init: 初始化 dpb 桃育种系统代码库
前后端 + 后端 FastAPI 全量源码、部署脚本与文档。
This commit is contained in:
34047007@qq.com
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"""育种统计模块（一期 2 个最基础：ABLUP·EBV + 配合力 GCA/SCA）"""
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"""育种统计 路由（一期 2 个最基础：ABLUP·EBV + 配合力 GCA/SCA）"""
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from typing import Annotated
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from fastapi import APIRouter, Body, Depends, Query
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.api.v1.module_bre.statistics.schema import (
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AnovaIn,
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AnovaOut,
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ClusterIn,
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CombiningAbilityOut,
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CorrelationIn,
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CvFoldOut,
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CvResultOut,
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CvRunIn,
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DataQualityIn,
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DecisionPreviewIn,
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DescribeStatsIn,
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GblupRunIn,
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GeneticCorrIn,
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GeneticCorrOut,
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GeneticGainIn,
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GenotypingDatasetOut,
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GwasQtlXEIn,
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GwasResultOut,
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GwasRunIn,
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GwasSnpOut,
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InbreedingDepressionIn,
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KinshipIn,
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MatingRecommendIn,
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MabcIn,
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MasPanelIn,
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MasPanelOut,
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MasPanelSetMarkersIn,
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OcsIn,
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PredictionOut,
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PredictionValueOut,
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QtlIn,
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QtlOut,
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RunStatsIn,
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SelectionIndexApplyIn,
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SelectionIndexIn,
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SelectionIndexOut,
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StabilityOut,
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StabilityRunIn,
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StatisticsJobOut,
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TrialDesignIn,
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TypeBIn,
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TypeBOut,
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)
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from app.api.v1.module_bre.statistics.service import StatisticsService
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from app.core.base_schema import AuthSchema
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from app.core.router_class import OperationLogRoute
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from app.core.dependencies import db_getter
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from app.core.dependencies import AuthPermission
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from app.common.response import SuccessResponse
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StatisticsRouter = APIRouter(route_class=OperationLogRoute, prefix="/statistics")
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@StatisticsRouter.post("/ablup/run", summary="运行 ABLUP/EBV 估计")
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async def run_ablup(
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data: RunStatsIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).run_ablup(
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data.trait_id, data.trait_code, data.year, data.fixed_effects, data.covariate,
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data.data_gate, data.min_clone_n, data.min_pedigree_rate, data.max_missing_rate,
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data.gxe, data.gxe_env, data.stage, data.gxr, data.spatial, data.spatial_aniso,
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data.block,
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))
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@StatisticsRouter.post("/combining/run", summary="运行配合力 GCA/SCA 分析")
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async def run_combining(
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data: RunStatsIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(
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await StatisticsService(auth, db).run_combining(
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data.trait_id, data.trait_code, data.year, data.design_type,
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)
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)
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@StatisticsRouter.get("/predictions", summary="育种值模型列表")
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async def list_predictions(
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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objs = await StatisticsService(auth, db).list_predictions()
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return SuccessResponse([PredictionOut.model_validate(o) for o in objs])
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@StatisticsRouter.get("/predictions/{prediction_id}/values", summary="EBV 排行")
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async def ebv_ranking(
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prediction_id: int,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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objs = await StatisticsService(auth, db).ebv_ranking(prediction_id)
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return SuccessResponse([PredictionValueOut.model_validate(o) for o in objs])
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@StatisticsRouter.get("/predictions/{prediction_id}/clones", summary="无性系级 EBV 排行")
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async def clone_ranking(
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prediction_id: int,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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result = await StatisticsService(auth, db).clone_ranking(prediction_id)
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return SuccessResponse(result)
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@StatisticsRouter.get("/combining", summary="配合力结果列表")
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async def list_combining(
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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objs = await StatisticsService(auth, db).list_combining()
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return SuccessResponse([CombiningAbilityOut.model_validate(o) for o in objs])
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@StatisticsRouter.get("/combining/{ca_id}", summary="配合力结果详情(GCA/SCA)")
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async def combining_detail(
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ca_id: int,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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obj = await StatisticsService(auth, db).combining_detail(ca_id)
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return SuccessResponse(CombiningAbilityOut.model_validate(obj))
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@StatisticsRouter.get("/jobs", summary="统计任务列表")
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async def list_jobs(
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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objs = await StatisticsService(auth, db).list_jobs()
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return SuccessResponse([StatisticsJobOut.model_validate(o) for o in objs])
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@StatisticsRouter.get("/jobs/{job_id}", summary="统计任务状态")
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async def job_status(
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job_id: int,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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obj = await StatisticsService(auth, db).job_status(job_id)
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return SuccessResponse(StatisticsJobOut.model_validate(obj))
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# ---------- k-fold 交叉验证（外部验证） ----------
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@StatisticsRouter.post("/cv/run", summary="运行 k-fold 交叉验证(外部验证预测准确度)")
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async def run_cv(
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data: CvRunIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).run_cv(
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data.trait_id, data.trait_code, data.year, data.fixed_effects, data.covariate,
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data.gxe, data.gxe_env, data.k,
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data.dataset_id, data.method, data.maf_min,
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data.split, data.seed,
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))
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@StatisticsRouter.get("/cv", summary="k-fold CV 结果批次列表")
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async def list_cv(
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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objs = await StatisticsService(auth, db).list_cv()
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return SuccessResponse([CvResultOut.model_validate(o) for o in objs])
