Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-20 06:31 UTC

BanerjeeLab-repertoire / pycat-napari

Python Condensate Analysis Toolbox (PyCAT) for bio-image analysis, especially biomolecular condensates and fluorescence microscopy images.

PythonCustom license★ 1 star⑂ 2 forkssince May 2024View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

BanerjeeLab-repertoire/pycat-napari holds a health index of 60 out of 100, placing it in the Moderate band. It scores highest on Vitality (75/100) and lowest on Community & Adoption (37/100). It was last updated today. A single contributor accounts for most of its recent work.

60
overall / 100
Moderate

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.

60
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 57 is calibrated to 60 on the published index scale (record calibration 2026-08-02).

Ownership

Condensate Biophysics LabPersonal account
4 followers8 public repossince Apr 2023

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
PyPIpycat-napari1.7.52,1336394 days agobio-imagecondensatefluorescenceimage-analysismicroscopynaparipycatscientific

Metrics by category

Vitality

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

75Good · 21% of overall

Development activity

73Good
How it's scored
36/36Push recency — last push 0 days ago
9/36Commit cadence — 13/52 weeks with commits
18/18Commit volume — 819 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_year819
human_commit_share1
days_since_last_push0
active_weeks_last_year13

Release discipline

78Good
How it's scored
16.2/27Ships releases — 100 version tags (no GitHub releases)
27/36Release recency — latest release 97 days ago
27/27Release cadence — a release every ~0 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv1.6.29
releases_from_tagsyes
days_since_latest_release97
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?

37Weak · 17% of overall

Popularity & adoption

1Critical
How it's scored
0/60Stars — 1 stars
0/25Forks — 2 forks
0/15Watchers — 1 watchers
Inputs used
forks2
stars1
watchers1
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_badges4
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

56Moderate
How it's scored
44.4/80Monthly downloads — 2,133 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagespycat-napari
dependents—
ecosystemspypi
total_downloads—
monthly_downloads2,133
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?

50Moderate · 23% of overall

Maintainer resilience (bus factor)

15Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
1.9/22.5Commit distribution — top contributor authored 92% of commits
4.1/13.5Contributor breadth — 3 contributors
0/10OpenSSF Scorecard: Contributors — project has 0 contributing companies or organizations -- score normalized to 0
Inputs used
bus_factor1
contributors_sampled3
top_contributor_share0.915

Issue & PR responsiveness

67Good
How it's scored
0/42Issue resolution — no issues or no data
30/30PR acceptance — 10/10 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_prs10
open_issues0
closed_issues0
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio—
closed_unmerged_prs0
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Issue resolution, Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

36Weak
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
5/25Owner reach — 4 followers of BanerjeeLab-repertoire
14/25Track record — 8 public repos, account ~3 yr old
Inputs used
followers4
owner_typeUser
is_verified—
owner_loginBanerjeeLab-repertoire
public_repos8
account_age_days1,296
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 4 days ago
20/20Version history — 639 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagespycat-napari
ecosystemspypi
any_deprecatedno
min_days_since_publish4

Engineering Quality

Are baseline engineering and documentation practices in place?

74Good · 19% of overall

Engineering practices

80Excellent
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff], [tool.black])
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
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

65Good
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
0/10Topics
0/10Wiki
Inputs used
topics—
has_wikino
homepage—
docs_site—
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

45Weak · 16% of overall

Security posture

31At Risk
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
0/2.5Contributors — project has 0 contributing companies or organizations -- score normalized to 0
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 0 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
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
0/7.5Vulnerabilities — 24 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate3.1
Excluded from scoring (no data or not applicable): CI-Tests, Packaging, 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_packages196
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:pycat-napari@1.7.5 runtime dependency closure — what installing the published package pulls in — 196 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)
39.5/40Legible commit history — 74 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.74
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

62Moderate
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.black])
0/11Static type checking
0/10Reproducible environment
10/10Demonstrated agent practice — 78 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
1/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.78
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
54.2/55Manageable file sizes — 12/787 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes100,861
source_files_sampled787
oversized_source_files12

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 — 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

1GitHub stars
3contributors
819commits, last 12 months
0days since last push
100releases
1bus factor
0open 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 ★ / 2 ⇿
0Stars
2Forks
100Releases

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.

111222212024-122025-102026-09
Major 0Minor 1Patch 99

Each point covers 2 days.

