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Merge pull request 'modified_db' (#2) from modified_db into main

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Reviewed-on: #2
This commit is contained in:
Sosees04ka 2024-11-30 01:45:56 +04:00
commit 9bc9c5553b
7 changed files with 214 additions and 10 deletions
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18
db/crud.py
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@ -42,6 +42,24 @@ async def update(model_class: Type[T], id: int, updated_data: Dict[str, Any]) ->
await session.commit()
return await get_by_id(model_class, id)
# Надо переписать/раасмотреть update
async def update_exp(model_class: Type[T], id: int, updated_data: Dict[str, Any]) -> Optional[T]:
async with async_session_postgres() as session:
async with session.begin(): # Явная транзакция
stmt = (
update_(model_class)
.where(model_class.id == id)
.values(**updated_data)
.returning(model_class) # Возвращаем обновленный объект
)
result = await session.execute(stmt)
updated_instance = result.scalars().first()
if not updated_instance:
return None
return updated_instance # Возвращаем сразу обновленный объект
async def delete(model_class: Type[T], id: int) -> bool:
async with async_session_postgres() as session:

3
db/models/experiment_parameters_model.py
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@ -9,7 +9,7 @@ from db.models.base import Base
class ExperimentParameters(Base):
__tablename__ = 'experiment_parameters'
id: Mapped[int] = mapped_column(Identity(start=11, cycle=True),
id: Mapped[int] = mapped_column(Identity(start=1100, cycle=True),
primary_key=True)
outer_blades_count: Mapped[int]
outer_blades_length: Mapped[float]
@ -19,6 +19,7 @@ class ExperimentParameters(Base):
recycling_id: Mapped[Optional[int]] = mapped_column(ForeignKey('recycling_parameters.id', ondelete='SET NULL'))
experiment_hash: Mapped[str] = mapped_column(unique=True)
experiment_category_id: Mapped[Optional[int]] = mapped_column(ForeignKey('experiment_category.id', ondelete='SET NULL'), nullable=True)
oxidizer_temp: Mapped[float] = mapped_column(nullable=True)
def __repr__(self):
return f"<ExperimentParameters>"

2
db/models/load_parameters_model.py
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@ -7,7 +7,7 @@ from db.models.base import Base
class LoadParameters(Base):
__tablename__ = 'load_parameters'
id: Mapped[int] = mapped_column(Identity(start=6, cycle=True),
id: Mapped[int] = mapped_column(Identity(start=1000, cycle=True),
primary_key=True)
load: Mapped[int]
primary_air_consumption: Mapped[float]

2
db/models/recycling_parameters_model.py
View File

@ -9,7 +9,7 @@ from db.models.base import Base
class RecyclingParameters(Base):
__tablename__ = 'recycling_parameters'
id: Mapped[int] = mapped_column(Identity(start=6, cycle=True),
id: Mapped[int] = mapped_column(Identity(start=1000, cycle=True),
primary_key=True)
load_id: Mapped[Optional[int]] = mapped_column(ForeignKey('load_parameters.id', ondelete='SET NULL'))

108
db/repositories/experiment_parameters_repos.py
View File

@ -1,9 +1,16 @@
import hashlib
from typing import Sequence
import pandas as pd
import yaml
from fastapi import HTTPException
from sqlalchemy.future import select
from db.crud import create, update, get_by_id, update_exp
from db.models import LoadParameters, RecyclingParameters
from db.models.experiment_parameters_model import ExperimentParameters
from db.postgres_db_connection import async_session_postgres
from macros_generator import load_calculation, recycling_calculation
async def get_exp_parameters_by_category(category_id: int) -> Sequence[ExperimentParameters]:
@ -18,4 +25,103 @@ async def get_exp_parameters_by_exp_hash(exp_hash: str) -> Sequence[ExperimentPa
result = await session.execute(
select(ExperimentParameters).where(ExperimentParameters.experiment_hash == exp_hash)
)
return result.scalars().all()
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 = int(row['load'])
recycling = int(row['recycling_level'])
# Генерация хеша для experiment_hash
experiment_hash = generate_experiment_hash(row)
exp = 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,
oxidizer_temp=float(row['oxidizer_temp'])
)
await process_and_save_experiment_data(exp.id, load, recycling)
except Exception as e:
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)
if experiment is None:
raise HTTPException(status_code=404, detail=f"ExperimentParameters с id {id} не найден.")
yaml_file_path = "config.yaml"
with open(yaml_file_path, "r", encoding="utf-8") as file:
data = yaml.safe_load(file)
diameters = data["parameters"]["diameters"]
dict_load = load_calculation(load, diameters, None)
primary_air_consumption = dict_load["primary_air_consumption"]
secondary_air_consumption = dict_load["secondary_air_consumption"]
gas_inlet_consumption = dict_load["gas_inlet_consumption"]
alpha = dict_load["alpha"]
gas_consumption = dict_load["gas_consumption"]
air_consumption = dict_load["air_consumption"]
dict_recycling = recycling_calculation(alpha, gas_consumption, air_consumption, recycling_level)
co2 = dict_recycling["CO2"]
n2 = dict_recycling["N2"]
h2o = dict_recycling["H2O"]
o2 = dict_recycling["O2"]
load_params = await create(
LoadParameters,
load=int(load),
primary_air_consumption=primary_air_consumption,
secondary_air_consumption=secondary_air_consumption,
gas_inlet_consumption=gas_inlet_consumption
)
recycling_params = await create(
RecyclingParameters,
load_id=load_params.id,
recycling_level=int(recycling_level),
co2=co2,
n2=n2,
h2o=h2o,
o2=o2
)
await update_exp(
ExperimentParameters,
id=experiment.id,
updated_data={
"load_id": load_params.id,
"recycling_id": recycling_params.id
}
)
return {
"message": "Данные успешно обработаны и сохранены.",
"load_parameters": load_params,
"recycling_parameters": recycling_params
}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")

