├── farodit ├── __init__.py ├── polynomial_model.py ├── kalman_model.py ├── polynomial_neural_layer.py ├── window_slider.py └── predictor.py ├── requirements.txt ├── examples ├── industrial_sensors │ ├── load.py │ ├── tags.py │ ├── plot.py │ └── sensors_example.py ├── airfoil │ ├── airfoil_example.py │ ├── airfoil_data_preparation.py │ └── AirfoilSelfNoise.csv └── kalman │ └── kalman_example.py ├── setup.py ├── README.md ├── docs.md └── LICENSE /farodit/__init__.py: -------------------------------------------------------------------------------- 1 | from .polynomial_neural_layer import PolynomialNeuralLayer 2 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy~=1.23.5 2 | tensorflow~=2.15.0 3 | matplotlib>=3.4.3, ==3.* 4 | scikit-learn~=1.2.1 5 | pandas~=1.5.3 6 | filterpy~=1.4.5 7 | -------------------------------------------------------------------------------- /examples/industrial_sensors/load.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | 4 | def load_csv(date_column, columns): 5 | file_name = 'data.csv' 6 | df = pd.read_csv(file_name) 7 | df[date_column] = pd.to_datetime(df[date_column]) 8 | df = df.sort_values(date_column).reset_index(drop=True) 9 | df = df[columns].dropna() 10 | 11 | return df[columns] 12 | -------------------------------------------------------------------------------- /examples/airfoil/airfoil_example.py: -------------------------------------------------------------------------------- 1 | from farodit.polynomial_model import PolynomialModel 2 | from airfoil_data_preparation import prepare_airfoil_data 3 | 4 | if __name__ == "__main__": 5 | poly_net = PolynomialModel(order=3, output_dim=1) 6 | X_train, X_test, Y_train, Y_test = prepare_airfoil_data() 7 | poly_net.fit(X_train, Y_train, num_epoch=15000, batch_size=500) 8 | poly_net.validate(X_test, Y_test) -------------------------------------------------------------------------------- /examples/industrial_sensors/tags.py: -------------------------------------------------------------------------------- 1 | base_tags = sensors = [f'sensor_{i + 1}' for i in range(31)] 2 | 3 | future_prediction_tags = [ 4 | 'sensor_22', 5 | 'sensor_23' 6 | ] 7 | 8 | sensor_22_predictors_tags = [tag for tag in base_tags if 9 | tag not in ['sensor_22', 'sensor_23', 'sensor_28', 'sensor_29', 'sensor_30', 'sensor_31']] 10 | sensor_23_predictors_tags = [tag for tag in base_tags if tag not in ['sensor_22', 'sensor_23', 'sensor_25']] 11 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | setup( 4 | name='farodit', 5 | version='1.0', 6 | packages=find_packages(), 7 | python_requires=">=3.9, <3.12", 8 | install_requires=[ 9 | 'numpy>=1.23.5, <1.24', 10 | 'tensorflow>=2.15.0, <2.16', 11 | 'scikit-learn>=1.2.1, <1.3', 12 | 'pandas>=1.5.3, <1.5.4', 13 | 'matplotlib>=3.4.3, <4', 14 | ], 15 | url='https://github.com/SPbU-AI-Center/farodit', 16 | license='Apache-2.0', 17 | author='SPbU-AI-Center', 18 | author_email='st023633@student.spbu.ru', 19 | description='Framework for Analysis and Prediction on Data in Tables' 20 | ) 21 | -------------------------------------------------------------------------------- /examples/industrial_sensors/plot.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | from datetime import datetime 4 | import os 5 | 6 | def plot(y_fit, lr_y_fit, y_pred, lr_y_pred, title, show_plot=True, save_plot=False): 7 | real = np.concatenate([y_fit, y_pred], axis=0) 8 | model = np.concatenate([lr_y_fit, lr_y_pred], axis=0) 9 | 10 | plt.plot(range(len(real)), real, color='blue') 11 | plt.plot(range(len(model)), model, color='orange') 12 | 13 | plt.axvline(len(lr_y_fit), color='g', linestyle='--') 14 | 15 | plt.title(title) 16 | 17 | if save_plot: 18 | if not os.path.isdir('plots'): 19 | os.makedirs('plots') 20 | timestamp = str(datetime.now().time()).replace(':', '.') 