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Sosees04ka merged 26 commits from modified_db into main 2024-11-05 21:26:38 +04:00
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.env Normal file
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DATABASE=SuperService
POSTGRES_USER=UserSuperService
POSTGRES_PASSWORD=NotWarningWord1
CLICKHOUSE_USER=UserMyHouse
CLICKHOUSE_PASSWORD=NotWarningWord2
DB_USER=postgres
DB_PASSWORD=password
DB_HOST=localhost
DB_PORT=5432
DB_NAME=test

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.venv
.idea
__pycache__

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Dockerfile Normal file
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FROM python:3.12
WORKDIR /app
COPY requirements.txt requirements.txt
RUN pip install --upgrade pip
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]

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README.md
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# PIbd-42_SSPR
# Описание проекта
Доработка автоматизированной системы планирования и выполнения численного моделирования процессов сжигания топлив в горелочных устройствах энергоустановок предназначена для оптимизации процессов проведения численных экспериментов с цифровой моделью горелочного устройства с целью поиска наиболее экономичных и низко-эмиссионных режимов работы
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2. Клюшенкова Ксения
3. Базунов Андрей
4. Жимолостнова Анна
5. Цуканова Ирина
5. Цуканова Ирина
# Запуск проекта
## 1. Создание окружения
```
py -m venv .venv
```
## 2. Переход в окружение
```
.\.venv\Scripts\activate
```
## 3. Скачивание библиотек
```
pip install -r .\requirements.txt
```
## 4. Создаем .env
Необходимо создать файл и поместить в него необходимые параметры.
```
DATABASE=SuperService
POSTGRES_USER=UserSuperService
POSTGRES_PASSWORD=NotWarningWord1
CLICKHOUSE_USER=UserMyHouse
CLICKHOUSE_PASSWORD=NotWarningWord2
```
## 5. Запускаем все контейнеры
```
docker-compose up --build
```
При необходимости можем закрыть контейнера
```
docker-compose down
```
## 6. Запускаем проект
```
python runner.py
```
## 7. Подключение к ClickHouse
Команда входа в ClickHouse
```
docker exec -it clickhouse-db clickhouse-client -u UserMyHouse --password NotWarningWord2 --host localhost
```
Использовать базу данных
```
USE SuperService;
```
## 8. Подключение к PostgreSQL
Команда входа в ClickHouse
```
docker exec -it postgres-db psql -U UserSuperService -d SuperService
```
## 9. Миграция alembic
```
alembic revision --autogenerate
```
```
alembic upgrade head
```
# Инициализация БД
## 1. Запустить docker-compose
```
docker-compose up --build
```
## 2. Зайти в ClickHouse
```
docker exec -it clickhouse-db clickhouse-client -u UserMyHouse --password NotWarningWord2 --host localhost
```
## 3. Создать базу данных
```
CREATE DATABASE SuperService;
```
## 4. Инициализировать БД
Зайти на fastapi и выполнить запрос:
```
http://localhost:8000/init_db_data
```
PostgreSQL и ClickHouse будут заполнены данными.

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alembic.ini Normal file
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# A generic, single database configuration.
[alembic]
# path to migration scripts
# Use forward slashes (/) also on windows to provide an os agnostic path
script_location = db/migrations
# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
# Uncomment the line below if you want the files to be prepended with date and time
# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
# for all available tokens
# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
# sys.path path, will be prepended to sys.path if present.
# defaults to the current working directory.
prepend_sys_path = . db
# timezone to use when rendering the date within the migration file
# as well as the filename.
# If specified, requires the python>=3.9 or backports.zoneinfo library.
# Any required deps can installed by adding `alembic[tz]` to the pip requirements
# string value is passed to ZoneInfo()
# leave blank for localtime
# timezone =
# max length of characters to apply to the "slug" field
# truncate_slug_length = 40
# set to 'true' to run the environment during
# the 'revision' command, regardless of autogenerate
# revision_environment = false
# set to 'true' to allow .pyc and .pyo files without
# a source .py file to be detected as revisions in the
# versions/ directory
# sourceless = false
# version location specification; This defaults
# to db/migrations/versions. When using multiple version
# directories, initial revisions must be specified with --version-path.
# The path separator used here should be the separator specified by "version_path_separator" below.
# version_locations = %(here)s/bar:%(here)s/bat:db/migrations/versions
# version path separator; As mentioned above, this is the character used to split
# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
# Valid values for version_path_separator are:
#
# version_path_separator = :
# version_path_separator = ;
# version_path_separator = space
# version_path_separator = newline
version_path_separator = os # Use os.pathsep. Default configuration used for new projects.
# set to 'true' to search source files recursively
# in each "version_locations" directory
# new in Alembic version 1.10
# recursive_version_locations = false
# the output encoding used when revision files
# are written from script.py.mako
# output_encoding = utf-8
sqlalchemy.url = driver://user:pass@localhost/dbname
[post_write_hooks]
# post_write_hooks defines scripts or Python functions that are run
# on newly generated revision scripts. See the documentation for further
# detail and examples
# format using "black" - use the console_scripts runner, against the "black" entrypoint
# hooks = black
# black.type = console_scripts
# black.entrypoint = black
# black.options = -l 79 REVISION_SCRIPT_FILENAME
# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
# hooks = ruff
# ruff.type = exec
# ruff.executable = %(here)s/.venv/bin/ruff
# ruff.options = --fix REVISION_SCRIPT_FILENAME
# Logging configuration
[loggers]
keys = root,sqlalchemy,alembic
[handlers]
keys = console
[formatters]
keys = generic
[logger_root]
level = WARN
handlers = console
qualname =
[logger_sqlalchemy]
level = WARN
handlers =
qualname = sqlalchemy.engine
[logger_alembic]
level = INFO
handlers =
qualname = alembic
[handler_console]
class = StreamHandler
args = (sys.stderr,)
level = NOTSET
formatter = generic
[formatter_generic]
format = %(levelname)-5.5s [%(name)s] %(message)s
datefmt = %H:%M:%S

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import time
import pandas as pd
import clickhouse_connect
class ClickHouseClient:
def __init__(self, host='localhost', port=9000, database="default", username="", password=""):
self.host = host
self.port = port
self.database = database
self.username = username
self.password = password
self.client = self.connect_clickhouse()
self.initialize_table()
def connect_clickhouse(self):
"""
Создает подключение к базе данных ClickHouse.
"""
return clickhouse_connect.get_client(host=self.host, port=self.port, database=self.database, username=self.username, password=self.password)
def initialize_table(self):
"""
Инициализирует таблицу в ClickHouse, если она не существует.
"""
create_table_query = """
CREATE TABLE IF NOT EXISTS experiment_data (
volume Float64,
nitrogen_oxide_emission Float64,
temperature Float64,
co_fraction Float64,
co2_fraction Float64,
x Float64,
y Float64,
z Float64,
file_id String
) ENGINE = MergeTree()
ORDER BY file_id
"""
self.client.command(create_table_query)
def get_data(self):
"""
Извлекает максимальные и взвешенные средние значения из ClickHouse, включая разницу max(x) - min(x) при температуре > 1150.
:return: DataFrame с данными из ClickHouse.
"""
query = """
SELECT
file_id,
MAX(temperature) AS max_temperature,
MAX(nitrogen_oxide_emission) AS max_nox,
MAX(co_fraction) AS max_co,
MAX(co2_fraction) AS max_co2,
SUM(volume * temperature) / SUM(volume) AS weighted_avg_temperature,
SUM(volume * nitrogen_oxide_emission) / SUM(volume) AS weighted_avg_nox,
SUM(volume * co_fraction) / SUM(volume) AS weighted_avg_co,
SUM(volume * co2_fraction) / SUM(volume) AS weighted_avg_co2,
MAX(if(temperature > 1150, x, NULL)) - MIN(if(temperature > 1150, x, NULL)) AS x_range_high_temp,
MAX(if(temperature > 1150, y, NULL)) - MIN(if(temperature > 1150, y, NULL)) AS y_range_high_temp,
MAX(if(temperature > 1150, z, NULL)) - MIN(if(temperature > 1150, z, NULL)) AS z_range_high_temp,
SUM(if(temperature > 1150, volume, NULL)) AS flame_volume
FROM
experiment_data
GROUP BY
file_id
"""
results = self.client.query(query)
columns = ["file_id", "max_temperature", "max_nox", "max_co", "max_co2",
"weighted_avg_temperature", "weighted_avg_nox", "weighted_avg_co", "weighted_avg_co2",
"x_range_high_temp", "y_range_high_temp", "z_range_high_temp", "flame_volume"]
return pd.DataFrame(results.result_rows, columns=columns)
def save_csv_to_clickhouse(self, csv_path, file_id):
"""
Загружает данные из CSV файла в ClickHouse.
:param csv_path: Путь к CSV файлу.
:param file_id: Идентификатор файла.
"""
# Чтение данных из CSV
df = pd.read_csv(csv_path, delimiter=';', decimal='.')
# Переименовать колонки
rename_dict = {
"Volume (m^3)": "volume",
"Mass Fraction of Nitrogen Oxide Emission": "nitrogen_oxide_emission",
"Temperature (K)": "temperature",
"Mass Fraction of CO": "co_fraction",
"Mass Fraction of CO2": "co2_fraction",
"X (m)": "x",
"Y (m)": "y",
"Z (m)": "z"
}
df.rename(columns=rename_dict, inplace=True)
df['x'] = abs(df['x'])
df['x'] = df['x'] - df['x'].min()
df['y'] = df['y'] - df['y'].min()
df['z'] = df['z'] - df['z'].min()
# Добавление столбца идентификатора файла
df["file_id"] = file_id
# Удаление существующих записей для данного файла
delete_query = "ALTER TABLE experiment_data DELETE WHERE file_id = %(file_id)s"
self.client.command(delete_query, parameters={'file_id': file_id})
# Вставка данных в ClickHouse
self.client.insert_df('experiment_data', df)

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config.yaml Normal file
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paths:
starccm: 'C:/Program Files/Siemens/19.04.007-R8/STAR-CCM+19.04.007-R8/star/bin/starccm+'
chemkin: '/media/user/Projects/burner_data/chemkin'
main: '/media/user/Data/experiment_data'
parameters:
number_processes: 16
mesh_base_size: 0.7
stopping_criterion: 7000
diameters:
d1: 1442
d2: 1016
d3: 640
d4: 325
d5: 325
d6: 245
default_values:
N1: 24
L1: 70.0
a1: 60.0
N2: 18
L2: 105.0
N3: 9
L3: 29.6
api_url: "http://10.6.23.120:8000/api/v1/generate_geom"

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data_models.py Normal file
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import matplotlib.pyplot as mp
import pandas as pd
import seaborn as sb
from settings import settings
from clickhouse_tools import ClickHouseClient
from postgres_tools import PostgresClient
class DataPreparer:
def __init__(self, clickhouse_host='localhost', postgres_host='localhost', postgres_db='your_db',
postgres_user='your_user', postgres_password='your_password'):
self.clickhouse_client = ClickHouseClient("localhost", 8123, database=settings.DATABASE, username=settings.CLICKHOUSE_USER, password=settings.CLICKHOUSE_PASSWORD)
self.postgres_client = PostgresClient(
dbname=settings.DATABASE,
user=settings.POSTGRES_USER,
password=settings.POSTGRES_PASSWORD,
host="localhost",
port="5432"
)
def prepare_ml_dataset(self):
"""
Подготавливает набор данных для машинного обучения, объединяя данные из ClickHouse и PostgreSQL.
:return: DataFrame с подготовленными данными.
"""
# clickhouse_data = self.clickhouse_client.get_data()
postgres_data = self.postgres_client.get_experiments()
result_data = self.postgres_client.get_data()
# Объединение данных по file_id
ml_dataset = pd.merge(postgres_data, result_data, on='file_id')
self.postgres_client.close()
return ml_dataset
data_preparer = DataPreparer()
# Подготовка набора данных для машинного обучения
ml_dataset = data_preparer.prepare_ml_dataset()
ml_dataset = ml_dataset.drop('file_id', axis=1)
ml_dataset.to_csv('burner_data_pg_2.csv', index=False)
# Находим колонки с одним уникальным значением
cols_to_drop = ml_dataset.columns[ml_dataset.nunique() == 1]
# Удаляем эти колонки
ml_dataset = ml_dataset.drop(columns=cols_to_drop)
fig, ax = mp.subplots(figsize=(40, 40))
dataplot = sb.heatmap(ml_dataset.corr(), cmap="YlGnBu", annot=True)
# displaying heatmap
mp.show()

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db/__init__.py Normal file
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from clickhouse_sqlalchemy import make_session, get_declarative_base, engines
from sqlalchemy import create_engine
from settings import settings
engine_clickhouse = create_engine(settings.clickhouse_url)
BaseClickhouse = get_declarative_base()
session_clickhouse = make_session(engine_clickhouse)

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from typing import Type, TypeVar, Sequence, Optional, Dict, Any
from sqlalchemy import update as update_, delete as delete_
from sqlalchemy.future import select
from db.models import ExperimentCategory, ExperimentData, ExperimentParameters, LoadParameters, RecyclingParameters
from db.postgres_db_connection import async_session_postgres
T = TypeVar("T", ExperimentCategory, ExperimentData, ExperimentParameters, LoadParameters, RecyclingParameters)
async def get_all(model_class: Type[T]) -> Sequence[T]:
async with async_session_postgres() as session:
result = await session.execute(select(model_class))
return result.scalars().all()
async def get_by_id(model_class: Type[T], id: int) -> Optional[T]:
async with async_session_postgres() as session:
result = await session.execute(select(model_class).where(model_class.id == id))
return result.scalar_one_or_none()
async def create(model_class: Type[T], **kwargs) -> T:
async with async_session_postgres() as session:
new_instance = model_class(**kwargs)
session.add(new_instance)
await session.commit()
await session.refresh(new_instance)
return new_instance
async def update(model_class: Type[T], id: int, updated_data: Dict[str, Any]) -> Optional[T]:
async with async_session_postgres() as session:
stmt = (
update_(model_class)
.where(model_class.id == id)
.values(**updated_data)
.execution_options(synchronize_session="fetch")
)
await session.execute(stmt)
await session.commit()
return await get_by_id(model_class, id)
async def delete(model_class: Type[T], id: int) -> bool:
async with async_session_postgres() as session:
stmt = delete_(model_class).where(model_class.id == id)
result = await session.execute(stmt)
await session.commit()
return result.rowcount > 0

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import asyncio
import pandas as pd
from sqlalchemy import insert, text
from sqlalchemy.exc import OperationalError, IntegrityError
from db.clickhouse_db_connection import BaseClickhouse, engine_clickhouse, session_clickhouse
from db.postgres_db_connection import async_session_postgres, engine_postgres
from db.models.base import Base
from db.models.ch_experimentdb_experiment_data_model import ChExperimentDBExperimentData
from db.models.experiment_data_model import ExperimentData
from db.models.experiment_parameters_model import ExperimentParameters
from db.models.load_parameters_model import LoadParameters
from db.models.recycling_parameters_model import RecyclingParameters
async def create_all_tables():
async with engine_postgres.begin() as conn:
await conn.run_sync(Base.metadata.create_all)
BaseClickhouse.metadata.create_all(engine_clickhouse)
async def load_data_to_postgres(file: str, model_class, chunk_size=100):
df = pd.read_csv(file).dropna()
async with async_session_postgres() as session:
for start in range(0, len(df), chunk_size):
end = start + chunk_size
chunk = df.iloc[start:end].to_dict(orient='records')
stmt = insert(model_class).values(chunk)
try:
await session.execute(stmt)
await session.commit()
except IntegrityError as e:
await session.rollback()
def load_data_to_clickhouse(file: str, model_class, chunk_size=100):
df = pd.read_csv(file).dropna()
with session_clickhouse as session:
for start in range(0, len(df), chunk_size):
end = start + chunk_size
chunk = df.iloc[start:end].to_dict(orient='records')
stmt = insert(model_class).values(chunk)
session.execute(stmt)
session.commit()
async def csv_to_db():
await create_all_tables()
await load_data_to_postgres('./db/files/load_parameters.csv', LoadParameters)
await load_data_to_postgres('./db/files/recycling_parameters.csv', RecyclingParameters)
await load_data_to_postgres('./db/files/experiment_parameters.csv', ExperimentParameters)
await load_data_to_postgres('./db/files/experiment_data.csv', ExperimentData)
load_data_to_clickhouse('./db/files/ch_experimentdb_experiment_data.csv', ChExperimentDBExperimentData)
print('csv_to_db выполнено')

