Öffentliches RegisterGut
Software-GesundheitsberichtSchema 0.16.0 · Metriken 1.13.0 · 2026-07-21 02:00 UTC
holoviz / datashader
Quickly and accurately render even the largest data.
holoviz/datashader erreicht einen Gesundheitsindex von 79 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Engineering Quality (95/100) ab, am schwächsten bei AI Readiness (35/100). Zuletzt vor 33 Tagen aktualisiert. 3 Mitwirkende tragen den Großteil der jüngsten Arbeit.
79
gesamt / 100Software-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.
79
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)
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
HoloVizOrganisation
446 Follower15 öffentliche Reposseit Juni 2019
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Paket-Ökosysteme
| Registry | Paket | Version | Downloads / Monat | Versionen | Zuletzt veröffentlicht |
|---|---|---|---|---|---|
| PyPI | datashader | 0.19.1 | - | 61 | vor 62 Tagen |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
69
Entwicklungsaktivität
60Mittel
Wie die Bewertung erfolgt
| 18/36 | Push-Aktualität — letzter Push vor 33 Tagen |
| 16.6/36 | Commit-Rhythmus — 24/52 Wochen mit Commits |
| 16.2/18 | Commit-Volumen — 63 Commits im letzten Jahr |
| 9/10 | OpenSSF Scorecard: Maintained — 11 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 9 |
Verwendete Eingangsdaten
| commits_last_year | 63 |
| human_commit_share | — |
| days_since_last_push | 33 |
| active_weeks_last_year | 24 |
Release-Disziplin
83Gut
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 42 Releases veröffentlicht |
| 36/36 | Release-Aktualität — letztes Release vor 62 Tagen |
| 19.8/27 | Release-Rhythmus — ein Release etwa alle 104 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases. |
Verwendete Eingangsdaten
| releases_count | 42 |
| latest_release_tag | v0.19.1 |
| releases_from_tags | nein |
| days_since_latest_release | 62 |
| mean_days_between_releases | 104 |
Community & Verbreitung
Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?
82
Popularität & Verbreitung
90Exzellent
Wie die Bewertung erfolgt
| 57.6/60 | Stars — 3.559 Stars |
| 21.4/25 | Forks — 375 Forks |
| 10.8/15 | Watcher — 90 Watcher |
Verwendete Eingangsdaten
| forks | 375 |
| stars | 3.559 |
| watchers | 90 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community-Gesundheit
72Gut
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 22.5/22.5 | Lizenz — anerkannte Lizenz (BSD-3-Clause) |
| 0/18 | CONTRIBUTING-Leitfaden |
| 13.5/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 6.3/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| has_contributing | nein |
| has_issue_template | nein |
| has_code_of_conduct | ja |
| has_pull_request_template | ja |
Nachhaltigkeit & Governance
Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?
78
Wie die Bewertung erfolgt
| 36/54 | Bus-Faktor — 3 Beitragende decken die Hälfte aller Commits ab |
| 14.4/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 36 % der Commits |
| 13.5/13.5 | Breite der Beitragenden — 59 Beitragende |
| 10/10 | OpenSSF Scorecard: Contributors — project has 37 contributing companies or organizations |
Verwendete Eingangsdaten
| bus_factor | 3 |
| contributors_sampled | 59 |
| top_contributor_share | 0,361 |
Wie die Bewertung erfolgt
| 36.4/46.8 | Issue-Lösungsquote — 78 % der Issues geschlossen |
| 35.2/38.3 | PR-Annahme — 809/878 entschiedene PRs gemergt |
| 1.5/15 | OpenSSF Scorecard: Code-Review — Found 5/30 approved changesets -- score normalized to 1 |
Verwendete Eingangsdaten
| merged_prs | 809 |
| open_issues | 135 |
| closed_issues | 473 |
| issue_closed_ratio | 0,778 |
| closed_unmerged_prs | 69 |
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 19.1/25 | Reichweite des Inhabers — 446 Follower von holoviz |
| 20.8/25 | Kontohistorie — 15 öffentliche Repos, Kontoalter ca. 7 Jahre |
Verwendete Eingangsdaten
| followers | 446 |
| owner_type | Organization |
| is_verified | — |
| owner_login | holoviz |
| public_repos | 15 |
| account_age_days | 2.597 |
Paketpflege
100Exzellent
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 1 Paket(e) auf pypi |
| 35/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 62 Tagen |
