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Software-GesundheitsberichtSchema 0.27.0 · Metriken 1.13.0 · 2026-07-25 12:19 UTC

equinor / fmu-tools

fmu-tools is a library containing tools for pre- and post-processing in a Fast Model Update (FMU) context

Python · OpenEdge ABLLGPL-3.0★ 13 Sterne⑂ 20 Forksseit März 2020Auf GitHub ansehen ↗

equinor/fmu-tools erreicht einen Gesundheitsindex von 80 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Vitality (94/100) ab, am schwächsten bei AI Readiness (46/100). Zuletzt heute aktualisiert. 3 Mitwirkende tragen den Großteil der jüngsten Arbeit.

80
gesamt / 100
Gut

Software-Gesundheitsindex

Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.

80
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
VitalitätCommunity &VerbreitungNachhaltigkeit &GovernanceEngineering-QualitätSicherheitAI Readiness

Bewertungsprofil

Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.

Eigentümerschaft

EquinorOrganisation
1.757 Follower751 öffentliche Reposseit Dez. 2010

Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlichtTags
PyPIfmu-tools1.32.1-39vor 23 Tagenenergysubsurfacereservoirscientificengineering

Metriken nach Kategorie

Vitalität

Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?

94Exzellent · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 0 Tagen
26.3/36Commit-Rhythmus — 38/52 Wochen mit Commits
18/18Commit-Volumen — 118 Commits im letzten Jahr
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
Verwendete Eingangsdaten
commits_last_year118
human_commit_share0,69
days_since_last_push0
active_weeks_last_year38

Release-Disziplin

100Exzellent
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 52 Releases veröffentlicht
36/36Release-Aktualität — letztes Release vor 23 Tagen
27/27Release-Rhythmus — ein Release etwa alle 29,4 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count52
latest_release_tag1.32.1
releases_from_tagsnein
days_since_latest_release23
mean_days_between_releases29,4
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.

Community & Verbreitung

Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?

55Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
17.5/60Stars — 13 Stars
10.7/25Forks — 20 Forks
6.5/15Watcher — 16 Watcher
Verwendete Eingangsdaten
forks20
stars13
watchers16
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Wie die Bewertung erfolgt
22.5/22.5README
22.5/22.5Lizenz — anerkannte Lizenz (LGPL-3.0)
18/18CONTRIBUTING-Leitfaden
0/13.5Verhaltenskodex
0/7.2Issue-Vorlage
6.3/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingja
has_issue_templatenein
has_code_of_conductnein
has_pull_request_templateja

Nachhaltigkeit & Governance

Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?

83Gut · 24 % des Gesamtindex
Wie die Bewertung erfolgt
36/54Bus-Faktor — 3 Beitragende decken die Hälfte aller Commits ab
17.6/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 22 % der Commits
13.5/13.5Breite der Beitragenden — 19 Beitragende
6/10OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6
Verwendete Eingangsdaten
bus_factor3
contributors_sampled19
top_contributor_share0,216
Wie die Bewertung erfolgt
40/46.8Issue-Lösungsquote — 86 % der Issues geschlossen
33.3/38.3PR-Annahme — 280/322 entschiedene PRs gemergt
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Verwendete Eingangsdaten
merged_prs280
open_issues22
closed_issues130
issue_closed_ratio0,855
closed_unmerged_prs42
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
23.3/25Reichweite des Inhabers — 1.757 Follower von equinor
25/25Kontohistorie — 751 öffentliche Repos, Kontoalter ca. 15 Jahre
Verwendete Eingangsdaten
followers1.757
owner_typeOrganization
is_verified
owner_loginequinor
public_repos751
account_age_days5.699

Paketpflege

100Exzellent
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
35/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 23 Tagen
20/20Versionshistorie — 39 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagesfmu-tools
ecosystemspypi
any_deprecatednein
min_days_since_publish23

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

77Gut · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 4 Workflow(s)
24/24Tests vorhanden
0/16Linter-Konfiguration
0/9.6Pre-Commit-Hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_confignein
has_precommit_confignein

Dokumentation

90Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — https://equinor.github.io/fmu-tools/
10/10Repository-Beschreibung
0/10Topics
10/10Wiki
Verwendete Eingangsdaten
topics
has_wikija
homepagehttps://equinor.github.io/fmu-tools/
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?

89Exzellent · 16 % des Gesamtindex

Sicherheitslage

86Exzellent
Wie die Bewertung erfolgt
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — keine Daten
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
1.5/2.5Contributors — project has 2 contributing companies or organizations -- score normalized to 6
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5Lizenz — license file detected
7.5/7.5Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
2/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
5/5SAST — SAST tool is run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — keine Daten
6.8/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate8,6
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection, signed_releases. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
35/35Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory
25/25Indirekte Abhängigkeiten ohne bekannte Advisories — keine indirekte Abhängigkeit trägt ein bekanntes Advisory
0/40Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum
Verwendete Eingangsdaten
sourceosv
advisories0
affected_packages0
assessed_packages50
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. Abgeglichen wurde die Laufzeit-Abhängigkeitshülle von pypi:fmu-tools@1.32.1 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 50 Paketen. Erreichbarkeit wird nicht analysiert.

