Registro público
Informe de salud del softwareesquema 0.14.0 · métricas 1.13.0 · 2026-07-19 00:41 UTC

mdolab / pyhyp

pyHyp generates volume meshes from surface meshes using hyperbolic marching.

Fortran · PythonLicencia propia★ 59 estrellas⑂ 44 forksdesde feb 2019Ver en GitHub ↗

mdolab/pyhyp tiene un índice de salud de 53 sobre 100, lo que lo sitúa en la banda Moderado. Su puntuación más alta es Sustainability & Governance (73/100) y la más baja, AI Readiness (34/100). Se actualizó por última vez hace 25 días. 2 personas concentran la mayor parte del trabajo reciente.

53
global / 100
Moderado

Índice de salud del software

Las métricas se agrupan en categorías ponderadas sobre una escala de 1 a 100. El resultado global parte de su media; cuando la evidencia pública activa la Política de Jurisdicciones de Alto Riesgo, la calificación se ajusta y recibe el límite 49 (En riesgo). Preparación para IA queda fuera.

53
Excelente85-100Ejemplar; cumple prácticamente todos los criterios evaluados
Bueno70-84Saludable; carencias menores
Moderado50-69Aceptable con carencias notables; se recomienda revisión
En riesgo30-49Debilidades significativas; su adopción exige cautela
Crítico1-29Problemas graves (proyecto abandonado, un solo mantenedor, sin higiene)
VitalidadComunidad yAdopciónSostenibilidady GobernanzaCalidad deIngenieríaSeguridadPreparaciónpara IA

Perfil de puntuación

Cada eje es una categoría. La forma importa más que la media: un proyecto sano llena toda la figura, mientras que un perfil de picos y cráteres indica que la fortaleza en una dimensión enmascara el riesgo en otra.

Titularidad

MDO LabOrganización
262 seguidores30 repositorios públicosdesde abr 2017

Este repositorio está respaldado por una organización: una custodia compartida y responsable que puede sobrevivir a cualquier mantenedor individual.

Métricas por categoría

Vitalidad

¿Está vivo el proyecto: se escribe código y se publican versiones?

48En riesgo · 22% del índice global
Cómo se puntúa
28.8/36Recencia de push — último push hace 25 días
1.4/36Cadencia de commits — 2/52 semanas con commits
5.4/18Volumen de commits — 3 commits en el último año
0/10OpenSSF Scorecard: Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Datos de entrada utilizados
commits_last_year3
human_commit_share
days_since_last_push25
active_weeks_last_year2
Cómo se puntúa
27/27Publica versiones — 14 versiones publicadas
27/36Recencia de las versiones — última versión hace 121 días
12.6/27Cadencia de publicación — una versión cada ~213,8 días
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Datos de entrada utilizados
releases_count14
latest_release_tagv2.6.3
releases_from_tagsno
days_since_latest_release121
mean_days_between_releases213,8

Comunidad y Adopción

¿Tiene el proyecto usuarios, descargas, atención y unas condiciones acogedoras para quienes contribuyen?

49En riesgo · 18% del índice global
Cómo se puntúa
28.6/60Estrellas — 59 estrellas
13.6/25Forks — 44 forks
5/15Observadores — 9 observadores
Datos de entrada utilizados
forks44
stars59
watchers9
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Cómo se puntúa
22.5/22.5README
16.9/22.5Licencia — archivo de licencia presente, no es una licencia reconocida
0/18Guía CONTRIBUTING
0/13.5Código de conducta
0/7.2Plantilla de issues
6.3/6.3Plantilla de PR
Datos de entrada utilizados
has_readme
has_license
has_contributingno
has_issue_templateno
has_code_of_conductno
has_pull_request_template

Sostenibilidad y Gobernanza

¿Sobrevivirá el proyecto a sus personas: factor bus, capacidad de respuesta, quién lo respalda y mantenimiento del paquete?