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@StatisticsRouter.get("/cv/{cv_id}", summary="k-fold CV 结果详情(含折明细)")
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async def cv_detail(
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cv_id: int,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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result = await StatisticsService(auth, db).cv_detail(cv_id)
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return SuccessResponse({
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"result": CvResultOut.model_validate(result["result"]),
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"folds": [CvFoldOut.model_validate(f) for f in result["folds"]],
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})
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# ---------- 纯 Python 统计分析（不依赖 R） ----------
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@StatisticsRouter.get("/describe", summary="描述性统计(无需R)")
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async def describe_stats(
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trait_codes: list[str] | None = Query(None),
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group_by: str = Query("none"),
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year: int | None = Query(None),
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).describe_stats(trait_codes, group_by, year))
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@StatisticsRouter.get("/correlation", summary="性状相关性矩阵(无需R；mode=pheno表型/genetic遗传)")
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async def correlation(
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trait_codes: list[str] | None = Query(None),
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mode: str = Query("pheno", description="pheno=皮尔逊表型相关；genetic=成对双性状BLUP遗传相关"),
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g_method: str = Query("mtblup", description="genetic 模式遗传相关来源：mtblup / calo(缺批次回退)"),
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year: int | None = Query(None, description="年份过滤(仅 genetic 模式生效)"),
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).correlation_matrix(
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trait_codes, mode, g_method, year))
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@StatisticsRouter.post("/selection-index", summary="选择指数排名(EBV或表型加权，落库)")
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async def selection_index(
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data: SelectionIndexIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).selection_index(
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data.weights, data.year, data.top_n, data.batch_ids, data.use_h2, data.method, data.aggregate,
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data.stage, data.g_method, data.auto_weights,
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data.restricted_traits, data.economic_weights,
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))
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@StatisticsRouter.get("/selection-index", summary="选择指数批次列表")
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async def list_index_batches(
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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objs = await StatisticsService(auth, db).list_index_batches()
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return SuccessResponse([SelectionIndexOut.model_validate(o) for o in objs])
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@StatisticsRouter.post("/selection-index/{index_id}/apply", summary="选择指数前N名写入决选")
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async def apply_selection(
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index_id: int,
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data: SelectionIndexApplyIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(
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await StatisticsService(auth, db).apply_selection(
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index_id, data.top_n, data.selection_year, data.rule_id, data.min_reliability)
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)
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@StatisticsRouter.post("/kinship", summary="亲缘/近交分析(复用系谱A矩阵)")
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async def kinship(
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data: KinshipIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).kinship_matrix(data.threshold, data.tree_ids))
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@StatisticsRouter.post("/inbreeding-depression", summary="近交衰退分析(F→表型线性回归,纯计算)")
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async def inbreeding_depression(
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data: InbreedingDepressionIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).inbreeding_depression(
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data.trait_id, data.trait_code, data.year, data.trial_study_id, data.min_n))
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@StatisticsRouter.post("/data-quality", summary="数据质量/异常值诊断(IQR+MAD稳健z)")
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async def data_quality(
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data: DataQualityIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).data_quality_report(
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data.trait_id, data.trait_code, data.year, data.trial_study_id, data.dataset_id))
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@StatisticsRouter.post("/genetic-gain", summary="ΔG 遗传增益投影(截断选择强度×PA×σ_A)")
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async def genetic_gain(
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data: GeneticGainIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).genetic_gain(
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data.trait_id, data.trait_code, data.prediction_id,
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data.top_p, data.top_n, data.generation_interval))
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@StatisticsRouter.post("/mating-recommend", summary="主动选配推荐(EBV互补−近交惩罚,S-等位硬过滤)")
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async def mating_recommend(
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data: MatingRecommendIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).mating_recommend(
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data.candidate_germplasm_ids, data.prediction_id, data.kinship_threshold,
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data.w_ebv, data.w_kin, data.max_pairs))
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@StatisticsRouter.post("/ocs", summary="最优贡献选择 OCS(群体配种贡献优化,纯计算)")
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async def ocs(
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data: OcsIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).ocs(
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data.candidate_germplasm_ids, data.n_select, data.lam, data.prediction_id))
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@StatisticsRouter.post("/mabc-progress", summary="MABC 标记辅助回交进度(前景MAS+背景恢复率+回交代建议,纯计算)")
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async def mabc_progress(
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data: MabcIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).mabc_progress(
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data.candidate_tree_ids, data.foreground_panel_ids, data.background_panel_ids,
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data.recurrent_parent_tree_id, data.foreground_min_hits, data.generation,
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data.background_target))
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@StatisticsRouter.post("/trial-design", summary="试验设计生成：RCBD/增广/α-格子(落库 block_no)")
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async def trial_design(
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data: TrialDesignIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).trial_design(
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data.trial_study_id, data.design_type, data.seed,
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data.check_germplasm_ids, data.block_size, data.reps))
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@StatisticsRouter.post("/anova/run", summary="运行 ANOVA / 广义遗传力 H²")
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async def run_anova(
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data: AnovaIn,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
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db: AsyncSession = Depends(db_getter),
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):
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return SuccessResponse(await StatisticsService(auth, db).run_anova(
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data.trait_id, data.trait_code, data.year, data.block))
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@StatisticsRouter.get("/anova", summary="ANOVA 结果列表")
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async def list_anova(
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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objs = await StatisticsService(auth, db).list_anova()
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return SuccessResponse([AnovaOut.model_validate(o) for o in objs])
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@StatisticsRouter.get("/anova/{anova_id}", summary="ANOVA 结果详情")
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async def anova_detail(
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anova_id: int,
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auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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db: AsyncSession = Depends(db_getter),
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):
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obj = await StatisticsService(auth, db).anova_detail(anova_id)
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return SuccessResponse(AnovaOut.model_validate(obj))
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@StatisticsRouter.get("/trait-values", summary="统一性状值视图(长表)")
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async def trait_values(
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trait_codes: list[str] | None = Query(None),