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db/files/ch_experimentdb_experiment_data.csv Normal file
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id,volume,nitrogen_oxide_emission,temperature,co_fraction,co2_fraction,x,y,z,file_id
1,1.25018009e-07,2.923801e-10,1546.8502257117334,0.0876590170721079,0.0825937575747152,0.0002862908352005,4.250519128266181,4.23689607830385,10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
2,1.26276707e-07,2.774402e-10,1546.5278876054947,0.0876535741398523,0.0826072611209551,0.0002970890883859,4.2633663477653,4.2118739518585935,10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
3,5.215560244e-07,2.800989e-10,1547.007375203737,0.087598352075182,0.0826205755990879,0.0023288861534884,4.258002712932174,4.224854019469968,10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
4,1.259521373e-07,2.756836e-10,1544.7738826604286,0.0879623682225424,0.0825022367344128,0.0003061405654724,4.294951651093852,4.228359039356472,10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
5,6.71204778e-08,2.747822e-10,1543.303419205242,0.0881230454580786,0.0824746033833566,0.0004276277312526,4.301939013567143,4.203422399444153,10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
6,3.982683542e-07,2.728959e-10,1544.1211163717342,0.0880178768655007,0.0824974031307205,0.0025840772927808,4.2895374677272695,4.241641279196477,10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
7,1.314501491e-07,2.729991e-10,1543.9585181296568,0.0881077612277477,0.0824593957518346,0.0003192230503286,4.282153424064967,4.253165462380294,10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
8,1.2711512153e-06,2.799785e-10,1545.337937815918,0.0878016656787745,0.0825684205234254,0.0031927006255012,4.279075221963268,4.242926141081111,10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
9,5.002643798e-07,2.907778e-10,1546.1911065379516,0.0876446332442777,0.0826230869307894,0.0012912004540912,4.259093754350848,4.2392143443160855,10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
10,4.372515903e-07,2.852153e-10,1545.394093220484,0.0877377183636819,0.0826003036520615,0.0010234056556132,4.268539978944442,4.241752821543271,10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
1 id volume nitrogen_oxide_emission temperature co_fraction co2_fraction x y z file_id
2 1 1.25018009e-07 2.923801e-10 1546.8502257117334 0.0876590170721079 0.0825937575747152 0.0002862908352005 4.250519128266181 4.23689607830385 10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
3 2 1.26276707e-07 2.774402e-10 1546.5278876054947 0.0876535741398523 0.0826072611209551 0.0002970890883859 4.2633663477653 4.2118739518585935 10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
4 3 5.215560244e-07 2.800989e-10 1547.007375203737 0.087598352075182 0.0826205755990879 0.0023288861534884 4.258002712932174 4.224854019469968 10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
5 4 1.259521373e-07 2.756836e-10 1544.7738826604286 0.0879623682225424 0.0825022367344128 0.0003061405654724 4.294951651093852 4.228359039356472 10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
6 5 6.71204778e-08 2.747822e-10 1543.303419205242 0.0881230454580786 0.0824746033833566 0.0004276277312526 4.301939013567143 4.203422399444153 10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
7 6 3.982683542e-07 2.728959e-10 1544.1211163717342 0.0880178768655007 0.0824974031307205 0.0025840772927808 4.2895374677272695 4.241641279196477 10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
8 7 1.314501491e-07 2.729991e-10 1543.9585181296568 0.0881077612277477 0.0824593957518346 0.0003192230503286 4.282153424064967 4.253165462380294 10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
9 8 1.2711512153e-06 2.799785e-10 1545.337937815918 0.0878016656787745 0.0825684205234254 0.0031927006255012 4.279075221963268 4.242926141081111 10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
10 9 5.002643798e-07 2.907778e-10 1546.1911065379516 0.0876446332442777 0.0826230869307894 0.0012912004540912 4.259093754350848 4.2392143443160855 10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d
11 10 4.372515903e-07 2.852153e-10 1545.394093220484 0.0877377183636819 0.0826003036520615 0.0010234056556132 4.268539978944442 4.241752821543271 10bf3b81cf73056684c0041f6adc5a35881bbeca2ba0e8bbc7d827c3ba058a3d