21 | plt.gcf().savefig('plots/' + timestamp + ' ' + title + '.jpg', bbox_inches='tight') 22 | 23 | if show_plot: 24 | plt.show() 25 | 26 | plt.close() 27 | -------------------------------------------------------------------------------- /examples/airfoil/airfoil_data_preparation.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from sklearn.model_selection import train_test_split 3 | from sklearn.preprocessing import MinMaxScaler 4 | from pathlib import Path 5 | 6 | 7 | def load_data(): 8 | filepath = "AirfoilSelfNoise.csv" 9 | return pd.read_csv(filepath) 10 | 11 | 12 | def split_data(data_frame): 13 | X = data_frame[["f", "alpha", "c", "U_infinity", "delta"]] 14 | Y = data_frame[["SSPL"]] 15 | return train_test_split(X, Y, test_size=0.33) 16 | 17 | 18 | def scale_data(X_train, X_test): 19 | scaler = MinMaxScaler() 20 | X_train = scaler.fit_transform(X_train) 21 | X_test = scaler.transform(X_test) 22 | return X_train, X_test 23 | 24 | 25 | def prepare_airfoil_data(): 26 | data = load_data() 27 | X_train, X_test, Y_train, Y_test = split_data(data) 28 | X_train, X_test = scale_data(X_train, X_test) 29 | return X_train, X_test, Y_train.values.ravel(), Y_test.values.ravel() 30 | -------------------------------------------------------------------------------- /farodit/polynomial_model.py: -------------------------------------------------------------------------------- 1 | import time 2 | 3 | from keras import Sequential, optimizers 4 | from matplotlib import pyplot as plt 5 | from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error 6 | 7 | from farodit import PolynomialNeuralLayer 8 | 9 | 10 | class PolynomialModel: 11 | def __init__(self, order, output_dim): 12 | self.model = Sequential() 13 | self.model.add(PolynomialNeuralLayer(output_dimension=output_dim, polynomial_order=order)) 14 | optimizer = optimizers.Adam(learning_rate=0.05) 15 | self.model.compile(loss='mean_squared_logarithmic_error', optimizer=optimizer, metrics=['mae', 'mape']) 16 | 17 | def fit(self, X_train, Y_train, num_epoch=10000, batch_size=500): 18 | start = time.time() 19 | self.model.fit(X_train, Y_train, epochs=num_epoch, batch_size=batch_size, shuffle=False, verbose=1) 20 | print(self.model.get_weights()) 21 | print(f'PNN is built in {time.time() - start} seconds') 22 | 23 | def predict(self, X_data): 24 | return self.model.predict(X_data) 25 | 26 | def validate(self, X_data, Y_data): 27 | Y_model = self.predict(X_data) 28 | print('MSE = ', mean_squared_error(Y_data, Y_model)) 29 | print('MAE = ', mean_absolute_error(Y_data, Y_model)) 30 | print('MAPE = ', mean_absolute_percentage_error(Y_data, Y_model)) 31 | 32 | t = range(len(X_data)) 33 | plt.scatter(t, Y_data.reshape(1, -1)) 34 | plt.scatter(t, Y_model.reshape(1, -1)) 35 | plt.show() 36 | -------------------------------------------------------------------------------- /examples/industrial_sensors/sensors_example.py: -------------------------------------------------------------------------------- 1 | from sklearn.model_selection import train_test_split 2 | from sklearn.linear_model import Ridge 3 | 4 | from farodit.predictor import Predictor 5 | from tags import base_tags, sensor_22_predictors_tags 6 | from load import load_csv 7 | from plot import plot 8 | 9 | window_size = 12 10 | future_response_size = 48 11 | test_dataset_size_ratio = 0.3 12 | timestamp_column_name = 'DATE' 13 | target_sensor_name = 'sensor_22' 14 | target_sensor = [target_sensor_name] 15 | predictor_columns = sensor_22_predictors_tags 