|
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group_by: str = Query("none"),
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||||
year: int | None = Query(None),
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
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return SuccessResponse(await StatisticsService(auth, db).trait_values(trait_codes, group_by, year))
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||||
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||||
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||||
@StatisticsRouter.get("/fairness", summary="按site EBV偏差/公平性报告(只读)")
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async def fairness_report(
|
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prediction_id: int = Query(..., description="EBV 预测批次 id"),
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||||
group_by: str = Query("site", description="分组维度（当前仅支持 site）"),
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||||
threshold_sd: float = Query(1.0, description="偏差标记阈值：|site均值-全体均值| > threshold_sd×全体sd 标记站点"),
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
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||||
db: AsyncSession = Depends(db_getter),
|
||||
):
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||||
return SuccessResponse(await StatisticsService(auth, db).fairness_report(
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prediction_id, group_by, threshold_sd))
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||||
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||||
|
||||
@StatisticsRouter.get("/predictions/{prediction_id}/compare", summary="模型健康监控(相邻两轮ABLUP对比)")
|
||||
async def compare_predictions(
|
||||
prediction_id: int,
|
||||
prev_prediction_id: int | None = Query(None),
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).compare_predictions(prediction_id, prev_prediction_id))
|
||||
|
||||
|
||||
@StatisticsRouter.post("/decision-preview", summary="选择规则决策预览(表型+EBV)")
|
||||
async def decision_preview(
|
||||
data: DecisionPreviewIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(
|
||||
auth, db).decision_preview(data.rule_ids, data.prediction_id, data.year, data.min_reliability,
|
||||
data.stage))
|
||||
|
||||
|
||||
@StatisticsRouter.get("/combination-funnel", summary="组合得失漏斗(花→果→种→苗→定植→树→入选，只读)")
|
||||
async def combination_funnel(
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).combination_funnel())
|
||||
|
||||
|
||||
@StatisticsRouter.get("/traits", summary="统计可用性状下拉(只读bre_trait)")
|
||||
async def list_traits(
|
||||
core_only: bool = Query(True, description="仅返回核心性状(对应 tree_evaluation 列)"),
|
||||
selection_only: bool = Query(False, description="仅返回选种目标性状(into_ebv=1，供指数/EBV排行/决策候选)"),
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).list_traits(core_only, selection_only))
|
||||
|
||||
|
||||
# ---------- AMMI / Finlay-Wilkinson 稳定性（§8.18） ----------
|
||||
@StatisticsRouter.post("/stability/run", summary="运行 AMMI/Finlay-Wilkinson 稳定性分析")
|
||||
async def run_stability(
|
||||
data: StabilityRunIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).run_stability(
|
||||
data.trait_id, data.trait_code, data.gxe_env, data.year, tuple(data.methods),
|
||||
))
|
||||
|
||||
|
||||
@StatisticsRouter.get("/stability", summary="稳定性分析结果列表")
|
||||
async def list_stability(
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
objs = await StatisticsService(auth, db).list_stability()
|
||||
return SuccessResponse([StabilityOut.model_validate(o) for o in objs])
|
||||
|
||||
|
||||
@StatisticsRouter.get("/stability/{sid}", summary="稳定性分析结果详情(AMMI/FW明细)")
|
||||
async def stability_detail(
|
||||
sid: int,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
obj = await StatisticsService(auth, db).stability_detail(sid)
|
||||
return SuccessResponse(StabilityOut.model_validate(obj))
|
||||
|
||||
|
||||
# ---------- 遗传相关矩阵（MT-BLUP 成对双性状，§8.18） ----------
|
||||
@StatisticsRouter.post("/genetic-corr/run", summary="运行遗传相关矩阵(MT-BLUP成对双性状)")
|
||||
async def run_genetic_corr(
|
||||
data: GeneticCorrIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).run_genetic_corr(
|
||||
data.trait_ids, data.year, data.full_mtblup))
|
||||
|
||||
|
||||
@StatisticsRouter.get("/genetic-corr", summary="遗传相关结果列表")
|
||||
async def list_genetic_corr(
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
objs = await StatisticsService(auth, db).list_genetic_corr()
|
||||
return SuccessResponse([GeneticCorrOut.model_validate(o) for o in objs])
|
||||
|
||||
|
||||
@StatisticsRouter.get("/genetic-corr/{cid}", summary="遗传相关结果详情")
|
||||
async def genetic_corr_detail(
|
||||
cid: int,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
obj = await StatisticsService(auth, db).genetic_corr_detail(cid)
|
||||
return SuccessResponse(GeneticCorrOut.model_validate(obj))
|
||||
|
||||
|
||||
# ---------- Type-B 多环境遗传相关 / UPGMA 聚类（§8.27） ----------
|
||||
@StatisticsRouter.post("/type-b-heredity", summary="运行 Type-B 多环境遗传相关(环境当性状逐对BLUP，落库)")
|
||||
async def run_type_b_heredity(
|
||||
data: TypeBIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).run_type_b_heredity(
|
||||
data.trait_id, data.trait_code, data.env_dim, data.method, data.year))
|
||||
|
||||
|
||||
@StatisticsRouter.get("/type-b", summary="Type-B 多环境遗传相关结果列表")
|
||||
async def list_type_b(
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
objs = await StatisticsService(auth, db).list_type_b()
|
||||
return SuccessResponse([TypeBOut.model_validate(o) for o in objs])
|
||||
|
||||
|
||||
@StatisticsRouter.get("/type-b/{rid}", summary="Type-B 多环境遗传相关结果详情")
|
||||
async def type_b_detail(
|
||||
rid: int,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
obj = await StatisticsService(auth, db).type_b_detail(rid)
|
||||
return SuccessResponse(TypeBOut.model_validate(obj))
|
||||
|
||||
|
||||
@StatisticsRouter.post("/cluster", summary="UPGMA 层次聚类(计算端点，不落库)")
|
||||
async def run_cluster(
|
||||
data: ClusterIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).run_cluster(
|
||||
data.trait_ids, data.entity_type, data.mode, data.distance, data.k))
|
||||
|
||||
|
||||
# ---------- GBLUP / ssGBLUP 基因组选择（§8.18） ----------
|
||||
@StatisticsRouter.post("/gblup/run", summary="运行 GBLUP/ssGBLUP 基因组选择")
|
||||
async def run_gblup(
|
||||
data: GblupRunIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).run_gblup(
|
||||
data.dataset_id, data.trait_id, data.trait_code, data.year, data.method, data.maf_min,
|
||||
data.seed,
|
||||
))
|
||||
|
||||
|
||||
@StatisticsRouter.get("/gblup/datasets", summary="基因型数据集列表(含样本数)")
|
||||
async def list_genotyping_datasets(
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
datasets = await StatisticsService(auth, db).list_genotyping_datasets()
|
||||
return SuccessResponse([GenotypingDatasetOut(**d) for d in datasets])
|
||||
|
||||
|
||||
# ---------- MLOps 漂移检测 / 手动重训 / 版本回滚（§8.18） ----------
|
||||
@StatisticsRouter.get("/model/drift", summary="模型漂移检测(输入快照哈希比对)")
|
||||
async def model_drift(
|
||||
prediction_id: int = Query(..., description="预测批次 id"),
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).model_drift(prediction_id))
|
||||
|
||||
|
||||
@StatisticsRouter.post("/model/retrain", summary="手动重训(漂移才重跑run_ablup，active自动转移)")
|
||||
async def model_retrain(
|
||||
prediction_id: int = Query(..., description="预测批次 id"),
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).model_retrain(prediction_id))
|
||||
|
||||
|
||||
@StatisticsRouter.post("/model/activate/{prediction_id}", summary="版本回滚(该批次设为当前生效版本)")
|
||||
async def model_activate(
|
||||
prediction_id: int,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).model_activate(prediction_id))
|
||||
|
||||
|
||||
# ---------- GWAS / QTL / MAS 标记辅助选择（§8.22） ----------
|
||||
@StatisticsRouter.post("/gwas/run", summary="运行 GWAS 关联分析(GLM+PC)")
|
||||
async def run_gwas(
|
||||
data: GwasRunIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).run_gwas(
|
||||
data.dataset_id, data.trait_id, data.trait_code, data.year,
|
||||
data.method, data.maf_min, data.n_pc, data.sig_level, data.qtl_window,
|
||||
))
|
||||
|
||||
|
||||
@StatisticsRouter.get("/gwas", summary="GWAS 批次列表")
|
||||
async def list_gwas(
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
objs = await StatisticsService(auth, db).list_gwas()
|
||||
return SuccessResponse([GwasResultOut.model_validate(o) for o in objs])
|
||||
|
||||
|
||||
@StatisticsRouter.get("/gwas/{gwas_id}", summary="GWAS 批次详情(Manhattan/QQ 数据 + QTL)")
|
||||
async def gwas_detail(
|
||||
gwas_id: int,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
result = await StatisticsService(auth, db).gwas_detail(gwas_id)
|
||||
return SuccessResponse({
|
||||
"result": GwasResultOut.model_validate(result["result"]),
|
||||
"snps": [GwasSnpOut.model_validate(s) for s in result["snps"]],
|
||||
"qtls": [QtlOut.model_validate(q) for q in result["qtls"]],
|
||||
})
|
||||
|
||||
|
||||
@StatisticsRouter.post("/gwas-qtl-x-e", summary="QTL×E 分层 GWAS + 稳定性判定(计算端点不建表)")
|
||||
async def gwas_qtl_x_e(
|
||||
data: GwasQtlXEIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).gwas_qtl_x_e(
|
||||
data.dataset_id, data.trait_id, data.trait_code, data.year,
|
||||
data.method, data.maf_min, data.n_pc, data.sig_level, data.qtl_window,
|
||||
data.env_dim,
|
||||
))
|
||||
|
||||
|
||||
@StatisticsRouter.post("/qtl", summary="录入已知 QTL")
|
||||
async def qtl_create(
|
||||
data: QtlIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).qtl_create(data))
|
||||
|
||||
|
||||
@StatisticsRouter.put("/qtl/{qtl_id}", summary="更新 QTL")
|
||||
async def qtl_update(
|
||||
qtl_id: int,
|
||||
data: QtlIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
obj = await StatisticsService(auth, db).qtl_update(qtl_id, data)
|
||||
return SuccessResponse(QtlOut.model_validate(obj))
|
||||
|
||||
|
||||
@StatisticsRouter.delete("/qtl/delete", summary="删除 QTL")
|
||||
async def qtl_delete(
|
||||
ids: Annotated[list[int], Body(description="ID列表")],
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:delete"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
await StatisticsService(auth, db).qtl_delete(ids)
|
||||
return SuccessResponse()
|
||||
|
||||
|
||||
@StatisticsRouter.get("/qtl", summary="QTL 列表(可按性状)")
|
||||
async def qtl_list(
|
||||
trait_id: int | None = Query(None, description="按性状过滤"),
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
objs = await StatisticsService(auth, db).qtl_list(trait_id)
|
||||
return SuccessResponse([QtlOut.model_validate(o) for o in objs])
|
||||
|
||||
|
||||
