Представь, пожалуйста, что ты программист и тебе необходимо помочь мне решить проблему. У меня есть код, предназначенный для обработки и анализа данных. 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
from sklearn.linear_model import LinearRegression, HuberRegressor
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import optuna
from scipy.optimize import minimize_scalar
import openpyxl
import warnings
warnings.filterwarnings("ignore")  # Скрыть предупреждения для чистоты вывода

# Функция загрузки данных из Excel
def load_data_from_excel(excel_file, columns_to_check):
    workbook = openpyxl.load_workbook(excel_file, data_only=True)
    sheet = workbook.active

    numeric_data = []

    # Вычисляем максимальный индекс столбца
    max_col_index = max([col - 1 for col in columns_to_check])

    for row_num, row in enumerate(sheet.iter_rows(min_row=3), start=3):
        # Проверяем, что в строке достаточно столбцов
        if len(row) <= max_col_index:
            print(f"Пропуск строки {row_num}: недостаточно столбцов (ожидалось минимум {max_col_index + 1}, получено {len(row)})")
            continue

        row_values = []
        is_full_numeric = True

        for col in columns_to_check:
            cell_value = row[col - 1].value
            if cell_value is None:
                # print(f"Пропуск строки {row_num}: пустое значение в столбце {col}")
                is_full_numeric = False
                break
            if isinstance(cell_value, (int, float)):
                row_values.append(cell_value)
            else:
                # print(f"Пропуск строки {row_num}: нечисловое значение в столбце {col}")
                is_full_numeric = False
                break

        if is_full_numeric:
            numeric_data.append(row_values)

    if not numeric_data:
        print("Нет данных для загрузки после обработки.")
    else:
        print(f"Загружено строк: {len(numeric_data)}")

    df = pd.DataFrame(numeric_data)
    # Удаление первой строки (если заголовки остались)
    df = df.drop(index=0).reset_index(drop=True)
    return df

# Остальные функции

def sort_data_by_temperature_and_flow_rate(df, temperature_column, flow_rate_column):
    return df.sort_values(by=[temperature_column, flow_rate_column])

def get_unique_temperatures(df, temperature_column):
    return df[temperature_column].unique()

def get_nominal_temperatures(unique_pt_values):
    nominal_temperatures = {}
    for pt in unique_pt_values:
        nominal_temperature = float(input(f"Введите номинальное значение температуры обратной сетевой воды для давления Pt = {pt} (например, 70): "))
        nominal_temperatures[pt] = nominal_temperature
    return nominal_temperatures

# Обновлённая функция для расчёта дельт с использованием интерполяции
def calculate_deltas_for_all_pressures(df, pressure_column, temperature_column,
                                       power_column, flow_rate_column, nominal_temperatures):
    df = df.copy()
    df['Дельта'] = np.nan  # Инициализируем 'Дельта' значениями NaN

    unique_pt_values = df[pressure_column].unique()

    for pt in unique_pt_values:
        df_pt = df[df[pressure_column] == pt]
        nominal_temperature = nominal_temperatures[pt]

        # Фильтруем и сортируем номинальный набор данных
        nominal_subset = df_pt[df_pt[temperature_column] == nominal_temperature].sort_values(by=flow_rate_column)

        if nominal_subset.empty:
            print(f"Нет данных для номинальной температуры {nominal_temperature} при давлении Pt = {pt}")
            continue

        unique_temperatures = get_unique_temperatures(df_pt, temperature_column)

        for temp in unique_temperatures:
            if temp == nominal_temperature:
                continue

            # Фильтруем и сортируем текущий набор данных
            subset = df_pt[df_pt[temperature_column] == temp].sort_values(by=flow_rate_column)

            if subset.empty:
                print(f"Нет данных для температуры {temp} при давлении Pt = {pt}")
                continue

            # Проверяем, достаточно ли точек для интерполяции
            if len(nominal_subset[flow_rate_column]) < 2:
                print(f"Недостаточно данных для интерполяции в номинальном наборе при Pt = {pt}, Tоб = {nominal_temperature}")
                continue

            # Интерполируем мощности номинального набора на расходах текущего набора
            interpolated_nominal_powers = np.interp(
                subset[flow_rate_column],
                nominal_subset[flow_rate_column],
                nominal_subset[power_column],
                left=np.nan,
                right=np.nan
            )

            # Вычисляем дельты, игнорируя значения с NaN (где интерполяция невозможна)
            delta_values = subset[power_column].values - interpolated_nominal_powers

            # Обновляем DataFrame, присваивая дельты
            df.loc[subset.index, 'Дельта'] = delta_values

    # Удаляем строки с NaN в 'Дельта', если такие есть
    df = df.dropna(subset=['Дельта'])
    return df

def find_temperature_pairs(pressure_temp_data):
    delta_to_temps = {}

    for pressure, data in pressure_temp_data.items():
        nominal = data['nominal']
        temperatures = data['temperatures']
        deltas = [int(round(temp - nominal)) for temp in temperatures]
        data['deltas'] = deltas

        for delta, temp in zip(deltas, temperatures):
            if delta not in delta_to_temps:
                delta_to_temps[delta] = []
            delta_to_temps[delta].append((pressure, temp))

    # Сортируем результаты по дельтам
    sorted_result = {delta: delta_to_temps[delta] for delta in sorted(delta_to_temps)}

    print("Соответствующие пары температур для общих дельт:")
    for delta, temps in sorted_result.items():
        print(f"{delta}: {temps}")

    return sorted_result

def get_temperatures_with_matches(delta_to_temps_pressures, base_pressure):
    pressure_to_matched_temperatures = {}
    for delta, temps in delta_to_temps_pressures.items():
        pressures_present = [pt for pt, temp in temps]
        for pt, temp in temps:
            if pt not in pressure_to_matched_temperatures:
                pressure_to_matched_temperatures[pt] = set()
            pressure_to_matched_temperatures[pt].add(temp)
    return pressure_to_matched_temperatures

def process_delta_groups(delta_to_temps_pressures, df, temperature_column, delta_column):
    for delta, temp_pressure_list in delta_to_temps_pressures.items():
        print(f"\nОбработка дельты {delta}: {temp_pressure_list}")
        # Берем первый элемент как базовый
        base_pressure, base_temperature = temp_pressure_list[0]
        print(f"Базовое давление и температура: Pt = {base_pressure}, Tоб = {base_temperature}")