16 | columns_to_load = [timestamp_column_name] + base_tags 17 | 18 | # load data 19 | dataset = load_csv(date_column=timestamp_column_name, columns=columns_to_load) 20 | 21 | # regressor test 22 | ridge_regression_model = Ridge(alpha=0.01, tol=1e-3, solver='auto') 23 | predictor = Predictor(ridge_regression_model, target_sensor_name, predictor_columns, is_using_previous_y=False) 24 | predictor.set_future_points_count(future_response_size) 25 | predictor.set_sliding_window_size(window_size) 26 | predictor.set_outliers_filter_settings(upper_percentile=98, lower_persentile=2, lower_bound=8) 27 | predictor.set_mean_filter_settings(mean_window_size=5) 28 | 29 | training_dataset, testing_dataset = train_test_split(dataset, test_size=test_dataset_size_ratio, shuffle=False) 30 | training_dataset.reset_index(inplace=True, drop=True) 31 | testing_dataset.reset_index(inplace=True, drop=True) 32 | predicted_testing_dataset = testing_dataset 33 | predictor.fit(training_dataset) 34 | predicted_training = predictor.predict(training_dataset) 35 | predicted_dataframe = predictor.predict(predicted_testing_dataset) 36 | 37 | print('test set shape', testing_dataset.shape) 38 | print('predicted shape', predicted_dataframe.shape) 39 | plot(training_dataset.loc[window_size - 1:, target_sensor_name].values, 40 | predicted_training[:, -future_response_size], 41 | predicted_testing_dataset.loc[window_size - 1:, target_sensor_name].values, 42 | predicted_dataframe[:, -future_response_size], 43 | f'windowed ({window_size}) {target_sensor_name} mae %.2f rmse %.2f', 44 | show_plot=True, 45 | save_plot=True) 46 | -------------------------------------------------------------------------------- /farodit/kalman_model.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from filterpy.kalman import KalmanFilter as kf 3 | from matplotlib import pyplot as plt 4 | from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error 5 | 6 | 7 | class KalmanModel: 8 | def fit(self, dim_x, dim_z, 9 | transition_matrix, 10 | observation_matrix, 11 | process_covariance, 12 | observation_covariance, 13 | initial_state, 14 | initial_covariance 15 | ): 16 | self.model = kf(dim_x, dim_z) 17 | 18 | # F - матрица процесса 19 | self.model.F = transition_matrix 20 | # Матрица наблюдения 21 | self.model.H = observation_matrix 22 | # Ковариационная матрица ошибки модели 23 | self.model.Q = process_covariance 24 | measurementSigma = 0.5 25 | # Ковариационная матрица ошибки измерения 26 | self.model.R = observation_covariance 27 | # Начальное состояние. 28 | self.model.x = initial_state 29 | # Ковариационная матрица для начального состояния 30 | self.model.P = initial_covariance 31 | 32 | def predict(self, X_data): 33 | filtered_state = np.zeros((len(X_data), self.model.dim_x)) 34 | state_covariance_history = np.zeros((len(X_data), self.model.dim_x, self.model.dim_x)) 35 | 36 | for i in range(0, len(X_data)): 37 | z = np.array([X_data[i]]) 38 | self.model.predict() 39 | self.model.update(z) 40 | 41 | filtered_state[i] = self.model.x 42 | state_covariance_history[i] = self.model.P 43 | 44 | return filtered_state, state_covariance_history 45 | 46 | def validate(self, X_data, Y_data): 47 | Y_model, _ = self.predict(X_data) 48 | 49 | mse = mean_squared_error(Y_data, Y_model[:, 0]) 50 | mae = mean_absolute_error(Y_data, Y_model[:, 0]) 51 | mape = mean_absolute_percentage_error(Y_data, Y_model[:, 0]) 52 | 53 | print('MSE = ', mse) 54 | print('MAE = ', mae) 55 | print('MAPE = ', mape) 56 | 57 | t = range(len(X_data)) 58 | plt.scatter(t, Y_data, label="Истинные значения") 59 | plt.scatter(t, Y_model[:, 0], label="Прогнозируемые значения") 60 | plt.legend() 61 | plt.show() 62 | 63 | -------------------------------------------------------------------------------- /examples/kalman/kalman_example.