@StatisticsRouter.get("/qtl/{qtl_id}", summary="QTL 详情")
|
||||
async def qtl_detail(
|
||||
qtl_id: int,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
obj = await StatisticsService(auth, db).qtl_detail(qtl_id)
|
||||
return SuccessResponse(QtlOut.model_validate(obj))
|
||||
|
||||
|
||||
@StatisticsRouter.post("/mas-panel", summary="创建 MAS 标记辅助选择面板")
|
||||
async def mas_panel_create(
|
||||
data: MasPanelIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
return SuccessResponse(await StatisticsService(auth, db).mas_panel_create(data))
|
||||
|
||||
|
||||
@StatisticsRouter.put("/mas-panel/{panel_id}", summary="更新 MAS 面板")
|
||||
async def mas_panel_update(
|
||||
panel_id: int,
|
||||
data: MasPanelIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
obj = await StatisticsService(auth, db).mas_panel_update(panel_id, data)
|
||||
return SuccessResponse(MasPanelOut.model_validate(obj))
|
||||
|
||||
|
||||
@StatisticsRouter.delete("/mas-panel/delete", summary="删除 MAS 面板")
|
||||
async def mas_panel_delete(
|
||||
ids: Annotated[list[int], Body(description="ID列表")],
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:delete"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
await StatisticsService(auth, db).mas_panel_delete(ids)
|
||||
return SuccessResponse()
|
||||
|
||||
|
||||
@StatisticsRouter.get("/mas-panel", summary="MAS 面板列表(可按性状)")
|
||||
async def mas_panel_list(
|
||||
trait_id: int | None = Query(None, description="按性状过滤"),
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
objs = await StatisticsService(auth, db).mas_panel_list(trait_id)
|
||||
return SuccessResponse([MasPanelOut.model_validate(o) for o in objs])
|
||||
|
||||
|
||||
@StatisticsRouter.get("/mas-panel/{panel_id}", summary="MAS 面板详情(含标记)")
|
||||
async def mas_panel_detail(
|
||||
panel_id: int,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:query"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
result = await StatisticsService(auth, db).mas_panel_detail(panel_id)
|
||||
return SuccessResponse({
|
||||
"panel": MasPanelOut.model_validate(result["panel"]),
|
||||
"markers": [m for m in result["markers"]],
|
||||
})
|
||||
|
||||
|
||||
@StatisticsRouter.post("/mas-panel/{panel_id}/markers", summary="设置面板标记(全量替换)")
|
||||
async def mas_panel_set_markers(
|
||||
panel_id: int,
|
||||
data: MasPanelSetMarkersIn,
|
||||
auth: AuthSchema = Depends(AuthPermission(permissions=["module_bre:statistics:create"])),
|
||||
db: AsyncSession = Depends(db_getter),
|
||||
):
|
||||
n = await StatisticsService(auth, db).mas_panel_set_markers(panel_id, data.markers)
|
||||
return SuccessResponse({"panel_id": panel_id, "n_markers": n})
|
||||
@@ -0,0 +1,62 @@
|
||||
"""育种统计 CRUD"""
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.core.base_crud import CRUDBase
|
||||
from app.core.base_schema import AuthSchema
|
||||
|
||||
from .model import (
|
||||
AnovaResultModel,
|
||||
CombiningAbilityModel,
|
||||
GeneticCorrResultModel,
|
||||
PredictionModel,
|
||||
PredictionValueModel,
|
||||
SelectionIndexModel,
|
||||
StabilityResultModel,
|
||||
StatisticsJobModel,
|
||||
TypeBResultModel,
|
||||
)
|
||||
|
||||
|
||||
class PredictionCRUD(CRUDBase[PredictionModel, dict, dict]):
|
||||
def __init__(self, auth: AuthSchema, db: AsyncSession) -> None:
|
||||
super().__init__(PredictionModel, auth, db)
|
||||
|
||||
|
||||
class PredictionValueCRUD(CRUDBase[PredictionValueModel, dict, dict]):
|
||||
def __init__(self, auth: AuthSchema, db: AsyncSession) -> None:
|
||||
super().__init__(PredictionValueModel, auth, db)
|
||||
|
||||
|
||||
class CombiningAbilityCRUD(CRUDBase[CombiningAbilityModel, dict, dict]):
|
||||
def __init__(self, auth: AuthSchema, db: AsyncSession) -> None:
|
||||
super().__init__(CombiningAbilityModel, auth, db)
|
||||
|
||||
|
||||
class StatisticsJobCRUD(CRUDBase[StatisticsJobModel, dict, dict]):
|
||||
def __init__(self, auth: AuthSchema, db: AsyncSession) -> None:
|
||||
super().__init__(StatisticsJobModel, auth, db)
|
||||
|
||||
|
||||
class SelectionIndexCRUD(CRUDBase[SelectionIndexModel, dict, dict]):
|
||||
def __init__(self, auth: AuthSchema, db: AsyncSession) -> None:
|
||||
super().__init__(SelectionIndexModel, auth, db)
|
||||
|
||||
|
||||
class AnovaResultCRUD(CRUDBase[AnovaResultModel, dict, dict]):
|
||||
def __init__(self, auth: AuthSchema, db: AsyncSession) -> None:
|
||||
super().__init__(AnovaResultModel, auth, db)
|
||||
|
||||
|
||||
class GeneticCorrResultCRUD(CRUDBase[GeneticCorrResultModel, dict, dict]):
|
||||
def __init__(self, auth: AuthSchema, db: AsyncSession) -> None:
|
||||
super().__init__(GeneticCorrResultModel, auth, db)
|
||||
|
||||
|
||||
class StabilityResultCRUD(CRUDBase[StabilityResultModel, dict, dict]):
|
||||
def __init__(self, auth: AuthSchema, db: AsyncSession) -> None:
|
||||
super().__init__(StabilityResultModel, auth, db)
|
||||
|
||||
|
||||
class TypeBResultCRUD(CRUDBase[TypeBResultModel, dict, dict]):
|
||||
def __init__(self, auth: AuthSchema, db: AsyncSession) -> None:
|
||||
super().__init__(TypeBResultModel, auth, db)
|
||||
@@ -0,0 +1,445 @@
|
||||
"""育种统计 数据模型（一期 2 个最基础：ABLUP·EBV + 配合力 GCA/SCA）"""
|
||||
from datetime import datetime
|
||||
|
||||
from sqlalchemy import (
|
||||
JSON,
|
||||
Boolean,
|
||||
Date,
|
||||
DateTime,
|
||||
Float,
|
||||
ForeignKey,
|
||||
Index,
|
||||
Integer,
|
||||
Numeric,
|
||||
String,
|
||||
Text,
|
||||
)
|
||||
from sqlalchemy.orm import Mapped, mapped_column
|
||||
|
||||
from app.core.base_model import MappedBase, ModelMixin, UserMixin
|
||||
|
||||
|
||||
class PredictionModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""育种值模型/批次元数据（§3.12）。"""
|
||||
|
||||
__tablename__ = "bre_prediction"
|
||||
|
||||
model_name: Mapped[str | None] = mapped_column(String(128), nullable=True, comment="模型名称")
|
||||
trait_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_trait.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="性状"
|
||||
)
|
||||
method: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="方法(ABLUP/COMBINING)")
|
||||
stage: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="测定阶段(juvenile幼龄/evaluation成株)")
|
||||
accuracy: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="模型精度(批次PA=√均值可靠性)")
|
||||
train_n: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="训练样本量")
|
||||
heritability: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="遗传力 h²")
|
||||
predict_date: Mapped[datetime | None] = mapped_column(Date, nullable=True, comment="预测日期")
|
||||
data_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="数据版本(输入快照版本)")
|
||||
input_hash: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="输入快照SHA256(表型+系谱)")
|
||||
engine_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="统计引擎版本")
|
||||
is_active: Mapped[bool | None] = mapped_column(Boolean, nullable=True, default=False, comment="当前生效版本(同性状最近批次，供版本回滚)")
|
||||
note: Mapped[str | None] = mapped_column(String(512), nullable=True, comment="备注")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_prediction_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class PredictionValueModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""个体预测值（EBV 持久化，V1.1 建表）。"""
|
||||
|
||||
__tablename__ = "bre_prediction_value"
|
||||
|
||||
prediction_id: Mapped[int] = mapped_column(
|
||||
Integer, ForeignKey("bre_prediction.id", ondelete="CASCADE"),
|
||||
index=True, nullable=False, comment="关联模型"
|
||||
)
|
||||
germplasm_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_germplasm.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="种质(亲本级)"
|
||||
)
|
||||
tree_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_tree.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="单株(树级)"
|
||||
)
|
||||
trait_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_trait.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="性状"
|
||||
)
|
||||
predicted_value: Mapped[float | None] = mapped_column(Float, nullable=True, comment="预测值/EBV")
|
||||
reliability: Mapped[float | None] = mapped_column(Float, nullable=True, comment="可靠性")
|
||||
pa: Mapped[float | None] = mapped_column(Float, nullable=True, comment="预测准确度 PA=√可靠性")
|
||||
rank: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="排名")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_prediction_value_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class CombiningAbilityModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""配合力分析结果（§3.12；GCA 按亲本、SCA 按组合）。"""
|
||||
|
||||
__tablename__ = "bre_combining_ability"
|
||||
|
||||
model_name: Mapped[str | None] = mapped_column(String(128), nullable=True, comment="模型名称")
|
||||
trait_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_trait.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="性状"
|
||||
)
|
||||
method: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="方法")
|
||||
design_type: Mapped[str | None] = mapped_column(
|
||||
String(32), nullable=True,
|
||||
comment="分析采用交配设计(full_diallel/partial_diallel/line_tester/nciii，结果快照)",
|
||||
)
|
||||
gca_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="亲本一般配合力 {parent: gca}")
|
||||
sca_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="组合特殊配合力 {combo: {p1,p2,sca}}")
|
||||
anova_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="配合力方差分解(F_gca/p_gca)")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_combining_ability_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class SelectionIndexModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""选择指数 批次（加权 Z 综合；可选接入 ABLUP 批次 EBV 与 h² 加权，结果持久化）。"""
|
||||
|
||||
__tablename__ = "bre_selection_index"
|
||||
|
||||
model_name: Mapped[str | None] = mapped_column(String(128), nullable=True, comment="批次名称")
|
||||
method: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="SI_EBV/SI_PHENO")
|
||||
weights_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="{trait_code: 用户权重}")
|
||||
batch_refs_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="{trait_code: ABLUP批次id}")
|
||||
heritability_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="{trait_code: h²}")
|
||||
top_n: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="返回前 N")
|
||||
result_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="{traits, top:[{tree_id,index,detail}]}")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_selection_index_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class AnovaResultModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""单因素 ANOVA 结果（家系方差组分 + 广义遗传力 H²）。"""
|
||||
|
||||
__tablename__ = "bre_anova_result"
|
||||
|
||||
model_name: Mapped[str | None] = mapped_column(String(128), nullable=True, comment="批次名称")
|
||||
