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Software-GesundheitsberichtSchema 0.11.0 · Metriken 1.13.0 · 2026-07-15 23:49 UTC

openclimatefix / graph_weather

Graph-based weather forecasting models. Originally, PyTorch implementation of Ryan Keisler's 2022 "Forecasting Global Weather with Graph Neural Networks" paper (https://arxiv.org/abs/2202.07575)

PythonMIT★ 301 Sterne⑂ 97 Forksseit Feb. 2022Auf GitHub ansehen ↗

openclimatefix/graph_weather erreicht einen Gesundheitsindex von 62 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Vitality (82/100) ab, am schwächsten bei Security (1/100). Zuletzt vor 2 Tagen aktualisiert. 2 Mitwirkende tragen den Großteil der jüngsten Arbeit.

62
gesamt / 100
Mittel

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.

62
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

Open Climate FixOrganisation
913 Follower44 öffentliche Reposseit März 2019

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

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlicht
PyPIgraph_weather1.0.146-115vor 2 Tagen

Metriken nach Kategorie

Vitalität

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

82Gut · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 2 Tagen
11.1/36Commit-Rhythmus — 16/52 Wochen mit Commits
16.1/18Commit-Volumen — 61 Commits im letzten Jahr
0/10OpenSSF Scorecard: Maintained — keine Daten
Verwendete Eingangsdaten
commits_last_year61
human_commit_share
days_since_last_push2
active_weeks_last_year16

Release-Disziplin

100Exzellent
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 100 Releases veröffentlicht
36/36Release-Aktualität — letztes Release vor 2 Tagen
27/27Release-Rhythmus — ein Release etwa alle 2,9 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count100
latest_release_tagv1.0.146
releases_from_tagsnein
days_since_latest_release2
mean_days_between_releases2,9

Community & Verbreitung

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

59Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
40.2/60Stars — 301 Stars
16.5/25Forks — 97 Forks
3.3/15Watcher — 5 Watcher
Verwendete Eingangsdaten
forks97
stars301
watchers5
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Wie die Bewertung erfolgt
22.5/22.5README
22.5/22.5Lizenz — anerkannte Lizenz (MIT)
0/18CONTRIBUTING-Leitfaden
0/13.5Verhaltenskodex
0/7.2Issue-Vorlage
6.3/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingnein
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?

71Gut · 24 % des Gesamtindex
Wie die Bewertung erfolgt
25.2/54Bus-Faktor — 2 Beitragende decken die Hälfte aller Commits ab
11.7/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 48 % der Commits
13.5/13.5Breite der Beitragenden — 23 Beitragende
0/10OpenSSF Scorecard: Contributors — keine Daten
Verwendete Eingangsdaten
bus_factor2
contributors_sampled23
top_contributor_share0,482
Wie die Bewertung erfolgt
18/46.8Issue-Lösungsquote — 38 % der Issues geschlossen
33.3/38.3PR-Annahme — 87/100 entschiedene PRs gemergt
0/15OpenSSF Scorecard: Code-Review — keine Daten
Verwendete Eingangsdaten
merged_prs87
open_issues69
closed_issues43
issue_closed_ratio0,384
closed_unmerged_prs13
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
21.3/25Reichweite des Inhabers — 913 Follower von openclimatefix
24/25Kontohistorie — 44 öffentliche Repos, Kontoalter ca. 7 Jahre
Verwendete Eingangsdaten
followers913
owner_typeOrganization
is_verified
owner_loginopenclimatefix
public_repos44
account_age_days2.686

Paketpflege

100Exzellent
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
35/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 2 Tagen
20/20Versionshistorie — 115 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagesgraph_weather
ecosystemspypi
any_deprecatednein
min_days_since_publish2

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

79Gut · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 2 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration
9.6/9.6Pre-Commit-Hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — keine Daten
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_configja
has_precommit_configja
Wie die Bewertung erfolgt
30/30README
0/25Dokumentationsverzeichnis
0/15Dokumentations-/Homepage-Site
10/10Repository-Beschreibung
10/10Topics — 4 Topics
10/10Wiki
Verwendete Eingangsdaten
topicspytorch, weather, graph-neural-networks, forecasting-models
has_wikija
homepage
has_readmeja
has_docs_dirnein
has_descriptionja