19
main.py
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@ -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
@ -72,11 +73,11 @@ async def init_db_data(background_tasks: BackgroundTasks):
# "oxidizer_temp": [471, 493]
# },
# "count_exp": 1440,
# "round_rules": [0, 1, 1, 0, 1, 1, 1]
# "round_rules": [0, 1, 1, 0, 0, 0, 0]
# }
@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")

72
network/routes/experiment_parameters_router.py
View File

@ -1,8 +1,11 @@
import yaml
from fastapi import APIRouter, HTTPException
from scipy.stats import alpha
from db.crud import *
from db.models import LoadParameters
from db.repositories import get_exp_parameters_by_category, get_exp_parameters_by_exp_hash
from macros_generator import load_calculation, recycling_calculation
from network.schemas import ExperimentParametersBody
router = APIRouter()
@ -81,10 +84,77 @@ async def get_experiment_parameters_by_exp_category(hash: str):
@router.delete('/{id}/delete')
async def delete_experiment_parameters(id: int):
try:
is_deleted = await delete(LoadParameters, id)
is_deleted = await delete(ExperimentParameters, id)
if is_deleted:
return {"message": "Запись <ExperimentParameters> успешно удалена"}
else:
return {"message": "Запись <ExperimentParameters> не найдена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
# @router.post('/process_and_save/{id}') было нужно для проверки
async def process_and_save_experiment_data(id: int, load: float, recycling_level: float) -> dict:
try:
experiment = await get_by_id(ExperimentParameters, id)
if experiment is None:
raise HTTPException(status_code=404, detail=f"ExperimentParameters с id {id} не найден.")
yaml_file_path = "config.yaml"
with open(yaml_file_path, "r", encoding="utf-8") as file:
data = yaml.safe_load(file)
diameters = data["parameters"]["diameters"]
dict_load = load_calculation(load, diameters, None)
primary_air_consumption = dict_load["primary_air_consumption"]
secondary_air_consumption = dict_load["secondary_air_consumption"]
gas_inlet_consumption = dict_load["gas_inlet_consumption"]
alpha = dict_load["alpha"]
gas_consumption = dict_load["gas_consumption"]
air_consumption = dict_load["air_consumption"]
dict_recycling = recycling_calculation(alpha, gas_consumption, air_consumption, recycling_level)
co2 = dict_recycling["CO2"]
n2 = dict_recycling["N2"]
h2o = dict_recycling["H2O"]
o2 = dict_recycling["O2"]
load_params = await create(
LoadParameters,
load=int(load),
primary_air_consumption=primary_air_consumption,
secondary_air_consumption=secondary_air_consumption,
gas_inlet_consumption=gas_inlet_consumption
)
recycling_params = await create(
RecyclingParameters,
load_id=load_params.id,
recycling_level=int(recycling_level),
co2=co2,
n2=n2,
h2o=h2o,
o2=o2
)
await update_exp(
ExperimentParameters,
id=experiment.id,
updated_data={
"load_id": load_params.id,
"recycling_id": recycling_params.id
}
)
return {
"message": "Данные успешно обработаны и сохранены.",
"load_parameters": load_params,
"recycling_parameters": recycling_params
}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")