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from filterpy import common 3 | from matplotlib import pyplot as plt 4 | 5 | from farodit.kalman_model import KalmanModel 6 | 7 | fig, axs = plt.subplots(1, 1, figsize=(12, 10)) 8 | 9 | transition_matrix = np.array([[1, 1], [0, 1]]) 10 | observation_matrix = np.array([[1, 0]]) 11 | process_covariance = np.array([[1e-5, 0], [0, 1e-5]]) 12 | observation_covariance = np.array([[1e-3]]) 13 | initial_state_mean = np.array([0, 0]) 14 | initial_state_covariance = np.array([[1, 0], [0, 1]]) 15 | 16 | dt = 0.01 17 | noiseSigma = 0.5 18 | samplesCount = 1000 19 | noise = np.random.normal(loc=0.0, scale=noiseSigma, size=samplesCount) 20 | 21 | trajectory = np.zeros((3, samplesCount)) 22 | 23 | position = 0 24 | velocity = 1.0 25 | acceleration = 0.0 26 | 27 | for i in range(1, samplesCount): 28 | position = position + velocity * dt + (acceleration * dt ** 2) / 2.0 + 0.1 * np.sin( 29 | 2 * np.pi * i * dt) # Добавлено синусоидальное колебание 30 | velocity = velocity + acceleration * dt + 0.2 * np.cos( 31 | 2 * np.pi * i * dt) # Добавлено косинусоидальное колебание к скорости 32 | acceleration = acceleration + 0.1 * np.sin(4 * np.pi * i * dt) # Добавлено синусоидальное колебание к ускорению 33 | 34 | trajectory[0][i] = position 35 | trajectory[1][i] = velocity 36 | trajectory[2][i] = acceleration 37 | 38 | measurement = trajectory[0] + noise 39 | processNoise = 1e-4 40 | 41 | # F - матрица процесса 42 | F = np.array([[1, dt, (dt**2) / 2], 43 | [0, 1.0, dt], 44 | [0, 0, 1.0]]) 45 | 46 | # Матрица наблюдения 47 | H = np.array([[1.0, 0.0, 0.0]]) 48 | 49 | # Ковариационная матрица ошибки модели 50 | Q = common.Q_discrete_white_noise(dim=3, dt=dt, var=processNoise) 51 | 52 | measurementSigma = 0.5 53 | # Ковариационная матрица ошибки измерения 54 | R = np.array([[measurementSigma * measurementSigma]]) 55 | 56 | # Начальное состояние. 57 | x = np.array([0.0, 0.0, 0.0]) 58 | 59 | # Ковариационная матрица для начального состояния 60 | P = np.array([[10.0, 0.0, 0.0], 61 | [0.0, 10.0, 0.0], 62 | [0.0, 0.0, 10.0]]) 63 | 64 | k = KalmanModel() 65 | k.fit(dim_x=3, 66 | dim_z=1, 67 | transition_matrix=F, 68 | observation_matrix=H, 69 | initial_state=x, 70 | initial_covariance=P, 71 | observation_covariance=R, 72 | process_covariance=Q 73 | ) 74 | 75 | filteredState, stateCovarianceHistory = k.predict(measurement) 76 | 77 | axs.set_title("Kalman filter (3rd order)") 78 | axs.plot(measurement, label="Измерение") 79 | axs.plot(trajectory[0], label="Истинное значение") 80 | axs.plot(filteredState[:, 0], label="Оценка фильтра") 81 | axs.legend() 82 | 83 | plt.tight_layout() 84 | plt.show() 85 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FARODIT: Framework for Analysis and Prediction on Data in Tables 2 | 3 | ### The repository has been moved to https://git-pub.ai-center.online/ai-center/farodit 4 | 5 | FARODIT is a software that facilitates the development and integration of artificial intelligence technology components, including cloud solution process automation services aimed at modernization, acceleration and adaptation of AI algorithms in the direction of predictive analytics for the digital industry and beyond. 6 | ## Goal 7 | We aimed to create a platform that helps machine learning experts quickly and easily develop and test predictive analytics models based on tabular data. The framework provides easy access to data, a set of tools for data preprocessing, and the options for selecting machine learning algorithms and tuning model parameters to maximize predictive accuracy. In addition, the framework is extensible and open to integration with other tools and libraries to meet the needs of different users. 