        # Запрашиваем целевое значение для базового элемента
        target_value_base = float(input(f"Введите целевое значение для температуры {base_temperature} при давлении {base_pressure} (например, 1.0): "))

        # Формируем подмножество данных для базового элемента
        subset_base = df[(df['Pt_rounded'] == base_pressure) & (df[temperature_column] == base_temperature)].copy()

        if subset_base.empty:
            print(f"Нет данных для базового элемента при Pt = {base_pressure}, Tоб = {base_temperature}")
            continue

        # Нормализуем дельты для базового элемента
        first_delta_base = subset_base.iloc[0][delta_column]
        if abs(first_delta_base) < 1e-6:
            print(f"Первое значение 'Дельта' слишком мало для базового элемента при Pt = {base_pressure}, Tоб = {base_temperature}")
            continue
        else:
            k_base = target_value_base / first_delta_base
        df.loc[subset_base.index, 'Модифицированное значение'] = subset_base[delta_column] * k_base

        # Получаем коэффициент регрессии для базового элемента
        X_base = subset_base[delta_column].values.reshape(-1, 1)
        y_base = df.loc[subset_base.index, 'Модифицированное значение'].values

        if len(X_base) > 1:
            model_base = LinearRegression()
            model_base.fit(X_base, y_base)
            coef_base = model_base.coef_[0]
            print(f"Коэффициент регрессии для базового элемента: {coef_base}")
        else:
            print(f"Недостаточно данных для построения модели для базового элемента при Pt = {base_pressure}, Tоб = {base_temperature}")
            continue

        # Обрабатываем остальные элементы
        for pressure, temperature in temp_pressure_list[1:]:
            print(f"Обработка элемента: Pt = {pressure}, Tоб = {temperature}")
            subset = df[(df['Pt_rounded'] == pressure) & (df[temperature_column] == temperature)].copy()

            if subset.empty:
                print(f"Нет данных для элемента при Pt = {pressure}, Tоб = {temperature}")
                continue

            first_delta = subset.iloc[0][delta_column]
            if abs(first_delta) < 1e-6:
                print(f"Первое значение 'Дельта' слишком мало для Pt = {pressure}, Tоб = {temperature}")
                continue

            # Определяем целевую функцию для оптимизации
            def objective_func(target_value):
                k = target_value / first_delta
                subset['Модифицированное значение'] = subset[delta_column] * k
                X = subset[delta_column].values.reshape(-1, 1)
                y = subset['Модифицированное значение'].values

                if len(X) < 2:
                    return float('inf')

                model = LinearRegression()
                model.fit(X, y)
                coef = model.coef_[0]
                return abs(coef - coef_base)

            # Выполняем оптимизацию
            res = minimize_scalar(objective_func, bounds=(0.01, 10), method='bounded')
            optimal_target_value = res.x
            k = optimal_target_value / first_delta

            # Обновляем 'Модифицированное значение' в основном датафрейме
            df.loc[subset.index, 'Модифицированное значение'] = df.loc[subset.index, delta_column] * k

            print(f"Оптимальное целевое значение для Pt = {pressure}, Tоб = {temperature}: {optimal_target_value}, коэффициент k: {k}")
            print(f"Коэффициент регрессии для Pt = {pressure}, Tоб = {temperature} после оптимизации совпадает с базовым.")

    return df

def save_data(df, output_file):
    df.to_csv(output_file, index=False)

def objective(trial, Gt_train, Gt_test, mod_train, mod_test):
    degree = trial.suggest_int('degree', 1, 5)

    poly = PolynomialFeatures(degree=degree)
    Gt_train_poly = poly.fit_transform(Gt_train)
    Gt_test_poly = poly.transform(Gt_test)

    model = LinearRegression()
    model.fit(Gt_train_poly, mod_train)

    mod_pred = model.predict(Gt_test_poly)

    r2 = r2_score(mod_test, mod_pred)

    return -r2

def remove_outliers_iqr(df, column_name):
    Q1 = df[column_name].quantile(0.25)
    Q3 = df[column_name].quantile(0.75)
    IQR = Q3 - Q1

    if IQR == 0:
        print(f"IQR равен нулю для столбца {column_name}. Не буду удалять выбросы.")
        return df
    else:
        lower_bound = Q1 - 1.5 * IQR
        upper_bound = Q3 + 1.5 * IQR
        return df[(df[column_name] >= lower_bound) & (df[column_name] <= upper_bound)]

# Функция для удаления выбросов с настраиваемыми параметрами
def remove_outliers_iqr_specific(df, column_name, lower_factor=1.5, upper_factor=1.5):
    Q1 = df[column_name].quantile(0.25)
    Q3 = df[column_name].quantile(0.75)
    IQR = Q3 - Q1

    lower_bound = Q1 - lower_factor * IQR
    upper_bound = Q3 + upper_factor * IQR

    df_clean = df[(df[column_name] >= lower_bound) & (df[column_name] <= upper_bound)]
    return df_clean

# Функция main_plot с учётом всех изменений
def main_plot(data_list, pt_values, pressure_to_matched_temperatures, delta_to_temps_pressures):
    fig, (ax_gt, ax_delta) = plt.subplots(1, 2, figsize=(20, 10))

    # Списки для хранения уравнений
    poly_equations = []
    delta_equations = []

    for idx, (data, pt) in enumerate(zip(data_list, pt_values)):
        print(f"\nОбработка давления Pt = {pt}")

        if pt == pt_values[0]:
            matched_temperatures = data['Tоб'].unique()
        else:
            matched_temperatures = pressure_to_matched_temperatures.get(pt, [])

        # Убеждаемся, что matched_temperatures является списком или множеством
        if isinstance(matched_temperatures, set):
            matched_temperatures = list(matched_temperatures)

        # Проверяем, если matched_temperatures пуст, пропускаем итерацию
        if len(matched_temperatures) == 0:
            print(f"Нет соответствующих температур для Pt={pt}")
            continue

        # Приведение типов и округление (если необходимо)
        data['Tоб'] = data['Tоб'].astype(float).round(2)
        matched_temperatures = np.array(matched_temperatures).astype(float).round(2)

        # Фильтруем данные, оставляя только необходимые температуры
        data_matched = data[data['Tоб'].isin(matched_temperatures)]

        if data_matched.empty:
            print(f"Нет данных с соответствующими температурами для Pt={pt}")
            continue