AI Readiness

Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.

46Gefährdet · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
17/40Lesbare Commit-Historie — 22 von 69 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text)
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share0,319
agent_instruction_files
agent_instruction_max_bytes
Wie die Bewertung erfolgt
0/18Bootstrap mit einem Befehl
22/22Automatisierte Tests
0/11Lint-/Format-Konfiguration
11/11Statische Typprüfung — mypy.ini
10/10Reproduzierbare Umgebung — lockfile
0/10Belegte Agentenpraxis — keine von Agenten verfassten Commits unter den letzten 100
8/8Automatisierte Wartung — 31 der letzten 100 Commits sind automatisierte Abhängigkeits-Updates
4/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfilesuv.lock
has_dockerfilenein
typed_languagenein
bootstrap_files
has_devcontainernein
has_linter_confignein
typecheck_configsmypy.ini
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0,31
Wie die Bewertung erfolgt
27/45Typprüfbarer Code — Python mit Typprüfungs-Konfiguration (mypy.ini)
54.3/55Handhabbare Dateigrößen — 1/84 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes74.386
source_files_sampled84
oversized_source_files1

Eckdaten

13GitHub-Sterne
19Mitwirkende
118Commits, letzte 12 Monate
0Tage seit letztem Push
52Releases
3Bus-Faktor
22offene Issues
PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

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

Weitere Details

Stern- und Fork-Verlauf 0 ★ / 20 ⇿
0Sterne
20Forks
46Releases

Wann jeder Stern und Fork hinzugefügt wurde, von GitHub erfasst und nach Tagen gruppiert. Das kumulierte Wachstum steht direkt über den täglichen Zugängen, aus denen es besteht, sodass beide gegeneinander lesbar sind: stetiger organischer Zuwachs sieht ganz anders aus als ein abrupter, kurzlebiger Ausschlag. Wo dieser Unterschied messbar ist, wird er als Wachstumsauthentizität ausgewiesen.

0481216202022020-062023-062026-06
Major 0Minor 30Patch 14

Jeder Punkt umfasst 6 Tage.

OpenSSF Scorecard 8.6 / 10
8.6Gesamtwert

Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).Scorecard v5.5.0 · 2026-07-25 12:19 UTC

10Binary-Artifactsno binaries found in the repo
k. A.Branch-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
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
4Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 4
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
k. A.Signed-Releasesno releases found
9Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direkte Abhängigkeiten 10
RegistryPaketVersionsvorgabeManifest
PyPIdisjoint-setpyproject.toml
PyPIjsonschema>=3.2.0pyproject.toml
PyPInumpy>=1.23.5pyproject.toml
PyPIopenpyxl>=2.6pyproject.toml
PyPIpandas>=0.21pyproject.toml
PyPIpydanticpyproject.toml
PyPIpyyaml>=5.3pyproject.toml
PyPIscipy>=1.2pyproject.toml
PyPIxlrd>=1.2pyproject.toml
PyPIxtgeo>=4.23.0pyproject.toml
Alle Abhängigkeiten 103