trait_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_trait.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="性状"
|
||||
)
|
||||
method: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="ANOVA/H2")
|
||||
result_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="方差组分/H²/F/p")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_anova_result_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class CvResultModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""k-fold 交叉验证结果（外部验证预测准确度，区别于 PEV 可靠性）。"""
|
||||
|
||||
__tablename__ = "bre_cv_result"
|
||||
|
||||
trait_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_trait.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="性状"
|
||||
)
|
||||
trait_code: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="性状编码")
|
||||
method: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="KFCV/ABLUP 或 KFCV/GXE")
|
||||
k: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="折数")
|
||||
n_total: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="总记录数")
|
||||
n_individuals: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="加性个体数")
|
||||
mean_pearson: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="有效折预测-观测 pearson 均值")
|
||||
mean_rmse: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="有效折 RMSE 均值")
|
||||
pooled_pearson: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="合并全部折 pearson")
|
||||
pooled_rmse: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="合并全部折 RMSE")
|
||||
cv_accuracy: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="预测准确度(=mean_pearson)")
|
||||
h2: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="有效折遗传力均值")
|
||||
folds_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="折明细快照")
|
||||
data_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="数据版本")
|
||||
input_hash: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="输入快照SHA256")
|
||||
engine_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="统计引擎版本")
|
||||
note: Mapped[str | None] = mapped_column(String(512), nullable=True, comment="备注")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_cv_result_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class CvFoldModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""k-fold CV 折明细。"""
|
||||
|
||||
__tablename__ = "bre_cv_fold"
|
||||
|
||||
cv_result_id: Mapped[int] = mapped_column(
|
||||
Integer, ForeignKey("bre_cv_result.id", ondelete="CASCADE"),
|
||||
index=True, nullable=False, comment="关联 CV 结果"
|
||||
)
|
||||
fold_idx: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="折号(1-based)")
|
||||
n_train: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="训练记录数")
|
||||
n_test: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="测试个体数")
|
||||
n_eval: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="有效评估个体数")
|
||||
pearson: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="折内预测-观测 pearson")
|
||||
rmse: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="折内 RMSE")
|
||||
error: Mapped[str | None] = mapped_column(String(512), nullable=True, comment="折失败原因")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_cv_fold_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class StatisticsJobModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""统计异步任务（R 引擎；ABLUP/COMBINING 提交后异步执行并回写结果）。"""
|
||||
|
||||
__tablename__ = "bre_statistics_job"
|
||||
|
||||
job_type: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="ABLUP/COMBINING")
|
||||
params_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="入参")
|
||||
status: Mapped[str] = mapped_column(String(16), nullable=False, default="PENDING", comment="PENDING/RUNNING/SUCCESS/FAILED")
|
||||
result_ref: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="结果主表 id")
|
||||
error_msg: Mapped[str | None] = mapped_column(Text, nullable=True, comment="错误信息")
|
||||
started_time: Mapped[datetime | None] = mapped_column(DateTime, nullable=True)
|
||||
finished_time: Mapped[datetime | None] = mapped_column(DateTime, nullable=True)
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_statistics_job_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class GeneticCorrResultModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""成对双性状 BLUP 遗传相关矩阵结果（MT-BLUP，§8.18）。
|
||||
|
||||
逐对 bivariate 估计 σ²a1/σ²a2/σ²a12（固定单性状方差、仅对遗传相关 ρ 作
|
||||
1-D 剖面 REML golden-max），输出遗传相关矩阵 r_g 供 Smith-Hazel 指数
|
||||
G 矩阵非对角替换 Calo 近似。
|
||||
"""
|
||||
|
||||
__tablename__ = "bre_genetic_corr_result"
|
||||
|
||||
trait_ids_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="{trait_code: trait_id}")
|
||||
matrix_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="遗传相关矩阵 {code: {code: r_g}}")
|
||||
sigma_a_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="加性方差矩阵 {code: {code: σa}}")
|
||||
heritability_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="{code: h²}")
|
||||
pairs_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="逐对详情 {pair: {r_g, va1, va2, ve1, ve2, n_common, converged, warning}}")
|
||||
n_common: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="成对共有树数(最小)")
|
||||
data_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="数据版本")
|
||||
input_hash: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="输入快照SHA256(表型+系谱)")
|
||||
engine_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="统计引擎版本")
|
||||
note: Mapped[str | None] = mapped_column(String(512), nullable=True, comment="备注")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_genetic_corr_result_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class TypeBResultModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""Type-B 多环境遗传相关结果（§8.27）。
|
||||
|
||||
同一性状在多个环境（site=研究点 / year=年份）的表型，以环境互为"性状"逐对
|
||||
REML 双性状 BLUP 估计遗传相关 r_g（复用 mtblup.solve_bivariate）；r_g 均值
|
||||
即 Type-B 遗传相关，衡量基因型×环境互作强度与跨环境遗传稳定性。
|
||||
"""
|
||||
|
||||
__tablename__ = "bre_type_b_result"
|
||||
|
||||
trait_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_trait.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="性状"
|
||||
)
|
||||
trait_code: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="性状编码")
|
||||
env_dim: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="环境维度(site/year)")
|
||||
method: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="方法(reml/calo，calo 无环境级EBV时回退reml)")
|
||||
envs_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="{env_code: env_label}")
|
||||
matrix_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="环境间遗传相关矩阵 {env: {env: r_g}}")
|
||||
pairs_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="逐对详情 {pair: {r_g, va1, va2, ve1, ve2, n_common, converged, warning}}")
|
||||
n_common: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="成对共有树数(最小)")
|
||||
data_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="数据版本")
|
||||
input_hash: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="输入快照SHA256")
|
||||
engine_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="统计引擎版本")
|
||||
note: Mapped[str | None] = mapped_column(String(512), nullable=True, comment="备注")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_type_b_result_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class StabilityResultModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""AMMI / Finlay-Wilkinson 稳定性分析结果（§8.18）。
|
||||
|
||||
两因素(基因型×环境)均值表 → AMMI SVD(IPC1/IPC2/ASV/ecovalence) 与
|
||||
Finlay-Wilkinson 回归斜率 b，输出稳定性排名。
|
||||
"""
|
||||
|
||||
__tablename__ = "bre_stability_result"
|
||||
|
||||
trait_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_trait.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="性状"
|
||||
)
|
||||
trait_code: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="性状编码")
|
||||
env_dim: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="环境维度(site/year)")
|
||||
methods: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="方法(ammi,finlay)")
|
||||
detail_json: Mapped[dict | None] = mapped_column(JSON, nullable=True, comment="稳定性分析明细快照")
|
||||
data_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="数据版本")
|
||||
input_hash: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="输入快照SHA256")
|
||||
engine_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="统计引擎版本")
|
||||
note: Mapped[str | None] = mapped_column(String(512), nullable=True, comment="备注")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_stability_result_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class GwasResultModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""GWAS 关联分析批次结果（§8.22）。
|
||||
|
||||
GLM+PC 逐标记单标记回归（纯 numpy + fdist），输出显著标记集与 QTL 区间。
|
||||
"""
|
||||
|
||||
__tablename__ = "bre_gwas_result"
|
||||
|
||||
dataset_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_genotyping_dataset.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="基因型数据集"
|
||||
)
|
||||
trait_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_trait.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="性状"
|
||||
)
|
||||
trait_code: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="性状编码")
|
||||
method: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="方法(gwas/ssgwas)")
|
||||
n_individuals: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="样本数(表型∩基因型)")
|
||||
n_markers: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="标记总数")
|
||||
m_after_maf: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="MAF 过滤后标记数")
|
||||
maf_min: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="MAF 过滤下限")
|
||||
n_pc: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="群体结构主成分数")
|
||||
sig_level: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="显著性水平")
|
||||
threshold_bonf: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="Bonferroni 阈值")
|
||||
n_sig_bonf: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="Bonferroni 显著标记数")
|
||||
n_sig_fdr: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="FDR q<α 标记数")
|
||||
n_qtl: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="QTL 区间数")
|
||||
data_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="数据版本")
|
||||
input_hash: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="输入快照SHA256(表型+系谱+基因型)")
|
||||
engine_version: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="统计引擎版本")
|
||||
remark: Mapped[str | None] = mapped_column(String(512), nullable=True, comment="备注")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_gwas_result_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class GwasSnpModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""GWAS 逐标记关联结果（§8.22）。"""
|
||||
|
||||
__tablename__ = "bre_gwas_snp"
|
||||
|
||||
gwas_result_id: Mapped[int] = mapped_column(
|
||||
Integer, ForeignKey("bre_gwas_result.id", ondelete="CASCADE"),
|