        # Дополнительная очистка от выбросов для давления Pt=0.4
        if pt == 0.4:
            print("Выполняется дополнительная очистка от выбросов для Pt=0.4")
            data_matched = remove_outliers_iqr_specific(data_matched, 'Модифицированное значение')
            print(f"Количество данных для Pt=0.4 после очистки: {data_matched.shape[0]}")

            if data_matched.shape[0] < 2:
                print("Недостаточно данных для построения модели после очистки для Pt=0.4")
                continue

        print(f"Количество строк в data_matched для Pt={pt}: {data_matched.shape[0]}")

        # График зависимости модифицированного значения от Gт
        Gt = data_matched['Gт'].values.reshape(-1, 1)
        mod_value = data_matched['Модифицированное значение'].values

        # Нормировка данных
        scaler = StandardScaler()
        Gt_scaled = scaler.fit_transform(Gt)

        Gt_train, Gt_test, mod_train, mod_test = train_test_split(Gt_scaled, mod_value, test_size=0.2, random_state=42)

        study = optuna.create_study(direction='minimize')
        study.optimize(lambda trial: objective(trial, Gt_train, Gt_test, mod_train, mod_test), n_trials=50)

        best_degree = study.best_params['degree']
        best_r2 = -study.best_value

        poly = PolynomialFeatures(degree=best_degree)
        Gt_poly = poly.fit_transform(Gt_scaled)

        model = LinearRegression()
        model.fit(Gt_poly, mod_value)

        mod_value_pred = model.predict(Gt_poly)

        # Преобразуем обратно для построения графика
        Gt_original = scaler.inverse_transform(Gt_scaled)
        order = np.argsort(Gt_original.flatten())

        ax_gt.scatter(data_matched['Gт'], data_matched['Модифицированное значение'], label=f'Данные Pt={pt}', color=plt.cm.tab10(idx % 10), alpha=0.6)
        ax_gt.plot(Gt_original[order].flatten(), mod_value_pred[order], '-', label=f'Полином степени {best_degree}, R²: {best_r2:.2f}, Pt={pt}', color=plt.cm.tab10(idx % 10))

        # Сохранение уравнения полинома
        coef = model.coef_
        intercept = model.intercept_
        equation = f"Pt={pt}: y = {intercept:.4f} "
        for i, c in enumerate(coef[1:], start=1):
            equation += f"+ ({c:.15f}) * x^{i} "
        poly_equations.append(equation)

    # Вывод всех уравнений после цикла
    print("\nУравнения полиномов для графика 'Модифицированное значение' от 'Gт':")
    for eq in poly_equations:
        print(eq)

    # Группировка данных по дельтам для графика 'Зависимость f(dNe, ΔTоб)'
    # Собираем все данные в один DataFrame
    combined_data = pd.concat(data_list, ignore_index=True)

    # Перебираем группы по дельтам
    for delta_value, temp_pressure_list in delta_to_temps_pressures.items():
        df_group = pd.DataFrame()

        for pt, tob in temp_pressure_list:
            df_temp = combined_data[(combined_data['Pt_rounded'] == pt) & (combined_data['Tоб'] == tob)]
            df_group = pd.concat([df_group, df_temp], ignore_index=True)

        if df_group.empty:
            print(f"Нет данных для дельты {delta_value}")
            continue

        df_group = df_group.sort_values(by='Gт')

        X = df_group['Дельта'].values.reshape(-1, 1)
        y = df_group['Модифицированное значение'].values

        if len(X) > 1:
            model = LinearRegression()
            model.fit(X, y)

            slope = model.coef_[0]
            intercept = model.intercept_

            ax_delta.plot(df_group['Дельта'], df_group['Модифицированное значение'], '.', label=f'ΔTоб: {delta_value}°C')

            # Сохранение уравнения для группы дельт
            delta_equations.append(f"Уравнение для ΔTоб {delta_value}°C: y = {slope:.4f} * x + {intercept:.4f}")
        else:
            print(f"Недостаточно данных для построения модели для ΔTоб {delta_value}°C")

    # Вывод всех уравнений после цикла
    print("\nУравнения для ΔTоб:")
    for eq in delta_equations:
        print(eq)

    # Настройка легенд и оформления графиков
    ax_gt.set_title('Зависимость f(Pот, Gтурб)')
    ax_gt.set_xlabel('Gт')
    ax_gt.set_ylabel('Модифицированное значение')
    # Перемещение легенды под график
    ax_gt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2)
    ax_gt.grid(True)

    ax_delta.set_title('Зависимость f(dNe, ΔTоб)')
    ax_delta.set_xlabel('Дельта')
    ax_delta.set_ylabel('Модифицированное значение')
    ax_delta.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2)
    ax_delta.grid(True)

    plt.tight_layout()
    plt.show()

# Главный блок программы
if __name__ == "__main__":
    excel_file = '/content/cleaned_file.xlsx'

    # Определяем индексы столбцов для загрузки
    columns_to_check = list(range(1, 27))  # Столбцы со 2 по 27 включительно

    # Загружаем данные
    df = load_data_from_excel(excel_file, columns_to_check)

    # Присваиваем названия столбцов
    column_names = [
        'Gт', 'Qт', 'Ne', 'qe', 'Gcн', 'Iпв', 'Tохл', 'Qот', 'w',
        'Tоб', 'Tпр', 'Pт', 'Pдиаф', 'Pсн', 'Pк', 'Gк', 'G:Д03', 'G:С29', 'T:Д03', 'P:Д03',
        'P:Ч13', 'T:Ч13', 'P:Ч17', 'P:Ч20', 'P:Ч29', 'T:Р27'
    ]

    # Проверяем соответствие количества столбцов
    if df.shape[1] != len(column_names):
        raise ValueError(f"Количество столбцов в DataFrame ({df.shape[1]}) не соответствует количеству названий столбцов ({len(column_names)}).")

    df.columns = column_names

    # Выводим информацию о загруженных данных
    print(f"Размеры DataFrame после загрузки: {df.shape}")
    print("Первые 5 строк DataFrame:")
    print(df.head())

    # Продолжаем с остальным кодом
    temperature_column = 'Tоб'
    flow_rate_column = 'Gт'
    power_column = 'Ne'
    pressure_column = 'Pт'

    # Проверяем наличие необходимых столбцов
    required_columns = [temperature_column, flow_rate_column, power_column, pressure_column]
    for col in required_columns:
        if col not in df.columns:
            raise KeyError(f"Столбец '{col}' не найден в данных.")