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 12 direkte und 91 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIdisjoint-set0.9.0direkt
PyPIjsonschema4.26.0direkt
PyPInumpy2.4.6direkt
PyPInumpy2.5.0direkt
PyPIopenpyxl3.1.5direkt
PyPIpandas3.0.3direkt
PyPIpydantic2.13.4direkt
PyPIpyyaml6.0.3direkt
PyPIscipy1.17.1direkt
PyPIscipy1.18.0direkt
PyPIxlrd2.0.2direkt
PyPIxtgeo4.24.5direkt
PyPIalabaster1.0.0indirekt
PyPIannotated-doc0.0.4indirekt
PyPIannotated-types0.7.0indirekt
PyPIast-serialize0.5.0indirekt
PyPIattrs26.1.0indirekt
PyPIautoapi2.0.1indirekt
PyPIbabel2.18.0indirekt
PyPIcertifi2026.6.17indirekt
PyPIcharset-normalizer3.4.7indirekt
PyPIcolorama0.4.6indirekt
PyPIcontourpy1.3.3indirekt
PyPIcoverage7.14.3indirekt
PyPIcycler0.12.1indirekt
PyPIdeprecation2.1.0indirekt
PyPIdocutils0.21.2indirekt
PyPIemcee3.1.6indirekt
PyPIet-xmlfile2.0.0indirekt
PyPIexecnet2.1.2indirekt
PyPIfonttools4.63.0indirekt
PyPIgstools1.7.0indirekt
PyPIgstools-cython1.2.0indirekt
PyPIh5py3.16.0indirekt
PyPIhankel1.2.2indirekt
PyPIhdf5plugin6.0.0indirekt
PyPIidna3.18indirekt
PyPIimagesize2.0.0indirekt
PyPIiniconfig2.3.0indirekt
PyPIjinja23.1.6indirekt
PyPIjoblib1.5.3indirekt
PyPIjsonschema-specifications2025.9.1indirekt
PyPIkiwisolver1.5.0indirekt
PyPIlibrt0.11.0indirekt
PyPImarkdown-it-py4.2.0indirekt
PyPImarkupsafe3.0.3indirekt
PyPImatplotlib3.11.0indirekt
PyPImdit-py-plugins0.6.1indirekt
PyPImdurl0.1.2indirekt
PyPImeshio5.3.5indirekt
PyPImpmath1.4.1indirekt
PyPImypy2.1.0indirekt
PyPImypy-extensions1.1.0indirekt
PyPImyst-parser5.1.0indirekt
PyPIpackaging26.2indirekt
PyPIpathspec1.1.1indirekt
PyPIpillow12.3.0indirekt
PyPIpluggy1.6.0indirekt
PyPIpyarrow24.0.0indirekt
PyPIpydantic-core2.46.4indirekt
PyPIpyevtk1.7.0indirekt
PyPIpygments2.20.0indirekt
PyPIpyparsing3.3.2indirekt
PyPIpytest9.1.1indirekt
PyPIpytest-cov7.1.0indirekt
PyPIpytest-mock3.15.1indirekt
PyPIpytest-runner6.0.1indirekt
PyPIpytest-snapshot0.9.0indirekt
PyPIpytest-xdist3.8.0indirekt
PyPIpython-dateutil2.9.0.post0indirekt
PyPIreferencing0.37.0indirekt
PyPIrequests2.34.2indirekt
PyPIresfo5.0.1indirekt
PyPIrich15.0.0indirekt
PyPIroffio1.1.1indirekt
PyPIrpds-py2026.5.1indirekt
PyPIrstcheck6.2.5indirekt
PyPIrstcheck-core1.3.0indirekt
PyPIruff0.15.20indirekt
PyPIsegyio1.9.14indirekt
PyPIshapely2.1.2indirekt
PyPIshellingham1.5.4indirekt
PyPIsix1.17.0indirekt
PyPIsnowballstemmer3.1.1indirekt
PyPIsphinx8.1.3indirekt
PyPIsphinx-argparse0.6.0indirekt
PyPIsphinx-autodoc-typehints2.3.0indirekt
PyPIsphinx-rtd-theme3.1.0indirekt
PyPIsphinxcontrib-applehelp2.0.0indirekt
PyPIsphinxcontrib-devhelp2.0.0indirekt
PyPIsphinxcontrib-htmlhelp2.1.0indirekt
PyPIsphinxcontrib-jquery4.1indirekt
PyPIsphinxcontrib-jsmath1.0.1indirekt
PyPIsphinxcontrib-qthelp2.0.0indirekt
PyPIsphinxcontrib-serializinghtml2.0.0indirekt
PyPItomli2.4.1indirekt
PyPItyper0.26.7indirekt
PyPItypes-pyyaml6.0.12.20260518indirekt
PyPItyping-extensions4.15.0indirekt
PyPItyping-inspection0.4.2indirekt
PyPItzdata2026.2indirekt
PyPIurllib32.7.0indirekt
PyPIxtgeoviz0.3.1indirekt
Abhängigkeits-Advisories 0

Die Installation von pypi:fmu-tools@1.32.1 zieht 50 Pakete nach sich, direkt und transitiv: 0 tragen bekannte Advisories, davon 0 direkte Abhängigkeiten.

Keine bekannten Advisories betreffen die bewerteten Abhängigkeiten.

Ein Advisory bedeutet, dass die im Abhängigkeitsgraphen erfasste Version in den betroffenen Bereich eines Advisories fällt. Erreichbarkeit wird nicht analysiert, und der Graph enthält Entwicklungs- und Test-Pins — ein Fund kann das Werkzeug betreffen und nicht die ausgelieferte Software.

JSON-Rohbericht maschinenlesbar
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          "published_at": "2026-01-07T10:19:01Z"
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                "key": "ownership_backing",
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                "key": "verified_domain",
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                "key": "owner_reach",
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            "notes": [
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        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
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  ],
  "report_type": "repository",
  "generated_at": "2026-07-25T12:19:44.077797Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/e/equinor/fmu-tools.svg",
  "full_name": "equinor/fmu-tools",
  "license_state": "standard",
  "license_spdx": "LGPL-3.0"
}

Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.

Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.27.0 — vollständige Methodik · Metriken-Wiki.

Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte StatistikenPyPI.