Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-21 03:58 UTC

marinerhemant / MIDAS

The repository provides codes under the Microstructural Identification using Diffraction Analysis Software

Jupyter Notebook · PythonCustom license★ 28 stars⑂ 21 forkssince Aug 2015View on GitHub ↗
KindCommand-line toolLibraryhow this is determined
#diffraction#midas#hedm#argonne

marinerhemant/MIDAS holds a health index of 77 out of 100, placing it in the Good band. It scores highest on Vitality (95/100) and lowest on Community & Adoption (52/100). It was last updated 6 days ago. A single contributor accounts for most of its recent work.

77
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.

77
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 66 is calibrated to 77 on the published index scale (record calibration 2026-08-02).

Ownership

Hemant SharmaPersonal account
13 followers1 public reposince Sep 2013@AdvancedPhotonSource

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPImidas-2d0.3.3145563 days ago—
PyPImidas-dt0.8.0122136 days agomidasxrd-ctdiffraction-tomographydtsynchrotronchemical-tomography
PyPImidas-ddd0.1.2625339 days agomidasdislocationdiscrete-dislocation-dynamicsdddexadisparadisstrohanisotropic-elasticitydislocation-loopeigenstraindifferentiablepytorch
PyPImidas-dfxm0.14.0901206 days ago—
PyPImidas-hkls0.16.01,6762020 days ago—
PyPImidas-index0.10.05822220 days agomidashedmindexingpytorchfar-fielddiffractioncrystallographypolycrystalgrain
PyPImidas-dct-tt0.1.3145440 days ago—
PyPImidas-defect0.9.11,616196 days ago—

Metrics by category

Vitality

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

95Exceptional · 21% of overall

Development activity

92Excellent
How it's scored
36/36Push recency — last push 6 days ago
28.4/36Commit cadence — 41/52 weeks with commits
18/18Commit volume — 2,589 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year2,589
human_commit_share1
days_since_last_push6
active_weeks_last_year41

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 6 days ago
27/27Release cadence — a release every ~0 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagmidas-saxs-v0.2.0
releases_from_tagsno
days_since_latest_release6
mean_days_between_releases0
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?

52Moderate · 17% of overall

Popularity & adoption

38Weak
How it's scored
23.2/60Stars — 28 stars
10.8/25Forks — 21 forks
3.9/15Watchers — 6 watchers
Inputs used
forks21
stars28
watchers6
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

64Moderate
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges1
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

58Moderate
How it's scored
46.4/80Monthly downloads — 3,005 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesmidas-dt, midas-ddd, midas-hkls, midas-index
dependents—
ecosystemspypi
total_downloads—
monthly_downloads3,005
unverified_packages_excludedmidas-2d, midas-dfxm, midas-dct-tt, midas-defect
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?

57Moderate · 23% of overall

Maintainer resilience (bus factor)

29At Risk
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.1/22.5Commit distribution — top contributor authored 100% of commits
9.5/13.5Contributor breadth — 7 contributors
10/10OpenSSF Scorecard: Contributors — project has 3 contributing companies or organizations -- score normalized to 10
Inputs used
bus_factor1
contributors_sampled7
top_contributor_share0.995

Issue & PR responsiveness

73Good
How it's scored
40.8/42Issue resolution — 97% of issues closed
22.3/30PR acceptance — 26/35 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs26
open_issues1
closed_issues34
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.971
closed_unmerged_prs9
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.

Ownership & stewardship

40Weak
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
8.2/25Owner reach — 13 followers of marinerhemant
14.2/25Track record — 1 public repos, account ~13 yr old
Inputs used
followers13
owner_typeUser
is_verified—
owner_loginmarinerhemant
public_repos1
account_age_days4,785
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 8 package(s) on pypi
35/35Publish recency — latest publish 6 days ago
20/20Version history — 22 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesmidas-2d, midas-dt, midas-ddd, midas-dfxm, midas-hkls, midas-index, midas-dct-tt, midas-defect
ecosystemspypi
any_deprecatedno
min_days_since_publish6

Engineering Quality

Are baseline engineering and documentation practices in place?

66Good · 19% of overall

Engineering practices

60Moderate
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
0/16Linter config
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_configno
has_precommit_configno
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

75Good
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://github.com/marinerhemant/MIDAS/tree/master/packages/midas_ddd
10/10Repository description
10/10Topics — 4 topics
10/10Wiki
Inputs used
topicsdiffraction, midas, hedm, argonne
has_wikiyes
homepage—
docs_sitehttps://github.com/marinerhemant/MIDAS/tree/master/packages/midas_ddd
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

55Moderate · 16% of overall

Security posture

44Weak
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
2.5/2.5Contributors — project has 3 contributing companies or organizations -- score normalized to 10
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.2/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 4 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 — no SAST tool detected
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_aggregate4.4
Excluded from scoring (no data or not applicable): CI-Tests, Signed-Releases. 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_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:midas-2d@0.3.3 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.