    # Преобразуем столбцы с числовыми данными в числовой тип и удаляем неверные строки
    numeric_columns = required_columns  # Добавьте сюда другие числовые столбцы, если нужно
    for col in numeric_columns:
        df[col] = pd.to_numeric(df[col], errors='coerce')

    # Удаляем строки с NaN в важных столбцах
    df = df.dropna(subset=numeric_columns)

    # Удаление строк с давлением равным нулю или отсутствующим давлением
    df = df[df[pressure_column] != 0]
    df = df.dropna(subset=[pressure_column])

    # Удаление строк, где все значения равны нулю
    df = df[(df != 0).any(axis=1)]

    # Округление значений давления до одного знака после запятой
    df['Pt_rounded'] = df[pressure_column].round(1)

    # Получение уникальных значений давления
    unique_pt_values = df['Pt_rounded'].unique()
    print(f"Уникальные значения давления: {unique_pt_values}")

    # Запрашиваем номинальные температуры для всех давлений
    nominal_temperatures = get_nominal_temperatures(unique_pt_values)

    # Сохраняем информацию о температурах и номинальных температурах
    pressure_temp_data = {}
    for pt in unique_pt_values:
        df_pt = df[df['Pt_rounded'] == pt]
        unique_temperatures = get_unique_temperatures(df_pt, temperature_column)
        pressure_temp_data[pt] = {
            'nominal': nominal_temperatures[pt],
            'temperatures': sorted(unique_temperatures.tolist())
        }

    # Расчет дельт для всех давлений
    df = calculate_deltas_for_all_pressures(
        df,
        'Pt_rounded',
        temperature_column,
        power_column,
        flow_rate_column,
        nominal_temperatures
    )

    # Создаем столбец для модифицированного значения
    df['Модифицированное значение'] = 0.0

    # Получаем пары температур для общих дельт
    delta_to_temps_pressures = find_temperature_pairs(pressure_temp_data)

    # Обработка каждой группы дельт
    df = process_delta_groups(delta_to_temps_pressures, df, temperature_column, 'Дельта')

    # Сохранение списка давлений и базового давления перед удалением номинальных температур
    pt_values = sorted(df['Pt_rounded'].unique())
    base_pressure = pt_values[0]

    # Удаление строк с номинальными температурами
    for pt in unique_pt_values:
        nominal_temp = nominal_temperatures[pt]
        df = df[~((df['Pt_rounded'] == pt) & (df[temperature_column] == nominal_temp))]

    # Удаление выбросов по столбцу 'Модифицированное значение'
    df = remove_outliers_iqr(df, 'Модифицированное значение')

    # Проверка количества данных для Pt=1.5
    print(f"Количество строк для Pt=1.5 до удаления выбросов: {df[df['Pt_rounded'] == 1.5].shape[0]}")
    print(f"Количество строк для Pt=1.5 после удаления выбросов: {df[df['Pt_rounded'] == 1.5].shape[0]}")

    # Сохранение результата
    output_file = 'final_output_with_delta.csv'
    save_data(df, output_file)
    print(f"Обработка завершена. Результаты сохранены в файл {output_file}.")

    # Подготовка data_list
    data_list = []
    for pt in pt_values:
        data_pt = df[df['Pt_rounded'] == pt].copy()
        data_list.append(data_pt)

    # Получаем pressure_to_matched_temperatures
    pressure_to_matched_temperatures = get_temperatures_with_matches(delta_to_temps_pressures, base_pressure)

    # Вызов функции main_plot
    main_plot(data_list, pt_values, pressure_to_matched_temperatures, delta_to_temps_pressures)
Он выводит:
Загружено строк: 4969
Размеры DataFrame после загрузки: (4968, 26)
Первые 5 строк DataFrame:
       Gт      Qт      Ne       qe  Gcн     Iпв  Tохл     Qот        w  Tоб  \
0  605.98  411.65  164.05  1071.54    0  247.06    20  227.86  3838.69   39   
1  608.01  412.81  164.56  1070.81    0  247.27    20  228.59  3849.45   39   
2  609.99  413.95  165.09  1070.04    0  247.48    20  229.27  3862.24   39   
3  612.01  415.10  165.60  1069.31    0  247.70    20  229.98  3873.66   39   
4  613.98  416.22  166.13  1068.58    0  247.90    20  230.66  3885.91   39   

   ...  G:Д03  G:С29  T:Д03  P:Д03  P:Ч13  T:Ч13  P:Ч17  P:Ч20  P:Ч29  T:Р27  
0  ...  511.4  2.133  257.4  26.54  23.37  537.5  11.48  6.081  0.088  166.8  
1  ...  513.1  2.140  257.8  26.62  23.44  537.5  11.52  6.101  0.088  166.5  
2  ...  514.8  2.146  258.1  26.71  23.52  537.5  11.56  6.120  0.088  166.2  
3  ...  516.4  2.152  258.5  26.80  23.60  537.5  11.60  6.140  0.088  165.9  
4  ...  518.1  2.158  258.8  26.89  23.68  537.5  11.64  6.159  0.088  165.6  

[5 rows x 26 columns]
Уникальные значения давления: [1.  0.8 0.6 1.5 1.2]
Введите номинальное значение температуры обратной сетевой воды для давления Pt = 1.0 (например, 70): 44
Введите номинальное значение температуры обратной сетевой воды для давления Pt = 0.8 (например, 70): 39
Введите номинальное значение температуры обратной сетевой воды для давления Pt = 0.6 (например, 70): 37
Введите номинальное значение температуры обратной сетевой воды для давления Pt = 1.5 (например, 70): 70
Введите номинальное значение температуры обратной сетевой воды для давления Pt = 1.2 (например, 70): 53
Соответствующие пары температур для общих дельт:
-20: [(1.5, 50)]
-15: [(1.5, 55), (1.2, 38)]
-10: [(1.5, 60), (1.2, 43)]
-5: [(1.0, 39), (1.5, 65), (1.2, 48)]
0: [(1.0, 44), (0.8, 39), (0.6, 37), (1.5, 70), (1.2, 53)]
5: [(1.0, 49), (0.8, 44), (0.6, 42), (1.2, 58)]
10: [(1.0, 54), (0.8, 49), (0.6, 47), (1.2, 63)]
15: [(1.0, 59), (0.8, 54), (0.6, 52), (1.2, 68)]
20: [(1.0, 64), (0.8, 59), (0.6, 57)]

Обработка дельты -20: [(1.5, 50)]
Базовое давление и температура: Pt = 1.5, Tоб = 50
Введите целевое значение для температуры 50 при давлении 1.5 (например, 1.0): 0.05
Коэффициент регрессии для базового элемента: 0.12685122854257086

Обработка дельты -15: [(1.5, 55), (1.2, 38)]
Базовое давление и температура: Pt = 1.5, Tоб = 55
Введите целевое значение для температуры 55 при давлении 1.5 (например, 1.0): 0.05
Коэффициент регрессии для базового элемента: 0.15543640769191316
Обработка элемента: Pt = 1.2, Tоб = 38
Оптимальное целевое значение для Pt = 1.2, Tоб = 38: 0.04227508213413696, коэффициент k: 0.15542864393417838
Коэффициент регрессии для Pt = 1.2, Tоб = 38 после оптимизации совпадает с базовым.