73Bueno · 24% del índice global
Cómo se puntúa
25.2/54Factor bus — la mitad de los commits recae en 2 contribuyente(s)
14/22.5Distribución de commits — el principal contribuyente firma el 38% de los commits
13.5/13.5Amplitud de contribuyentes — 24 contribuyentes
10/10OpenSSF Scorecard: Contributors — project has 3 contributing companies or organizations -- score normalized to 10
Datos de entrada utilizados
bus_factor2
contributors_sampled24
top_contributor_share0,378
Cómo se puntúa
39.6/46.8Resolución de issues — 85% de issues cerradas
36.1/38.3Aceptación de PR — 68/72 PR decididos fusionados
12/15OpenSSF Scorecard: Code-Review — Found 26/30 approved changesets -- score normalized to 8
Datos de entrada utilizados
merged_prs68
open_issues4
closed_issues22
issue_closed_ratio0,846
closed_unmerged_prs4
Cómo se puntúa
30/30Respaldo de la propiedad — propiedad de una organización
0/20Dominio verificado
17.4/25Alcance del propietario — 262 seguidores de mdolab
22.9/25Trayectoria — 30 repos públicos, cuenta de ~9 años
Datos de entrada utilizados
followers262
owner_typeOrganization
is_verified
owner_loginmdolab
public_repos30
account_age_days3391

Calidad de Ingeniería

¿Existen unas prácticas mínimas de ingeniería y documentación?

52Moderado · 20% del índice global
Cómo se puntúa
0/24Flujos de trabajo de CI
24/24Pruebas presentes
0/16Configuración de linter
0/9.6Hooks de pre-commit
0/6.4.editorconfig
2/20OpenSSF Scorecard: CI-Tests — 3 out of 26 merged PRs checked by a CI test -- score normalized to 1
Datos de entrada utilizados
has_cino
has_tests
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentación

90Excelente
Cómo se puntúa
30/30README
25/25Directorio de documentación
15/15Sitio de documentación / página del proyecto — https://mdolab-pyhyp.readthedocs-hosted.com/en/latest/
10/10Descripción del repositorio
0/10Topics
10/10Wiki
Datos de entrada utilizados
topics
has_wiki
homepagehttps://mdolab-pyhyp.readthedocs-hosted.com/en/latest/
has_readme
has_docs_dir
has_description

Seguridad

¿Son sólidas las prácticas visibles de seguridad y de cadena de suministro, sin exposición jurisdiccional de alto riesgo sin resolver?

35En riesgo · 16% del índice global
Cómo se puntúa
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — sin datos
0.2/2.5CI-Tests — 3 out of 26 merged PRs checked by a CI test -- score normalized to 1
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
6/7.5Code-Review — Found 26/30 approved changesets -- score normalized to 8
2.5/2.5Contributors — project has 3 contributing companies or organizations -- score normalized to 10
0/10Dangerous-Workflow — sin datos
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.2/2.5Licencia — license file detected
0/7.5Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packaging — sin datos
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — sin datos
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Datos de entrada utilizados
sourceopenssf_scorecard
checks_evaluated14
scorecard_versionv5.5.0
checks_inconclusive4
scorecard_aggregate3,5
Excluidos de la puntuación (sin datos o no aplicable): branch_protection, dangerous_workflow, packaging, token_permissions. Los pesos restantes se han renormalizado.

Preparación para IA

¿Hasta qué punto está el repositorio preparado para desarrollarse y mantenerse con agentes de codificación de IA? Es una insignia independiente y experimental — peso 0,0, de modo que se presenta por separado y no afecta a la puntuación de salud global.

34En riesgo · 0% del índice global
Cómo se puntúa
0/45Instrucciones para agentes — sin CLAUDE.md / AGENTS.md / reglas de editor
0/15Documentación legible por máquinas (llms.txt)
0/40Historial de commits legible — sin datos
Datos de entrada utilizados
has_llms_txtno
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Excluidos de la puntuación (sin datos o no aplicable): Historial de commits legible. Los pesos restantes se han renormalizado.
Cómo se puntúa
18/18Arranque con un solo comando — Makefile, doc/Makefile, lib/Makefile, src/3D/Makefile, src/modules/Makefile, src/python/f2py/Makefile, src/utils/Makefile
22/22Pruebas automatizadas
0/11Configuración de lint / formato
0/11Verificación estática de tipos
0/10Entorno reproducible
0/10Práctica demostrada con agentes — sin datos
0/8Mantenimiento automatizado — sin datos
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Datos de entrada utilizados
has_nixno
has_tests
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, doc/Makefile, lib/Makefile, src/3D/Makefile, src/modules/Makefile, src/python/f2py/Makefile, src/utils/Makefile
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Excluidos de la puntuación (sin datos o no aplicable): Práctica demostrada con agentes, Mantenimiento automatizado. Los pesos restantes se han renormalizado.
Cómo se puntúa
0/45Código verificable por tipos — Fortran sin configuración de verificación de tipos
55/55Tamaños de archivo manejables — 0/18 archivos fuente de más de 60 KB
Datos de entrada utilizados
primary_languageFortran
largest_source_bytes42.799
source_files_sampled18
oversized_source_files0
Cómo se puntúa
0/40Esquema de API (OpenAPI/GraphQL/proto)
0/20Servidor MCP
40/40Ejemplos ejecutables — examples
Datos de entrada utilizados
example_dirsexamples
has_mcp_signalno
api_schema_files