404603
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db/files/experiment_parameters.csv Normal file
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@ -0,0 +1,868 @@
id,outer_blades_count,outer_blades_length,outer_blades_angle,middle_blades_count,load_id,recycling_id,experiment_hash
1,24,70,30,18,1,1,f3440e566eedd3cb6bceaa2b8e16396456c2801120fabd283561b6ee2100b0b5
2,24,70,30,18,1,2,a47bd45c541df0f1ed9f853bf21ad4157a69aea5f3bd91925536af75a51aa3dc
3,24,70,30,18,1,3,ed1525ca364458eb337f71632366a4ba17528e3a966019f38e9c52be73f893aa
4,24,70,30,18,1,4,9d30eb894c46d1c09fb739530efd743debff8cc40ad9e31cfe7645bb163ef1e0
5,24,70,30,18,1,5,d3a0ddf904a96bdf79d5ba6c62bf7c0035e36c11dfce48bf9e6768f738e96893
6,24,70,30,18,1,6,2c11dd64bbc6b7b1d6dd4aabb7bc278ae39c10843fb9e45360ad4d56382600db
7,24,70,30,18,1,7,1b8eebbbf139120224cd35221a0c55fa85568e3e29ecc3967963ab8d243151ec
8,24,70,30,18,1,8,7b3c151a9712638709836f54ad10e671c98cd77d69a0ef7d3225427cc12568ea
9,24,70,30,18,2,9,d088c77f22a9d7598a5af46ef623e2611d1bab914c2ab4877f79175fb654d6aa
10,24,70,30,18,2,10,de5054f2198651c958a57a816eb85d5aa58cd061268a5195cae60e4a1c681e85
11,24,70,30,18,2,11,448581cea9fbb47236889fc7a5a18e175e4256e2cc8ebd7bb202c2463411280f
12,24,70,30,18,2,12,6cca36dc2f51aa1735a622404fd594a31609f337a26c0924c241793f84fe0ab3
13,24,70,30,18,2,13,3b446baa52e9268697b8acb2c005d7c0400d9d6a90e39f62944ba694910c45f5
14,24,70,30,18,2,14,cc93de3f50f3892f461424b7b11ea02527af20b70e707a5aae0aaab8f947f392
15,24,70,30,18,2,15,f46e555206f2ab5c308c467c8b2c0eb724f027f9b9b3d57da278a50483ffe2bb
16,24,70,30,18,2,16,7d327b5a4ca0d16d054c7775a2f6cd66e9f1714fd3a3be40437c02d077de5c67
17,24,70,30,18,3,17,51ab3b741aee17c59ef82c338d7fdde79697e6226cc3a7e4b3b40ec5dfcba647
18,24,70,30,18,3,18,fbcec5f6a53659317dbd8ec4047bb9215ed39b84001f7cda673becb37e74ba51
19,24,70,30,18,3,19,7bf6e8cd421222c7bc17c5e6ab43dcee4316e9b885903db1ca41741123009ff1
20,24,70,30,18,3,20,aad101f9418d0bb182d64da455caeec0bf410e65a3efdb44a025adc53f8ce216
21,24,70,30,18,3,21,c609ff027d988b6ea282e544d3191f4f9f47f912cc891b3d2ab32e6191d3e739
22,24,70,30,18,3,22,a460f48676a062c64103ff8cadd1faacf993f1253cdefe6c4b99a8505d09814a
23,24,70,30,18,3,23,c41ab44cb00cbe24ee9d9c29dfc847b7bf5954a136f07d95d5ef1c821b6e751c
24,24,70,30,18,3,24,3a56cfd1d1a3cd97d51bc489e63a1682e1495311900f3f9d337fe22c1a9884b5
25,24,70,30,18,4,25,c01ec654b3825e770fdfcded717e9d0862cd6d1d1303ffbf5a11974e5f583849
26,24,70,30,18,4,26,d6af15b0829a64320158a05684faeec8dbdb7599177ba0b95d2428f517025b19
27,24,70,30,18,4,27,58ff2e215f22abe9ca172b21db0f3ee3c89c8a77a5afa3c33cde6b1490d788ef
28,24,70,30,18,4,28,78b2430505297f9d16d1ee31b1781b3425f5ca8af81f758318f9bcc513b16056
29,24,70,30,18,4,29,3c9d9f05dc256a57766f7448bfbd1dc4090742092942a09c30a0cc4775901df8
30,24,70,30,18,4,30,78f91bf6c8e291f85582e96a5e0afcdd8b70c71d887cf7f45534268bfcbe065b
31,24,70,30,18,4,31,f2c809d7de702b3f76f22e87ce25687c75ba3239fff494b5f80d44397e0365fd
32,24,70,30,18,4,32,87b7070cb8e3c39cb1a00c132abef4ce1e5bdf082bb21715d3e5ea063d65cb6c
33,24,70,30,18,5,33,7e044e9dbaf5d043699ee22876ea8ae05659ec03b024a10849af140243726fbd
34,24,70,30,18,5,34,ebe73f29770dec56bb3ce6b2832e723d8a1f0ddf1f63698ccd057535bc2e7c8b
35,24,70,30,18,5,35,e673d4cc2675fda210549e533de77618a4a003c067bf0f2740d2fb35c320d1c1
36,24,70,30,18,5,36,f6373fe5dd40b33cf6f902705479e4e611e9e3f002e3d53b74d4467db6461603
37,24,70,30,18,5,37,f0dfcd828ab3edf2822d869f39fb24111eee5e8d54f5e67d6cf29d12d091a493
38,24,70,30,18,5,38,c7513526483d1f52282a177f5db8bce4223dbea44302e5b50cb00fd95436dc5e
39,24,70,30,18,5,39,937554b847c97b1545bd6169f594cb6a57cc3fbfe462fa976cf5b5c30cade75d
40,24,70,30,18,5,40,bbf8878c97bb3d1d55b85450c500416f5ff4254d098b6329966ccd1af6fec198
41,24,70,45,18,1,1,d93465f1368a0bc246d9ac814417d55eba2674dd96b6deee8b14f3496b44a5d4
42,24,70,45,18,1,2,63f441456c37857e11d9194e55ba304433365a38f9e2395e0f7826a8cb52979d
43,24,70,45,18,1,3,0b6f75922844a41b9e517dc675dc41bc592afccc7a9fb108b078011197058a0e
44,24,70,45,18,1,4,d231c7b7601c2fcde797acf2eed7ed6553a059e5bbf59cbe01d68f568f57ace8
45,24,70,45,18,1,5,019ec639ed4ac56a5d1017ba10914acc122da59ebaaf8f929dda7b351ff5224c
46,24,70,45,18,1,6,2d439156da14719d43e33ba034782fee7fb83033f1a4897f0b20788c5ebca65e
47,24,70,45,18,1,7,8f31f444564af0a7c70bcf2299fe80e9b9fa1a8f84edc4afbff81c0e4969fd6b
48,24,70,45,18,1,8,af2f9f938b771242ab4b0221a0d937e03ece383a2192d3036e05988fb2442245
49,24,70,45,18,2,9,26c261c66a5e93b4b88e93290324f5706ad2de59dd59e123c9db34d76494966f
50,24,70,45,18,2,10,f73948ca1404ac1ef598d32967ff590beac673c1f128633281e5f91d20439598
51,24,70,45,18,2,11,ea7ec1c838494ef086813bed27ae2c0c23f8875334392058cf1069615a333e42
52,24,70,45,18,2,12,4fa7e25f2c9c12088e4bb783ec7330a24744c9b83357256b2e21ce6bb6e139ca
53,24,70,45,18,2,13,a94578891fd1dd142acce45268da610890c2b148da88caae75c6033b655f93dd
54,24,70,45,18,2,14,cca92f84a1ccca51108ac4ceaa5acb74d993b17200a3ae4db3081d0025304969
55,24,70,45,18,2,15,8ce57236cacbca39e548e83ea1bcc342b62df29f3bde0caf2c0c2db1cd3b432d
56,24,70,45,18,2,16,8b647157123d4f7bcbcc625b054e3e4d83a0804e57731005a8dd903ef72ea936
57,24,70,45,18,3,17,7d660b53b68eeb2d66b78089f71c4ed2c872369a1593a7d284d4d0df3eace407
58,24,70,45,18,3,18,3b29424168040cbd1a2947c94a8e61be3776ed45bbc1112909a55cf5ebe1bbbe
59,24,70,45,18,3,19,788aaab9df7f831a3e6056bd7758a218ebdb923c0ebf5be142a47c59a494270a
60,24,70,45,18,3,20,a4bb724f51b10dcc325ec19cbe0b8c5b41c8b9b0eae296dc61c353314db1b36e
61,24,70,45,18,3,21,6cafc249aa6b9e95afb63f60afd62255f2c95a8445b0b9e57a39cb92ae348f31
62,24,70,45,18,3,22,24ae10d92145ca93e6acbe22bb510f22af67453bb371bf13f904cb9a2d06d4d2
63,24,70,45,18,3,23,0405c139bc6e1bc882c3767d7603d02803acda222870e38ddc94dcefeb76179e
64,24,70,45,18,3,24,fd604232c9eb21c707c1c0afe99e72f5f131537850c51bf2a6709705e82b284f
65,24,70,45,18,4,25,34c86de16b8c7a3002bba420710d099ddd31591f78f8783caae64129d5f1fa6d
66,24,70,45,18,4,26,3be010d0e6a1daabc7a5e21ca17fc68508665bb4b99ec16a4912f88f0c1bfee2
67,24,70,45,18,4,27,19058a20083e7652aceebe4079d7918e0afabcb2b634ea0956c5cd8006a12082
68,24,70,45,18,4,28,ffba62754ec42c376894013792d126e31c307cf52f5aec10a148c74becbfc692
69,24,70,45,18,4,29,0cc7665eb716cf2851156fa45bf1c241cfccd43b25d757d798140a75f40b2354
70,24,70,45,18,4,30,eb20ea5909a55f61b92ff4337727242240765993931d9175043cbac3dac6caa5
71,24,70,45,18,4,31,a4a3baccc35da76517d3a16d379ab8416dabcaed429a72ef26e9ebc757377867
72,24,70,45,18,4,32,550c05d891ee0a19a37a0a614cc9dbece86e922973965583d22ceacb846e1581
73,24,70,45,18,5,33,995a45405eedeb6e463642d6f723b6fdb35df6035e7198d787e5a14fca8b4a85
74,24,70,45,18,5,34,66c882d8ee6b3f9b1563ba3c18dce8e7592b3e548d9b0aa2b54f9cec6d487889
75,24,70,45,18,5,35,a1856cfffa7b32a6c553a548f6cb52187fa9789f414a3a29712735965fcab90d
76,24,70,45,18,5,36,c504f3e398c0850397f0fea4bdf2df3eec3c5cf8a9200d164f34b2c92d59be9a
77,24,70,45,18,5,37,2f9ccc8409dbdcd01f9c1853b713c48e3f31b5ea0ede68cc956abd2529ed9bf5
78,24,70,45,18,5,38,9dfdcb6d0d69d6f54da0170c0b5bbb6c382ccfaa94c803ec093a01db452c2f24
79,24,70,45,18,5,39,09682926ea871314009a2e8340c4e0597573ec925c8d5666f7deb0c97c649fbf
80,24,70,45,18,5,40,97018fa18631ed88974f799858c3e9731aefd3d308f693e0d9e095d9ed6b5cff
81,24,70,60,18,1,1,b54c62da0bfea8f337d0c380d534bf98b25c1be9f014db4b8c2e4b455e85279b
82,24,70,60,18,1,2,cffbb4238b69f5b51d93fda58847314f90277073caba72eac0969368d2bf72af
83,24,70,60,18,1,3,2ba7288af1d84d07e239dd78baa790b6ada943681bbdfdc27e7f489da8046e8b
84,24,70,60,18,1,4,afa966ce1f0dcfaa6282a049c0c30febc999f856885199056dc570b97b6adff9
85,24,70,60,18,1,5,8f8df0abd28895a93112a57c4d4b32be7eb9c9d7f9ac40f016603173185a78e1
86,24,70,60,18,1,6,1e5b48a6d463e88f4bf49c29dca892d77b2a16a9cffb3cb4fab341f5645cdb77
87,24,70,60,18,1,7,879e6b65a692343e84e8e02e92a49d63053180a22f4b42676e2f29bbf581bd6a
88,24,70,60,18,1,8,0f76f567f474e5b41351eb42f0c6dae27240f7c69a65e0a02fd631ae7a30b088
89,24,70,60,18,2,9,5c68cce55587a05f51a597e0e2c57971e111e412e66b29d933cddddc892ba911
90,24,70,60,18,2,10,307e271391f0090938521a4ed707064dad973cb360497602b23dc69bd13daae6
91,24,70,60,18,2,11,bddfe29d9e272a9990190682aee23bb57da690ce391cf9dab1ba4f1e80e3e932
92,24,70,60,18,2,12,efcd861cc0eea9334341de08ef26a690c961fd7f37d37b96e9d3a8496a7b96de
93,24,70,60,18,2,13,32a460c67c2a9442b1b103523ca6a116d7ae2de8c2245f4cf51b2d666ceb2dad
94,24,70,60,18,2,14,8eac61db97a0cf8c060e9d4c235ca2fafe8fa85129837c74417429d781f4d8b6
95,24,70,60,18,2,15,73d6e002b5cbdb9888f5dfe4530371d1efba05da843b47343ec6a24d358f970e
96,24,70,60,18,2,16,0064dd418bb85442ca1641bd7d4659bd2a1c472123a30072b6a2e4280083ea85
97,24,70,60,18,3,17,75fb2ad1605612509d86e932eaa930221d6b26dff9646e6680524d88e65b3dd3
98,24,70,60,18,3,18,b701734dd3e549ceb97d8a82682b2728814b92e08fbeb949bbf67e7a79f1365c
99,24,70,60,18,3,19,d327280a452af12ee949f54a09cc47f3fc855bbd4b559a021c5632ce1bf7f9e6
100,24,70,60,18,3,20,d8470f3ee4730ea1b618cb1e9f61d53378b1b139725df661e8cca6a75ecea753
101,24,70,60,18,3,21,b289c625935178c4389206037e68c6f52c4bc5be990a25a26c223df840560356
102,24,70,60,18,3,22,a3d18f7ca7cee4a8aec93c65c9a14a3b3fb64f28981c6501f8f2fac4db09b561
103,24,70,60,18,3,23,5218ef80c1efd4f752a6cfa499d71d63245f4a1a013abd40e1cf56e100c1c58f
104,24,70,60,18,3,24,1b16c43ada345c628d9bf487f5114c2730fde939e25152b011aee810ba70c163
105,24,70,60,18,4,25,0471851d3b19ea655e1498f5b7099e10fe069d9f69235270252adc2044f9a86c
106,24,70,60,18,4,26,ee0b78999cc119ae2c697dcc7c5cf3cc34cc8fab0be267eccd484f3a811555e9
107,24,70,60,18,4,27,35228f2dee7eda00408c5878511762d740f4c9e0ee27d59cb8624ec116925068
108,24,70,60,18,4,28,86f3de97013f53d9c9486c25cd04b97d52892f52c72c69007b46b06e8665c2b6
109,24,70,60,18,4,29,0ee70f3eedb98ac680768601cf84d76f7b9fa195ebc6f6850c1cd5a752acaf4b
110,24,70,60,18,4,30,5112d4b18b376287c286863d52f824062d90590fa4c3928041b30258f80fc8e1
111,24,70,60,18,4,31,ed71b644bcc7479cff5bb1b74dec0f1868258707be53a2b6a186b3d366b83796
112,24,70,60,18,4,32,a2c54f86299c603fb20444001e5eb7029d0367232e47b05a5c7833fe38df1d07
113,24,70,60,18,5,33,56d4f83a0f1dc14643d621adb03676f5193f64ef8d03e598a8c5bfd8af034d50
114,24,70,60,18,5,34,603d47e77e49f235dc7ddc3971d07ca923a8b173708b1d56e2cd2f715fffc899
115,24,70,60,18,5,35,03222d207ac70db5ca2632d1c1b64eab35ddc337c4007d67da96d7d9256e9c60
116,24,70,60,18,5,36,7977f6c305b18e6f336dfec451dd8fee5ed95619ebd7ed25e720c170cff7055a
117,24,70,60,18,5,37,c6289646d1d7e7c3a5f3473dd8f54e3ab810b7274c11842a4f8cef37f6c850d9
118,24,70,60,18,5,38,5d2155f7aa0189d24fe10cacb93491e1629df381f3c296afb5c36ac50528c801
119,24,70,60,18,5,39,5ca59876b7808331ced53127a0dae0a464a1da3610825f4d14c468a080bcf8cc
120,24,70,60,18,5,40,80d0b251451977827cb7841ab36fd5b4ff2ac65f1004acbf5409e59fd4de252e
121,24,70,75,18,1,1,6d545739aef47e07fb946a0a802758733dbf559f62ab82eb0e218e50e0a067d4
122,24,70,75,18,1,2,de34f86cbf88081563e5976013233f0fc1c2c1684139020e7564c6297fbb3a6c
123,24,70,75,18,1,3,0b2faa7f9a7b122ffcf2091e1714eba7330dc6899b7c529344b26190ccdd11ad
124,24,70,75,18,1,4,cd4c16b5e4ee651821cd586db529765f530845753dd7ba89faf704742c2f1874
125,24,70,75,18,1,5,3e81dd88a44e2b866f15a19a6cc78c1fe07d5e45caa1a3779b5659ecd241255a
126,24,70,75,18,1,6,72ecd10b16fdb357f89a14a48a38b81fbdc7cc2850f608e547b1654324df0ddc
127,24,70,75,18,1,7,8e53935b91ef51de3f49c27776d6f33887f7b2575589bdde5db8a74065c9caab
128,24,70,75,18,1,8,a211936012fbacfd7ba8f4a84503907b02a67fb8ae76f92eb24318c3577c5605
129,24,70,75,18,2,9,9223dcc932346a888aa0c05f9397bb200408f09b590c02b0c5a9b4f511984d4b
130,24,70,75,18,2,10,cecd917ae056d26891c3a2695081a248f894654dc9f0766f98b9cd84436d5da1
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132,24,70,75,18,2,12,8d1ecbd4bb674d0faae4701466a2d014221f0fa0cd4a627753079e1f4e335f6f
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822,24,70.0,45.0,27,3,20,23f2808530b950186123fb354ecbaa9065fe4bb80639ce8af7643d1fc5fd0778
823,24,70.0,45.0,27,3,22,27ffb87ab25351b7bbcd5f17d50c24e88ff55f7bdf0b80a6c8488d4fa8604f50
824,24,70.0,45.0,27,4,25,4613f06c939f1c611958a7153afc0bb57dc86a775a7908b386d0ed963d766e10
825,24,70.0,45.0,27,4,26,cba374793aa40c7821ccf3e4f57c95da998f7dcd3856aabebe0312c3ea833da2
826,24,70.0,45.0,27,4,28,6663b83f2bfec70981dfae791212edf22dd758171e1406b6a7ece1152272218c
827,24,70.0,45.0,27,4,30,f7643ae1b6d74e64e55c6dc87b7bde5580f7ca9269242e9e37f12854e2df516b
828,24,70.0,45.0,27,5,33,c0cef2a3a5be3e3566650d75bb494dd1ac6c344640c16801e5f87956e68ff493
829,24,70.0,45.0,27,5,34,a41498b106cdc87cada4da79851382ccaca8d72de47a1e3872a72eb22571c791
830,24,70.0,45.0,27,5,36,6208d19ab8e8894678c06c6dbb176fe86734dc09846049b950fbd83ede999d2b
831,24,70.0,45.0,27,5,38,14e2433cdbb3d09db341749109691f4f887b4e587a73a77ba8666724124281d4
832,24,70.0,60.0,27,3,17,a47302f39abab1d61631f77a2a66ac5f5f19e0cce1e545c5b11e09ba0243b1f7
833,24,70.0,60.0,27,3,18,910e24bdd59209a7940dac87eca8a8833e40a707ee138e580979c4bfd8f5d2ad
834,24,70.0,60.0,27,3,20,1d27d3ebc3ed8aab99e891f193a479ae8ea59e65302c1553b8cbf51cc030dd36
835,24,70.0,60.0,27,3,22,307007fd709f4ccb26bb8a256d31e7dca30ef3f3292bc2dbba4771f085ca1dc9
836,24,70.0,60.0,27,4,25,8115089eef04b47cb6275050d907618d538c345f7756f8020d8fb289e6f23bae
837,24,70.0,60.0,27,4,26,af68f473a3c45905db8197e0aadf5c159317ec79b4d8e0ae1fa5d353469b9c40
838,24,70.0,60.0,27,4,28,21b76603d35fb4acc8780880304393952b106c64157da0c92df6ac0aa51af9d3
839,24,70.0,60.0,27,4,30,096680f71554f3cc58a3d1b628e9c8a97d2955fbc9a9b84372d7f7174e9e48d6
840,24,70.0,60.0,27,5,33,520845528e0465821a43ca6fc021ca03f78f25e7dd7a9e773313a8db8ca1bee7
841,24,70.0,60.0,27,5,34,bf0b065c3844d07027586b12677d5a8a14b6966953725ba78332d998d4e9ef25
842,24,70.0,60.0,27,5,36,fdd34e8aa7234c1d2d464018bb6ac32d2cd866881b2233271ce4371c8b3a4743
843,24,70.0,60.0,27,5,38,b9f4c40bb1e5ad8add62918b8db821e3b5b0b41569d89f9d53026f44659cf4b0
844,24,70.0,45.0,36,3,17,57e89909f924237ed35aafa2d600f65a5f1839c9b11285dfc2022a9e546fa643
845,24,70.0,45.0,36,3,18,70b5b7d36c76399fe2c0b1d2381bc173d2052b29896aef86013eb75b7ad878db
846,24,70.0,45.0,36,3,20,6620a225f2d346a4ee5dc3a17a4b4eb07bfefa6ccba668b680aad054559e33ce
847,24,70.0,45.0,36,3,22,f44480200440e9f85f6c9e918b24e17633bc863943af87b33e536fdc1cda2acb
848,24,70.0,45.0,36,4,25,a64ad90bf862aa0fa705c6f052beff71e9912adc6b0ac4b414a367687ed8964c
849,24,70.0,45.0,36,4,26,125b0a5ba773eed31ac3030936e59d82667497a17e904968df715542c2f8f6b6
850,24,70.0,45.0,36,4,28,fd2d2a2acf023872ecd8905be920ff50e81d842c7c1a9ad7609ed25ab80d7b8e
851,24,70.0,45.0,36,4,30,a328c4f01bd8355b93bc9add8261c74c62374ad4509e89b220d0a7cefe0264f2
852,24,70.0,45.0,36,5,33,f237fbb4af2fee18e1a50241c11ad184bd3815ca98e546de9b4219f01ad6d597
853,24,70.0,45.0,36,5,34,407115d033aa176b3251a1c6d6e535ba83ca191c9a3ae6aca45c733672eb4168
854,24,70.0,45.0,36,5,36,ac799afd672da0d65efcb9ad24e482851031ca25862b748fd3842f49159d846c
855,24,70.0,45.0,36,5,38,5e4bff496f363e2abb8447dcd3eeb181df92d10315ef19a485d334b8382bb7ae
856,24,70.0,60.0,36,3,17,72acfe55190ed57cebf4f1156986564e0416b0b2af0e44a320c9194c7f2ea8cf
857,24,70.0,60.0,36,3,18,2805e6f098d15b27284eb510e1c1362e525cf0b0babce5d1fc08edab3b95e4c5
858,24,70.0,60.0,36,3,20,8a50464709efe7d242c053efa8e950450108cb815e1a26dc121d0cd593da68d4
859,24,70.0,60.0,36,3,22,c3f631cf2a02a7c64044ba26389db67a638453be3d2b0b5a9dff87d6f0a45da1
860,24,70.0,60.0,36,4,25,1ea10a95e4b72824a094fff9a416831d105715d1190b55afe1b9d156f4e4b2fc
861,24,70.0,60.0,36,4,26,88946925060027a3a3472036c3e82033b9dc468ad99e44d895552cf0a6a641ef
862,24,70.0,60.0,36,4,28,e7ce645c4ccb5b38624068326842d053686718abb3f5761ea82aedc6e609861c
863,24,70.0,60.0,36,4,30,3d549d0fd3d08be83150bdf31346d01add6c7fb80f84e2477f95988cd439e9e8
864,24,70.0,60.0,36,5,33,e395a9dbc44ff4a21cf99ab1e1ab42d864ac35c98956c231aebf0c571df29b53
865,24,70.0,60.0,36,5,34,3b9f3cd1723bca4b49699491b87a4a88d2401530f4f0983376f7fba088d95b5e
866,24,70.0,60.0,36,5,36,f02e26d93e8f9469c685957428babff6ea1f3db5321c0e37f0723af16a3c5e97
867,24,70.0,60.0,36,5,38,ad7f8512bad46de4c706baa550d33fb859c68996936052d7fff4d8fed8f75f68
1 id outer_blades_count outer_blades_length outer_blades_angle middle_blades_count load_id recycling_id experiment_hash
2 1 24 70 30 18 1 1 f3440e566eedd3cb6bceaa2b8e16396456c2801120fabd283561b6ee2100b0b5
3 2 24 70 30 18 1 2 a47bd45c541df0f1ed9f853bf21ad4157a69aea5f3bd91925536af75a51aa3dc
4 3 24 70 30 18 1 3 ed1525ca364458eb337f71632366a4ba17528e3a966019f38e9c52be73f893aa
5 4 24 70 30 18 1 4 9d30eb894c46d1c09fb739530efd743debff8cc40ad9e31cfe7645bb163ef1e0
6 5 24 70 30 18 1 5 d3a0ddf904a96bdf79d5ba6c62bf7c0035e36c11dfce48bf9e6768f738e96893
7 6 24 70 30 18 1 6 2c11dd64bbc6b7b1d6dd4aabb7bc278ae39c10843fb9e45360ad4d56382600db
8 7 24 70 30 18 1 7 1b8eebbbf139120224cd35221a0c55fa85568e3e29ecc3967963ab8d243151ec
9 8 24 70 30 18 1 8 7b3c151a9712638709836f54ad10e671c98cd77d69a0ef7d3225427cc12568ea
10 9 24 70 30 18 2 9 d088c77f22a9d7598a5af46ef623e2611d1bab914c2ab4877f79175fb654d6aa
11 10 24 70 30 18 2 10 de5054f2198651c958a57a816eb85d5aa58cd061268a5195cae60e4a1c681e85
12 11 24 70 30 18 2 11 448581cea9fbb47236889fc7a5a18e175e4256e2cc8ebd7bb202c2463411280f
13 12 24 70 30 18 2 12 6cca36dc2f51aa1735a622404fd594a31609f337a26c0924c241793f84fe0ab3
14 13 24 70 30 18 2 13 3b446baa52e9268697b8acb2c005d7c0400d9d6a90e39f62944ba694910c45f5
15 14 24 70 30 18 2 14 cc93de3f50f3892f461424b7b11ea02527af20b70e707a5aae0aaab8f947f392
16 15 24 70 30 18 2 15 f46e555206f2ab5c308c467c8b2c0eb724f027f9b9b3d57da278a50483ffe2bb
17 16 24 70 30 18 2 16 7d327b5a4ca0d16d054c7775a2f6cd66e9f1714fd3a3be40437c02d077de5c67
18 17 24 70 30 18 3 17 51ab3b741aee17c59ef82c338d7fdde79697e6226cc3a7e4b3b40ec5dfcba647
19 18 24 70 30 18 3 18 fbcec5f6a53659317dbd8ec4047bb9215ed39b84001f7cda673becb37e74ba51
20 19 24 70 30 18 3 19 7bf6e8cd421222c7bc17c5e6ab43dcee4316e9b885903db1ca41741123009ff1
21 20 24 70 30 18 3 20 aad101f9418d0bb182d64da455caeec0bf410e65a3efdb44a025adc53f8ce216
22 21 24 70 30 18 3 21 c609ff027d988b6ea282e544d3191f4f9f47f912cc891b3d2ab32e6191d3e739
23 22 24 70 30 18 3 22 a460f48676a062c64103ff8cadd1faacf993f1253cdefe6c4b99a8505d09814a
24 23 24 70 30 18 3 23 c41ab44cb00cbe24ee9d9c29dfc847b7bf5954a136f07d95d5ef1c821b6e751c
25 24 24 70 30 18 3 24 3a56cfd1d1a3cd97d51bc489e63a1682e1495311900f3f9d337fe22c1a9884b5
26 25 24 70 30 18 4 25 c01ec654b3825e770fdfcded717e9d0862cd6d1d1303ffbf5a11974e5f583849
27 26 24 70 30 18 4 26 d6af15b0829a64320158a05684faeec8dbdb7599177ba0b95d2428f517025b19
28 27 24 70 30 18 4 27 58ff2e215f22abe9ca172b21db0f3ee3c89c8a77a5afa3c33cde6b1490d788ef
29 28 24 70 30 18 4 28 78b2430505297f9d16d1ee31b1781b3425f5ca8af81f758318f9bcc513b16056
30 29 24 70 30 18 4 29 3c9d9f05dc256a57766f7448bfbd1dc4090742092942a09c30a0cc4775901df8
31 30 24 70 30 18 4 30 78f91bf6c8e291f85582e96a5e0afcdd8b70c71d887cf7f45534268bfcbe065b
32 31 24 70 30 18 4 31 f2c809d7de702b3f76f22e87ce25687c75ba3239fff494b5f80d44397e0365fd
33 32 24 70 30 18 4 32 87b7070cb8e3c39cb1a00c132abef4ce1e5bdf082bb21715d3e5ea063d65cb6c
34 33 24 70 30 18 5 33 7e044e9dbaf5d043699ee22876ea8ae05659ec03b024a10849af140243726fbd
35 34 24 70 30 18 5 34 ebe73f29770dec56bb3ce6b2832e723d8a1f0ddf1f63698ccd057535bc2e7c8b
36 35 24 70 30 18 5 35 e673d4cc2675fda210549e533de77618a4a003c067bf0f2740d2fb35c320d1c1
37 36 24 70 30 18 5 36 f6373fe5dd40b33cf6f902705479e4e611e9e3f002e3d53b74d4467db6461603
38 37 24 70 30 18 5 37 f0dfcd828ab3edf2822d869f39fb24111eee5e8d54f5e67d6cf29d12d091a493
39 38 24 70 30 18 5 38 c7513526483d1f52282a177f5db8bce4223dbea44302e5b50cb00fd95436dc5e
40 39 24 70 30 18 5 39 937554b847c97b1545bd6169f594cb6a57cc3fbfe462fa976cf5b5c30cade75d
41 40 24 70 30 18 5 40 bbf8878c97bb3d1d55b85450c500416f5ff4254d098b6329966ccd1af6fec198
42 41 24 70 45 18 1 1 d93465f1368a0bc246d9ac814417d55eba2674dd96b6deee8b14f3496b44a5d4
43 42 24 70 45 18 1 2 63f441456c37857e11d9194e55ba304433365a38f9e2395e0f7826a8cb52979d
44 43 24 70 45 18 1 3 0b6f75922844a41b9e517dc675dc41bc592afccc7a9fb108b078011197058a0e
45 44 24 70 45 18 1 4 d231c7b7601c2fcde797acf2eed7ed6553a059e5bbf59cbe01d68f568f57ace8
46 45 24 70 45 18 1 5 019ec639ed4ac56a5d1017ba10914acc122da59ebaaf8f929dda7b351ff5224c
47 46 24 70 45 18 1 6 2d439156da14719d43e33ba034782fee7fb83033f1a4897f0b20788c5ebca65e
48 47 24 70 45 18 1 7 8f31f444564af0a7c70bcf2299fe80e9b9fa1a8f84edc4afbff81c0e4969fd6b
49 48 24 70 45 18 1 8 af2f9f938b771242ab4b0221a0d937e03ece383a2192d3036e05988fb2442245
50 49 24 70 45 18 2 9 26c261c66a5e93b4b88e93290324f5706ad2de59dd59e123c9db34d76494966f
51 50 24 70 45 18 2 10 f73948ca1404ac1ef598d32967ff590beac673c1f128633281e5f91d20439598
52 51 24 70 45 18 2 11 ea7ec1c838494ef086813bed27ae2c0c23f8875334392058cf1069615a333e42
53 52 24 70 45 18 2 12 4fa7e25f2c9c12088e4bb783ec7330a24744c9b83357256b2e21ce6bb6e139ca
54 53 24 70 45 18 2 13 a94578891fd1dd142acce45268da610890c2b148da88caae75c6033b655f93dd
55 54 24 70 45 18 2 14 cca92f84a1ccca51108ac4ceaa5acb74d993b17200a3ae4db3081d0025304969
56 55 24 70 45 18 2 15 8ce57236cacbca39e548e83ea1bcc342b62df29f3bde0caf2c0c2db1cd3b432d
57 56 24 70 45 18 2 16 8b647157123d4f7bcbcc625b054e3e4d83a0804e57731005a8dd903ef72ea936
58 57 24 70 45 18 3 17 7d660b53b68eeb2d66b78089f71c4ed2c872369a1593a7d284d4d0df3eace407
59 58 24 70 45 18 3 18 3b29424168040cbd1a2947c94a8e61be3776ed45bbc1112909a55cf5ebe1bbbe
60 59 24 70 45 18 3 19 788aaab9df7f831a3e6056bd7758a218ebdb923c0ebf5be142a47c59a494270a
61 60 24 70 45 18 3 20 a4bb724f51b10dcc325ec19cbe0b8c5b41c8b9b0eae296dc61c353314db1b36e
62 61 24 70 45 18 3 21 6cafc249aa6b9e95afb63f60afd62255f2c95a8445b0b9e57a39cb92ae348f31
63 62 24 70 45 18 3 22 24ae10d92145ca93e6acbe22bb510f22af67453bb371bf13f904cb9a2d06d4d2
64 63 24 70 45 18 3 23 0405c139bc6e1bc882c3767d7603d02803acda222870e38ddc94dcefeb76179e
65 64 24 70 45 18 3 24 fd604232c9eb21c707c1c0afe99e72f5f131537850c51bf2a6709705e82b284f
66 65 24 70 45 18 4 25 34c86de16b8c7a3002bba420710d099ddd31591f78f8783caae64129d5f1fa6d
67 66 24 70 45 18 4 26 3be010d0e6a1daabc7a5e21ca17fc68508665bb4b99ec16a4912f88f0c1bfee2
68 67 24 70 45 18 4 27 19058a20083e7652aceebe4079d7918e0afabcb2b634ea0956c5cd8006a12082
69 68 24 70 45 18 4 28 ffba62754ec42c376894013792d126e31c307cf52f5aec10a148c74becbfc692
70 69 24 70 45 18 4 29 0cc7665eb716cf2851156fa45bf1c241cfccd43b25d757d798140a75f40b2354
71 70 24 70 45 18 4 30 eb20ea5909a55f61b92ff4337727242240765993931d9175043cbac3dac6caa5
72 71 24 70 45 18 4 31 a4a3baccc35da76517d3a16d379ab8416dabcaed429a72ef26e9ebc757377867
73 72 24 70 45 18 4 32 550c05d891ee0a19a37a0a614cc9dbece86e922973965583d22ceacb846e1581
74 73 24 70 45 18 5 33 995a45405eedeb6e463642d6f723b6fdb35df6035e7198d787e5a14fca8b4a85
75 74 24 70 45 18 5 34 66c882d8ee6b3f9b1563ba3c18dce8e7592b3e548d9b0aa2b54f9cec6d487889
76 75 24 70 45 18 5 35 a1856cfffa7b32a6c553a548f6cb52187fa9789f414a3a29712735965fcab90d
77 76 24 70 45 18 5 36 c504f3e398c0850397f0fea4bdf2df3eec3c5cf8a9200d164f34b2c92d59be9a
78 77 24 70 45 18 5 37 2f9ccc8409dbdcd01f9c1853b713c48e3f31b5ea0ede68cc956abd2529ed9bf5
79 78 24 70 45 18 5 38 9dfdcb6d0d69d6f54da0170c0b5bbb6c382ccfaa94c803ec093a01db452c2f24
80 79 24 70 45 18 5 39 09682926ea871314009a2e8340c4e0597573ec925c8d5666f7deb0c97c649fbf
81 80 24 70 45 18 5 40 97018fa18631ed88974f799858c3e9731aefd3d308f693e0d9e095d9ed6b5cff
82 81 24 70 60 18 1 1 b54c62da0bfea8f337d0c380d534bf98b25c1be9f014db4b8c2e4b455e85279b
83 82 24 70 60 18 1 2 cffbb4238b69f5b51d93fda58847314f90277073caba72eac0969368d2bf72af
84 83 24 70 60 18 1 3 2ba7288af1d84d07e239dd78baa790b6ada943681bbdfdc27e7f489da8046e8b
85 84 24 70 60 18 1 4 afa966ce1f0dcfaa6282a049c0c30febc999f856885199056dc570b97b6adff9
86 85 24 70 60 18 1 5 8f8df0abd28895a93112a57c4d4b32be7eb9c9d7f9ac40f016603173185a78e1
87 86 24 70 60 18 1 6 1e5b48a6d463e88f4bf49c29dca892d77b2a16a9cffb3cb4fab341f5645cdb77
88 87 24 70 60 18 1 7 879e6b65a692343e84e8e02e92a49d63053180a22f4b42676e2f29bbf581bd6a
89 88 24 70 60 18 1 8 0f76f567f474e5b41351eb42f0c6dae27240f7c69a65e0a02fd631ae7a30b088
90 89 24 70 60 18 2 9 5c68cce55587a05f51a597e0e2c57971e111e412e66b29d933cddddc892ba911
91 90 24 70 60 18 2 10 307e271391f0090938521a4ed707064dad973cb360497602b23dc69bd13daae6
92 91 24 70 60 18 2 11 bddfe29d9e272a9990190682aee23bb57da690ce391cf9dab1ba4f1e80e3e932
93 92 24 70 60 18 2 12 efcd861cc0eea9334341de08ef26a690c961fd7f37d37b96e9d3a8496a7b96de
94 93 24 70 60 18 2 13 32a460c67c2a9442b1b103523ca6a116d7ae2de8c2245f4cf51b2d666ceb2dad
95 94 24 70 60 18 2 14 8eac61db97a0cf8c060e9d4c235ca2fafe8fa85129837c74417429d781f4d8b6
96 95 24 70 60 18 2 15 73d6e002b5cbdb9888f5dfe4530371d1efba05da843b47343ec6a24d358f970e
97 96 24 70 60 18 2 16 0064dd418bb85442ca1641bd7d4659bd2a1c472123a30072b6a2e4280083ea85
98 97 24 70 60 18 3 17 75fb2ad1605612509d86e932eaa930221d6b26dff9646e6680524d88e65b3dd3
99 98 24 70 60 18 3 18 b701734dd3e549ceb97d8a82682b2728814b92e08fbeb949bbf67e7a79f1365c
100 99 24 70 60 18 3 19 d327280a452af12ee949f54a09cc47f3fc855bbd4b559a021c5632ce1bf7f9e6
101 100 24 70 60 18 3 20 d8470f3ee4730ea1b618cb1e9f61d53378b1b139725df661e8cca6a75ecea753
102 101 24 70 60 18 3 21 b289c625935178c4389206037e68c6f52c4bc5be990a25a26c223df840560356
103 102 24 70 60 18 3 22 a3d18f7ca7cee4a8aec93c65c9a14a3b3fb64f28981c6501f8f2fac4db09b561
104 103 24 70 60 18 3 23 5218ef80c1efd4f752a6cfa499d71d63245f4a1a013abd40e1cf56e100c1c58f
105 104 24 70 60 18 3 24 1b16c43ada345c628d9bf487f5114c2730fde939e25152b011aee810ba70c163
106 105 24 70 60 18 4 25 0471851d3b19ea655e1498f5b7099e10fe069d9f69235270252adc2044f9a86c
107 106 24 70 60 18 4 26 ee0b78999cc119ae2c697dcc7c5cf3cc34cc8fab0be267eccd484f3a811555e9
108 107 24 70 60 18 4 27 35228f2dee7eda00408c5878511762d740f4c9e0ee27d59cb8624ec116925068
109 108 24 70 60 18 4 28 86f3de97013f53d9c9486c25cd04b97d52892f52c72c69007b46b06e8665c2b6
110 109 24 70 60 18 4 29 0ee70f3eedb98ac680768601cf84d76f7b9fa195ebc6f6850c1cd5a752acaf4b
111 110 24 70 60 18 4 30 5112d4b18b376287c286863d52f824062d90590fa4c3928041b30258f80fc8e1
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6
db/files/load_parameters.csv Normal file
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@ -0,0 +1,6 @@
id,load,primary_air_consumption,secondary_air_consumption,gas_inlet_consumption
1,190,20.3354886853634,51.7975452731847,0.345208189376503
2,260,20.9617016507624,53.3926037902321,0.453841651629505
3,315,23.6372054386887,60.2075139567843,0.559493985383952
4,400,30.4431292303094,77.5432245058036,0.731285900359563
5,465,35.3184718352393,89.9614546849929,0.848336676986426
1 id load primary_air_consumption secondary_air_consumption gas_inlet_consumption
2 1 190 20.3354886853634 51.7975452731847 0.345208189376503
3 2 260 20.9617016507624 53.3926037902321 0.453841651629505
4 3 315 23.6372054386887 60.2075139567843 0.559493985383952
5 4 400 30.4431292303094 77.5432245058036 0.731285900359563
6 5 465 35.3184718352393 89.9614546849929 0.848336676986426