Обработка дельты -10: [(1.5, 60), (1.2, 43)]
Базовое давление и температура: Pt = 1.5, Tоб = 60
Введите целевое значение для температуры 60 при давлении 1.5 (например, 1.0): 0.05
Коэффициент регрессии для базового элемента: 0.22983222247760346
Обработка элемента: Pt = 1.2, Tоб = 43
Оптимальное целевое значение для Pt = 1.2, Tоб = 43: 0.04979459077068766, коэффициент k: 0.22983835309794517
Коэффициент регрессии для Pt = 1.2, Tоб = 43 после оптимизации совпадает с базовым.

Обработка дельты -5: [(1.0, 39), (1.5, 65), (1.2, 48)]
Базовое давление и температура: Pt = 1.0, Tоб = 39
Введите целевое значение для температуры 39 при давлении 1.0 (например, 1.0): 0.05
Коэффициент регрессии для базового элемента: 0.40513833992094406
Обработка элемента: Pt = 1.5, Tоб = 65
Оптимальное целевое значение для Pt = 1.5, Tоб = 65: 0.05258663483732436, коэффициент k: 0.4051358616127034
Коэффициент регрессии для Pt = 1.5, Tоб = 65 после оптимизации совпадает с базовым.
Обработка элемента: Pt = 1.2, Tоб = 48
Оптимальное целевое значение для Pt = 1.2, Tоб = 48: 0.04240096749701759, коэффициент k: 0.40512983786575996
Коэффициент регрессии для Pt = 1.2, Tоб = 48 после оптимизации совпадает с базовым.

Обработка дельты 0: [(1.0, 44), (0.8, 39), (0.6, 37), (1.5, 70), (1.2, 53)]
Базовое давление и температура: Pt = 1.0, Tоб = 44
Введите целевое значение для температуры 44 при давлении 1.0 (например, 1.0): 0.05
Нет данных для базового элемента при Pt = 1.0, Tоб = 44

Обработка дельты 5: [(1.0, 49), (0.8, 44), (0.6, 42), (1.2, 58)]
Базовое давление и температура: Pt = 1.0, Tоб = 49
Введите целевое значение для температуры 49 при давлении 1.0 (например, 1.0): 0.05
Коэффициент регрессии для базового элемента: -0.39849325931802193
Обработка элемента: Pt = 0.8, Tоб = 44
Оптимальное целевое значение для Pt = 0.8, Tоб = 44: 0.07287074683290058, коэффициент k: -0.39849678384867826
Коэффициент регрессии для Pt = 0.8, Tоб = 44 после оптимизации совпадает с базовым.
Обработка элемента: Pt = 0.6, Tоб = 42
Оптимальное целевое значение для Pt = 0.6, Tоб = 42: 0.08498445524845018, коэффициент k: -0.398489787804916
Коэффициент регрессии для Pt = 0.6, Tоб = 42 после оптимизации совпадает с базовым.
Обработка элемента: Pt = 1.2, Tоб = 58
Оптимальное целевое значение для Pt = 1.2, Tоб = 58: 0.049947220412121046, коэффициент k: -0.39849447733917964
Коэффициент регрессии для Pt = 1.2, Tоб = 58 после оптимизации совпадает с базовым.

Обработка дельты 10: [(1.0, 54), (0.8, 49), (0.6, 47), (1.2, 63)]
Базовое давление и температура: Pt = 1.0, Tоб = 54
Введите целевое значение для температуры 54 при давлении 1.0 (например, 1.0): 0.05
Коэффициент регрессии для базового элемента: -0.16394806924101238
Обработка элемента: Pt = 0.8, Tоб = 49
Оптимальное целевое значение для Pt = 0.8, Tоб = 49: 0.06483058439416159, коэффициент k: -0.16394672704637173
Коэффициент регрессии для Pt = 0.8, Tоб = 49 после оптимизации совпадает с базовым.
Обработка элемента: Pt = 0.6, Tоб = 47
Оптимальное целевое значение для Pt = 0.6, Tоб = 47: 0.07213069193245022, коэффициент k: -0.1639518371819009
Коэффициент регрессии для Pt = 0.6, Tоб = 47 после оптимизации совпадает с базовым.
Обработка элемента: Pt = 1.2, Tоб = 63
Оптимальное целевое значение для Pt = 1.2, Tоб = 63: 0.04273819385817454, коэффициент k: -0.16394912355277214
Коэффициент регрессии для Pt = 1.2, Tоб = 63 после оптимизации совпадает с базовым.

Обработка дельты 15: [(1.0, 59), (0.8, 54), (0.6, 52), (1.2, 68)]
Базовое давление и температура: Pt = 1.0, Tоб = 59
Введите целевое значение для температуры 59 при давлении 1.0 (например, 1.0): 0.05
Коэффициент регрессии для базового элемента: -0.11111111111111395
Обработка элемента: Pt = 0.8, Tоб = 54
Оптимальное целевое значение для Pt = 0.8, Tоб = 54: 0.0736498490905543, коэффициент k: -0.11110852371039459
Коэффициент регрессии для Pt = 0.8, Tоб = 54 после оптимизации совпадает с базовым.
Обработка элемента: Pt = 0.6, Tоб = 52
Оптимальное целевое значение для Pt = 0.6, Tоб = 52: 0.08185827326029121, коэффициент k: -0.11110987231943634
Коэффициент регрессии для Pt = 0.6, Tоб = 52 после оптимизации совпадает с базовым.
Обработка элемента: Pt = 1.2, Tоб = 68
Оптимальное целевое значение для Pt = 1.2, Tоб = 68: 0.04733517221157309, коэффициент k: -0.11111036321313089
Коэффициент регрессии для Pt = 1.2, Tоб = 68 после оптимизации совпадает с базовым.

