meets essentially all checked criteria
Good70-84Healthy; minor gaps
Moderate50-69Acceptable with notable gaps; review recommended
At risk30-49Significant weaknesses; adoption warrants caution
Critical1-29Severe 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.

Ownership

59 followers9 public repossince Oct 2017

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPImtpy-v22.1.48,021200 days agomagnetotellurics

Metrics by category

Vitality

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

88Excellent · 22% of overall
How it's scored
36/36Push recency — last push 0 days ago
21.5/36Commit cadence — 31/52 weeks with commits
18/18Commit volume — 422 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year422
human_commit_share0.98
days_since_last_push0
active_weeks_last_year31
How it's scored
27/27Ships releases — 19 releases published
36/36Release recency — latest release 0 days ago
19.8/27Release cadence — a release every ~73.5 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count19
latest_release_tagv2.1.4
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases73.5
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?

56Moderate · 18% of overall
How it's scored
28.8/60Stars — 61 stars
11.3/25Forks — 24 forks
3.3/15Watchers — 5 watchers
Inputs used
forks24
stars61
watchers5
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
7.2/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
has_contributingno
has_issue_templateyes
has_code_of_conductno
has_pull_request_templateyes
How it's scored
52.1/80Monthly downloads — 8,021 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesmtpy-v2
dependents
ecosystemspypi
total_downloads
monthly_downloads8,021
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?

54Moderate · 24% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
1.4/22.5Commit distribution — top contributor authored 94% of commits
5.4/13.5Contributor breadth — 4 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled4
top_contributor_share0.938
How it's scored
12.9/46.8Issue resolution — 28% of issues closed
37.5/38.3PR acceptance — 53/54 decided PRs merged
1.5/15OpenSSF Scorecard: Code-Review — Found 1/8 approved changesets -- score normalized to 1
Inputs used
merged_prs53
open_issues34
closed_issues13
issue_closed_ratio0.277
closed_unmerged_prs1
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
12.8/25Owner reach — 59 followers of MTgeophysics
19.3/25Track record — 9 public repos, account ~8 yr old
Inputs used
followers59
owner_typeOrganization
is_verified
owner_loginMTgeophysics
public_repos9
account_age_days3,194
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 20 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesmtpy-v2
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

87Excellent · 20% of overall
How it's scored
24/24CI workflows — 5 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
4/20OpenSSF Scorecard: CI-Tests — 1 out of 5 merged PRs checked by a CI test -- score normalized to 2
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://mtpy-v2.readthedocs.io/en/latest/index.html
10/10Repository description
10/10Topics — 3 topics
10/10Wiki
Inputs used
topicsmagnetotellurics, mt-metadata, mth5
has_wikiyes
homepagehttps://mtpy-v2.readthedocs.io/en/latest/index.html
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

36At risk · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
0.5/2.5CI-Tests — 1 out of 5 merged PRs checked by a CI test -- score normalized to 2
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-Review — Found 1/8 approved changesets -- score normalized to 1
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
0/10Dangerous-Workflow — dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 0 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_aggregate3.6
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? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score.

55Moderate · 0% of overall
How it's scored
45/45Agent instructions — .github/copilot-instructions.md
0/15Machine-readable docs (llms.txt)
23.9/40Legible commit history — 44 of 98 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
legible_history_share0.449
agent_instruction_files.github/copilot-instructions.md
agent_instruction_max_bytes704
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practice — no agent-authored commits among the last 100
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_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
53/55Manageable file sizes — 8/224 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes164,225
source_files_sampled224
oversized_source_files8
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — notebooks, recipes
Inputs used
example_dirsnotebooks, recipes
has_mcp_signalno
api_schema_files

Key facts

61GitHub stars
4contributors
422commits, last 12 months
0days since last push
19releases
1bus factor
34open 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:mtpy-v2@2.1.4; 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 ★ / 24 ⇿
0Stars
24Forks
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.

048121620242422023-122025-042026-07
Major 0Minor 1Patch 11

Each point covers 3 days.