41
db/files/recycling_parameters.csv Normal file
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@ -0,0 +1,41 @@
id,load_id,recycling_level,co2,n2,h2o,o2
1,1,0,0,0.7685,0,0.2315
2,1,5,0.00364653315945826,0.754018809386459,0.00768250189905284,0.234652155555029
3,1,8,0.00583445305513322,0.745330095018335,0.0122920030384845,0.236543448888047
4,1,10,0.00729306631891652,0.739537618772919,0.0153650037981057,0.237804311110059
5,1,15,0.0109395994783748,0.725056428159379,0.0230475056971585,0.240956466665088
6,1,20,0.014586132637833,0.710575237545838,0.0307300075962114,0.244108622220117
7,1,25,0.0182326657972913,0.696094046932298,0.0384125094952642,0.247260777775147
8,1,30,0.0218791989567496,0.681612856318757,0.046095011394317,0.250412933330176
9,2,0,0,0.7685,0,0.2315
10,2,5,0.00465084114980632,0.760613280911782,0.00979837407289293,0.224937503865519
11,2,8,0.00744134583969012,0.755881249458851,0.0156773985166287,0.22100000618483
12,2,10,0.00930168229961265,0.752726561823564,0.0195967481457859,0.218375007731037
13,2,15,0.013952523449419,0.744839842735346,0.0293951222186788,0.211812511596556
14,2,20,0.0186033645992253,0.736953123647129,0.0391934962915717,0.205250015462074
15,2,25,0.0232542057490316,0.729066404558911,0.0489918703644647,0.198687519327593
16,2,30,0.0279050468988379,0.721179685470693,0.0587902444373576,0.192125023193112
17,3,0,0,0.7685,0,0.2315
18,3,5,0.0050845553927608,0.763461128636561,0.0107121215556223,0.220742194415056
19,3,8,0.00813528862841727,0.760437805818498,0.0171393944889956,0.214287511064089
20,3,10,0.0101691107855216,0.758422257273122,0.0214242431112445,0.209984388830112
21,3,15,0.0152536661782824,0.753383385909684,0.0321363646668668,0.199226583245167
22,3,20,0.0203382215710432,0.748344514546245,0.042848486222489,0.188468777660223
23,3,25,0.025422776963804,0.743305643182806,0.0535606077781113,0.177710972075279
24,3,30,0.0305073323565648,0.738266771819367,0.0642727293337336,0.166953166490335
25,4,0,0,0.7685,0,0.2315
26,4,5,0.00516002254880722,0.763956659904288,0.0108711154669045,0.22001220208
27,4,8,0.00825603607809156,0.761230655846861,0.0173937847470473,0.213119523328001
28,4,10,0.0103200450976144,0.759413319808576,0.0217422309338091,0.208524404160001
29,4,15,0.0154800676464217,0.754869979712864,0.0326133464007136,0.197036606240001
30,4,20,0.0206400901952289,0.750326639617152,0.0434844618676182,0.185548808320001
31,4,25,0.0258001127440361,0.74578329952144,0.0543555773345227,0.174061010400002
32,4,30,0.0309601352928433,0.741239959425727,0.0652266928014272,0.162573212480002
33,5,0,0,0.7685,0,0.2315
34,5,5,0.00515964747536643,0.763954197102887,0.0108703252636361,0.22001583015811
35,5,8,0.00825543596058629,0.76122671536462,0.0173925204218177,0.213125328252976
36,5,10,0.0103192949507329,0.759408394205775,0.0217406505272721,0.20853166031622
37,5,15,0.0154789424260993,0.754862591308662,0.0326109757909082,0.19704749047433
38,5,20,0.0206385899014657,0.750316788411549,0.0434813010545443,0.185563320632441
39,5,25,0.0257982373768322,0.745770985514437,0.0543516263181804,0.174079150790551
40,5,30,0.0309578848521986,0.741225182617324,0.0652219515818164,0.162594980948661
1 id load_id recycling_level co2 n2 h2o o2
2 1 1 0 0 0.7685 0 0.2315
3 2 1 5 0.00364653315945826 0.754018809386459 0.00768250189905284 0.234652155555029
4 3 1 8 0.00583445305513322 0.745330095018335 0.0122920030384845 0.236543448888047
5 4 1 10 0.00729306631891652 0.739537618772919 0.0153650037981057 0.237804311110059
6 5 1 15 0.0109395994783748 0.725056428159379 0.0230475056971585 0.240956466665088
7 6 1 20 0.014586132637833 0.710575237545838 0.0307300075962114 0.244108622220117
8 7 1 25 0.0182326657972913 0.696094046932298 0.0384125094952642 0.247260777775147
9 8 1 30 0.0218791989567496 0.681612856318757 0.046095011394317 0.250412933330176
10 9 2 0 0 0.7685 0 0.2315
11 10 2 5 0.00465084114980632 0.760613280911782 0.00979837407289293 0.224937503865519
12 11 2 8 0.00744134583969012 0.755881249458851 0.0156773985166287 0.22100000618483
13 12 2 10 0.00930168229961265 0.752726561823564 0.0195967481457859 0.218375007731037
14 13 2 15 0.013952523449419 0.744839842735346 0.0293951222186788 0.211812511596556
15 14 2 20 0.0186033645992253 0.736953123647129 0.0391934962915717 0.205250015462074
16 15 2 25 0.0232542057490316 0.729066404558911 0.0489918703644647 0.198687519327593
17 16 2 30 0.0279050468988379 0.721179685470693 0.0587902444373576 0.192125023193112
18 17 3 0 0 0.7685 0 0.2315
19 18 3 5 0.0050845553927608 0.763461128636561 0.0107121215556223 0.220742194415056
20 19 3 8 0.00813528862841727 0.760437805818498 0.0171393944889956 0.214287511064089
21 20 3 10 0.0101691107855216 0.758422257273122 0.0214242431112445 0.209984388830112
22 21 3 15 0.0152536661782824 0.753383385909684 0.0321363646668668 0.199226583245167
23 22 3 20 0.0203382215710432 0.748344514546245 0.042848486222489 0.188468777660223
24 23 3 25 0.025422776963804 0.743305643182806 0.0535606077781113 0.177710972075279
25 24 3 30 0.0305073323565648 0.738266771819367 0.0642727293337336 0.166953166490335
26 25 4 0 0 0.7685 0 0.2315
27 26 4 5 0.00516002254880722 0.763956659904288 0.0108711154669045 0.22001220208
28 27 4 8 0.00825603607809156 0.761230655846861 0.0173937847470473 0.213119523328001
29 28 4 10 0.0103200450976144 0.759413319808576 0.0217422309338091 0.208524404160001
30 29 4 15 0.0154800676464217 0.754869979712864 0.0326133464007136 0.197036606240001
31 30 4 20 0.0206400901952289 0.750326639617152 0.0434844618676182 0.185548808320001
32 31 4 25 0.0258001127440361 0.74578329952144 0.0543555773345227 0.174061010400002
33 32 4 30 0.0309601352928433 0.741239959425727 0.0652266928014272 0.162573212480002
34 33 5 0 0 0.7685 0 0.2315
35 34 5 5 0.00515964747536643 0.763954197102887 0.0108703252636361 0.22001583015811
36 35 5 8 0.00825543596058629 0.76122671536462 0.0173925204218177 0.213125328252976
37 36 5 10 0.0103192949507329 0.759408394205775 0.0217406505272721 0.20853166031622
38 37 5 15 0.0154789424260993 0.754862591308662 0.0326109757909082 0.19704749047433
39 38 5 20 0.0206385899014657 0.750316788411549 0.0434813010545443 0.185563320632441
40 39 5 25 0.0257982373768322 0.745770985514437 0.0543516263181804 0.174079150790551
41 40 5 30 0.0309578848521986 0.741225182617324 0.0652219515818164 0.162594980948661