| 20/20 | Versionshistorie — 61 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | datashader |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 62 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
95
Engineering-Praktiken
92Exzellent
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 6 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration |
| 9.6/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 18/20 | OpenSSF Scorecard: CI-Tests — 27 out of 29 merged PRs checked by a CI test -- score normalized to 9 |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | ja |
Dokumentation
100Exzellent
Wie die Bewertung erfolgt
| 30/30 | README |
| 25/25 | Dokumentationsverzeichnis |
| 15/15 | Dokumentations-/Homepage-Site — http://datashader.org |
| 10/10 | Repository-Beschreibung |
| 10/10 | Topics — 4 Topics |
| 10/10 | Wiki |
Verwendete Eingangsdaten
| topics | holoviz, datashader, data-visualizations, rasterization |
| has_wiki | ja |
| homepage | http://datashader.org |
| has_readme | ja |
| has_docs_dir | ja |
| has_description | ja |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
68
Sicherheitslage
60Mittel
Wie die Bewertung erfolgt
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — keine Daten |
| 2.2/2.5 | CI-Tests — 27 out of 29 merged PRs checked by a CI test -- score normalized to 9 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 0.8/7.5 | Code-Review — Found 5/30 approved changesets -- score normalized to 1 |
| 2.5/2.5 | Contributors — project has 37 contributing companies or organizations |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 0/7.5 | Dependency-Update-Tool — no update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.5/2.5 | Lizenz — license file detected |
| 6.8/7.5 | Maintained — 11 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 9 |
| 5/5 | Packaging — packaging workflow detected |
| 5/5 | Pinned-Dependencies — all dependencies are pinned |
| 1/5 | SAST — SAST tool is not run on all commits -- score normalized to 2 |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — Project has not signed or included provenance with any releases. |
| 7.5/7.5 | Token-Permissions — GitHub workflow tokens follow principle of least privilege |
| 7.5/7.5 | Vulnerabilities — 0 existing vulnerabilities detected |
Verwendete Eingangsdaten
| source | openssf_scorecard |
| checks_evaluated | 17 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 1 |
| scorecard_aggregate | 6 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection. Die verbleibenden Gewichte wurden renormalisiert.
Abhängigkeits-Advisories
100Exzellent
Wie die Bewertung erfolgt
| 35/35 | Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory |
| 25/25 | Indirekte Abhängigkeiten ohne bekannte Advisories — keine indirekte Abhängigkeit trägt ein bekanntes Advisory |
| 0/40 | Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum |
Verwendete Eingangsdaten
| source | osv |
| advisories | 0 |
| affected_packages | 0 |
| assessed_packages | 19 |
| unassessed_packages | 0 |
| affected_by_severity | none |
| direct_affected_packages | 0 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. Abgeglichen wurde die Laufzeit-Abhängigkeitshülle von pypi:datashader@0.19.1 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 19 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.
35
AI-Agentenkontext & Leitlinien
1Kritisch
Wie die Bewertung erfolgt
| 0/45 | Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln |
| 0/15 | Maschinenlesbare Doku (llms.txt) |
| 0/40 | Lesbare Commit-Historie — keine Daten |
Verwendete Eingangsdaten
| has_llms_txt | nein |
| 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
| 0/18 | Bootstrap mit einem Befehl |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration |
| 0/11 | Statische Typprüfung |
| 0/10 | Reproduzierbare Umgebung |
| 0/10 | Belegte Agentenpraxis — keine Daten |
| 0/8 | Automatisierte Wartung — keine Daten |
| 10/10 | OpenSSF Scorecard: Pinned-Dependencies — all dependencies are pinned |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | — |
| has_dockerfile | nein |
| typed_language | nein |
| bootstrap_files | — |
| has_devcontainer | nein |
| has_linter_config | ja |
| 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.