OpenSSF Scorecard 3.6 / 10
3.6aggregate

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 · 2026-07-24 21:11 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
2CI-Tests1 out of 5 merged PRs checked by a CI test -- score normalized to 2
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 1/8 approved changesets -- score normalized to 1
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
0Dangerous-Workflowdangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 0 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
PyPInumpypyproject.toml
PyPIscipypyproject.toml
PyPIxarray>=2024.10.0pyproject.toml
PyPImatplotlib>=3.7pyproject.toml
PyPIpyprojpyproject.toml
PyPIconfigparserpyproject.toml
PyPImt_metadatapyproject.toml
PyPImth5pyproject.toml
PyPIgeopandaspyproject.toml
PyPIrasteriopyproject.toml
PyPIcontextilypyproject.toml
PyPIpyevtkpyproject.toml
PyPIsimpegpyproject.toml
PyPIaurorapyproject.toml
PyPIbokehpyproject.toml
PyPIpanelpyproject.toml
All dependencies 13

Full resolved dependency set from the GitHub dependency graph: 3 direct and 10 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPImatplotlibdirect
PyPImth5direct
PyPIxarraydirect
PyPIipykernelindirect
PyPIipythonindirect
PyPIjupyter-clientindirect
PyPIloguruindirect
PyPInbsphinxindirect
PyPIpytestindirect
PyPIpyyamlindirect
PyPIsetuptoolsindirect
PyPIsphinxindirect
PyPIsphinx-rtd-themeindirect
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
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                "key": "openssf_scorecard_contributors",
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                "max_points": 10
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                "key": "pr_acceptance",
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              "followers": 59,
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                "key": "owner_reach",
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              {
                "key": "fuzzing",
                "name": "Fuzzing",
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                "key": "license",
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                "key": "maintained",
                "name": "Maintained",
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                "key": "packaging",
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                "key": "pinned_dependencies",
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                "detail": "dependency not pinned by hash detected -- score normalized to 0",
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                "key": "sast",
                "name": "SAST",
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                "key": "security_policy",
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              {
                "key": "signed_releases",
                "name": "Signed-Releases",
                "detail": "no releases found",
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                "status": "excluded",
                "details": [
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                ],
                "max_points": 7.5
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              {
                "key": "token_permissions",
                "name": "Token-Permissions",
                "detail": "detected GitHub workflow tokens with excessive permissions",
                "points": 0,
                "status": "missed",
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              },
              {
                "key": "vulnerabilities",
                "name": "Vulnerabilities",
                "detail": "0 existing vulnerabilities detected",
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            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
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            "notes": [
              {
                "code": "jurisdiction_evidence_limits",
                "params": {}
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            ],
            "value": 100,
            "inputs": {
              "meaning": "self-published location evidence; not nationality or citizenship",
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              "exposures": [],
              "policy_countries": [
                "Russia",
                "Iran",
                "North Korea"
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              "review_only_matches": 0,
              "assessed_self_published_locations": 1
            },
            "components": [
              {
                "key": "policy_exposure_multiplier",
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                "points": 100,
                "status": "met",
                "details": [
                  {
                    "code": "jurisdiction_no_match",
                    "params": {}
                  }
                ],
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              }
            ]
          }
        ],
        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
      },
      {
        "key": "ai_readiness",
        "band": "moderate",
        "name": "AI Readiness",
        "value": 55,
        "weight": 0,
        "metrics": [
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            "note": null,
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                "status": "partial",
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                    "code": "legible_history",
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                    }
                  }
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              }
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            "name": "Verify loop (build / test / typecheck)",
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            "inputs": {
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              },
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              {
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                ],
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              },
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                "key": "automated_maintenance",
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                "key": "openssf_scorecard_pinned_dependencies",
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            },
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                ],
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              },
              {
                "key": "manageable_file_sizes",
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                "status": "partial",
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                    }
                  }
                ],
                "max_points": 55
              }
            ]
          },
          {
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            "notes": [],
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            "inputs": {
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            },
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              },
              {
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                "points": 0,
                "status": "missed",
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              },
              {
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            ]
          }
        ],
        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [
    "Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
    "deps.dev does not index pypi:mtpy-v2@2.1.4; advisories assessed against the repository dependency graph instead",
    "No resolved dependencies carried a version and a supported ecosystem"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-24T21:12:16.183636Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/m/MTgeophysics/mtpy-v2.svg",
  "full_name": "MTgeophysics/mtpy-v2",
  "license_state": "standard",
  "license_spdx": "MIT"
}

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 v1.13.0, schema v0.27.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.