74
db/migrations/env.py Normal file
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from logging.config import fileConfig
from sqlalchemy import engine_from_config
from sqlalchemy import pool
from alembic import context
from db.config import settings
from db.models.experiment_data_model import ExperimentData
from db.models.experiment_parameters_model import ExperimentParameters
from db.models.load_parameters_model import LoadParameters
from db.models.recycling_parameters_model import RecyclingParameters
from db.models.base import Base
config = context.config
if config.config_file_name is not None:
fileConfig(config.config_file_name)
config.set_main_option('sqlalchemy.url', settings.db_url_asyncpg + '?async_fallback=True')
target_metadata = Base.metadata
def run_migrations_offline() -> None:
"""Run migrations in 'offline' mode.
This configures the context with just a URL
and not an Engine, though an Engine is acceptable
here as well. By skipping the Engine creation
we don't even need a DBAPI to be available.
Calls to context.execute() here emit the given string to the
script output.
"""
url = config.get_main_option("sqlalchemy.url")
context.configure(
url=url,
target_metadata=target_metadata,
literal_binds=True,
dialect_opts={"paramstyle": "named"},
)
with context.begin_transaction():
context.run_migrations()
def run_migrations_online() -> None:
"""Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.
"""
connectable = engine_from_config(
config.get_section(config.config_ini_section, {}),
prefix="sqlalchemy.",
poolclass=pool.NullPool,
)
with connectable.connect() as connection:
context.configure(
connection=connection, target_metadata=target_metadata
)
with context.begin_transaction():
context.run_migrations()
if context.is_offline_mode():
run_migrations_offline()
else:
run_migrations_online()

26
db/migrations/script.py.mako Normal file
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"""${message}
Revision ID: ${up_revision}
Revises: ${down_revision | comma,n}
Create Date: ${create_date}
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
${imports if imports else ""}
# revision identifiers, used by Alembic.
revision: str = ${repr(up_revision)}
down_revision: Union[str, None] = ${repr(down_revision)}
branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
def upgrade() -> None:
${upgrades if upgrades else "pass"}
def downgrade() -> None:
${downgrades if downgrades else "pass"}

7
db/models/__init__.py Normal file
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from .base import *
from .experiment_parameters_model import *
from .experiment_data_model import *
from .ch_experimentdb_experiment_data_model import *
from .load_parameters_model import *
from .recycling_parameters_model import *
from .experiment_category import *

8
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from typing import Annotated
from sqlalchemy.orm import DeclarativeBase, mapped_column
int_pk_incr = Annotated[int, mapped_column(primary_key=True, autoincrement=True)]
class Base(DeclarativeBase):
pass

26
db/models/ch_experimentdb_experiment_data_model.py Normal file
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from clickhouse_sqlalchemy import engines
from clickhouse_sqlalchemy.types import Float64, String
from sqlalchemy import Integer
from sqlalchemy.orm import Mapped, mapped_column
from db.clickhouse_db_connection import BaseClickhouse
class ChExperimentDBExperimentData(BaseClickhouse):
__tablename__ = 'experiment_data'
__table_args__ = (
engines.MergeTree(order_by='id'),
)
id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
volume: Mapped[float] = mapped_column(Float64)
nitrogen_oxide_emission: Mapped[float] = mapped_column(Float64)
temperature: Mapped[float] = mapped_column(Float64)
co_fraction: Mapped[float] = mapped_column(Float64)
co2_fraction: Mapped[float] = mapped_column(Float64)
x: Mapped[float] = mapped_column(Float64)
y: Mapped[float] = mapped_column(Float64)
z: Mapped[float] = mapped_column(Float64)
file_id: Mapped[str] = mapped_column(String)
def __repr__(self):
return f"<ChExperimentDBExperimentData id={self.id}, volume={self.volume}>"

17
db/models/experiment_category.py Normal file
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from typing import Optional
from sqlalchemy import Identity, ForeignKey
from sqlalchemy.orm import Mapped, mapped_column
from db.models.base import Base
class ExperimentCategory(Base):
__tablename__ = 'experiment_category'
id: Mapped[int] = mapped_column(Identity(start=1, cycle=True),
primary_key=True)
name: Mapped[str]
def __repr__(self):
return f"<ExperimentCategory>"

22
db/models/experiment_data_model.py Normal file
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from typing import Optional
from sqlalchemy import Identity, ForeignKey
from sqlalchemy.orm import Mapped, mapped_column
from db.models.base import Base
class ExperimentData(Base):
__tablename__ = 'experiment_data'
id: Mapped[int] = mapped_column(Identity(start=21, cycle=True),
primary_key=True)
direction: Mapped[float]
temperature: Mapped[float]
nox: Mapped[float]
co2: Mapped[float]
co: Mapped[float]
file_id: Mapped[Optional[str]] = mapped_column(ForeignKey('experiment_parameters.experiment_hash', ondelete='SET NULL'))
def __repr__(self):
return f"<ExperimentData>"

24
db/models/experiment_parameters_model.py Normal file
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from typing import Optional
from sqlalchemy import ForeignKey, Identity
from sqlalchemy.orm import Mapped, mapped_column
from db.models.base import Base
class ExperimentParameters(Base):
__tablename__ = 'experiment_parameters'
id: Mapped[int] = mapped_column(Identity(start=11, cycle=True),
primary_key=True)
outer_blades_count: Mapped[int]
outer_blades_length: Mapped[float]
outer_blades_angle: Mapped[float]
middle_blades_count: Mapped[int]
load_id: Mapped[Optional[int]] = mapped_column(ForeignKey('load_parameters.id', ondelete='SET NULL'))
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)
def __repr__(self):
return f"<ExperimentParameters>"

18
db/models/load_parameters_model.py Normal file
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from sqlalchemy import Identity
from sqlalchemy.orm import Mapped, mapped_column
from db.models.base import Base
class LoadParameters(Base):
__tablename__ = 'load_parameters'
id: Mapped[int] = mapped_column(Identity(start=6, cycle=True),
primary_key=True)
load: Mapped[int]
primary_air_consumption: Mapped[float]
secondary_air_consumption: Mapped[float]
gas_inlet_consumption: Mapped[float]
def __repr__(self):
return f"<LoadParameters>"

23
db/models/recycling_parameters_model.py Normal file
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from typing import Optional
from sqlalchemy import ForeignKey, Identity
from sqlalchemy.orm import Mapped, mapped_column
from db.models.base import Base
class RecyclingParameters(Base):
__tablename__ = 'recycling_parameters'
id: Mapped[int] = mapped_column(Identity(start=6, cycle=True),
primary_key=True)
load_id: Mapped[Optional[int]] = mapped_column(ForeignKey('load_parameters.id', ondelete='SET NULL'))
recycling_level: Mapped[int]
co2: Mapped[float]
n2: Mapped[float]
h2o: Mapped[float]
o2: Mapped[float]
def __repr__(self):
return f"<RecyclingParameters>"

11
db/postgres_db_connection.py Normal file
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from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker
from settings import settings
engine_postgres = create_async_engine(url=settings.db_url_asyncpg_docker, echo=True)
async_session_postgres = async_sessionmaker(engine_postgres, expire_on_commit=False)
# Функция для получения новой сессии
async def get_async_session():
async with async_session_postgres() as session:
yield session

6
db/repositories/__init__.py Normal file
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from .experiment_data_repos import *
from .experiment_parameters_repos import *
from .load_parameters_repos import *
from .recycling_parameters_repos import *
from .ch_experimentdb_experiment_data_repos import *
from .experiment_category_repos import *

41
db/repositories/ch_experimentdb_experiment_data_repos.py Normal file
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from db.models.ch_experimentdb_experiment_data_model import ChExperimentDBExperimentData
from sqlalchemy import select,func
class ChExperimentDBExperimentDataRepository:
def __init__(self, session):
self.session = session
def get_all(self):
return self.session.query(ChExperimentDBExperimentData).all()
def get_by_id(self, id: int) -> ChExperimentDBExperimentData:
return self.session.query(ChExperimentDBExperimentData).filter(ChExperimentDBExperimentData.id == id).one_or_none()
def get_by_file_id(self, file_id: str) -> ChExperimentDBExperimentData:
return self.session.query(ChExperimentDBExperimentData).filter(ChExperimentDBExperimentData.file_id == file_id).all()
def create(self, ch_experiment_data: ChExperimentDBExperimentData) -> ChExperimentDBExperimentData:
max_id_query = select(func.max(ChExperimentDBExperimentData.id))
max_id_result = self.session.execute(max_id_query).scalar()
max_id = max_id_result or 0
ch_experiment_data.id = max_id + 1
self.session.add(ch_experiment_data)
self.session.commit()
return ch_experiment_data
def update(self, id: int, updated_data: dict) -> ChExperimentDBExperimentData:
self.session.query(ChExperimentDBExperimentData).filter(ChExperimentDBExperimentData.id == id).update(updated_data)
self.session.commit()
return self.get_by_id(id)
def delete(self, id: int) -> bool:
item = self.get_by_id(id)
if item:
self.session.delete(item)
self.session.commit()
return True
return False

41
db/repositories/experiment_category_repos.py Normal file
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from typing import Sequence
from sqlalchemy import update, delete
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy.future import select
from typing_extensions import deprecated
from db.models import ExperimentCategory
@deprecated(
"теперь есть параметризованный круд, а уже свои специфичные методы у каждой сущности в своем репозитории"
)
class ExperimentCategoryRepository:
def __init__(self, session: AsyncSession):
self.session = session
async def get_all(self) -> Sequence[ExperimentCategory]:
result = await self.session.execute(select(ExperimentCategory))
return result.scalars().all()
async def get_by_id(self, id: int) -> ExperimentCategory:
result = await self.session.execute(select(ExperimentCategory).where(ExperimentCategory.id == id))
return result.scalar_one_or_none()
async def create(self, new_data: ExperimentCategory):
self.session.add(new_data)
await self.session.commit()
async def update(self, id: int, updated_data: dict):
stmt = (
update(ExperimentCategory).
where(ExperimentCategory.id == id).
values(**updated_data)
)
await self.session.execute(stmt)
await self.session.commit()
async def delete(self, id: int):
stmt = delete(ExperimentCategory).where(ExperimentCategory.id == id)
await self.session.execute(stmt)
await self.session.commit()

24
db/repositories/experiment_data_repos.py Normal file
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from typing import Sequence
from sqlalchemy.future import select
from db.models.experiment_data_model import ExperimentData
from db.postgres_db_connection import async_session_postgres
async def get_exp_data_by_file_id(file_id: str) -> Sequence[ExperimentData]:
async with async_session_postgres() as session:
result = await session.execute(
select(ExperimentData).where(ExperimentData.file_id == file_id)
)
return result.scalars().all()
async def get_all_exp_data(page: int, page_size: int) -> Sequence[ExperimentData]:
async with async_session_postgres() as session:
offset = (page - 1) * page_size
result = await session.execute(
select(ExperimentData).offset(offset).limit(page_size)
)
return result.scalars().all()

21
db/repositories/experiment_parameters_repos.py Normal file
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from typing import Sequence
from sqlalchemy.future import select
from db.models.experiment_parameters_model import ExperimentParameters
from db.postgres_db_connection import async_session_postgres
async def get_exp_parameters_by_category(category_id: int) -> Sequence[ExperimentParameters]:
async with async_session_postgres() as session:
result = await session.execute(
select(ExperimentParameters).where(ExperimentParameters.experiment_category_id == category_id)
)
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(
select(ExperimentParameters).where(ExperimentParameters.experiment_hash == exp_hash)
)
return result.scalars().all()

76
db/repositories/load_parameters_repos.py Normal file
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from typing import Optional, Sequence
from sqlalchemy import delete as delete_
from sqlalchemy import update as update_
from sqlalchemy.future import select
from db.models.load_parameters_model import LoadParameters
from db.postgres_db_connection import async_session_postgres
# async def get_all() -> Sequence[LoadParameters]:
# async with async_session_postgres() as session:
# result = await session.execute(select(LoadParameters))
# return result.scalars().all()
#
#
# async def get_by_id(id: int) -> LoadParameters:
# async with async_session_postgres() as session:
# result = await session.execute(select(LoadParameters).where(LoadParameters.id == id))
# return result.scalar_one_or_none()
#
#
# async def create(new_data: LoadParameters):
# async with async_session_postgres() as session:
# session.add(new_data)
# await session.commit()
#
#
# async def update(id: int, updated_data: dict):
# async with async_session_postgres() as session:
# stmt = (
# update_(LoadParameters).
# where(LoadParameters.id == id).
# values(**updated_data)
# )
# await session.execute(stmt)
# await session.commit()
#
#
# async def delete(id: int) -> bool:
# async with async_session_postgres() as session:
# stmt = delete_(LoadParameters).where(LoadParameters.id == id)
# result = await session.execute(stmt)
# await session.commit()
# return result.rowcount > 0
# class LoadParametersRepository:
# def __init__(self, session: AsyncSession):
# self.session = session
#
# async def get_all(self) -> Sequence[LoadParameters]:
# result = await self.session.execute(select(LoadParameters))
# return result.scalars().all()
#
# async def get_by_id(self, id: int) -> LoadParameters:
# result = await self.session.execute(select(LoadParameters).where(LoadParameters.id == id))
# return result.scalar_one_or_none()
#
# async def create(self, new_data: LoadParameters):
# self.session.add(new_data)
# await self.session.commit()
#
# async def update(self, id: int, updated_data: dict):
# stmt = (
# update_(LoadParameters).
# where(LoadParameters.id == id).
# values(**updated_data)
# )
# await self.session.execute(stmt)
# await self.session.commit()
#
# async def delete(self, id: int):
# stmt = delete_(LoadParameters).where(LoadParameters.id == id)
# await self.session.execute(stmt)
# await self.session.commit()

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db/repositories/recycling_parameters_repos.py Normal file
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from typing import Sequence
from typing_extensions import deprecated
from sqlalchemy import select
from sqlalchemy import update, delete
from sqlalchemy.ext.asyncio import AsyncSession
from db.models.recycling_parameters_model import RecyclingParameters
@deprecated(
"теперь есть параметризованный круд, а уже свои специфичные методы у каждой сущности в своем репозитории"
)
class RecyclingParametersRepository:
def __init__(self, session: AsyncSession):
self.session = session
async def get_all(self) -> Sequence[RecyclingParameters]:
result = await self.session.execute(select(RecyclingParameters))
return result.scalars().all()
async def get_by_id(self, id: int) -> RecyclingParameters:
result = await self.session.execute(select(RecyclingParameters).where(RecyclingParameters.id == id))
return result.scalar_one_or_none()
async def create(self, new_data: RecyclingParameters):
self.session.add(new_data)
await self.session.commit()
async def update(self, id: int, updated_data: dict):
stmt = (
update(RecyclingParameters).
where(RecyclingParameters.id == id).
values(**updated_data)
)
await self.session.execute(stmt)
await self.session.commit()
async def delete(self, id: int):
stmt = delete(RecyclingParameters).where(RecyclingParameters.id == id)
await self.session.execute(stmt)
await self.session.commit()

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docker-compose.yml Normal file
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services:
db:
image: postgres
container_name: postgres-db
environment:
POSTGRES_DB: ${DATABASE}
POSTGRES_USER: ${POSTGRES_USER}
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
ports:
- "5432:5432"
volumes:
- postgres_data:/var/lib/postgresql/data
clickhouse:
image: clickhouse/clickhouse-server:latest
container_name: clickhouse-db
environment:
CLICKHOUSE_USER: ${CLICKHOUSE_USER}
CLICKHOUSE_PASSWORD: ${CLICKHOUSE_PASSWORD}
ports:
- "8123:8123"
- "9000:9000"
volumes:
- clickhouse_data:/var/lib/clickhouse
fastapi:
build: .
container_name: fastapi-app
ports:
- "8000:8000"
depends_on:
- db
- clickhouse
volumes:
- .:/app
volumes:
postgres_data:
clickhouse_data:

