Update dev release

db/repositories/experiment_parameters_repos.py

@@ -20,6 +20,7 @@ async def get_exp_parameters_by_category(category_id: int) -> Sequence[Experimen
         )
         return result.scalars().all()
 
+
 async def get_exp_parameters_by_exp_hash(exp_hash: str) -> Sequence[ExperimentParameters]:
     async with async_session_postgres() as session:
         result = await session.execute(
@@ -34,7 +35,7 @@ def generate_experiment_hash(data: dict) -> str:
     return hashlib.sha256(hash_input.encode()).hexdigest()
 
 
-async def save_experiment_to_db(df: pd.DataFrame):
+async def save_experiment_to_db(df: pd.DataFrame, experiment_category_id: int):
     for _, row in df.iterrows():
         try:
             # Преобразуем load и recycling_level в соответствующие id
@@ -42,7 +43,7 @@ async def save_experiment_to_db(df: pd.DataFrame):
             recycling = int(row['recycling_level'])
 
             # Генерация хеша для experiment_hash
-            experiment_hash = generate_experiment_hash(row)
+            experiment_hash = generate_experiment_hash(row.to_dict())
 
             exp = await create(
                 ExperimentParameters,
@@ -50,10 +51,11 @@ async def save_experiment_to_db(df: pd.DataFrame):
                 outer_blades_length=float(row['outer_blades_length']),
                 outer_blades_angle=float(row['outer_blades_angle']),
                 middle_blades_count=int(row['middle_blades_count']),
-                load_id= None,
+                load_id=None,
                 recycling_id=None,
                 experiment_hash=experiment_hash,
-                oxidizer_temp=float(row['oxidizer_temp'])
+                oxidizer_temp=float(row['oxidizer_temp']),
+                experiment_category_id=experiment_category_id
             )
 
             await process_and_save_experiment_data(exp.id, load, recycling)
@@ -61,6 +63,7 @@ async def save_experiment_to_db(df: pd.DataFrame):
             print(f"Ошибка при сохранении данных: {e}")
             raise HTTPException(status_code=500, detail=f"Ошибка при сохранении данных: {e}")
 
+
 async def process_and_save_experiment_data(id: int, load: float, recycling_level: float) -> dict:
     try:
         experiment = await get_by_id(ExperimentParameters, id)

docker-compose.yml

@@ -1,4 +1,3 @@
-
 services:
   db:
     image: postgres
@@ -8,7 +7,7 @@ services:
       POSTGRES_USER: ${POSTGRES_USER}
       POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
     ports:
-      - "5432:5432"
+      - ${DB_PORT}
     volumes:
       - postgres_data:/var/lib/postgresql/data
 

main.py

@@ -1,5 +1,6 @@
 import pandas as pd
 from fastapi import FastAPI, HTTPException, BackgroundTasks
+from fastapi.middleware.cors import CORSMiddleware
 from pyDOE3 import pbdesign, lhs
 
 from db.csv_to_db import csv_to_db
@@ -13,6 +14,14 @@ from new_experiment_planner_pyDOE3 import scale_design, scale_design_lhs, round_
 
 app = FastAPI()
 
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=['*'],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 app.include_router(ch_experimentdb_experiment_data_router.router,
                    prefix="/ch_experimentdb_experiment_data",
                    tags=["ch_experimentdb_experiment_data"])
@@ -79,6 +88,7 @@ async def init_db_data(background_tasks: BackgroundTasks):
 @app.post("/pyDOE3_screening_design")
 async def generate_screening_design(request: ExperimentParametersPyDOE3) -> List[Dict[str, float]]:
     param_ranges = request.param_ranges
+    category = request.category
 
     # Создаем screening design и масштабируем его
     num_factors = len(param_ranges)
@@ -89,7 +99,7 @@ async def generate_screening_design(request: ExperimentParametersPyDOE3) -> List
     df_screening = pd.DataFrame(scaled_screening_design, columns=param_ranges.keys())
 
     # Сохраняем результаты в базу данных
-    await save_experiment_to_db(df_screening)
+    await save_experiment_to_db(df_screening, category)
 
     return df_screening.to_dict(orient="records")
 
@@ -99,6 +109,7 @@ async def generate_lhs_design(request: ExperimentParametersPyDOE3) -> List[Dict[
     param_ranges = request.param_ranges
     count_exp = request.count_exp
     round_rules = request.round_rules
+    category = request.category
 
     # Создаем lhs design и масштабируем его
     num_factors = len(param_ranges)
@@ -112,6 +123,6 @@ async def generate_lhs_design(request: ExperimentParametersPyDOE3) -> List[Dict[
     df_lhs = pd.DataFrame(round_scaled_lhs_samples, columns=param_ranges.keys())
 
     # Сохраняем результаты в базу данных
-    await save_experiment_to_db(df_lhs)
+    await save_experiment_to_db(df_lhs, category)
 
     return df_lhs.to_dict(orient="records")

network/routes/experiment_parameters_router.py

@@ -21,7 +21,8 @@ async def create_experiment_parameters(body: ExperimentParametersBody):
                      middle_blades_count=body.middle_blades_count,
                      load_id=body.load_id,
                      recycling_id=body.recycling_id,
-                     experiment_hash=body.experiment_hash
+                     experiment_hash=body.experiment_hash,
+                     experiment_category_id=body.experiment_category_id
                      )
 
         return {"message": "Новая запись <ExperimentParameters> успешно добавлена"}

network/schemas.py

@@ -2,10 +2,14 @@ from typing import Optional, Dict, Tuple, List
 
 from pydantic import BaseModel, ConfigDict
 
+
 class ExperimentParametersPyDOE3(BaseModel):
     param_ranges: Dict[str, Tuple[float, float]]
+    category: int
     count_exp: int
     round_rules: List[int]
+    category: int
+
 
 class ExperimentParameters(BaseModel):
     outer_blades_count: str
@@ -53,6 +57,7 @@ class ExperimentParametersBody(BaseModel):
     load_id: Optional[int]
     recycling_id: Optional[int]
     experiment_hash: str
+    experiment_category_id: int
 
 
 class LoadParametersBody(BaseModel):