AI-Code-Lesbarkeit für Modelle
52Mittel
Wie die Bewertung erfolgt
| 0/45 | Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration |
| 51.9/55 | Handhabbare Dateigrößen — 6/108 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | Python |
| largest_source_bytes | 152.409 |
| source_files_sampled | 108 |
| oversized_source_files | 6 |
Maschinenlesbare AI-Schnittstellen
40Gefährdet
Wie die Bewertung erfolgt
| 0/40 | API-Schema (OpenAPI/GraphQL/proto) |
| 0/20 | MCP-Server |
| 40/40 | Lauffähige Beispiele — examples, notebooks |
Verwendete Eingangsdaten
| example_dirs | examples, notebooks |
| has_mcp_signal | nein |
| api_schema_files | — |
Eckdaten
3.559GitHub-Sterne
59Mitwirkende
63Commits, letzte 12 Monate
33Tage seit letztem Push
42Releases
3Bus-Faktor
135offene Issues
PyPIPaket-Ökosysteme
Weitere Details
OpenSSF Scorecard 6.0 / 10
6.0Gesamtwert
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).
| 10 | Binary-Artifacts | no binaries found in the repo |
| k. A. | Branch-Protection | internal 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 |
| 9 | CI-Tests | 27 out of 29 merged PRs checked by a CI test -- score normalized to 9 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 1 | Code-Review | Found 5/30 approved changesets -- score normalized to 1 |
| 10 | Contributors | project has 37 contributing companies or organizations |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 0 | Dependency-Update-Tool | no update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 10 | License | license file detected |
| 9 | Maintained | 11 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 9 |
| 10 | Packaging | packaging workflow detected |
| 10 | Pinned-Dependencies | all dependencies are pinned |
| 2 | SAST | SAST tool is not run on all commits -- score normalized to 2 |
| 0 | Security-Policy | security policy file not detected |
| 0 | Signed-Releases | Project has not signed or included provenance with any releases. |
| 10 | Token-Permissions | GitHub workflow tokens follow principle of least privilege |
| 10 | Vulnerabilities | 0 existing vulnerabilities detected |
Direkte Abhängigkeiten 12
| Registry | Paket | Versionsvorgabe | Manifest |
|---|---|---|---|
| PyPI | colorcet | — | pyproject.toml |
| PyPI | multipledispatch | — | pyproject.toml |
| PyPI | numba | — | pyproject.toml |
| PyPI | numpy | — | pyproject.toml |
| PyPI | pandas | — | pyproject.toml |
| PyPI | param | — | pyproject.toml |
| PyPI | pyct | — | pyproject.toml |
| PyPI | requests | — | pyproject.toml |
| PyPI | scipy | — | pyproject.toml |
| PyPI | toolz | — | pyproject.toml |
| PyPI | packaging | — | pyproject.toml |
| PyPI | xarray | — | pyproject.toml |
Alle Abhängigkeiten 12
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 12 direkte und 0 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
| Registry | Paket | Version | Beziehung |
|---|---|---|---|
| PyPI | colorcet | — | direkt |
| PyPI | multipledispatch | — | direkt |
| PyPI | numba | — | direkt |
| PyPI | numpy | — | direkt |
| PyPI | packaging | — | direkt |
| PyPI | pandas | — | direkt |
| PyPI | param | — | direkt |
| PyPI | pyct | — | direkt |
| PyPI | requests | — | direkt |
| PyPI | scipy | — | direkt |
| PyPI | toolz | — | direkt |
| PyPI | xarray | — | direkt |
Abhängigkeits-Advisories 0
Die Installation von pypi:datashader@0.19.1 zieht 19 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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"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
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"dependencies": [
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{
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{
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{
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{
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{
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],
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{
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"note": null,
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{
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{
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{
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{
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{
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{
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{
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{
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],
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{
"key": "security",
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"weight": 0.16,
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{
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{
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{
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{
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{
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{
"key": "vulnerabilities",
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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 v0.16.0 — vollständige Methodik · Metriken-Wiki.
Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken — PyPI.