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import requests
import os
import subprocess
import time
import psutil
import argparse
import macros_generator as mg
STARCCM_PATH = '/home/user/Siemens/19.02.009-R8/STAR-CCM+19.02.009-R8/star/bin/starccm+'
NUMBER_PROCESSES = 16
def download_file_from_fastapi(api_url, params, full_file_name):
response = requests.post(api_url, json=params)
if response.status_code == 200:
with open(full_file_name, "wb") as f:
f.write(response.content)
print("File downloaded successfully.")
else:
print(f"Failed to download file. Status code: {response.status_code}")
def terminate_process_by_name(process_name):
# Проходим по всем процессам в системе
for proc in psutil.process_iter(['pid', 'name']):
try:
# Если имя процесса совпадает с искомым
if proc.info['name'] == process_name:
# Завершаем процесс
proc.terminate()
print(f"Процесс '{process_name}' с PID {proc.pid} был завершен.")
except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess) as e:
print(f"Не удалось завершить процесс '{process_name}': {e}")
def create_directory(path):
if not os.path.exists(path):
os.makedirs(path)
def delete_directory(path):
if os.path.exists(path):
os.remove(path)
def run_macros(macros_name, model_name=None, new_model=False, is_gpgpu=False):
np_value = '1 -gpgpu auto' if is_gpgpu else str(NUMBER_PROCESSES)
if new_model:
macros_command = f'{STARCCM_PATH} -np {np_value} -new -batch \'{macros_name}\''
else:
if model_name is None:
raise ValueError("model_name must be provided if new_model is False")
macros_command = f'{STARCCM_PATH} -np {np_value} \'{model_name}\' -batch \'{macros_name}\''
subprocess.run(
["bash", "-c", macros_command])
def run_experiment(angle):
chemkin_path = "/media/user/Новый том/burner_automation/burner_data/chemkin"
mesh_base_size = 0.7
cpu_stopping_criterion = 200
stopping_criterion = 7000
main_path = '/media/user/Новый том/burner_automation/experiment_data'
create_directory(main_path)
diameters = {'d1': 1442, 'd2': 1016, 'd3': 640, 'd4': 325, 'd5': 325, 'd6': 245}
default_values = {
"N1": 24,
"L1": 70.0,
"a1": 60.0,
"N2": 18,
"L2": 105.0,
"N3": 9,
"L3": 30.0
}
# number_outer_blades = [12, 24, 36, 48]
#number_outer_blades = 24.0
# length_outer_blades = [44, 70, 86, 107.5]
#length_outer_blades = 70.0
# angle_outer_blades = [30.0, 45.0, 60.0, 75.0]
# number_middle_blades = [9, 18, 27, 36]
#number_middle_blades = 18.0
# load = [190, 260, 315, 400, 465]
# load_eco = [260, 315, 400, 465]
load = 450
# recycling_eco = [0, 7, 14, 21, 28]
# recycling = [0, 5, 10, 15, 20, 25, 30]
# recycling = [0, 6, 12, 18, 24, 30]
recycling = 0
start_time = time.time()
directories_list = []
api_url = "http://10.6.23.120:8000/api/v1/generate_geom"
params = {
"a1": angle
}
geometry_path = str(24) + '_' + str(70) + '_' + str(angle) + '_' + str(18)
geometry_path_full = os.path.join(main_path, geometry_path, 'geometry')
create_directory(geometry_path_full)
geometry_file_name = os.path.join(geometry_path_full, 'geometry.stp')
download_file_from_fastapi(api_url, params, geometry_file_name)
macros_path = os.path.join(main_path, geometry_path, 'general_macros')
directories_list.append(macros_path)
MODEL_PATH = os.path.join(main_path, geometry_path, 'model')
directories_list.append(MODEL_PATH)
model_parameters = {
'geometry_path': geometry_file_name,
'chemkin_path': chemkin_path,
'init_model_folder': MODEL_PATH,
'bladeCount': 18,
'mesh_base_size': mesh_base_size
}
fuel_parameters = mg.load_calculation(load, diameters)
recycling_parameters = mg.recycling_calculation(fuel_parameters['alpha'], fuel_parameters['gas_consumption'],
fuel_parameters['air_consumption'], recycling)
experiments_path = os.path.join(main_path, geometry_path, 'experiments')
directories_list.append(experiments_path)
load_path = os.path.join(experiments_path, str(load))
directories_list.append(load_path)
load_macros_experiment_path = os.path.join(load_path, 'macros')
directories_list.append(load_macros_experiment_path)
load_model_experiment_path = os.path.join(load_path, 'model')
directories_list.append(load_model_experiment_path)
recycling_experiment_path = os.path.join(load_path, str(recycling))
directories_list.append(recycling_experiment_path)
recycling_macros_experiment_path = os.path.join(recycling_experiment_path, 'macros')
directories_list.append(recycling_macros_experiment_path)
solver_parameters = {
'experiment_path': recycling_experiment_path,
'stopping_criterion': stopping_criterion
}
prc_macros_file = os.path.join(macros_path, 'preprocess_macro.java')
fuel_macros_file = os.path.join(load_macros_experiment_path, 'fuel_macro.java')
rec_macros_file = os.path.join(recycling_macros_experiment_path, 'recycle_macro.java')
run_macros_file = os.path.join(recycling_macros_experiment_path, 'run_macros.java')
for directory in directories_list:
create_directory(directory)
model_file = os.path.join(MODEL_PATH, 'init_model.sim')
mg.preprocessor_settings(prc_macros_file, model_parameters, model_file)
load_model_file = os.path.join(load_model_experiment_path, "load_"+str(load)+".sim")
mg.fuel_settings(fuel_macros_file, fuel_parameters, load_model_file)
exp_file = os.path.join(recycling_experiment_path, "recycling_"+str(recycling)+".sim")
mg.fgm_table_settings(rec_macros_file, recycling_parameters, exp_file)
mg.setting_and_running_solver(run_macros_file, solver_parameters, exp_file)
run_macros(prc_macros_file, new_model=True)
run_macros(fuel_macros_file, model_file)
run_macros(rec_macros_file, load_model_file)
run_macros(run_macros_file, exp_file, is_gpgpu=True)
_EXP_FILE = exp_file + "~"
delete_directory(_EXP_FILE)
# solver_parameters['stopping_criterion'] = STOPPING_CRITERION
# mg.setting_and_running_solver(run_macros_file, solver_parameters, EXP_FILE)
# run_macros(run_macros_file, EXP_FILE, is_gpgpu=True)
# _EXP_FILE = EXP_FILE + "~"
# delete_directory(_EXP_FILE)
# Конец замера времени
end_time = time.time()
# Вычисление времени выполнения
execution_time = end_time - start_time
print(f"Execution time: {execution_time}")
# time.sleep(10)
#
# processes_name = ["starccm+", "star-ccm+"]
#
# for process in processes_name:
# terminate_process_by_name(process)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Запуск экспериментов с энергетической установкой ГМУ-45")
parser.add_argument("angle", type=str, help="Угол наклона лопаток во внешнем контуре")
args = parser.parse_args()
run_experiment(args.angle)

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from pathlib import Path
import yaml
from settings import settings
from clickhouse_tools import ClickHouseClient
import utils
from postgres_tools import PostgresClient
# Загрузка конфигурации из файла config.yaml
with open('config.yaml', 'r') as config_file:
config = yaml.safe_load(config_file)
MAIN_PATH = config['paths']['main']
main_path = Path(MAIN_PATH)
def add_data_to_db(experiment_parameters, load_parameters, recycling_parameters):
geometry_path = (f"{experiment_parameters['outer_blades_count']}_{experiment_parameters['outer_blades_length']}_"
f"{experiment_parameters['outer_blades_angle']}_{experiment_parameters['middle_blades_count']}")
experiments_path = main_path / geometry_path / 'experiments'
load_path = experiments_path / str(experiment_parameters['load'])
load_parameters_path = load_path / 'parameters'
recycling_path = load_path / str(experiment_parameters['recycling'])
load_parameters_file = load_parameters_path / f"load_{experiment_parameters['load']}_parameters.yaml"
plot_csv = recycling_path / 'plot.csv'
table_csv = recycling_path / 'data_table.csv'
file_id = utils.calculate_hash(experiment_parameters)
clickhouse_client = ClickHouseClient("localhost", 8123, settings.DATABASE, settings.CLICKHOUSE_USER, settings.CLICKHOUSE_PASSWORD)
# Инициализация базы данных
db = PostgresClient(
dbname=settings.DATABASE,
user=settings.POSTGRES_USER,
password=settings.POSTGRES_PASSWORD,
host="localhost",
port="5432"
)
try:
if load_parameters_file.exists():
with open(load_parameters_file, 'r') as fuel_dict_file:
fuel_parameters = yaml.safe_load(fuel_dict_file)
load_parameters['primary_air_consumption'] = fuel_parameters['primary_air_consumption']
load_parameters['secondary_air_consumption'] = fuel_parameters['secondary_air_consumption']
load_parameters['gas_inlet_consumption'] = fuel_parameters['gas_inlet_consumption']
# Вставка данных в load_parameters и получение id
load_id = db.insert_load_parameters(load_parameters)
# Вставка данных в recycling_parameters и получение id
recycling_id = db.insert_recycling_parameters(recycling_parameters, load_id)
# Вставка данных в experiment_parameters
db.insert_experiment_parameters(experiment_parameters, load_id, recycling_id, file_id)
# Сохранение изменений
db.connection.commit()
db.save_csv_to_postgres(plot_csv, file_id)
clickhouse_client.save_csv_to_clickhouse(table_csv, file_id)
print('Загружено: ', experiment_parameters)
finally:
db.close()

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import pandas as pd
from fastapi import FastAPI, HTTPException, BackgroundTasks
from pyDOE3 import pbdesign, lhs
from db.csv_to_db import csv_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
from network.schemas import *
from new_experiment_planner import run_experiment
from new_experiment_planner_pyDOE3 import scale_design, scale_design_lhs, round_by_index
app = FastAPI()
app.include_router(ch_experimentdb_experiment_data_router.router,
prefix="/ch_experimentdb_experiment_data",
tags=["ch_experimentdb_experiment_data"])
app.include_router(experiment_category_router.router,
prefix="/experiment_category",
tags=["experiment_category"])
app.include_router(experiment_data_router.router,
prefix="/experiment_data",
tags=["experiment_data"])
app.include_router(experiment_parameters_router.router,
prefix="/experiment_parameters",
tags=["experiment_parameters"])
app.include_router(load_parameters_router.router,
prefix="/load_parameters",
tags=["load_parameters"])
app.include_router(recycling_parameters_router.router,
prefix="/recycling_parameters",
tags=["recycling_parameters"])
# Эндпоинт для запуска эксперимента
@app.post("/run_experiment/")
def run_experiment_api(params: ExperimentParameters):
try:
# Вызываем функцию run_experiment с параметрами
run_experiment(
params.outer_blades_count,
params.outer_blades_length,
params.outer_blades_angle,
params.middle_blades_count,
params.load,
params.recycling
)
return {"status": "success", "message": "Experiment started successfully."}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
# эндпоинт инициализации бд из csv файлов
@app.get('/init_db_data')
async def init_db_data(background_tasks: BackgroundTasks):
try:
background_tasks.add_task(csv_to_db)
return {"status": "success", "message": "Инициализация БД запущена в фоне"}
except Exception as e:
print(str(e.with_traceback()))
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
# Пример запроса
# {
# "param_ranges": {
# "outer_blades_count": [12, 48],
# "outer_blades_length": [44, 107.5],
# "outer_blades_angle": [30, 75],
# "middle_blades_count": [9, 36],
# "load": [315, 465],
# "recycling_level": [0, 20],
# "oxidizer_temp": [471, 493]
# },
# "count_exp": 1440,
# "round_rules": [0, 1, 1, 0, 1, 1, 1]
# }
@app.post("/pyDOE3_screening_design")
def generate_screening_design(request: ExperimentParametersPyDOE3) -> List[Dict[str, float]]:
param_ranges = request.param_ranges
# Создаем screening design и масштабируем его
num_factors = len(param_ranges)
screening_design = pbdesign(num_factors)
scaled_screening_design = scale_design(screening_design, param_ranges)
# Преобразуем в DataFrame и возвращаем результат
df_screening = pd.DataFrame(scaled_screening_design, columns=param_ranges.keys())
return df_screening.to_dict(orient="records")
@app.post("/pyDOE3_lhs_design")
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
# Создаем lhs design и масштабируем его
num_factors = len(param_ranges)
lhs_samples = lhs(num_factors, samples=count_exp)
scaled_lhs_samples = scale_design_lhs(lhs_samples, param_ranges)
# Округляем значения
round_scaled_lhs_samples = round_by_index(scaled_lhs_samples, round_rules)
# Преобразуем в DataFrame и возвращаем результат
df_lhs = pd.DataFrame(round_scaled_lhs_samples, columns=param_ranges.keys())
return df_lhs.to_dict(orient="records")

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network/routes/ch_experimentdb_experiment_data_router.py Normal file
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from fastapi import APIRouter, HTTPException
from db.clickhouse_db_connection import session_clickhouse
from db.models import ChExperimentDBExperimentData
from db.repositories.ch_experimentdb_experiment_data_repos import ChExperimentDBExperimentDataRepository
from network.schemas import ChExperimentDBExperimentDataBody
from typing import List
router = APIRouter()
repository = ChExperimentDBExperimentDataRepository(session_clickhouse)
@router.post('/create')
async def create_ch_experimentdb_experiment_data(data: ChExperimentDBExperimentDataBody):
try:
new_record = repository.create(ChExperimentDBExperimentData(
volume=data.volume,
nitrogen_oxide_emission=data.nitrogen_oxide_emission,
temperature=data.temperature,
co_fraction=data.co_fraction,
co2_fraction=data.co2_fraction,
x=data.x,
y=data.y,
z=data.z,
file_id=data.file_id
))
return {"message": "Новая запись успешно добавлена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/all', response_model=List[ChExperimentDBExperimentDataBody])
async def get_all_ch_experimentdb_experiment_data():
try:
result = repository.get_all()
if result:
return result
else:
return {"message": "Нет записей"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/{id}')
async def get_ch_experimentdb_experiment_data_by_id(id: int):
try:
record = repository.get_by_id(id)
if record:
return record
else:
raise HTTPException(status_code=404, detail="Запись не найдена")
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/file_id/{file_id}')
async def get_ch_experimentdb_experiment_data_by_file_id(file_id: str):
try:
record = repository.get_by_file_id(file_id)
if record:
return record
else:
raise HTTPException(status_code=404, detail="Запись не найдена")
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.delete('/{id}/delete')
async def delete_ch_experimentdb_experiment_data(id: int):
try:
is_deleted = repository.delete(id)
if is_deleted:
return {"message": "Запись успешно удалена"}
else:
raise HTTPException(status_code=404, detail="Запись не найдена")
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")

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from fastapi import APIRouter, HTTPException
from db.crud import *
from db.models import ExperimentCategory
from network.schemas import ExperimentCategoryBody
router = APIRouter()
@router.post('/create')
async def create_experiment_category(body: ExperimentCategoryBody):
try:
await create(ExperimentCategory,
name=body.name
)
return {"message": "Новая запись <ExperimentCategory> успешно добавлена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/all')
async def get_all_experiment_category():
try:
result = await get_all(ExperimentCategory)
if result is not None:
return result
else:
return {"message": "Нет записей в <ExperimentCategory>"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/{id}')
async def get_by_id_experiment_category(id: int):
try:
result = await get_by_id(ExperimentCategory, id)
if result is not None:
return result
else:
return {"message": "Запись <ExperimentCategory> не найдена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.delete('/{id}/delete')
async def delete_experiment_category(id: int):
try:
is_deleted = await delete(ExperimentCategory, id)
if is_deleted:
return {"message": "Запись <ExperimentCategory> успешно удалена"}
else:
return {"message": "Запись <ExperimentCategory> не найдена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")

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import math
from fastapi import APIRouter, HTTPException, Query
from db.crud import *
from db.models import ExperimentData
from db.repositories import get_exp_data_by_file_id, get_all_exp_data
from network.schemas import ExperimentDataBody
router = APIRouter()
@router.post('/create')
async def create_experiment_data(body: ExperimentDataBody):
try:
await create(ExperimentData,
direction=body.direction,
temperature=body.temperature,
nox=body.nox,
co2=body.co2,
co=body.co,
file_id=body.file_id
)
return {"message": "Новая запись <ExperimentData> успешно добавлена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/all')
async def get_all_experiment_data(page: int = Query(ge=1, default=1),
size: int = Query(ge = 1, le=100, default=15)):
try:
result = await get_all_exp_data(page, size)
return {
"items": result,
"page": page,
"size": size,
}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/{id}')
async def get_by_id_experiment_data(id: int):
try:
result = await get_by_id(ExperimentData, id)
if result is not None:
return result
else:
return {"message": "Запись <ExperimentData> не найдена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/file_id/{file_id}')
async def get_experiment_data_by_file_id(file_id: str):
try:
result = await get_exp_data_by_file_id(file_id)
if result is not None:
return result
else:
return {"message": f'<ExperimentData> с file_id = {id} - не найдены'}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.delete('/{id}/delete')
async def delete_experiment_data(id: int):
try:
is_deleted = await delete(ExperimentData, id)
if is_deleted:
return {"message": "Запись <ExperimentData> успешно удалена"}
else:
return {"message": "Запись <ExperimentData> не найдена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")