60Moderate · 4% of overall

Agent context & guidance

40Weak
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

53Moderate
How it's scored
18/18One-command bootstrap — packages/midas_defect/docs/Makefile
22/22Automated tests
0/11Lint / format config
11/11Static type checking — packages/midas_ff_pipeline/midas_ff_pipeline/py.typed, packages/midas_pipeline/midas_pipeline/py.typed
0/10Reproducible environment
2/10Demonstrated agent practice — 1 of the last 100 commits agent-authored or agent-credited
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_filespackages/midas_defect/docs/Makefile
has_devcontainerno
has_linter_configno
typecheck_configspackages/midas_ff_pipeline/midas_ff_pipeline/py.typed, packages/midas_pipeline/midas_pipeline/py.typed
agent_commit_share0.01
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

81Excellent
How it's scored
27/45Type-checkable code — Jupyter Notebook with type-check config (packages/midas_ff_pipeline/midas_ff_pipeline/py.typed, packages/midas_pipeline/midas_pipeline/py.typed)
54/55Manageable file sizes — 51/2,699 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes302,319
source_files_sampled2,699
oversized_source_files51

Machine-readable interfaces

100Exceptional
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 — example, examples, notebooks
Inputs used
example_dirsexample, examples, 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

28GitHub stars
7contributors
2,589commits, last 12 months
6days since last push
100releases
1bus factor
1open issues
—package ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 21 ⇿
0Stars
21Forks

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.

048121620242112016-062021-062026-05

Each point covers 10 days.

OpenSSF Scorecard 4.4 / 10
4.4aggregate

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 · 2026-10-21 03:57 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 4 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
0SASTno SAST tool detected
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 143
RegistryPackageVersion constraintManifest
PyPInumpy>=1.20packages/midas_2d/pyproject.toml
PyPItorch>=2.0packages/midas_2d/pyproject.toml
PyPImidas-hkls>=0.5packages/midas_2d/pyproject.toml
PyPImidas-pink>=0.1packages/midas_2d/pyproject.toml
PyPImidas-invert>=0.1.1packages/midas_2d/pyproject.toml
PyPImidas-params>=0.9.0packages/midas_calibrate/pyproject.toml
PyPInumpy>=1.22packages/midas_calibrate/pyproject.toml
PyPIscipy>=1.9packages/midas_calibrate/pyproject.toml
PyPItorch>=2.1packages/midas_calibrate/pyproject.toml
PyPImidas-distortion>=0.1.0packages/midas_calibrate/pyproject.toml
PyPImidas-hkls>=0.1.0packages/midas_calibrate/pyproject.toml
PyPImidas-integrate>=0.9.0packages/midas_calibrate/pyproject.toml
PyPImidas-peakfit>=0.2.0packages/midas_calibrate/pyproject.toml
PyPIh5py>=3.0packages/midas_calibrate/pyproject.toml
PyPIhdf5plugin>=4.0packages/midas_calibrate/pyproject.toml
PyPItifffile>=2022.0packages/midas_calibrate/pyproject.toml
PyPIpsutil>=5.8packages/midas_calibrate/pyproject.toml
PyPImidas-params>=0.9.0packages/midas_calibrate_v2/pyproject.toml
PyPInumpy>=1.22packages/midas_calibrate_v2/pyproject.toml
PyPIscipy>=1.9packages/midas_calibrate_v2/pyproject.toml
PyPItorch>=2.1packages/midas_calibrate_v2/pyproject.toml
PyPImidas-distortion>=0.2.0packages/midas_calibrate_v2/pyproject.toml
PyPImidas-hkls>=0.1.0packages/midas_calibrate_v2/pyproject.toml
PyPImidas-integrate>=0.1.0packages/midas_calibrate_v2/pyproject.toml
PyPImidas-peakfit>=0.4.0packages/midas_calibrate_v2/pyproject.toml
PyPImidas-calibrate>=0.6.0packages/midas_calibrate_v2/pyproject.toml
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PyPItifffile>=2022.0packages/midas_calibrate_v2/pyproject.toml
PyPImidas-zipper>=0.1.1packages/midas_calibrate_v2/pyproject.toml
PyPIscikit-image>=0.19packages/midas_calibrate_v2/pyproject.toml
PyPIhdf5plugin>=4.0packages/midas_dct_tt/pyproject.toml
PyPInumpy>=1.20packages/midas_dct_tt/pyproject.toml
PyPItorch>=2.0packages/midas_dct_tt/pyproject.toml
PyPImidas-dfxm>=0.3.1packages/midas_dct_tt/pyproject.toml
PyPImidas-stress>=0.8.1packages/midas_dct_tt/pyproject.toml
PyPImidas-diffract>=0.7packages/midas_dct_tt/pyproject.toml
PyPImidas-hkls>=0.7.2packages/midas_dct_tt/pyproject.toml
PyPImidas-invert>=0.1.1packages/midas_dct_tt/pyproject.toml
PyPImidas-distortion>=0.2packages/midas_dct_tt/pyproject.toml
PyPInumpy>=1.22packages/midas_ddd/pyproject.toml
PyPItorch>=2.0packages/midas_ddd/pyproject.toml
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PyPImidas-params>=0.9.0packages/midas_defect/pyproject.toml
PyPInumpy>=1.20packages/midas_defect/pyproject.toml
PyPItorch>=2.0packages/midas_defect/pyproject.toml
PyPIpandas>=1.5packages/midas_defect/pyproject.toml
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PyPItifffile>=2023.0packages/midas_defect/pyproject.toml
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PyPImidas-transforms>=0.1packages/midas_defect/pyproject.toml
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PyPImidas-integrate>=0.9.0packages/midas_integrate_v2/pyproject.toml
PyPImidas-calibrate-v2>=0.11.0packages/midas_integrate_v2/pyproject.toml
PyPImidas-distortion>=0.2.0packages/midas_integrate_v2/pyproject.toml
PyPInumpy>=1.20packages/midas_invert/pyproject.toml
PyPItorch>=2.0packages/midas_invert/pyproject.toml
PyPIscipy>=1.9packages/midas_joint_ff_calibrate/pyproject.toml
PyPImidas-params>=0.9.0packages/midas_joint_ff_calibrate/pyproject.toml
PyPInumpy>=1.22packages/midas_joint_ff_calibrate/pyproject.toml
PyPItorch>=2.1packages/midas_joint_ff_calibrate/pyproject.toml
PyPImidas-peakfit>=0.4.0packages/midas_joint_ff_calibrate/pyproject.toml
PyPImidas-calibrate-v2>=0.1.0packages/midas_joint_ff_calibrate/pyproject.toml
PyPImidas-calibrate>=0.6.0packages/midas_joint_ff_calibrate/pyproject.toml
PyPImidas-fit-grain>=0.11.0packages/midas_joint_ff_calibrate/pyproject.toml
PyPImidas-diffract>=0.9.0packages/midas_joint_ff_calibrate/pyproject.toml
PyPImidas-hkls>=0.1.0packages/midas_joint_ff_calibrate/pyproject.toml
PyPImidas-process-grains>=0.11.0packages/midas_joint_ff_calibrate/pyproject.toml
All dependencies 68