Datos clave

59estrellas de GitHub
24contribuidores
3commits en los últimos 12 meses
25días desde el último push
14versiones publicadas
2factor bus
4issues abiertas
PyPIecosistemas de paquetes

Advertencias de recopilación de datos

  • Could not fetch pypi package 'pyhyp' from its registry

Más detalle

OpenSSF Scorecard 3.5 / 10
3.5agregado

Evaluación de seguridad independiente y agnóstica en cuanto a herramientas, procedente del proyecto de código abierto OpenSSF Scorecard. Cada comprobación premia una práctica de seguridad, no la herramienta de un proveedor concreto. Las comprobaciones que Scorecard no pudo determinar se marcan como n/d y se excluyen de la puntuación de seguridad (nunca se cuentan como cero).Scorecard v5.5.0 · 2026-07-19 00:41 UTC

10Binary-Artifactsno binaries found in the repo
n/dBranch-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
1CI-Tests3 out of 26 merged PRs checked by a CI test -- score normalized to 1
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
8Code-ReviewFound 26/30 approved changesets -- score normalized to 8
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
n/dDangerous-Workflowno workflows found
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
n/dPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
n/dToken-PermissionsNo tokens found
10Vulnerabilities0 existing vulnerabilities detected
Todas las dependencias 6

Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 0 paquetes directos y 6 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.