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from fastapi import APIRouter, HTTPException
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 network.schemas import ExperimentParametersBody
router = APIRouter()
@router.post('/create')
async def create_experiment_parameters(body: ExperimentParametersBody):
try:
await create(ExperimentParameters,
outer_blades_count=body.outer_blades_count,
outer_blades_length=body.outer_blades_length,
outer_blades_angle=body.outer_blades_angle,
middle_blades_count=body.middle_blades_count,
load_id=body.load_id,
recycling_id=body.recycling_id,
experiment_hash=body.experiment_hash
)
return {"message": "Новая запись <ExperimentParameters> успешно добавлена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/all')
async def get_all_experiment_parameters():
try:
result = await get_all(ExperimentParameters)
if result is not None:
return result
else:
return {"message": "Нет записей в <ExperimentParameters>"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/{id}')
async def get_by_id_experiment_parameters(id: int):
try:
result = await get_by_id(ExperimentParameters, id)
if result is not None:
return result
else:
return {"message": "Запись <ExperimentParameters> не найдена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/by_category/{id}')
async def get_experiment_parameters_by_exp_category(id: int):
try:
result = await get_exp_parameters_by_category(id)
if result is not None:
return result
else:
return {"message": f'<ExperimentParameters> с идентификатором категории - {id} - не найдены'}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/by_exp_hash/{hash}')
async def get_experiment_parameters_by_exp_category(hash: str):
try:
result = await get_exp_parameters_by_exp_hash(hash)
if result is not None:
return result
else:
return {"message": f'<ExperimentParameters> с experiment_hash = {id} - не найдены'}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.delete('/{id}/delete')
async def delete_experiment_parameters(id: int):
try:
is_deleted = await delete(LoadParameters, 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)}")

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from fastapi import APIRouter, HTTPException
from db.crud import *
from db.models import LoadParameters
from network.schemas import LoadParametersBody
router = APIRouter()
@router.post('/create')
async def create_load_parameters(body: LoadParametersBody):
try:
await create(LoadParameters,
load=body.load,
primary_air_consumption=body.primary_air_consumption,
secondary_air_consumption=body.secondary_air_consumption,
gas_inlet_consumption=body.gas_inlet_consumption
)
return {"message": "Новая запись <LoadParameters> успешно добавлена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/all')
async def get_all_load_parameters():
try:
result = await get_all(LoadParameters)
if result is not None:
return result
else:
return {"message": "Нет записей в <LoadParameters>"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/{id}')
async def get_by_id_load_parameters(id: int):
try:
result = await get_by_id(LoadParameters, id)
if result is not None:
return result
else:
return {"message": "Запись <LoadParameters> не найдена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.delete('/{id}/delete')
async def delete_load_parameters(id: int):
try:
is_deleted = await delete(LoadParameters, id)
if is_deleted:
return {"message": "Запись <LoadParameters> успешно удалена"}
else:
return {"message": "Запись <LoadParameters> не найдена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")

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from fastapi import APIRouter, HTTPException
from db.crud import *
from db.models import RecyclingParameters
from network.schemas import RecyclingParametersBody
router = APIRouter()
@router.post('/create')
async def create_recycling_parameters(body: RecyclingParametersBody):
try:
await create(RecyclingParameters,
load_id=body.load_id,
recycling_level=body.recycling_level,
co2=body.co2,
n2=body.n2,
h2o=body.h2o,
o2=body.o2
)
return {"message": "Новая запись <RecyclingParameters> успешно добавлена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/all')
async def get_all_recycling_parameters():
try:
result = await get_all(RecyclingParameters)
if result is not None:
return result
else:
return {"message": "Нет записей в <RecyclingParameters>"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.get('/{id}')
async def get_by_id_recycling_parameters(id: int):
try:
result = await get_by_id(RecyclingParameters, id)
if result is not None:
return result
else:
return {"message": "Запись <RecyclingParameters> не найдена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@router.delete('/{id}/delete')
async def delete_recycling_parameters(id: int):
try:
is_deleted = await delete(RecyclingParameters, id)
if is_deleted:
return {"message": "Запись <RecyclingParameters> успешно удалена"}
else:
return {"message": "Запись <RecyclingParameters> не найдена"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")

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from typing import Optional, Dict, Tuple, List
from pydantic import BaseModel, ConfigDict
class ExperimentParametersPyDOE3(BaseModel):
param_ranges: Dict[str, Tuple[float, float]]
count_exp: int
round_rules: List[int]
class ExperimentParameters(BaseModel):
outer_blades_count: str
outer_blades_length: str
outer_blades_angle: str
middle_blades_count: str
load: str
recycling: str
class ChExperimentDBExperimentDataBody(BaseModel):
model_config = ConfigDict(from_attributes=True)
volume: float
nitrogen_oxide_emission: float
temperature: float
co_fraction: float
co2_fraction: float
x: float
y: float
z: float
file_id: str
class ExperimentCategoryBody(BaseModel):
model_config = ConfigDict(from_attributes=True)
name: str
class ExperimentDataBody(BaseModel):
model_config = ConfigDict(from_attributes=True)
direction: float
temperature: float
nox: float
co2: float
co: float
file_id: Optional[str]
class ExperimentParametersBody(BaseModel):
model_config = ConfigDict(from_attributes=True)
outer_blades_count: int
outer_blades_length: float
outer_blades_angle: float
middle_blades_count: int
load_id: Optional[int]
recycling_id: Optional[int]
experiment_hash: str
class LoadParametersBody(BaseModel):
model_config = ConfigDict(from_attributes=True)
load: int
primary_air_consumption: float
secondary_air_consumption: float
gas_inlet_consumption: float
class RecyclingParametersBody(BaseModel):
model_config = ConfigDict(from_attributes=True)
load_id: Optional[int]
recycling_level: int
co2: float
n2: float
h2o: float
o2: float

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import requests
import subprocess
import argparse
import yaml
import psutil
import time
import macros_generator as mg
from settings import settings
from clickhouse_tools import ClickHouseClient
import utils
from postgres_tools import PostgresClient
from pathlib import Path
from contextlib import contextmanager
# Загрузка конфигурации из файла config.yaml
with open('config.yaml', 'r') as config_file:
config = yaml.safe_load(config_file)
STARCCM_PATH = config['paths']['starccm']
CHEMKIN_PATH = config['paths']['chemkin']
MAIN_PATH = config['paths']['main']
NUMBER_PROCESSES = config['parameters']['number_processes']
MESH_BASE_SIZE = config['parameters']['mesh_base_size']
STOPPING_CRITERION = config['parameters']['stopping_criterion']
DIAMETERS = config['parameters']['diameters']
DEFAULT_VALUES = config['parameters']['default_values']
API_URL = config['api_url']
def download_file_from_fastapi(api_url, params, full_file_name):
try:
response = requests.post(api_url, json=params)
response.raise_for_status()
with open(full_file_name, "wb") as f:
f.write(response.content)
print("File downloaded successfully.")
except requests.RequestException as e:
print(f"Failed to download file: {e}")
def terminate_process_by_name(process_name):
for proc in psutil.process_iter(['pid', 'name']):
try:
if proc.info['name'] == process_name:
proc.terminate()
print(f"Process '{process_name}' with PID {proc.pid} was terminated.")
except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess) as e:
print(f"Failed to terminate process '{process_name}': {e}")
def create_directory(path):
Path(path).mkdir(parents=True, exist_ok=True)
@contextmanager
def remove_file_on_exit(file_path):
try:
yield
finally:
if file_path.exists():
file_path.unlink()
def run_macros(macros_name, model_name=None, new_model=False, is_gpgpu=False):
np_value = '1 -gpgpu auto' if is_gpgpu else str(NUMBER_PROCESSES)
if new_model:
macros_command = f'{STARCCM_PATH} -np {np_value} -new -batch \'{macros_name}\''
else:
if model_name is None:
raise ValueError("model_name must be provided if new_model is False")
macros_command = f'{STARCCM_PATH} -np {np_value} \'{model_name}\' -batch \'{macros_name}\''
subprocess.run(["bash", "-c", macros_command], check=True)
def run_experiment(outer_blades_count, outer_blades_length, outer_blades_angle, middle_blades_count, load, recycling):
main_path = Path(MAIN_PATH)
create_directory(main_path)
geometry_path = f"{outer_blades_count}_{outer_blades_length}_{outer_blades_angle}_{middle_blades_count}"
geometry_path_full = main_path / geometry_path / 'geometry'
create_directory(geometry_path_full)
geometry_file_name = geometry_path_full / 'geometry.stp'
general_macros_path = main_path / geometry_path / 'general_macros'
create_directory(general_macros_path)
model_path = main_path / geometry_path / 'model'
create_directory(model_path)
model_file = model_path / 'init_model.sim'
experiments_path = main_path / geometry_path / 'experiments'
load_path = experiments_path / str(load)
load_parameters_path = load_path / 'parameters'
load_macros_path = load_path / 'macros'
load_model_path = load_path / 'model'
recycling_path = load_path / str(recycling)
recycling_macros_path = recycling_path / 'macros'
for directory in [experiments_path, load_path, load_parameters_path, load_macros_path, load_model_path,
recycling_path, recycling_macros_path]:
create_directory(directory)
load_parameters_file = load_parameters_path / f"load_{load}_parameters.yaml"
load_model_file = load_model_path / f"load_{load}.sim"
exp_file = recycling_path / f"recycling_{recycling}.sim"
# Проверка наличия файла init_model.sim
if not model_file.exists():
download_file_from_fastapi(API_URL, {"N1": outer_blades_count,
"L1": outer_blades_length,
"a1": outer_blades_angle,
"N2": middle_blades_count},
geometry_file_name)
prc_macros_file = general_macros_path / 'preprocess_macro.java'
model_parameters = {
'geometry_path': geometry_file_name,
'chemkin_path': CHEMKIN_PATH,
'init_model_folder': model_path,
'bladeCount': middle_blades_count,
'mesh_base_size': MESH_BASE_SIZE
}
mg.preprocessor_settings(prc_macros_file, model_parameters, model_file)
run_macros(prc_macros_file, new_model=True)
if not load_parameters_file.exists():
fuel_parameters = mg.load_calculation(float(load), DIAMETERS)
with open(load_parameters_file, 'w') as fuel_dict_file:
yaml.dump(fuel_parameters, fuel_dict_file, default_flow_style=False, allow_unicode=True)
else:
with open(load_parameters_file, 'r') as fuel_dict_file:
fuel_parameters = yaml.safe_load(fuel_dict_file)
# Проверка наличия файла load_{load}.sim
if not load_model_file.exists():
fuel_macros_file = load_macros_path / 'fuel_macro.java'
mg.fuel_settings(fuel_macros_file, fuel_parameters, load_model_file)
run_macros(fuel_macros_file, model_file)
# Проверка наличия файла recycling_{recycling}.sim
if not exp_file.exists():
rec_macros_file = recycling_macros_path / 'recycle_macro.java'
run_macros_file = recycling_macros_path / 'run_macros.java'
recycling_parameters = mg.recycling_calculation(
fuel_parameters['alpha'], fuel_parameters['gas_consumption'], fuel_parameters['air_consumption'],
float(recycling))
solver_parameters = {
'experiment_path': recycling_path,
'stopping_criterion': STOPPING_CRITERION
}
mg.fgm_table_settings(rec_macros_file, recycling_parameters, exp_file)
mg.setting_and_running_solver(run_macros_file, solver_parameters, exp_file)
run_macros(rec_macros_file, load_model_file)
run_macros(run_macros_file, exp_file, is_gpgpu=True)
experiment_parameters = {
'outer_blades_count': int(float(outer_blades_count)),
'outer_blades_length': outer_blades_length,
'outer_blades_angle': outer_blades_angle,
'middle_blades_count': int(float(middle_blades_count)),
'load': float(load),
'recycling': float(recycling),
}
fields_to_select = ['primary_air_consumption', 'secondary_air_consumption', 'gas_inlet_consumption']
load_parameters = {key: fuel_parameters[key] for key in fields_to_select}
load_parameters['load'] = float(load)
recycling_parameters['load'] = float(load)
recycling_parameters['recycling_level'] = float(recycling)
plot_csv = recycling_path / 'plot.csv'
table_csv = recycling_path / 'data_table.csv'
clickhouse_client = ClickHouseClient("localhost", 8123, settings.DATABASE, settings.CLICKHOUSE_USER,
settings.CLICKHOUSE_PASSWORD)
db = PostgresClient(
dbname=settings.DATABASE,
user=settings.POSTGRES_USER,
password=settings.POSTGRES_PASSWORD,
host="localhost",
port="5432"
)
file_id = utils.calculate_hash(experiment_parameters)
try:
clickhouse_client.save_csv_to_clickhouse(table_csv, file_id)
print("Clickhouse saved successfully")
load_id = db.insert_load_parameters(load_parameters)
recycling_id = db.insert_recycling_parameters(recycling_parameters, load_id)
db.insert_experiment_parameters(experiment_parameters, load_id, recycling_id, file_id)
db.connection.commit()
db.save_csv_to_postgres(plot_csv, file_id)
print("Postgres saved successfully")
finally:
db.close()
with remove_file_on_exit(exp_file.with_suffix(".sim~")):
pass
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Запуск экспериментов с энергетической установкой ГМУ-45")
parser.add_argument("outer_blades_count", type=str, help="Количество лопаток во внешнем контуре")
parser.add_argument("outer_blades_length", type=str, help="Ширина лопаток во внешнем контуре")
parser.add_argument("outer_blades_angle", type=str, help="Угол наклона лопаток во внешнем контуре")
parser.add_argument("middle_blades_count", type=str, help="Количество лопаток в среднем контуре")
parser.add_argument("load", type=str, help="Паровая нагрузка")
parser.add_argument("recycling", type=str, help="Уровень рециркуляции уходящих газов")
args = parser.parse_args()
run_experiment(
args.outer_blades_count,
args.outer_blades_length,
args.outer_blades_angle,
args.middle_blades_count,
args.load,
args.recycling
)

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import numpy as np
# Функция для масштабирования значений дизайна
def scale_design(design, param_ranges):
scaled_design = []
for row in design:
scaled_row = []
for i, val in enumerate(row):
min_val, max_val = param_ranges[list(param_ranges.keys())[i]]
scaled_val = (val + 1) / 2 * (max_val - min_val) + min_val
scaled_row.append(scaled_val)
scaled_design.append(scaled_row)
return np.array(scaled_design)
def scale_design_lhs(design, param_ranges):
scaled_design = []
for row in design:
scaled_row = []
for i, val in enumerate(row):
min_val, max_val = param_ranges[list(param_ranges.keys())[i]]
scaled_val = val * (max_val - min_val) + min_val
scaled_row.append(scaled_val)
scaled_design.append(scaled_row)
return np.array(scaled_design)
# Функция для округления значений
def round_by_index(array, rules):
rounded_array = np.zeros(array.shape)
for i in range(array.shape[0]):
for j in range(array.shape[1]):
rounded_array[i, j] = round(array[i, j], rules[j])
return rounded_array