Обработка дельты 20: [(1.0, 64), (0.8, 59), (0.6, 57)]
Базовое давление и температура: Pt = 1.0, Tоб = 64
Введите целевое значение для температуры 64 при давлении 1.0 (например, 1.0): 0.05
Коэффициент регрессии для базового элемента: -0.07660060975609753
Обработка элемента: Pt = 0.8, Tоб = 59
Оптимальное целевое значение для Pt = 0.8, Tоб = 59: 0.07163281016584495, коэффициент k: -0.07660070023963846
Коэффициент регрессии для Pt = 0.8, Tоб = 59 после оптимизации совпадает с базовым.
Обработка элемента: Pt = 0.6, Tоб = 57
Оптимальное целевое значение для Pt = 0.6, Tоб = 57: 0.0804380711845084, коэффициент k: -0.07660046379063758
Коэффициент регрессии для Pt = 0.6, Tоб = 57 после оптимизации совпадает с базовым.
Количество строк для Pt=1.5 до удаления выбросов: 269
Количество строк для Pt=1.5 после удаления выбросов: 269
[I 2024-12-05 08:41:43,734] A new study created in memory with name: no-name-d2873686-ab6d-45c1-b8d1-cdbf77cb922a
[I 2024-12-05 08:41:43,747] Trial 0 finished with value: -0.8899668043088795 and parameters: {'degree': 3}. Best is trial 0 with value: -0.8899668043088795.
[I 2024-12-05 08:41:43,753] Trial 1 finished with value: -0.7478124266428201 and parameters: {'degree': 1}. Best is trial 0 with value: -0.8899668043088795.
[I 2024-12-05 08:41:43,760] Trial 2 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 2 with value: -0.906696416547354.
[I 2024-12-05 08:41:43,769] Trial 3 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,777] Trial 4 finished with value: -0.8899668043088795 and parameters: {'degree': 3}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,785] Trial 5 finished with value: -0.8899668043088795 and parameters: {'degree': 3}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,792] Trial 6 finished with value: -0.7478124266428201 and parameters: {'degree': 1}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,798] Trial 7 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,806] Trial 8 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,815] Trial 9 finished with value: -0.8899668043088795 and parameters: {'degree': 3}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,835] Trial 10 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,856] Trial 11 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,872] Trial 12 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
Обработка завершена. Результаты сохранены в файл final_output_with_delta.csv.

Обработка давления Pt = 0.6
Количество строк в data_matched для Pt=0.6: 837
[I 2024-12-05 08:41:43,890] Trial 13 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,909] Trial 14 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,926] Trial 15 finished with value: -0.8877750469054011 and parameters: {'degree': 2}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,943] Trial 16 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,960] Trial 17 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,977] Trial 18 finished with value: -0.8877750469054011 and parameters: {'degree': 2}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:43,995] Trial 19 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,013] Trial 20 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,030] Trial 21 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,048] Trial 22 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,073] Trial 23 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,097] Trial 24 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,121] Trial 25 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,143] Trial 26 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,166] Trial 27 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,188] Trial 28 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,206] Trial 29 finished with value: -0.8877750469054011 and parameters: {'degree': 2}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,230] Trial 30 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,252] Trial 31 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,275] Trial 32 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,290] Trial 33 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,309] Trial 34 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,332] Trial 35 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,352] Trial 36 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,371] Trial 37 finished with value: -0.7478124266428201 and parameters: {'degree': 1}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,392] Trial 38 finished with value: -0.8899668043088795 and parameters: {'degree': 3}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,412] Trial 39 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,431] Trial 40 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,449] Trial 41 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,471] Trial 42 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,493] Trial 43 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,512] Trial 44 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,531] Trial 45 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,554] Trial 46 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,575] Trial 47 finished with value: -0.906696416547354 and parameters: {'degree': 4}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,593] Trial 48 finished with value: -0.9067227524432894 and parameters: {'degree': 5}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,611] Trial 49 finished with value: -0.8899668043088795 and parameters: {'degree': 3}. Best is trial 3 with value: -0.9067227524432894.
[I 2024-12-05 08:41:44,628] A new study created in memory with name: no-name-3179ab89-3f5c-479e-8e3a-5dc62016ce62
[I 2024-12-05 08:41:44,636] Trial 0 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9565894568319617.
[I 2024-12-05 08:41:44,649] Trial 1 finished with value: -0.955830200200557 and parameters: {'degree': 2}. Best is trial 0 with value: -0.9565894568319617.
[I 2024-12-05 08:41:44,657] Trial 2 finished with value: -0.955830200200557 and parameters: {'degree': 2}. Best is trial 0 with value: -0.9565894568319617.
[I 2024-12-05 08:41:44,668] Trial 3 finished with value: -0.9513563361170578 and parameters: {'degree': 1}. Best is trial 0 with value: -0.9565894568319617.
[I 2024-12-05 08:41:44,677] Trial 4 finished with value: -0.955830200200557 and parameters: {'degree': 2}. Best is trial 0 with value: -0.9565894568319617.
[I 2024-12-05 08:41:44,684] Trial 5 finished with value: -0.9513563361170578 and parameters: {'degree': 1}. Best is trial 0 with value: -0.9565894568319617.
[I 2024-12-05 08:41:44,692] Trial 6 finished with value: -0.9566362828913243 and parameters: {'degree': 3}. Best is trial 6 with value: -0.9566362828913243.
[I 2024-12-05 08:41:44,700] Trial 7 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,707] Trial 8 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,715] Trial 9 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,732] Trial 10 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,753] Trial 11 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,778] Trial 12 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,798] Trial 13 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,817] Trial 14 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.

