db/repositories/experiment_parameters_repos.py

@@ -1,7 +1,11 @@
+import hashlib
 from typing import Sequence
 
+import pandas as pd
+from fastapi import HTTPException
 from sqlalchemy.future import select
 
+from db.crud import create
 from db.models.experiment_parameters_model import ExperimentParameters
 from db.postgres_db_connection import async_session_postgres
 
@@ -19,3 +23,34 @@ async def get_exp_parameters_by_exp_hash(exp_hash: str) -> Sequence[ExperimentPa
             select(ExperimentParameters).where(ExperimentParameters.experiment_hash == exp_hash)
         )
         return result.scalars().all()
+
+
+def generate_experiment_hash(data: dict) -> str:
+    """Генерация уникального хеша на основе данных эксперимента"""
+    hash_input = f"{data['outer_blades_count']}_{data['outer_blades_length']}_{data['outer_blades_angle']}_{data['middle_blades_count']}_{data['load']}_{data['recycling_level']}"
+    return hashlib.sha256(hash_input.encode()).hexdigest()
+
+
+async def save_experiment_to_db(df: pd.DataFrame):
+    for _, row in df.iterrows():
+        try:
+            # Преобразуем load и recycling_level в соответствующие id
+            load_id = int(row['load'])
+            recycling_id = int(row['recycling_level'])
+
+            # Генерация хеша для experiment_hash
+            experiment_hash = generate_experiment_hash(row)
+
+            await create(
+                ExperimentParameters,
+                outer_blades_count=int(row['outer_blades_count']),
+                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,
+                recycling_id=None,
+                experiment_hash=experiment_hash
+            )
+        except Exception as e:
+            print(f"Ошибка при сохранении данных: {e}")
+            raise HTTPException(status_code=500, detail=f"Ошибка при сохранении данных: {e}")

main.py

@@ -3,6 +3,7 @@ from fastapi import FastAPI, HTTPException, BackgroundTasks
 from pyDOE3 import pbdesign, lhs
 
 from db.csv_to_db import csv_to_db
+from db.repositories import save_experiment_to_db
 from network.routes import (ch_experimentdb_experiment_data_router, experiment_data_router,
                             experiment_parameters_router, experiment_category_router)
 from network.routes import load_parameters_router, recycling_parameters_router
@@ -76,7 +77,7 @@ async def init_db_data(background_tasks: BackgroundTasks):
 # }
 
 @app.post("/pyDOE3_screening_design")
-def generate_screening_design(request: ExperimentParametersPyDOE3) -> List[Dict[str, float]]:
+async def generate_screening_design(request: ExperimentParametersPyDOE3) -> List[Dict[str, float]]:
     param_ranges = request.param_ranges
 
     # Создаем screening design и масштабируем его
@@ -84,13 +85,17 @@ def generate_screening_design(request: ExperimentParametersPyDOE3) -> List[Dict[
     screening_design = pbdesign(num_factors)
     scaled_screening_design = scale_design(screening_design, param_ranges)
 
-    # Преобразуем в DataFrame и возвращаем результат
+    # Преобразуем в DataFrame
     df_screening = pd.DataFrame(scaled_screening_design, columns=param_ranges.keys())
+
+    # Сохраняем результаты в базу данных
+    await save_experiment_to_db(df_screening)
+
     return df_screening.to_dict(orient="records")
 
 
 @app.post("/pyDOE3_lhs_design")
-def generate_lhs_design(request: ExperimentParametersPyDOE3) -> List[Dict[str, float]]:
+async def generate_lhs_design(request: ExperimentParametersPyDOE3) -> List[Dict[str, float]]:
     param_ranges = request.param_ranges
     count_exp = request.count_exp
     round_rules = request.round_rules
@@ -103,6 +108,10 @@ def generate_lhs_design(request: ExperimentParametersPyDOE3) -> List[Dict[str, f
     # Округляем значения
     round_scaled_lhs_samples = round_by_index(scaled_lhs_samples, round_rules)
 
-    # Преобразуем в DataFrame и возвращаем результат
+    # Преобразуем в DataFrame
     df_lhs = pd.DataFrame(round_scaled_lhs_samples, columns=param_ranges.keys())
+
+    # Сохраняем результаты в базу данных
+    await save_experiment_to_db(df_lhs)
+
     return df_lhs.to_dict(orient="records")