Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-04 20:42 UTC

ipython / matplotlib-inline

Inline Matplotlib backend for Jupyter

Jupyter NotebookBSD-3-Clause★ 31 stars⑂ 39 forkssince Feb 2021View on GitHub ↗
KindPluginLibraryhow this is determined

ipython/matplotlib-inline holds a health index of 75 out of 100, placing it in the Good band. It scores highest on Sustainability & Governance (79/100) and lowest on Community & Adoption (45/100). It was last updated 3 days ago. 3 contributors account for most of its recent work.

75
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.

75
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 65 is calibrated to 75 on the published index scale (record calibration 2026-08-02).

Ownership

IPythonOrganization
552 followers28 public repossince Mar 2010

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPImatplotlib-inline0.2.2-1088 days agoipythonjupytermatplotlibpython

Metrics by category

Vitality

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

66Good · 21% of overall
How it's scored
36/36Push recencylast push 3 days ago
4.8/36Commit cadence7/52 weeks with commits
15.1/18Commit volume47 commits in the last year
6/10OpenSSF Scorecard: Maintained7 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 6
Inputs used
commits_last_year47
human_commit_share0.98
days_since_last_push3
active_weeks_last_year7
How it's scored
16.2/27Ships releases11 version tags (no GitHub releases)
36/36Release recencylatest release 88 days ago
12.6/27Release cadencea release every ~205.6 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count11
latest_release_tag0.2.2
releases_from_tagsyes
days_since_latest_release88
mean_days_between_releases205.6
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?

45Weak · 17% of overall
How it's scored
24/60Stars31 stars
13.2/25Forks39 forks
2.7/15Watchers4 watchers
Inputs used
forks39
stars31
watchers4
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (BSD-3-Clause)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

79Good · 23% of overall
How it's scored
36/54Bus factor3 contributor(s) cover half of all commits
15.8/22.5Commit distributiontop contributor authored 30% of commits
13.5/13.5Contributor breadth23 contributors
10/10OpenSSF Scorecard: Contributorsproject has 54 contributing companies or organizations
Inputs used
bus_factor3
contributors_sampled23
top_contributor_share0.299
How it's scored
18.8/42Issue resolution45% of issues closed
26.1/30PR acceptance33/38 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
3/15OpenSSF Scorecard: Code-ReviewFound 2/10 approved changesets -- score normalized to 2
Inputs used
merged_prs33
open_issues16
closed_issues13
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.448
closed_unmerged_prs5
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 backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
19.7/25Owner reach552 followers of ipython
22.6/25Track record28 public repos, account ~16 yr old
Inputs used
followers552
owner_typeOrganization
is_verified
owner_loginipython
public_repos28
account_age_days5,976
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 88 days ago
20/20Version history10 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesmatplotlib-inline
ecosystemspypi
any_deprecatedno
min_days_since_publish88

Engineering Quality

Are baseline engineering and documentation practices in place?

58Moderate · 19% of overall
How it's scored
24/24CI workflows2 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
16/20OpenSSF Scorecard: CI-Tests7 out of 8 merged PRs checked by a CI test -- score normalized to 8
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

50Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics
has_wikiyes
homepage
docs_site
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

77Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
2/2.5CI-Tests7 out of 8 merged PRs checked by a CI test -- score normalized to 8
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1.5/7.5Code-ReviewFound 2/10 approved changesets -- score normalized to 2
2.5/2.5Contributorsproject has 54 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
4.5/7.5Maintained7 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 6
5/5Packagingpackaging workflow detected
1/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 2
0/5SASTSAST tool is not run on all commits -- score normalized to 0
4.5/5Security-Policysecurity policy file detected
0/7.5Signed-Releasesno data
7.5/7.5Token-PermissionsGitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate7.1
Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages1
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:matplotlib-inline@0.2.2 runtime dependency closure — what installing the published package pulls in — 1 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.

49Weak · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
12.5/40Legible commit history23 of 98 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.235
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
11/11Static type checkingmatplotlib_inline/py.typed
0/10Reproducible environment
2/10Demonstrated agent practice1 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance2 of the last 100 commits are automated dependency updates
2/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 2
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configno
typecheck_configsmatplotlib_inline/py.typed
agent_commit_share0.01
toolchain_manifests
dependency_bot_commit_share0.02
How it's scored
27/45Type-checkable codeJupyter Notebook with type-check config (matplotlib_inline/py.typed)
55/55Manageable file sizes0/4 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes12,171
source_files_sampled4
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesnotebooks
Inputs used
example_dirsnotebooks
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

31GitHub stars
23contributors
47commits, last 12 months
3days since last push
11releases
3bus factor
16open 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 ★ / 39 ⇿
0Stars
39Forks
8Releases

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.

0102030403812021-042023-102026-05
Major 0Minor 1Patch 7

Each point covers 5 days.

OpenSSF Scorecard 7.1 / 10
7.1aggregate

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 · 2026-08-04 20:42 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
8CI-Tests7 out of 8 merged PRs checked by a CI test -- score normalized to 8
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2Code-ReviewFound 2/10 approved changesets -- score normalized to 2
10Contributorsproject has 54 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
6Maintained7 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 6
10Packagingpackaging workflow detected
2Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 2
0SASTSAST tool is not run on all commits -- score normalized to 0
9Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 1
RegistryPackageVersion constraintManifest
PyPItraitletspyproject.toml
All dependencies 1

Full resolved dependency set from the GitHub dependency graph: 0 direct and 1 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIflit-coreindirect
Dependency advisories 0

Installing pypi:matplotlib-inline@0.2.2 pulls in 1 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 v0.31.0 — full methodology · metrics wiki.

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