Обработка давления Pt = 0.8
Количество строк в data_matched для Pt=0.8: 771
[I 2024-12-05 08:41:44,843] Trial 15 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,862] Trial 16 finished with value: -0.9566362828913243 and parameters: {'degree': 3}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,879] Trial 17 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,898] Trial 18 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,919] Trial 19 finished with value: -0.9566362828913243 and parameters: {'degree': 3}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,945] Trial 20 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,970] Trial 21 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:44,990] Trial 22 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,010] Trial 23 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,034] Trial 24 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,054] Trial 25 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,076] Trial 26 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,098] Trial 27 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,119] Trial 28 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,142] Trial 29 finished with value: -0.9566362828913243 and parameters: {'degree': 3}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,165] Trial 30 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,187] Trial 31 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,210] Trial 32 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,231] Trial 33 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,253] Trial 34 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,275] Trial 35 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,297] Trial 36 finished with value: -0.955830200200557 and parameters: {'degree': 2}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,320] Trial 37 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,342] Trial 38 finished with value: -0.9513563361170578 and parameters: {'degree': 1}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,366] Trial 39 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,391] Trial 40 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,418] Trial 41 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,445] Trial 42 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,469] Trial 43 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,495] Trial 44 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,521] Trial 45 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,543] Trial 46 finished with value: -0.955830200200557 and parameters: {'degree': 2}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,571] Trial 47 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,594] Trial 48 finished with value: -0.9565894568319617 and parameters: {'degree': 4}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,619] Trial 49 finished with value: -0.9570844400210016 and parameters: {'degree': 5}. Best is trial 7 with value: -0.9570844400210016.
[I 2024-12-05 08:41:45,639] A new study created in memory with name: no-name-14336823-d646-42dc-99e4-42ce4a281529
[I 2024-12-05 08:41:45,647] Trial 0 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,655] Trial 1 finished with value: -0.9615022567709656 and parameters: {'degree': 3}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,663] Trial 2 finished with value: -0.9601303039378464 and parameters: {'degree': 2}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,675] Trial 3 finished with value: -0.9601303039378464 and parameters: {'degree': 2}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,683] Trial 4 finished with value: -0.9615022567709656 and parameters: {'degree': 3}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,693] Trial 5 finished with value: -0.9598860894233034 and parameters: {'degree': 1}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,701] Trial 6 finished with value: -0.9601303039378464 and parameters: {'degree': 2}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,711] Trial 7 finished with value: -0.9601303039378464 and parameters: {'degree': 2}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,720] Trial 8 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,731] Trial 9 finished with value: -0.9601303039378464 and parameters: {'degree': 2}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,752] Trial 10 finished with value: -0.961801876419622 and parameters: {'degree': 5}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,773] Trial 11 finished with value: -0.961801876419622 and parameters: {'degree': 5}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,799] Trial 12 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,824] Trial 13 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.

Обработка давления Pt = 1.0
Количество строк в data_matched для Pt=1.0: 961
[I 2024-12-05 08:41:45,856] Trial 14 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,880] Trial 15 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,906] Trial 16 finished with value: -0.961801876419622 and parameters: {'degree': 5}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,927] Trial 17 finished with value: -0.9615022567709656 and parameters: {'degree': 3}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,952] Trial 18 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:45,976] Trial 19 finished with value: -0.9615022567709656 and parameters: {'degree': 3}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,004] Trial 20 finished with value: -0.961801876419622 and parameters: {'degree': 5}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,025] Trial 21 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,046] Trial 22 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,071] Trial 23 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,097] Trial 24 finished with value: -0.9615022567709656 and parameters: {'degree': 3}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,123] Trial 25 finished with value: -0.961801876419622 and parameters: {'degree': 5}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,150] Trial 26 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,178] Trial 27 finished with value: -0.9615022567709656 and parameters: {'degree': 3}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,204] Trial 28 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,227] Trial 29 finished with value: -0.961801876419622 and parameters: {'degree': 5}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,252] Trial 30 finished with value: -0.9615022567709656 and parameters: {'degree': 3}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,276] Trial 31 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,299] Trial 32 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,323] Trial 33 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,350] Trial 34 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,377] Trial 35 finished with value: -0.9598860894233034 and parameters: {'degree': 1}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,402] Trial 36 finished with value: -0.961801876419622 and parameters: {'degree': 5}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,430] Trial 37 finished with value: -0.9615022567709656 and parameters: {'degree': 3}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,448] Trial 38 finished with value: -0.9615022567709656 and parameters: {'degree': 3}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,466] Trial 39 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,487] Trial 40 finished with value: -0.961801876419622 and parameters: {'degree': 5}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,509] Trial 41 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,540] Trial 42 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,566] Trial 43 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,587] Trial 44 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,610] Trial 45 finished with value: -0.961801876419622 and parameters: {'degree': 5}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,634] Trial 46 finished with value: -0.9615022567709656 and parameters: {'degree': 3}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,658] Trial 47 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,681] Trial 48 finished with value: -0.9618475090282309 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,698] Trial 49 finished with value: -0.9601303039378464 and parameters: {'degree': 2}. Best is trial 0 with value: -0.9618475090282309.
[I 2024-12-05 08:41:46,720] A new study created in memory with name: no-name-3a0b6111-5ee4-4ee2-a20e-b617eedaa607
[I 2024-12-05 08:41:46,729] Trial 0 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 0 with value: -0.9461803279178675.
[I 2024-12-05 08:41:46,735] Trial 1 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,745] Trial 2 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,754] Trial 3 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,761] Trial 4 finished with value: -0.9461914412588097 and parameters: {'degree': 2}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,771] Trial 5 finished with value: -0.9461914412588097 and parameters: {'degree': 2}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,778] Trial 6 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,786] Trial 7 finished with value: -0.9350090102587694 and parameters: {'degree': 1}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,795] Trial 8 finished with value: -0.9461772271511589 and parameters: {'degree': 3}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,803] Trial 9 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,820] Trial 10 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,842] Trial 11 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,866] Trial 12 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,888] Trial 13 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,913] Trial 14 finished with value: -0.9461772271511589 and parameters: {'degree': 3}. Best is trial 1 with value: -0.9463512800236288.