Full resolved dependency set from the GitHub dependency graph: 35 direct and 33 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIh5py—direct
PyPIhdf5plugin—direct
PyPIjoblib—direct
PyPImidas-2d—direct
PyPImidas-calibrate—direct
PyPImidas-calibrate-v2—direct
PyPImidas-ddd—direct
PyPImidas-defect—direct
PyPImidas-dfxm—direct
PyPImidas-diffract—direct
PyPImidas-distortion—direct
PyPImidas-fit-grain—direct
PyPImidas-hkls—direct
PyPImidas-index—direct
PyPImidas-integrate—direct
PyPImidas-integrate-v2—direct
PyPImidas-invert—direct
PyPImidas-params—direct
PyPImidas-peakfit—direct
PyPImidas-pink—direct
PyPImidas-pipeline—direct
PyPImidas-process-grains—direct
PyPImidas-stress—direct
PyPImidas-tomo—direct
PyPImidas-transforms—direct
PyPImidas-zipper—direct
PyPInumba—direct
PyPInumpy—direct
PyPIpandas—direct
PyPIpsutil—direct
PyPIscikit-image—direct
PyPIscipy—direct
PyPItifffile—direct
PyPItorch—direct
PyPIzarr—direct
PyPIfabio—indirect
PyPIgemmi—indirect
PyPImatplotlib—indirect
PyPImidas-dct-tt—indirect
PyPImidas-dt—indirect
PyPImidas-ff-pipeline—indirect
PyPImidas-grain-odf—indirect
PyPImidas-joint-ff-calibrate—indirect
PyPImidas-nf-fitorientation—indirect
PyPImidas-nf-pipeline—indirect
PyPImidas-nf-preprocess—indirect
PyPImidas-parsl-configs—indirect
PyPImidas-pdf—indirect
PyPImidas-pf-odf—indirect
PyPImidas-plotting—indirect
PyPImidas-propagate—indirect
PyPImidas-saxs—indirect
PyPImidas-uq—indirect
PyPImidas-xaf—indirect
PyPImultiprocess—indirect
PyPInumcodecs—indirect
PyPIpillow—indirect
PyPIplotly—indirect
PyPIpycifrw—indirect
PyPIpyro-ppl—indirect
PyPIpytest—indirect
PyPIpytest-benchmark—indirect
PyPIpytest-cov—indirect
PyPIscikit-build-core—indirect
PyPIsetuptools—indirect
PyPIsr-midas—indirect
PyPIstreamlit—indirect
PyPItqdm—indirect
Dependency advisories 0

Installing pypi:midas-2d@0.3.3 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.

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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.