||||
index=True, nullable=False, comment="关联 GWAS 批次"
|
||||
)
|
||||
marker_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_marker.id", ondelete="SET NULL"),
|
||||
index=True, nullable=True, comment="标记"
|
||||
)
|
||||
marker_name: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="标记名称")
|
||||
chromosome: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="染色体")
|
||||
position: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="物理位置(bp)")
|
||||
maf: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="次要等位频率")
|
||||
effect: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="加性效应")
|
||||
se: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="效应标准误")
|
||||
t_value: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="t 值")
|
||||
p_value: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="p 值")
|
||||
neg_log10p: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="−log10(p)")
|
||||
q_value: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="BH-FDR q 值")
|
||||
sig_bonf: Mapped[bool | None] = mapped_column(Boolean, nullable=True, comment="Bonferroni 显著")
|
||||
sig_fdr: Mapped[bool | None] = mapped_column(Boolean, nullable=True, comment="FDR 显著")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_gwas_snp_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class QtlModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""QTL 区间（§8.22）。
|
||||
|
||||
source=known 为人工录入已知 QTL（桃成熟期/果重等位点）；source=gwas 为 GWAS
|
||||
显著标记聚类自动定位（gwas_result_id 关联批次）。
|
||||
"""
|
||||
|
||||
__tablename__ = "bre_qtl"
|
||||
|
||||
trait_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_trait.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="性状"
|
||||
)
|
||||
chromosome: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="染色体")
|
||||
start_bp: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="区间起点(bp)")
|
||||
end_bp: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="区间终点(bp)")
|
||||
peak_marker_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_marker.id", ondelete="SET NULL"),
|
||||
index=True, nullable=True, comment="峰标记"
|
||||
)
|
||||
peak_marker_name: Mapped[str | None] = mapped_column(String(64), nullable=True, comment="峰标记名称")
|
||||
peak_p: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="峰标记 p 值")
|
||||
n_markers: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="区间显著标记数")
|
||||
effect: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="区间峰效应")
|
||||
source: Mapped[str | None] = mapped_column(String(8), nullable=True, default="known", comment="来源(known/gwas)")
|
||||
gwas_result_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_gwas_result.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="来源 GWAS 批次(source=gwas 时)"
|
||||
)
|
||||
remark: Mapped[str | None] = mapped_column(String(512), nullable=True, comment="备注")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_qtl_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class MasPanelModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""MAS 标记辅助选择面板（§8.22）。
|
||||
|
||||
一组与目标性状关联的标记（QTL 峰标记），供决策预览按有利剂量命中童期幼苗。
|
||||
"""
|
||||
|
||||
__tablename__ = "bre_mas_panel"
|
||||
|
||||
panel_name: Mapped[str | None] = mapped_column(String(128), nullable=True, comment="面板名称")
|
||||
trait_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_trait.id", ondelete="CASCADE"),
|
||||
index=True, nullable=True, comment="目标性状"
|
||||
)
|
||||
n_markers: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="标记数")
|
||||
remark: Mapped[str | None] = mapped_column(String(512), nullable=True, comment="备注")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_mas_panel_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
|
||||
|
||||
class MasPanelMarkerModel(ModelMixin, UserMixin, MappedBase):
|
||||
"""MAS 面板标记（§8.22）。"""
|
||||
|
||||
__tablename__ = "bre_mas_panel_marker"
|
||||
|
||||
panel_id: Mapped[int] = mapped_column(
|
||||
Integer, ForeignKey("bre_mas_panel.id", ondelete="CASCADE"),
|
||||
index=True, nullable=False, comment="关联面板"
|
||||
)
|
||||
marker_id: Mapped[int | None] = mapped_column(
|
||||
Integer, ForeignKey("bre_marker.id", ondelete="SET NULL"),
|
||||
index=True, nullable=True, comment="标记"
|
||||
)
|
||||
favorable_dose: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="有利剂量(0/1/2)")
|
||||
effect: Mapped[float | None] = mapped_column(Numeric, nullable=True, comment="效应(绝对值参考)")
|
||||
direction: Mapped[str | None] = mapped_column(String(8), nullable=True, default="high", comment="方向(high=剂量≥favorable_dose命中 / low=剂量≤命中)")
|
||||
mode: Mapped[str | None] = mapped_column(
|
||||
String(16), nullable=True, default="additive",
|
||||
comment="基因作用模式：additive加性(剂量≥fav)/dominance显性(剂量≥1高/≤1低)/recessive隐性(剂量=2高/0低)/allele等位存在(有利等位)/haplotype单倍型(同组全命中)")
|
||||
favorable_allele: Mapped[str | None] = mapped_column(String(16), nullable=True, comment="有利等位(allele/haplotype 模式用，SSR 等位索引)")
|
||||
haplotype_group: Mapped[str | None] = mapped_column(String(32), nullable=True, comment="单倍型组(haplotype 模式：同组标记全命中才计 1)")
|
||||
|
||||
__table_args__ = (
|
||||
Index("ix_bre_mas_panel_marker_created_deleted", "created_time", "is_deleted"),
|
||||
)
|
||||
@@ -0,0 +1,673 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""育种统计 Schema（一期 2 个最基础：ABLUP·EBV + 配合力 GCA/SCA）"""
|
||||
from datetime import datetime
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
|
||||
class RunStatsIn(BaseModel):
|
||||
"""提交统计任务的入参。"""
|
||||
trait_id: int = Field(..., description="性状 id（bre_trait）")
|
||||
trait_code: str = Field(..., description="性状编码（bre_trait.trait_code，如 avg_fruit_weight）")
|
||||
year: int | None = Field(None, description="评价年份过滤；为空则全部")
|
||||
fixed_effects: list[str] | None = Field(
|
||||
None, description="固定效应因子：支持 trial_study(试验/站点) / rootstock(砧木)"
|
||||
)
|
||||
covariate: str | None = Field(
|
||||
None, description="协变量（BLUP 校正）：crop_load(负载量) / competition(空间竞争:相邻株数)"
|
||||
)
|
||||
data_gate: bool = Field(
|
||||
True, description="数据就绪门禁(G1)：每 clone/家系最小 n、系谱完整率、缺失率任一不达标则拒绝运行"
|
||||
)
|
||||
min_clone_n: int = Field(
|
||||
2, ge=0, description="每 clone/家系最小样本量（观测树数低于该值的组列入未达标）"
|
||||
)
|
||||
min_pedigree_rate: float = Field(
|
||||
0.3, ge=0.0, le=1.0, description="系谱完整率下限（有父母或组合亲本的观测树占比）"
|
||||
)
|
||||
max_missing_rate: float = Field(
|
||||
0.8, ge=0.0, le=1.0, description="表型缺失率上限（缺失=有评价记录但无该性状数值）"
|
||||
)
|
||||
gxe: bool = Field(
|
||||
False, description="启用 G×E 交互随机效应（clone×site / 组合×site；year 同理）"
|
||||
)
|
||||
gxe_env: str = Field(
|
||||
"site", description="G×E 环境维度：site(trial_study_id) / year(evaluate_year)"
|
||||
)
|
||||
gxr: bool = Field(
|
||||
False, description="启用砧木×接穗随机互作（G×R）：rootstock 从固定效应移到随机效应，与 gxe 互斥"
|
||||
)
|
||||
spatial: bool = Field(
|
||||
False,
|
||||
description=(
|
||||
"启用 AR1×AR1 空间协方差（残差 e~N(0,σ²e·R)，R_ij=ρ^(|Δrow|+|Δcol|)）："
|
||||
"需观测株均有 bre_tree.row_no/col_no；与 gxe/gxr 互斥"
|
||||
),
|
||||
)
|
||||
spatial_aniso: bool = Field(
|
||||
False,
|
||||
description=(
|
||||
"空间协方差各向异性双参数（需 spatial=True）：R_ij=ρ_row^|Δrow|·ρ_col^|Δcol|，"
|
||||
"方法 AR1×AR1(aniso)，输出 ρ_row/ρ_col；False 时用单参 ρ（v1 兼容）"
|
||||
),
|
||||
)
|
||||
block: bool = Field(
|
||||
False,
|
||||
description=(
|
||||
"启用区组随机效应（不完全区组/增广/α-格子）：bre_tree.block_no 作第二随机效应，"
|
||||
"精度收益进入遗传评估；需 ≥2 个区组；与 gxe/gxr/spatial 互斥"
|
||||
),
|
||||
)
|
||||
stage: str | None = Field(
|
||||
None, description="发育阶段拆分：juvenile(童期)/evaluation(成株)；给定则只取该阶段观测建模并拆独立批次"
|
||||
)
|
||||
design_type: str = Field(
|
||||
"full_diallel",
|
||||
description=(
|
||||
"交配设计（配合力分析用）：full_diallel=完全双列(Griffing) / "
|
||||
"partial_diallel=部分双列 / line_tester=line×tester(NCII 两因素模型) / "
|
||||
"nciii=NCIII 测交"
|
||||
),
|
||||
)
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class PredictionOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
model_name: str | None = None
|
||||
trait_id: int | None = None
|
||||
method: str | None = None
|
||||
stage: str | None = None
|
||||
accuracy: float | None = None
|
||||
train_n: int | None = None
|
||||
heritability: float | None = None
|
||||
data_version: str | None = None
|
||||
input_hash: str | None = None
|
||||
engine_version: str | None = None
|
||||
is_active: bool | None = None
|
||||
note: str | None = None
|
||||
created_time: datetime | None = None
|
||||
|
||||
|
||||
class PredictionValueOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
prediction_id: int
|
||||
germplasm_id: int | None = None
|
||||
tree_id: int | None = None
|
||||
trait_id: int | None = None
|
||||
predicted_value: float | None = None
|
||||
reliability: float | None = None
|
||||
pa: float | None = None
|
||||
rank: int | None = None
|
||||
|
||||
|
||||
class CombiningAbilityOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
model_name: str | None = None
|
||||
trait_id: int | None = None
|
||||
method: str | None = None
|
||||
design_type: str | None = None
|
||||
gca_json: dict | None = None
|
||||
sca_json: dict | None = None
|
||||
anova_json: dict | None = None
|
||||
|
||||
|
||||
class StatisticsJobOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
job_type: str | None = None
|
||||
status: str
|
||||
params_json: dict | None = None
|
||||
result_ref: int | None = None
|
||||
error_msg: str | None = None
|
||||
started_time: datetime | None = None
|
||||
finished_time: datetime | None = None
|
||||
|
||||
|
||||
# ---------- 纯 Python 统计分析（不依赖 R） ----------
|
||||
class DescribeStatsIn(BaseModel):
|
||||
"""描述性统计入参。"""
|
||||
trait_codes: list[str] | None = Field(None, description="数值性状列名；为空则用全部数值列")
|
||||
group_by: str = Field("none", description="分组维度：none/year/combination")
|
||||
year: int | None = Field(None, description="评价年份过滤")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class CorrelationIn(BaseModel):
|
||||
"""相关分析入参。"""
|
||||
trait_codes: list[str] | None = Field(None, description="参与相关的数值性状；至少2个")
|
||||
mode: str = Field(
|
||||
"pheno",
|
||||
description="相关类型：pheno=表型相关（皮尔逊，纯Python）；genetic=遗传相关（成对双性状BLUP REML）",
|
||||
)
|
||||
g_method: str = Field(
|
||||
"mtblup",
|
||||
description="遗传相关 r_g 来源（仅 mode=genetic）：mtblup=成对双性状BLUP(REML精确)；calo=可靠性校正EBV相关，无生效EBV批次时回退mtblup",
|
||||
)
|
||||
year: int | None = Field(None, description="评价年份过滤")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class SelectionIndexIn(BaseModel):
|
||||
"""选择指数入参。"""
|
||||
weights: dict[str, float] | None = Field(None, description="性状->权重，缺省等权；自动归一化")
|
||||
year: int | None = Field(None, description="评价年份过滤（仅表型回退路径使用）")
|
||||
top_n: int = Field(50, description="返回排名前 N 的单株")
|
||||
batch_ids: dict[str, int] | None = Field(None, description="性状->ABLUP批次id，指定则用该批次的EBV作为指数输入")