OpenSSF Scorecard 3.1 / 10
3.1aggregate

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 · 2026-10-20 06:30 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
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
1Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 1
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities24 existing vulnerabilities detected
Direct dependencies 29
RegistryPackageVersion constraintManifest
PyPIbioio>=3.5,<4pyproject.toml
PyPIbioio-ome-tiff>=1.4,<2pyproject.toml
PyPIbioio-tifffile>=1.3,<2pyproject.toml
PyPIbioio-czi>=3.0,<4pyproject.toml
PyPIbioio-imageio>=1.3,<2pyproject.toml
PyPIcellpose<4pyproject.toml
PyPImatplotlib—pyproject.toml
PyPInapari>=0.9.1,<0.10pyproject.toml
PyPInumpy>=1.22pyproject.toml
PyPIopencv-python-headless—pyproject.toml
PyPIpandas—pyproject.toml
PyPIpydantic—pyproject.toml
PyPIqtpy>=2.4pyproject.toml
PyPIPySide6-Essentials>=6.12pyproject.toml
PyPItorch—pyproject.toml
PyPIpywavelets—pyproject.toml
PyPIscikit-image—pyproject.toml
PyPIscikit-learn—pyproject.toml
PyPInetworkx—pyproject.toml
PyPIscipy—pyproject.toml
PyPIopenpyxl—pyproject.toml
PyPIlargestinteriorrectangle—pyproject.toml
PyPIimaris-ims-file-reader—pyproject.toml
PyPIav—pyproject.toml
PyPIhdf5plugin—pyproject.toml
PyPInumba—pyproject.toml
PyPIseaborn—pyproject.toml
PyPIsimpleitk—pyproject.toml
PyPIzarr>=2.12pyproject.toml
All dependencies 70

Full resolved dependency set from the GitHub dependency graph: 29 direct and 41 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIav—direct
PyPIbioio—direct
PyPIbioio-czi—direct
PyPIbioio-imageio—direct
PyPIbioio-ome-tiff—direct
PyPIbioio-tifffile—direct
PyPIcellpose—direct
PyPIhdf5plugin—direct
PyPIimaris-ims-file-reader—direct
PyPIlargestinteriorrectangle—direct
PyPImatplotlib—direct
PyPInapari—direct
PyPInetworkx—direct
PyPInumba—direct
PyPInumpy—direct
PyPIopencv-python-headless—direct
PyPIopenpyxl—direct
PyPIpandas—direct
PyPIpydantic—direct
PyPIpyside6-essentials—direct
PyPIpywavelets—direct
PyPIqtpy—direct
PyPIscikit-image—direct
PyPIscikit-learn—direct
PyPIscipy—direct
PyPIseaborn—direct
PyPIsimpleitk—direct
PyPItorch—direct
PyPIzarr—direct
PyPIbioio-bioformats—indirect
PyPIhatchling—indirect
PyPIimageio—indirect
PyPIjupyter-server—indirect
PyPIjupyterlab—indirect
PyPImyst-parser—indirect
PyPInapari-accelerated-pixel-and-object-classification—indirect
PyPInapari-animation—indirect
PyPInapari-blob-detection—indirect
PyPInapari-brightness-contrast—indirect
PyPInapari-clusters-plotter—indirect
PyPInapari-crop—indirect
PyPInapari-curtain—indirect
PyPInapari-czifile2—indirect
PyPInapari-folder-browser—indirect
PyPInapari-layer-details-display—indirect
PyPInapari-mouse-controls—indirect
PyPInapari-plot-profile—indirect
PyPInapari-plugin-engine—indirect
PyPInapari-plugin-search—indirect
PyPInapari-process-points-and-surfaces—indirect
PyPInapari-pyclesperanto-assistant—indirect
PyPInapari-pystackreg—indirect
PyPInapari-roi—indirect
PyPInapari-segment-blobs-and-things-with-membranes—indirect
PyPInapari-simpleitk-image-processing—indirect
PyPInapari-skimage-regionprops—indirect
PyPInapari-stl-exporter—indirect
PyPInapari-tabu—indirect
PyPInapari-workflow-inspector—indirect
PyPInapari-workflow-optimizer—indirect
PyPInpe2—indirect
PyPIplatymatch—indirect
PyPIpydata-sphinx-theme—indirect
PyPIpytest—indirect
PyPIredlionfish—indirect
PyPIsphinx—indirect
PyPIsphinx-autodoc-typehints—indirect
PyPIsphinx-copybutton—indirect
PyPIthe-segmentation-game—indirect
PyPIvispy—indirect
Dependency advisories 0

Installing pypi:pycat-napari@1.7.5 pulls in 196 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.

Related records

Inspected repositories sharing catalogue tags or ecosystems with BanerjeeLab-repertoire/pycat-napari. Read them side by side →

94cytomining/pycytominerPython package for image-based profilingExceptional91computational-cell-analytics/micro-samSegment Anything for MicroscopyExcellent73OpenHCSDev/openhcsTyped, reproducible high-content microscopy workflows across GUI, Python, CellProfiler, Napari/Fiji, GPU, and local MCP agents.Good73Dana-Farber-AIOS/pathmlTools for computational pathologyGood98InsightSoftwareConsortium/ITKInsight Toolkit (ITK) -- Official Repository. ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.Exceptional98equinor/ertERT - Ensemble based Reservoir Tool - is designed for running ensembles of dynamical models such as reservoir models, in order to do sensitivity analysis and data assimilation. ERT supports data assimilation using the Ensemble Smoother (ES) and Ensemble Smoother with Multiple Data Assimilation (ES-MDA).Exceptional

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.