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oop_experiment_planner.py Normal file
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import psycopg
import pandas as pd
class PostgresClient:
def __init__(self, dbname, user, password, host, port):
self.connection = psycopg.connect(
dbname=dbname,
user=user,
password=password,
host=host,
port=port
)
self.init_db()
def init_db(self):
with self.connection.cursor() as cur:
cur.execute("""
CREATE TABLE IF NOT EXISTS load_parameters (
id SERIAL PRIMARY KEY,
load NUMERIC NOT NULL UNIQUE,
primary_air_consumption NUMERIC NOT NULL,
secondary_air_consumption NUMERIC NOT NULL,
gas_inlet_consumption NUMERIC NOT NULL
);
CREATE TABLE IF NOT EXISTS recycling_parameters (
id SERIAL PRIMARY KEY,
load_id INTEGER NOT NULL,
recycling_level NUMERIC NOT NULL,
CO2 NUMERIC NOT NULL,
N2 NUMERIC NOT NULL,
H2O NUMERIC NOT NULL,
O2 NUMERIC NOT NULL,
UNIQUE(load_id, recycling_level),
FOREIGN KEY (load_id) REFERENCES load_parameters(id) ON DELETE CASCADE
);
CREATE TABLE IF NOT EXISTS experiment_parameters (
id SERIAL PRIMARY KEY,
outer_blades_count INTEGER NOT NULL,
outer_blades_length NUMERIC NOT NULL,
outer_blades_angle NUMERIC NOT NULL,
middle_blades_count INTEGER NOT NULL,
load_id INTEGER NOT NULL,
recycling_id INTEGER NOT NULL,
experiment_hash CHAR(64) NOT NULL UNIQUE,
FOREIGN KEY (load_id) REFERENCES load_parameters(id) ON DELETE CASCADE,
FOREIGN KEY (recycling_id) REFERENCES recycling_parameters(id) ON DELETE CASCADE
);
CREATE TABLE IF NOT EXISTS experiment_data (
id BIGSERIAL PRIMARY KEY,
Direction DOUBLE PRECISION,
Temperature DOUBLE PRECISION,
NOx DOUBLE PRECISION,
CO2 DOUBLE PRECISION,
CO DOUBLE PRECISION,
file_id CHAR(64) NOT NULL
);
""")
self.connection.commit()
def insert_load_parameters(self, load_parameters):
with self.connection.cursor() as cur:
cur.execute("SELECT id FROM load_parameters WHERE load = %s", (load_parameters['load'],))
load_id = cur.fetchone()
if load_id is None:
cur.execute("""
INSERT INTO load_parameters (load, primary_air_consumption, secondary_air_consumption, gas_inlet_consumption)
VALUES (%s, %s, %s, %s)
RETURNING id;
""", (load_parameters['load'], load_parameters['primary_air_consumption'],
load_parameters['secondary_air_consumption'], load_parameters['gas_inlet_consumption']))
load_id = cur.fetchone()[0]
else:
load_id = load_id[0]
self.connection.commit()
return load_id
def insert_recycling_parameters(self, recycling_parameters, load_id):
with self.connection.cursor() as cur:
cur.execute("SELECT id FROM recycling_parameters WHERE load_id = %s AND recycling_level = %s",
(load_id, recycling_parameters['recycling_level']))
recycling_id = cur.fetchone()
if recycling_id is None:
cur.execute("""
INSERT INTO recycling_parameters (load_id, recycling_level, CO2, N2, H2O, O2)
VALUES (%s, %s, %s, %s, %s, %s)
RETURNING id;
""", (load_id, recycling_parameters['recycling_level'], recycling_parameters['CO2'],
recycling_parameters['N2'], recycling_parameters['H2O'], recycling_parameters['O2']))
recycling_id = cur.fetchone()[0]
else:
recycling_id = recycling_id[0]
self.connection.commit()
return recycling_id
def insert_experiment_parameters(self, experiment_parameters, load_id, recycling_id, file_id):
with self.connection.cursor() as cur:
cur.execute("SELECT id FROM experiment_parameters WHERE experiment_hash = %s", (file_id,))
experiment_id = cur.fetchone()
if experiment_id is None:
cur.execute("""
INSERT INTO experiment_parameters (outer_blades_count, outer_blades_length, outer_blades_angle, middle_blades_count, load_id, recycling_id, experiment_hash)
VALUES (%s, %s, %s, %s, %s, %s, %s);
""", (experiment_parameters['outer_blades_count'], experiment_parameters['outer_blades_length'],
experiment_parameters['outer_blades_angle'], experiment_parameters['middle_blades_count'], load_id,
recycling_id, file_id))
self.connection.commit()
def get_load_parameters(self, load):
with self.connection.cursor() as cur:
cur.execute("SELECT * FROM load_parameters WHERE load = %s", (load,))
row = cur.fetchone()
if row:
return {
'load': row[1],
'primary_air_consumption': row[2],
'secondary_air_consumption': row[3],
'gas_inlet_consumption': row[4]
}
return None
def get_recycling_parameters(self, load, recycling_level):
with self.connection.cursor() as cur:
cur.execute("""
SELECT rp.* FROM recycling_parameters rp
JOIN load_parameters lp ON rp.load_id = lp.id
WHERE lp.load = %s AND rp.recycling_level = %s
""", (load, recycling_level))
row = cur.fetchone()
if row:
return {
'load': load,
'recycling_level': row[2],
'CO2': row[3],
'N2': row[4],
'H2O': row[5],
'O2': row[6]
}
return None
def get_experiment_parameters(self, experiment_hash):
with self.connection.cursor() as cur:
cur.execute("SELECT * FROM experiment_parameters WHERE experiment_hash = %s", (experiment_hash,))
row = cur.fetchone()
if row:
load_params = self.get_load_parameters(row[5])
recycling_params = self.get_recycling_parameters(load_params['load'], row[6])
return {
'outer_blades_count': row[1],
'outer_blades_length': row[2],
'outer_blades_angle': row[3],
'middle_blades_count': row[4],
'load': load_params['load'],
'recycling': recycling_params['recycling_level'],
'experiment_hash': row[7]
}
return None
def get_experiments(self):
# query = """
# SELECT
# ep.experiment_hash AS file_id,
# ep.outer_blades_count,
# ep.outer_blades_length,
# ep.outer_blades_angle,
# ep.middle_blades_count,
# lp.primary_air_consumption,
# lp.secondary_air_consumption,
# lp.gas_inlet_consumption,
# rp.n2,
# rp.o2,
# rp.h2o,
# rp.co2
# FROM
# experiment_parameters ep
# JOIN
# load_parameters lp ON ep.load_id = lp.id
# JOIN
# recycling_parameters rp ON ep.recycling_id = rp.id
# """
query = """
SELECT
ep.experiment_hash AS file_id,
ep.outer_blades_count,
ep.outer_blades_length,
ep.outer_blades_angle,
ep.middle_blades_count,
lp.load,
rp.recycling_level
FROM
experiment_parameters ep
JOIN
load_parameters lp ON ep.load_id = lp.id
JOIN
recycling_parameters rp ON ep.recycling_id = rp.id
"""
with self.connection.cursor() as cursor:
cursor.execute(query)
data = cursor.fetchall()
columns = [desc[0] for desc in cursor.description]
df = pd.DataFrame(data, columns=columns)
return df
def save_csv_to_postgres(self, csv_path, file_id):
try:
# Прочитать файл и добавить хэш как новую колонку
df = pd.read_csv(csv_path)
first_col = df.columns[0]
df = df[[first_col] + [col for col in df.columns if "Line Section: Direction [-1,0,0] (m)" not in col]]
# Переименовать колонки
rename_dict = {
"Line Section: Direction [-1,0,0] (m)": "Direction",
"Line Section: Temperature (K)": "Temperature",
"Line Section: Mass Fraction of Nitrogen Oxide Emission": "NOx",
"Line Section: Mass Fraction of CO2": "CO2",
"Line Section: Mass Fraction of CO": "CO"
}
df.rename(columns=rename_dict, inplace=True)
df['file_id'] = file_id
with self.connection.cursor() as cur:
cur.execute("SELECT file_id FROM experiment_data WHERE file_id = %s", (file_id,))
row = cur.fetchone()
if row:
cur.execute("DELETE FROM experiment_data WHERE file_id = %s", (file_id,))
self.connection.commit()
# Вставка новых данных из DataFrame в таблицу
insert_query = '''
INSERT INTO experiment_data (Direction, Temperature, NOx, CO2, CO, file_id)
VALUES (%s, %s, %s, %s, %s, %s)
'''
data_to_insert = df.to_records(index=False).tolist()
cur.executemany(insert_query, data_to_insert)
self.connection.commit()
# Закрытие соединения
cur.close()
return "Success"
except Exception as e:
return f"Failed: {str(e)}"
def get_data(self):
query = """
WITH max_temp AS (
SELECT
file_id,
temperature AS max_temperature,
direction AS direction_for_max_temp,
ROW_NUMBER() OVER (PARTITION BY file_id ORDER BY temperature DESC) AS temp_rank
FROM
experiment_data
),
max_co2 AS (
SELECT
file_id,
co2 AS max_co2,
direction AS direction_for_max_co2,
ROW_NUMBER() OVER (PARTITION BY file_id ORDER BY co2 DESC) AS co2_rank
FROM
experiment_data
),
max_co AS (
SELECT
file_id,
co AS max_co,
direction AS direction_for_max_co,
ROW_NUMBER() OVER (PARTITION BY file_id ORDER BY co DESC) AS co_rank
FROM
experiment_data
),
max_nox AS (
SELECT
file_id,
nox AS max_nox,
direction AS direction_for_max_nox,
ROW_NUMBER() OVER (PARTITION BY file_id ORDER BY nox DESC) AS nox_rank
FROM
experiment_data
)
SELECT
t.file_id,
t.direction_for_max_temp,
t.max_temperature,
cx.direction_for_max_co2,
cx.max_co2,
c.direction_for_max_co,
c.max_co,
n.direction_for_max_nox,
n.max_nox
FROM
(SELECT * FROM max_temp WHERE temp_rank = 1) t
LEFT JOIN
(SELECT * FROM max_nox WHERE nox_rank = 1) n ON t.file_id = n.file_id
LEFT JOIN
(SELECT * FROM max_co2 WHERE co2_rank = 1) cx ON t.file_id = cx.file_id
LEFT JOIN
(SELECT * FROM max_co WHERE co_rank = 1) c ON t.file_id = c.file_id;
"""
with self.connection.cursor() as cursor:
cursor.execute(query)
data = cursor.fetchall()
columns = [desc[0] for desc in cursor.description]
df = pd.DataFrame(data, columns=columns)
return df
def close(self):
self.connection.close()
# Основной скрипт
# def main():
# # Данные
# experiment_parameters = {
# 'outer_blades_count': 24,
# 'outer_blades_length': 74.0,
# 'outer_blades_angle': 65.0,
# 'middle_blades_count': 18,
# 'load': 315.0,
# 'recycling': 8.0,
# }
#
# load_parameters = {
# 'load': 315.0,
# 'primary_air_consumption': 15.2239,
# 'secondary_air_consumption': 63.9876,
# 'gas_inlet_consumption': 0.8648
# }
#
# recycling_parameters = {
# 'load': 315.0,
# 'recycling_level': 8.0,
# 'CO2': 0.04,
# 'N2': 0.70,
# 'H2O': 0.06,
# 'O2': 0.20
# }
#
# # Инициализация базы данных
# db = PostgresClient(
# dbname="your_db_name",
# user="your_db_user",
# password="your_db_password",
# host="your_db_host",
# port="your_db_port"
# )
#
# try:
#
# # Извлечение и печать данных
# retrieved_experiment = db.get_experiment_parameters(experiment_parameters['experiment_hash'])
# print("Retrieved experiment parameters:", retrieved_experiment)
#
# retrieved_load = db.get_load_parameters(load_parameters['load'])
# print("Retrieved load parameters:", retrieved_load)
#
# retrieved_recycling = db.get_recycling_parameters(recycling_parameters['load'],
# recycling_parameters['recycling_level'])
# print("Retrieved recycling parameters:", retrieved_recycling)
# finally:
# db.close()
#
#
# if __name__ == "__main__":
# main()

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runner.py Normal file
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import subprocess
import time
def run_script():
# outer_blades_count = [12.0, 24.0, 36.0, 48.0]
# outer_blades_count = [24.0, 36.0]
outer_blades_count = [24.0]
# outer_blades_length = [44.0, 86.0, 107.5]
outer_blades_length = [70.0]
# outer_blades_angle = [30.0, 45.0, 60.0, 75.0]
outer_blades_angle = [45.0, 60.0]
middle_blades_count = [9.0, 27.0, 36.0]
# middle_blades_count = 18.0
# load = [190, 260, 315, 400, 465]
load = [315, 400, 465]
# load_eco = [260, 315, 400, 465]
# load = [190, 260, 465]
# recycling_eco = [0, 7, 14, 21, 28]
# recycling_full = [0, 5, 8, 10, 15, 20, 25, 30]
recycling_full = [0, 5, 10, 20]
# recycling = [0, 6, 12, 18, 24, 30]
# recycling = [0, 5, 8]
for middle_count in middle_blades_count:
for length in outer_blades_length:
for outer_blade in outer_blades_count:
for angle in outer_blades_angle:
for current_load in load:
for current_recycling in recycling_full:
# Начало замера времени
start_time = time.time()
result = subprocess.run(['python', 'new_experiment_planner.py', str(outer_blade),
str(length), str(angle), str(middle_count),
str(current_load), str(current_recycling)], capture_output=True, text=True)
# Конец замера времени
end_time = time.time()
# Вычисление времени выполнения
execution_time = end_time - start_time
print("Output of the script:")
print(result.stdout)
print("Errors (if any):")
print(result.stderr)
print("Execution time:", execution_time)
if __name__ == "__main__":
run_script()

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runner_db.py Normal file
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from insert_to_db import add_data_to_db
import macros_generator as mg
import yaml
# Загрузка конфигурации из файла config.yaml
with open('config.yaml', 'r') as config_file:
config = yaml.safe_load(config_file)
DIAMETERS = config['parameters']['diameters']
# outer_blades_count = [12, 24, 36, 48]
outer_blades_count = 24.0
# outer_blades_length = [44, 70, 86, 107.5]
outer_blades_length = 70.0
outer_blades_angle = [30.0, 45.0, 60.0, 75.0]
# middle_blades_count = [9, 18, 27, 36]
middle_blades_count = 18.0
# load = [190, 260, 315, 400, 465]
# load_eco = [260, 315, 400, 465]
load = [190, 260, 315, 400, 465]
# recycling_eco = [0, 7, 14, 21, 28]
recycling_full = [0, 5, 8, 10, 15, 20, 25, 30]
# recycling = [0, 6, 12, 18, 24, 30]
# recycling = [0, 5, 8]
for angle in outer_blades_angle:
for current_load in load:
for current_recycling in recycling_full:
experiment_parameters = {
'outer_blades_count': outer_blades_count,
'outer_blades_length': outer_blades_length,
'outer_blades_angle': angle,
'middle_blades_count': middle_blades_count,
'load': current_load,
'recycling': current_recycling,
}
_cur_diameters = DIAMETERS.copy()
fuel_parameters = mg.load_calculation(float(current_load), _cur_diameters)
load_parameters = {
'load': current_load,
'primary_air_consumption': fuel_parameters['primary_air_consumption'],
'secondary_air_consumption': fuel_parameters['secondary_air_consumption'],
'gas_inlet_consumption': fuel_parameters['gas_inlet_consumption']
}
recycling_parameters = mg.recycling_calculation(
fuel_parameters['alpha'], fuel_parameters['gas_consumption'], fuel_parameters['air_consumption'],
float(current_recycling))
recycling_parameters['load'] = float(current_load)
recycling_parameters['recycling_level'] = float(current_recycling)
add_data_to_db(experiment_parameters, load_parameters, recycling_parameters)

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settings.py Normal file
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from pydantic_settings import BaseSettings, SettingsConfigDict
class Settings(BaseSettings):
DB_USER: str
DB_PASSWORD: str
DB_HOST: str
DB_PORT: int
DB_NAME: str
DATABASE: str
POSTGRES_USER: str
POSTGRES_PASSWORD: str
CLICKHOUSE_USER: str
CLICKHOUSE_PASSWORD: str
@property
def db_url_asyncpg(self):
# 'postgresql+asyncpg://username:password@localhost:5432/database_name'
return f'postgresql+asyncpg://{self.DB_USER}:{self.DB_PASSWORD}@{self.DB_HOST}:{self.DB_PORT}/{self.DB_NAME}'
@property
def db_url_asyncpg_docker(self):
# 'postgresql+asyncpg://username:password@localhost:5432/database_name'
return f'postgresql+asyncpg://{self.POSTGRES_USER}:{self.POSTGRES_PASSWORD}@db:{self.DB_PORT}/{self.DATABASE}'
@property
def clickhouse_url(self):
return f'clickhouse://{self.CLICKHOUSE_USER}:{self.CLICKHOUSE_PASSWORD}@clickhouse:8123/{self.DATABASE}'
model_config = SettingsConfigDict(env_file=".env")
settings = Settings()

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test_db.py Normal file
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from clickhouse_tools import ClickHouseClient
from settings import settings
from pathlib import Path
import yaml
import utils
experiment_parameters = {
'outer_blades_count': 24.0,
'outer_blades_length': 70.0,
'outer_blades_angle': 60.0,
'middle_blades_count': 18.0,
'load': 400,
'recycling': 15,
}
with open('config.yaml', 'r') as config_file:
config = yaml.safe_load(config_file)
MAIN_PATH = config['paths']['main']
main_path = Path(MAIN_PATH)
geometry_path = (f"{experiment_parameters['outer_blades_count']}_{experiment_parameters['outer_blades_length']}_"
f"{experiment_parameters['outer_blades_angle']}_{experiment_parameters['middle_blades_count']}")
experiments_path = main_path / geometry_path / 'experiments'
load_path = experiments_path / str(experiment_parameters['load'])
recycling_path = load_path / str(experiment_parameters['recycling'])
table_csv = recycling_path / 'data_table.csv'
file_id = utils.calculate_hash(experiment_parameters)
clickhouse_client = ClickHouseClient("localhost", 8123, 'SuperService', 'UserMyHouse',
'NotWarningWord2')
clickhouse_client.save_csv_to_clickhouse(table_csv, file_id)

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utils.py Normal file
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import hashlib
import json
def calculate_hash(experiment_params):
params_str = json.dumps(experiment_params, sort_keys=True)
return hashlib.sha256(params_str.encode()).hexdigest()