|
||||
use_h2: bool = Field(True, description="有效权重 = 用户权重 × 遗传力(h²) 加权（仅 zsum 模式生效）")
|
||||
method: str = Field(
|
||||
"zsum",
|
||||
description=(
|
||||
"zsum=加权Z综合（轻量，可表型回退）；"
|
||||
"smith_hazel=真Smith-Hazel b=P⁻¹Ga（需各性状指定EBV批次且含h²）；"
|
||||
"restricted=约束指数 b=P⁻¹G(I−M)a（Kempthorne-Nordskog，受限性状 ΔG=0，需 restricted_traits）"
|
||||
),
|
||||
)
|
||||
aggregate: str = Field(
|
||||
"clone",
|
||||
description=(
|
||||
"聚合粒度：clone=无性系级（同系多株可靠加权聚合为一个遗传实体再排名，桃无性繁殖默认）；"
|
||||
"tree=单株级（旧行为）"
|
||||
),
|
||||
)
|
||||
stage: str | None = Field(
|
||||
None, description="发育阶段过滤：juvenile(童期)/evaluation(成株)；不传且性状横跨两阶段时返回 stage_warning"
|
||||
)
|
||||
g_method: str = Field(
|
||||
"calo", description="遗传相关 r_g 来源（smith_hazel / restricted 生效）：calo=可靠性校正EBV相关；mtblup=成对双性状BLUP(REML精确估计，单对不收敛回退calo)"
|
||||
)
|
||||
auto_weights: bool = Field(
|
||||
False,
|
||||
description="自动权重：以各性状 default_h2（实测h²优先，兜底0.1）为权重；强制 use_h2=False 防双重相乘",
|
||||
)
|
||||
restricted_traits: list[str] | None = Field(
|
||||
None, description="受限性状列表（仅 method=restricted 生效）：这些性状的遗传增益被约束为 0，其余性状自由响应（Kempthorne-Nordskog 闭式投影）"
|
||||
)
|
||||
economic_weights: dict[str, float] | None = Field(
|
||||
None, description="经济权重（经济价值，绝对尺度不归一化；smith_hazel / restricted 生效）：直接作为聚合基因型系数 a，未列出性状按 0 处理"
|
||||
)
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class SelectionIndexOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
model_name: str | None = None
|
||||
method: str | None = None
|
||||
weights_json: dict | None = None
|
||||
batch_refs_json: dict | None = None
|
||||
heritability_json: dict | None = None
|
||||
top_n: int | None = None
|
||||
result_json: dict | None = None
|
||||
created_time: datetime | None = None
|
||||
|
||||
|
||||
class SelectionIndexApplyIn(BaseModel):
|
||||
"""选择指数 -> 决选 入参。"""
|
||||
top_n: int = Field(50, description="写入前 N 名单株")
|
||||
selection_year: int | None = Field(None, description="入选年份；为空用当年")
|
||||
rule_id: int | None = Field(None, description="来源选择规则(决策预览选定,用于溯源 rule_id/from_stage/to_stage)")
|
||||
min_reliability: float = Field(
|
||||
0.2, ge=0.0, le=1.0,
|
||||
description="EBV 可靠性硬门槛：指数引用批次的 EBV 可靠性低于该值的单株跳过(证据不足不入选)",
|
||||
)
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class KinshipIn(BaseModel):
|
||||
"""亲缘/近交分析入参。"""
|
||||
threshold: float = Field(0.25, description="亲缘系数预警阈值（r>A 预警）")
|
||||
tree_ids: list[int] | None = Field(None, description="限定单株范围；为空用全部活动单株")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class DataQualityIn(BaseModel):
|
||||
"""数据质量/异常值诊断入参。"""
|
||||
trait_id: int = Field(..., description="性状 id（bre_trait）")
|
||||
trait_code: str = Field(..., description="性状编码")
|
||||
year: int | None = Field(None, description="评价年份过滤；为空则全部")
|
||||
trial_study_id: int | None = Field(None, description="限定试验/站点(MET)；为空则全部")
|
||||
dataset_id: int | None = Field(
|
||||
None, description="基因型数据集：提供时附加孟德尔检验段（最新批次计数+flag树+隔离状态）")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class GeneticGainIn(BaseModel):
|
||||
"""ΔG 遗传增益投影入参。"""
|
||||
trait_id: int = Field(..., description="性状 id（bre_trait）")
|
||||
trait_code: str = Field(..., description="性状编码")
|
||||
prediction_id: int | None = Field(None, description="指定 EBV 批次；为空用该性状最新批次")
|
||||
top_p: float | None = Field(None, gt=0, lt=1, description="入选比例（0-1，与 top_n 二选一）")
|
||||
top_n: int | None = Field(None, ge=1, description="入选株数（与 top_p 二选一）")
|
||||
generation_interval: float = Field(1.0, gt=0, description="世代间隔(年)；年增益=ΔG/L")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class InbreedingDepressionIn(BaseModel):
|
||||
"""近交衰退分析入参。"""
|
||||
trait_id: int = Field(..., description="性状 id（bre_trait）")
|
||||
trait_code: str = Field(..., description="性状编码")
|
||||
year: int | None = Field(None, description="评价年份过滤；为空则全部")
|
||||
trial_study_id: int | None = Field(None, description="限定试验/站点(MET)；为空则全部")
|
||||
min_n: int = Field(10, ge=2, description="回归最小样本量（同时具备 F 与表型的树）")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class TrialDesignIn(BaseModel):
|
||||
"""试验设计生成入参（rcbd / augmented / alpha）。"""
|
||||
trial_study_id: int = Field(..., description="试验研究点 id（bre_trial_study）")
|
||||
design_type: str = Field("rcbd", description="设计类型：rcbd=随机完全区组、augmented=增广、alpha=α-格子")
|
||||
seed: int | None = Field(None, description="随机种子；固定 seed 可复现同一设计")
|
||||
check_germplasm_ids: list[int] = Field(default_factory=list,
|
||||
description="增广设计对照种质 id 列表（每区组重复）")
|
||||
block_size: int | None = Field(None, description="α-格子区组大小 k（每重复区组块大小）")
|
||||
reps: int | None = Field(None, description="α-格子重复数 r（平方格子缺省 k+1）")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class MatingRecommendIn(BaseModel):
|
||||
"""主动选配推荐入参。"""
|
||||
candidate_germplasm_ids: list[int] = Field(..., description="候选亲本种质 id 集合（≥2）")
|
||||
prediction_id: int | None = Field(None, description="EBV 批次；为空用最新 ABLUP/GBLUP 批次")
|
||||
kinship_threshold: float = Field(0.25, description="亲缘惩罚阈值（r>阈值开始扣分）")
|
||||
w_ebv: float = Field(1.0, ge=0, description="EBV 互补权重")
|
||||
w_kin: float = Field(1.0, ge=0, description="近交惩罚权重")
|
||||
max_pairs: int = Field(20, ge=1, le=500, description="返回 top N 配对")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class OcsIn(BaseModel):
|
||||
"""最优贡献选择（OCS）入参。"""
|
||||
candidate_germplasm_ids: list[int] = Field(..., description="候选亲本种质 id 集合")
|
||||
n_select: int = Field(5, ge=1, description="选择数量（贡献总和 Σc=n_select）")
|
||||
lam: float = Field(0.1, ge=0, description="近交约束权重 λ：越大越压低子代平均亲缘（avg_kinship 单调非增），0=无近交约束（满额投最高 EBV 单亲）")
|
||||
prediction_id: int | None = Field(None, description="EBV 批次；为空用最新 ABLUP/GBLUP 批次")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class MabcIn(BaseModel):
|
||||
"""MABC 标记辅助回交进度入参。"""
|
||||
candidate_tree_ids: list[int] = Field(..., description="候选回交单株树 id 集合（BC 世代分离群体）")
|
||||
foreground_panel_ids: list[int] = Field(..., description="前景选择面板 id（目标性状 MAS 面板）")
|
||||
background_panel_ids: list[int] = Field(..., description="背景恢复面板 id（全基因组标记面板）")
|
||||
recurrent_parent_tree_id: int = Field(..., description="轮回亲本树 id（背景纯合一致对照）")
|
||||
foreground_min_hits: int = Field(1, ge=1, description="前景通过阈值（有利剂量命中标记数 ≥ 此值，stage 无关、童期可用）")
|
||||
generation: str = Field("BC1", description="候选当前回交代（F1/BC1/BC2/BC3），用于晋级建议")
|
||||
background_target: float = Field(90.0, gt=0, le=100, description="背景恢复目标 %（达到则建议晋级下一回交代）")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class AnovaIn(BaseModel):
|
||||
"""ANOVA / 广义遗传力 H² 入参。"""
|
||||
trait_id: int = Field(..., description="性状 id（bre_trait）")
|
||||
trait_code: str = Field(..., description="性状编码")
|
||||
year: int | None = Field(None, description="评价年份过滤；为空则全部")
|
||||
block: bool = Field(
|
||||
False, description="RCBD 设计基 ANOVA：True=从残差析出区组效应(SS_block/F_block/p_block)，遗传差异为区组校正后；需观测株 bre_tree.block_no")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class AnovaOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
model_name: str | None = None
|
||||
trait_id: int | None = None
|
||||
method: str | None = None
|
||||
result_json: dict | None = None
|
||||
created_time: datetime | None = None
|
||||
|
||||
|
||||
class CvRunIn(BaseModel):
|
||||
"""k-fold 交叉验证入参。"""
|
||||
trait_id: int = Field(..., description="性状 id（bre_trait）")
|
||||
trait_code: str = Field(..., description="性状编码")
|
||||
year: int | None = Field(None, description="评价年份过滤；为空则全部")
|
||||
fixed_effects: list[str] | None = Field(
|
||||
None, description="固定效应因子：trial_study(试验/站点) / rootstock(砧木)"
|
||||
)
|
||||
covariate: str | None = Field(
|
||||
None, description="协变量：crop_load(负载量) / competition(空间竞争)"
|
||||
)
|
||||
gxe: bool = Field(False, description="启用 G×E 交互随机效应")
|
||||
gxe_env: str = Field("site", description="G×E 环境维度：site / year")
|
||||
k: int = Field(5, ge=2, le=10, description="折数")
|
||||
dataset_id: int | None = Field(
|
||||
None, description="基因型数据集 id；给定则走 GS 交叉验证（GBLUP/ssGBLUP/rrBLUP/BayesB）"
|
||||
)
|
||||
method: str | None = Field(
|
||||
None, description="GS 方法：gblup / ssgblup / rrblup / bayesb（仅 dataset_id 给定时生效）"
|
||||
)
|
||||
maf_min: float = Field(0.05, description="MAF 下限（GS 交叉验证用）")
|
||||
split: str = Field(
|
||||
"random", description="GS 折划分：random=固定种子随机分层（默认）/ family=家系阻塞折（同父半同胞同折，防亲缘泄漏）"
|
||||
)
|
||||
seed: int = Field(20260805, description="BayesB 随机种子（折划分与 Gibbs 共用，固定可复现）")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class CvResultOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
trait_id: int | None = None
|
||||
trait_code: str | None = None
|
||||
method: str | None = None
|
||||
k: int | None = None
|
||||
n_total: int | None = None
|
||||
n_individuals: int | None = None
|
||||
mean_pearson: float | None = None
|
||||
mean_rmse: float | None = None
|
||||
pooled_pearson: float | None = None
|
||||
pooled_rmse: float | None = None
|
||||
cv_accuracy: float | None = None
|
||||
h2: float | None = None
|
||||
data_version: str | None = None
|
||||
input_hash: str | None = None
|
||||
engine_version: str | None = None
|
||||
note: str | None = None
|
||||
created_time: datetime | None = None
|
||||
|
||||
|
||||
class CvFoldOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
cv_result_id: int
|
||||
fold_idx: int | None = None
|
||||
n_train: int | None = None
|
||||
n_test: int | None = None
|
||||
n_eval: int | None = None
|
||||
pearson: float | None = None
|
||||
rmse: float | None = None
|
||||
error: str | None = None
|
||||
|
||||
|
||||
class DecisionPreviewIn(BaseModel):
|
||||
"""选择规则 × 表型/EBV/标记(MAS) 决策预览入参。"""
|
||||
rule_ids: list[int] | None = Field(None, description="指定规则；为空用全部启用规则")
|
||||
prediction_id: int | None = Field(None, description="EBV 源批次；为空用最新 ABLUP 批次")
|
||||
year: int | None = Field(None, description="表型均值年份过滤")
|
||||
min_reliability: float = Field(
|
||||
0.2, ge=0.0, le=1.0,
|
||||
description="EBV 可靠性门槛：单株 EBV 可靠性低于该值视为证据不足，不参与晋级/淘汰判定；"
|
||||
"ebv 条件内可配 min_reliability 逐性状覆盖",
|
||||
)
|
||||
stage: str | None = Field(
|
||||
None, description="发育阶段过滤：juvenile(童期)/evaluation(成株)；不传且规则引用性状横跨两阶段时返回 stage_warning"
|
||||
)
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class DecisionPreviewOut(BaseModel):
|
||||
rule_id: int | None = None
|
||||
rule_name: str | None = None
|
||||
action: str | None = None
|
||||
stage: str | None = None
|
||||
matched: bool | None = None
|
||||
reasons: list[str] | None = None
|
||||
tree_id: int | None = None
|
||||
tree_no: str | None = None
|
||||
combination_id: int | None = None
|
||||
|
||||
|
||||
# ---------- AMMI / Finlay-Wilkinson 稳定性（§8.18） ----------
|
||||
class StabilityRunIn(BaseModel):
|
||||
"""稳定性分析入参。"""
|
||||