Обработка давления Pt = 1.2
Количество строк в data_matched для Pt=1.2: 1160
[I 2024-12-05 08:41:46,935] Trial 15 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,960] Trial 16 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:46,986] Trial 17 finished with value: -0.9461772271511589 and parameters: {'degree': 3}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,013] Trial 18 finished with value: -0.9461914412588097 and parameters: {'degree': 2}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,033] Trial 19 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,055] Trial 20 finished with value: -0.9350090102587694 and parameters: {'degree': 1}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,082] Trial 21 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,108] Trial 22 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,129] Trial 23 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,150] Trial 24 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,176] Trial 25 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,204] Trial 26 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,226] Trial 27 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,250] Trial 28 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,269] Trial 29 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,289] Trial 30 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,315] Trial 31 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,337] Trial 32 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,364] Trial 33 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,390] Trial 34 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,409] Trial 35 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,426] Trial 36 finished with value: -0.9461772271511589 and parameters: {'degree': 3}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,443] Trial 37 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,460] Trial 38 finished with value: -0.9461914412588097 and parameters: {'degree': 2}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,477] Trial 39 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,498] Trial 40 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,515] Trial 41 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,532] Trial 42 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,551] Trial 43 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,570] Trial 44 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,589] Trial 45 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,609] Trial 46 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,628] Trial 47 finished with value: -0.9463512800236288 and parameters: {'degree': 5}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,647] Trial 48 finished with value: -0.9461803279178675 and parameters: {'degree': 4}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,664] Trial 49 finished with value: -0.9461914412588097 and parameters: {'degree': 2}. Best is trial 1 with value: -0.9463512800236288.
[I 2024-12-05 08:41:47,681] A new study created in memory with name: no-name-742fec23-3aa5-4f31-9eb0-ef42033331ba
[I 2024-12-05 08:41:47,690] Trial 0 finished with value: -0.46606975889217805 and parameters: {'degree': 2}. Best is trial 0 with value: -0.46606975889217805.
[I 2024-12-05 08:41:47,696] Trial 1 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 0 with value: -0.46606975889217805.
[I 2024-12-05 08:41:47,703] Trial 2 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,710] Trial 3 finished with value: -0.46606975889217805 and parameters: {'degree': 2}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,718] Trial 4 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,726] Trial 5 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,737] Trial 6 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,743] Trial 7 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,751] Trial 8 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,758] Trial 9 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,774] Trial 10 finished with value: -0.46764817386787616 and parameters: {'degree': 1}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,792] Trial 11 finished with value: -0.4658712087566771 and parameters: {'degree': 3}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,810] Trial 12 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,826] Trial 13 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,842] Trial 14 finished with value: -0.4658712087566771 and parameters: {'degree': 3}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,864] Trial 15 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.

Обработка давления Pt = 1.5
Количество строк в data_matched для Pt=1.5: 269
[I 2024-12-05 08:41:47,882] Trial 16 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,898] Trial 17 finished with value: -0.4658712087566771 and parameters: {'degree': 3}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,914] Trial 18 finished with value: -0.46606975889217805 and parameters: {'degree': 2}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,931] Trial 19 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,949] Trial 20 finished with value: -0.46764817386787616 and parameters: {'degree': 1}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,966] Trial 21 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:47,989] Trial 22 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,016] Trial 23 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,039] Trial 24 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,071] Trial 25 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,100] Trial 26 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,127] Trial 27 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,156] Trial 28 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,183] Trial 29 finished with value: -0.4658712087566771 and parameters: {'degree': 3}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,211] Trial 30 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,235] Trial 31 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,269] Trial 32 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,292] Trial 33 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,318] Trial 34 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,345] Trial 35 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,370] Trial 36 finished with value: -0.46606975889217805 and parameters: {'degree': 2}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,394] Trial 37 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,422] Trial 38 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,447] Trial 39 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,476] Trial 40 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,499] Trial 41 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,516] Trial 42 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,534] Trial 43 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,554] Trial 44 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,579] Trial 45 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,603] Trial 46 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,627] Trial 47 finished with value: -0.471356511568231 and parameters: {'degree': 5}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,649] Trial 48 finished with value: -0.45961194919625237 and parameters: {'degree': 4}. Best is trial 2 with value: -0.471356511568231.
[I 2024-12-05 08:41:48,667] Trial 49 finished with value: -0.4658712087566771 and parameters: {'degree': 3}. Best is trial 2 with value: -0.471356511568231.

Уравнения полиномов для графика 'Модифицированное значение' от 'Gт':
Pt=0.6: y = 0.2113 + (0.062722277612552) * x^1 + (-0.043588948689341) * x^2 + (-0.006047003832203) * x^3 + (0.008887847083934) * x^4 + (-0.000502891771810) * x^5 
Pt=0.8: y = 0.1541 + (0.047324884117152) * x^1 + (-0.003851001180537) * x^2 + (0.001936346629164) * x^3 + (0.000343317705896) * x^4 + (-0.001072032656373) * x^5 
Pt=1.0: y = 0.1044 + (0.038526335403930) * x^1 + (-0.000169919097599) * x^2 + (-0.001127804776384) * x^3 + (0.000918716108576) * x^4 
Pt=1.2: y = 0.0885 + (0.031471232798673) * x^1 + (0.003335399935386) * x^2 + (-0.001497292180443) * x^3 + (-0.000054639918445) * x^4 + (0.000424377558477) * x^5 
Pt=1.5: y = 0.0710 + (0.017929176898934) * x^1 + (0.005091227848858) * x^2 + (-0.007329393006961) * x^3 + (-0.001024530096914) * x^4 + (0.002679901175004) * x^5 
Нет данных для дельты 0

Уравнения для ΔTоб:
Уравнение для ΔTоб -20°C: y = 0.1269 * x + 0.0000
Уравнение для ΔTоб -15°C: y = 0.1554 * x + 0.0000
Уравнение для ΔTоб -10°C: y = 0.2298 * x + -0.0000
Уравнение для ΔTоб -5°C: y = 0.4051 * x + -0.0000
Уравнение для ΔTоб 5°C: y = -0.3985 * x + 0.0000
Уравнение для ΔTоб 10°C: y = -0.1640 * x + -0.0000
Уравнение для ΔTоб 15°C: y = -0.1111 * x + 0.0000
Уравнение для ΔTоб 20°C: y = -0.0766 * x + 0.0000
Мой код некорректно выводит уравнения в консоль, когда я попытался построить по данным уравнениям = 0.2113 + (0.062722277612552) * C3 + (-0.043588948689341) * C3^2 + (-0.006047003832203) * C3^3 + (0.008887847083934) * C3^4 + (-0.00050289177181) * C3^5 графики в excel, получаются значения -15160644167
-18471164691
-22336257494
-26822848481
-32003043410
-37954321001
-44759726048
-52508062530
-61294086719
-71218700291
-82389143440
-94919187983
-1.08929E+11
-1.24547E+11
-1.41907E+11
-1.6115E+11
-1.82427E+11
-2.05894E+11
-2.31716E+11
-2.60066E+11
-2.91126E+11
-3.25084E+11
-3.6214E+11
-4.02501E+11
-4.46382E+11
Хотя по графикам они порядка 0.2256
0.2576
0.2896
0.3216
0.3536
0.3856