Sicherheit

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

1Kritisch · 16 % des Gesamtindex
Wie die Bewertung erfolgt
0/30Sicherheitsrichtlinie (SECURITY.md)
0/25Dependabot-Konfiguration
0/25Lockfiles für Abhängigkeiten — veröffentlichte Bibliothek — Lockfiles sind Sache der Anwendung, hier nicht erwartet
0/20CodeQL-Workflow
Verwendete Eingangsdaten
sourcefile_signals
lockfiles
manifestspyproject.toml, setup.py
has_codeql_workflownein
has_security_policynein
has_dependabot_confignein
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lockfiles für Abhängigkeiten. Die verbleibenden Gewichte wurden renormalisiert.

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.

38Gefährdet · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
0/40Lesbare Commit-Historie — keine Daten
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lesbare Commit-Historie. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
0/18Bootstrap mit einem Befehl
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration
0/11Statische Typprüfung
10/10Reproduzierbare Umgebung — Dockerfile
0/10Belegte Agentenpraxis — keine Daten
0/8Automatisierte Wartung — keine Daten
0/10OpenSSF Scorecard: Pinned-Dependencies — keine Daten
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfiles
has_dockerfileja
typed_languagenein
bootstrap_files
has_devcontainernein
has_linter_configja
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Belegte Agentenpraxis, Automatisierte Wartung. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
0/45Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration
54.5/55Handhabbare Dateigrößen — 1/113 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes240.252
source_files_sampled113
oversized_source_files1

Eckdaten

301GitHub-Sterne
23Mitwirkende
61Commits, letzte 12 Monate
2Tage seit letztem Push
100Releases
2Bus-Faktor
69offene Issues
PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

Weitere Details

Direkte Abhängigkeiten 2
RegistryPaketVersionsvorgabeManifest
PyPItorch-harmonicspyproject.toml
PyPIanemoi-datasets>=0.4.5,<0.5pyproject.toml
Alle Abhängigkeiten 1

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 1 direkte und 0 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIanemoi-datasetsdirekt
JSON-Rohbericht maschinenlesbar
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        ],
        "description": "Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?"
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                "key": "openssf_scorecard_ci_tests",
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            ]
          }
        ],
        "description": "Are baseline engineering and documentation practices in place?"
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      {
        "key": "security",
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        "metrics": [
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            "key": "security_posture",
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                "code": "excluded_no_data",
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              {
                "code": "weights_renormalized",
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            "value": 1,
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            "components": [
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                "name": "Security policy (SECURITY.md)",
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                "status": "missed",
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              {
                "key": "dependabot_config",
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                "key": "dependency_lockfiles",
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                "key": "codeql_workflow",
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          },
          {
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            "notes": [
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            ],
            "value": 100,
            "inputs": {
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              "policy_countries": [
                "Russia",
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              "review_only_matches": 0,
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            "components": [
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                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
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                "status": "met",
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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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      {
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                "params": {
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              {
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            "value": 1,
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            "components": [
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                "key": "agent_instructions",
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                "status": "missed",
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                "max_points": 45
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              {
                "key": "machine_readable_docs_llms_txt",
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              },
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                ],
                "max_points": 40
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            ]
          },
          {
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            "value": 60,
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            },
            "components": [
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                "key": "one_command_bootstrap",
                "name": "One-command bootstrap",
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              },
              {
                "key": "automated_tests",
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              },
              {
                "key": "lint_format_config",
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                "points": 11,
                "status": "met",
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              },
              {
                "key": "static_type_checking",
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                "points": 0,
                "status": "missed",
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              },
              {
                "key": "reproducible_environment",
                "name": "Reproducible environment",
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                "points": 10,
                "status": "met",
                "details": [
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                  }
                ],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
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                "status": "excluded",
                "details": [
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                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
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                    "code": "no_data",
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                ],
                "max_points": 8
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              {
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            "notes": [],
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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."
      }
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  "warnings": [
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  "report_type": "repository",
  "generated_at": "2026-07-15T23:49:40.829901Z",
  "schema_version": "0.11.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/o/openclimatefix/graph_weather.svg",
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}

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 v0.11.0 — vollständige Methodik · Metriken-Wiki.

Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte StatistikenPyPI.