trait_id: int = Field(..., description="性状 id（bre_trait）")
|
||||
trait_code: str = Field(..., description="性状编码")
|
||||
gxe_env: str = Field("site", description="环境维度：site(trial_study_id) / year(evaluate_year)")
|
||||
year: int | None = Field(None, description="评价年份过滤；为空则全部")
|
||||
methods: list[str] = Field(
|
||||
["ammi", "finlay"], description="分析方法：ammi / finlay，可多选"
|
||||
)
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class StabilityOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
trait_id: int | None = None
|
||||
trait_code: str | None = None
|
||||
env_dim: str | None = None
|
||||
methods: str | None = None
|
||||
detail_json: dict | None = None
|
||||
data_version: str | None = None
|
||||
input_hash: str | None = None
|
||||
engine_version: str | None = None
|
||||
note: str | None = None
|
||||
created_time: datetime | None = None
|
||||
|
||||
|
||||
# ---------- 遗传相关矩阵（MT-BLUP，§8.18） ----------
|
||||
class GeneticCorrIn(BaseModel):
|
||||
"""遗传相关矩阵入参。"""
|
||||
trait_ids: list[int] = Field(..., description="参与遗传相关的性状 id（≥2）")
|
||||
year: int | None = Field(None, description="评价年份过滤；为空则全部")
|
||||
full_mtblup: bool = Field(
|
||||
False, description="True=全多变量 EM-REML（一次估计完整 G0⊗A，替代逐对 bivariate）")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class GeneticCorrOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
trait_ids_json: dict | None = None
|
||||
matrix_json: dict | None = None
|
||||
sigma_a_json: dict | None = None
|
||||
heritability_json: dict | None = None
|
||||
pairs_json: dict | None = None
|
||||
n_common: int | None = None
|
||||
data_version: str | None = None
|
||||
input_hash: str | None = None
|
||||
engine_version: str | None = None
|
||||
note: str | None = None
|
||||
created_time: datetime | None = None
|
||||
|
||||
|
||||
# ---------- Type-B 多环境遗传相关（§8.27） ----------
|
||||
class TypeBIn(BaseModel):
|
||||
"""Type-B 多环境遗传相关入参。"""
|
||||
trait_id: int = Field(..., description="性状 id（bre_trait，数值型）")
|
||||
trait_code: str = Field(..., description="性状编码")
|
||||
env_dim: str = Field("site", description="环境维度：site(研究点 trial_study_id) / year(评价年份) / stage(发育阶段：juvenile童期 vs evaluation成株，估幼年-成年遗传相关)")
|
||||
method: str = Field(
|
||||
"reml", description="方法：reml=环境互为性状逐对REML双性状BLUP(默认)；calo=分环境EBV相关/√rel(无环境级EBV时回退reml)"
|
||||
)
|
||||
year: int | None = Field(None, description="评价年份过滤；为空则全部")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class TypeBOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
trait_id: int | None = None
|
||||
trait_code: str | None = None
|
||||
env_dim: str | None = None
|
||||
method: str | None = None
|
||||
envs_json: dict | None = None
|
||||
matrix_json: dict | None = None
|
||||
pairs_json: dict | None = None
|
||||
n_common: int | None = None
|
||||
data_version: str | None = None
|
||||
input_hash: str | None = None
|
||||
engine_version: str | None = None
|
||||
note: str | None = None
|
||||
created_time: datetime | None = None
|
||||
|
||||
|
||||
# ---------- UPGMA 聚类（§8.27，计算端点不落库） ----------
|
||||
class ClusterIn(BaseModel):
|
||||
"""UPGMA 聚类入参（实体=单株或无性系，按多性状轮廓聚类）。"""
|
||||
trait_ids: list[int] = Field(..., description="参与聚类的数值性状 id（≥2）")
|
||||
entity_type: str = Field("tree", description="聚类实体：tree=单株；clone=无性系(同系多株均值聚合)")
|
||||
mode: str = Field("pheno", description="值来源：pheno=表型均值；genetic=EBV(无生效批次回退表型)")
|
||||
distance: str = Field(
|
||||
"corr", description="实体距离：corr=1-|皮尔逊r|(轮廓相似)；euclidean=标准化欧氏距离"
|
||||
)
|
||||
k: int | None = Field(None, description="聚成 k 类；为空按合并距离最大跳变自动选择")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
# ---------- GBLUP / ssGBLUP 基因组选择（§8.18） ----------
|
||||
class GblupRunIn(BaseModel):
|
||||
"""GBLUP/ssGBLUP/rrBLUP/BayesB 基因组选择入参（§8.18）。"""
|
||||
dataset_id: int = Field(..., description="基因型数据集 id（bre_genotyping_dataset）")
|
||||
trait_id: int = Field(..., description="性状 id（bre_trait）")
|
||||
trait_code: str = Field(..., description="性状编码")
|
||||
year: int | None = Field(None, description="评价年份过滤；为空则全部")
|
||||
method: str = Field("gblup", description="gblup（仅基因型株）/ ssgblup（单步法，含非基因型亲属）/ rrblup（岭回归逐标记，输出 marker effects）/ bayesb（贝叶斯可变选择，固定 seed 可复现）")
|
||||
maf_min: float = Field(0.05, ge=0.0, lt=0.5, description="MAF 过滤下限")
|
||||
seed: int = Field(20260805, description="BayesB 随机种子（固定保证可复现；MLOps 铁律）")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class GenotypingDatasetOut(BaseModel):
|
||||
id: int
|
||||
dataset_name: str | None = None
|
||||
platform: str | None = None
|
||||
panel: str | None = None
|
||||
purpose: str | None = None
|
||||
run_date: datetime | None = None
|
||||
n_samples: int | None = None
|
||||
|
||||
|
||||
# ---------- GWAS / QTL / MAS 标记辅助选择（§8.22） ----------
|
||||
class GwasRunIn(BaseModel):
|
||||
"""GWAS 关联分析入参（GLM+PC 首版）。"""
|
||||
dataset_id: int = Field(..., description="基因型数据集 id（bre_genotyping_dataset）")
|
||||
trait_id: int = Field(..., description="性状 id（bre_trait）")
|
||||
trait_code: str = Field(..., description="性状编码")
|
||||
year: int | None = Field(None, description="评价年份过滤；为空则全部")
|
||||
method: str = Field("gwas", description="gwas（GLM+PC）/ emmax（混合模型 EMMAX）/ ssgwas（单步 GWAS）")
|
||||
maf_min: float = Field(0.05, ge=0.0, lt=0.5, description="MAF 过滤下限")
|
||||
n_pc: int = Field(3, ge=1, le=10, description="群体结构主成分数")
|
||||
sig_level: float = Field(0.05, gt=0.0, le=1.0, description="显著性水平")
|
||||
qtl_window: int = Field(1_000_000, ge=1, description="QTL 合并窗口(bp)")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class GwasQtlXEIn(BaseModel):
|
||||
"""QTL×E 入参（按环境分层 GWAS + 稳定性判定，计算端点不建表）。"""
|
||||
dataset_id: int = Field(..., description="基因型数据集 id（bre_genotyping_dataset）")
|
||||
trait_id: int = Field(..., description="性状 id（bre_trait）")
|
||||
trait_code: str = Field(..., description="性状编码")
|
||||
year: int | None = Field(None, description="评价年份过滤；为空则全部")
|
||||
method: str = Field("gwas", description="gwas（GLM+PC）/ emmax（混合模型 EMMAX）")
|
||||
maf_min: float = Field(0.05, ge=0.0, lt=0.5, description="MAF 过滤下限")
|
||||
n_pc: int = Field(3, ge=1, le=10, description="群体结构主成分数")
|
||||
sig_level: float = Field(0.05, gt=0.0, le=1.0, description="显著性水平")
|
||||
qtl_window: int = Field(1_000_000, ge=1, description="QTL 合并窗口(bp)")
|
||||
env_dim: str = Field("site", description="环境维度：site(trial_study_id) / year(evaluate_year)")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class GwasResultOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
dataset_id: int | None = None
|
||||
trait_id: int | None = None
|
||||
trait_code: str | None = None
|
||||
method: str | None = None
|
||||
n_individuals: int | None = None
|
||||
n_markers: int | None = None
|
||||
m_after_maf: int | None = None
|
||||
maf_min: float | None = None
|
||||
n_pc: int | None = None
|
||||
sig_level: float | None = None
|
||||
threshold_bonf: float | None = None
|
||||
n_sig_bonf: int | None = None
|
||||
n_sig_fdr: int | None = None
|
||||
n_qtl: int | None = None
|
||||
data_version: str | None = None
|
||||
input_hash: str | None = None
|
||||
engine_version: str | None = None
|
||||
remark: str | None = None
|
||||
created_time: datetime | None = None
|
||||
|
||||
|
||||
class GwasSnpOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
gwas_result_id: int
|
||||
marker_id: int | None = None
|
||||
marker_name: str | None = None
|
||||
chromosome: str | None = None
|
||||
position: int | None = None
|
||||
maf: float | None = None
|
||||
effect: float | None = None
|
||||
se: float | None = None
|
||||
t_value: float | None = None
|
||||
p_value: float | None = None
|
||||
neg_log10p: float | None = None
|
||||
q_value: float | None = None
|
||||
sig_bonf: bool | None = None
|
||||
sig_fdr: bool | None = None
|
||||
|
||||
|
||||
class QtlIn(BaseModel):
|
||||
"""已知 QTL 录入（source=known）入参。"""
|
||||
trait_id: int | None = Field(None, description="性状 id（bre_trait）")
|
||||
chromosome: str | None = Field(None, description="染色体")
|
||||
start_bp: int | None = Field(None, description="区间起点(bp)")
|
||||
end_bp: int | None = Field(None, description="区间终点(bp)")
|
||||
peak_marker_id: int | None = Field(None, description="峰标记 id（bre_marker）")
|
||||
peak_p: float | None = Field(None, description="峰标记 p 值")
|
||||
n_markers: int | None = Field(None, description="区间显著标记数")
|
||||
effect: float | None = Field(None, description="峰效应")
|
||||
remark: str | None = Field(None, description="备注")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class QtlOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
trait_id: int | None = None
|
||||
chromosome: str | None = None
|
||||
start_bp: int | None = None
|
||||
end_bp: int | None = None
|
||||
peak_marker_id: int | None = None
|
||||
peak_marker_name: str | None = None
|
||||
peak_p: float | None = None
|
||||
n_markers: int | None = None
|
||||
effect: float | None = None
|
||||
source: str | None = None
|
||||
gwas_result_id: int | None = None
|
||||
remark: str | None = None
|
||||
created_time: datetime | None = None
|
||||
|
||||
|
||||
class MasPanelIn(BaseModel):
|
||||
"""MAS 标记辅助选择面板入参。"""
|
||||
panel_name: str = Field(..., description="面板名称")
|
||||
trait_id: int | None = Field(None, description="目标性状 id（bre_trait）")
|
||||
remark: str | None = Field(None, description="备注")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class MasPanelOut(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
id: int
|
||||
panel_name: str | None = None
|
||||
trait_id: int | None = None
|
||||
n_markers: int | None = None
|
||||
remark: str | None = None
|
||||
created_time: datetime | None = None
|
||||
|
||||
|
||||
class MasPanelMarkerIn(BaseModel):
|
||||
"""面板标记（有利剂量 + 方向 + 基因作用模式）。"""
|
||||
marker_id: int | None = Field(None, description="标记 id（bre_marker）")
|
||||
favorable_dose: int | None = Field(None, description="有利剂量(0/1/2)")
|
||||
effect: float | None = Field(None, description="效应(参考)")
|
||||
direction: str | None = Field("high", description="high=剂量≥favorable_dose命中 / low=剂量≤命中")
|
||||
mode: str | None = Field("additive", description="基因作用模式：additive/dominance/recessive/allele/haplotype")
|
||||
favorable_allele: str | None = Field(None, description="有利等位(allele/haplotype 模式用，SSR 等位索引)")
|
||||
haplotype_group: str | None = Field(None, description="单倍型组(haplotype 模式：同组标记全命中才计 1)")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
|
||||
|
||||
class MasPanelSetMarkersIn(BaseModel):
|
||||
"""面板标记全量替换入参。"""
|
||||
markers: list[MasPanelMarkerIn] = Field(default_factory=list, description="面板标记列表")
|
||||
model_config = ConfigDict(extra="ignore")
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user