RegistroPaqueteVersiónRelación
PyPIcgnsutilitiesindirecta
PyPImdolab-baseclassesindirecta
PyPImpi4pyindirecta
PyPInumpyindirecta
PyPIsphinx-mdolab-themeindirecta
PyPItabulateindirecta
Informe JSON sin procesar legible por máquina
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          {
            "name": "Dangerous-Workflow",
            "score": null,
            "reason": "no workflows found",
            "documentation_url": "https://github.com/ossf/scorecard/blob/c395761df6afe1a69e476bc60a013a94bcbc153f/docs/checks.md#dangerous-workflow"
          },
          {
            "name": "Dependency-Update-Tool",
            "score": 0,
            "reason": "no update tool detected",
            "documentation_url": "https://github.com/ossf/scorecard/blob/c395761df6afe1a69e476bc60a013a94bcbc153f/docs/checks.md#dependency-update-tool"
          },
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          {
            "name": "License",
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          {
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                "max_points": 5
              },
              {
                "key": "license",
                "name": "License",
                "detail": "license file detected",
                "points": 2.2,
                "status": "partial",
                "details": [],
                "max_points": 2.5
              },
              {
                "key": "maintained",
                "name": "Maintained",
                "detail": "0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "packaging",
                "name": "Packaging",
                "detail": "packaging workflow not detected",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 5
              },
              {
                "key": "pinned_dependencies",
                "name": "Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "sast",
                "name": "SAST",
                "detail": "SAST tool is not run on all commits -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "security_policy",
                "name": "Security-Policy",
                "detail": "security policy file not detected",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "signed_releases",
                "name": "Signed-Releases",
                "detail": "Project has not signed or included provenance with any releases.",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "token_permissions",
                "name": "Token-Permissions",
                "detail": "No tokens found",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 7.5
              },
              {
                "key": "vulnerabilities",
                "name": "Vulnerabilities",
                "detail": "0 existing vulnerabilities detected",
                "points": 7.5,
                "status": "met",
                "details": [],
                "max_points": 7.5
              }
            ]
          }
        ],
        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
      },
      {
        "key": "ai_readiness",
        "band": "at_risk",
        "name": "AI Readiness",
        "value": 34,
        "weight": 0,
        "metrics": [
          {
            "key": "ai_agent_context",
            "band": "critical",
            "name": "Agent context & guidance",
            "note": "Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "legible_commit_history"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              }
            ],
            "value": 1,
            "inputs": {
              "has_llms_txt": false,
              "legible_history_share": null,
              "agent_instruction_files": [],
              "agent_instruction_max_bytes": null
            },
            "components": [
              {
                "key": "agent_instructions",
                "name": "Agent instructions",
                "detail": "no CLAUDE.md / AGENTS.md / editor rules",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_agent_instructions",
                    "params": {}
                  }
                ],
                "max_points": 45
              },
              {
                "key": "machine_readable_docs_llms_txt",
                "name": "Machine-readable docs (llms.txt)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
              },
              {
                "key": "legible_commit_history",
                "name": "Legible commit history",
                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
            "band": "at_risk",
            "name": "Verify loop (build / test / typecheck)",
            "note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "demonstrated_agent_practice",
                    "automated_maintenance"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              }
            ],
            "value": 49,
            "inputs": {
              "has_nix": false,
              "has_tests": true,
              "lockfiles": [],
              "has_dockerfile": false,
              "typed_language": false,
              "bootstrap_files": [
                "Makefile",
                "doc/Makefile",
                "lib/Makefile",
                "src/3D/Makefile",
                "src/modules/Makefile",
                "src/python/f2py/Makefile",
                "src/utils/Makefile"
              ],
              "has_devcontainer": false,
              "has_linter_config": false,
              "typecheck_configs": [],
              "agent_commit_share": null,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": null
            },
            "components": [
              {
                "key": "one_command_bootstrap",
                "name": "One-command bootstrap",
                "detail": "Makefile, doc/Makefile, lib/Makefile, src/3D/Makefile, src/modules/Makefile, src/python/f2py/Makefile, src/utils/Makefile",
                "points": 18,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "Makefile, doc/Makefile, lib/Makefile, src/3D/Makefile, src/modules/Makefile, src/python/f2py/Makefile, src/utils/Makefile"
                    }
                  }
                ],
                "max_points": 18
              },
              {
                "key": "automated_tests",
                "name": "Automated tests",
                "detail": null,
                "points": 22,
                "status": "met",
                "details": [],
                "max_points": 22
              },
              {
                "key": "lint_format_config",
                "name": "Lint / format config",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 11
              },
              {
                "key": "static_type_checking",
                "name": "Static type checking",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 11
              },
              {
                "key": "reproducible_environment",
                "name": "Reproducible environment",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
            "band": "moderate",
            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 55,
            "inputs": {
              "primary_language": "Fortran",
              "largest_source_bytes": 42799,
              "source_files_sampled": 18,
              "oversized_source_files": 0
            },
            "components": [
              {
                "key": "type_checkable_code",
                "name": "Type-checkable code",
                "detail": "Fortran without a type-check config",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_typecheck_config_language",
                    "params": {
                      "language": "Fortran"
                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "0/18 source files over 60KB",
                "points": 55,
                "status": "met",
                "details": [
                  {
                    "code": "oversized_source_files",
                    "params": {
                      "kb": 60,
                      "sampled": 18,
                      "oversized": 0
                    }
                  }
                ],
                "max_points": 55
              }
            ]
          },
          {
            "key": "ai_interfaces",
            "band": "at_risk",
            "name": "Machine-readable interfaces",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
              "example_dirs": [
                "examples"
              ],
              "has_mcp_signal": false,
              "api_schema_files": []
            },
            "components": [
              {
                "key": "api_schema_openapi_graphql_proto",
                "name": "API schema (OpenAPI/GraphQL/proto)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 40
              },
              {
                "key": "mcp_server",
                "name": "MCP server",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "runnable_examples",
                "name": "Runnable examples",
                "detail": "examples",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "examples"
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          }
        ],
        "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."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [
    "Could not fetch pypi package 'pyhyp' from its registry"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-19T00:41:52.783703Z",
  "schema_version": "0.14.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/m/mdolab/pyhyp.svg",
  "full_name": "mdolab/pyhyp",
  "license_state": "custom",
  "license_spdx": null
}

Las puntuaciones son señales, no garantías. Reflejan prácticas públicamente visibles en GitHub; no son una auditoría de código ni una garantía de seguridad.

Los datos ausentes se excluyen y los pesos se renormalizan; nunca se puntúan como cero. La metodología es versionada y abierta: métricas v1.13.0, esquema v0.14.0 — metodología completa · wiki de métricas.

Cómo se sitúa un resultado dentro del registro general: estadísticas agregadas.