8 | ## Functionality 9 | ### Data preparation 10 | * tools for cleaning and preprocessing pandas dataframe data 11 | ### Model selection 12 | * selection of ML algorithms that can be used to build predictive analytics models 13 | * polynomial neural networks built on the basis of the tensorflow library. 14 | ### Training 15 | * model training on the basis of processed data 16 | * parameters tuning parameters and model performance optimization 17 | ### Evaluation 18 | * tools to evaluate the accuracy of the model on test data 19 | ## Technical specifications 20 | 21 | - handles data of numeric types 22 | - is able to process data with different discreteness 23 | - can work with a large number of features and measurements 24 | - supports a wide range of machine learning algorithms, including linear regression, decision trees, random forests, gradient bousting, and neural networks, including polynomial networks 25 | ## Project Structure 26 | The repository includes the following directories: 27 | * `farodit` contains the main classes and scripts 28 | * `examples` includes several *how-to-use-cases* where you can start to discover how framework works: 29 | * `airfoil` - simple case of use 30 | * `industrial_sensors` - case with WindowSlider 31 | ## Installation 32 | You can use: 33 | ``` 34 | pip install git+https://github.com/SPbU-AI-Center/farodit.git 35 | ``` 36 | or: 37 | ``` 38 | git clone https://github.com/SPbU-AI-Center/farodit.git 39 | cd farodit 40 | pip install -r requirements.txt 41 | ``` 42 | ## Future Development 43 | Any contribution is welcome. Our R&D team is open for cooperation with other scientific teams as well as with industrial partners. 44 | -------------------------------------------------------------------------------- /farodit/polynomial_neural_layer.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | 4 | class PolynomialNeuralLayer(tf.keras.layers.Layer): 5 | def __init__(self, output_dimension, polynomial_order, initial_weights=None, **kwargs): 6 | super(PolynomialNeuralLayer, self).__init__(**kwargs) 7 | self.polynomial_order = polynomial_order 8 | self.output_dimension = output_dimension 9 | self.initial_weights = initial_weights or [] 10 | self.polynomial_weights = [] 11 | self.polynomial_dimensions = [] 12 | 13 | def build(self, input_shape): 14 | input_dimension = input_shape[1] 15 | self.polynomial_dimensions = [input_dimension ** (n + 1) for n in range(self.polynomial_order)] 16 | 17 | initial_values = [] 18 | if self.initial_weights: 19 | transposed_weights = [weights.T for weights in self.initial_weights] 20 | bias_initial_value = transposed_weights[0] 21 | initial_values = transposed_weights[1:] 22 | else: 23 | bias_initial_value = tf.zeros_initializer()(shape=(1, self.output_dimension)) 24 | initial_values.append(tf.eye(self.polynomial_dimensions[0], self.output_dimension)) 25 | for i in range(1, self.polynomial_order): 26 | shape = (self.polynomial_dimensions[i], self.output_dimension) 27 | initial_values.append(tf.zeros_initializer()(shape=shape)) 28 | 29 | if self.polynomial_order + 1 > len(self.initial_weights): 30 | shape = (self.polynomial_dimensions[self.polynomial_order - 1], self.output_dimension) 31 | initial_values.append(tf.zeros_initializer()(shape=shape)) 32 | 33 | self.bias = tf.Variable(initial_value=bias_initial_value, dtype=tf.float32) 34 | 35 | for i, value in enumerate(initial_values): 36 | var = tf.Variable(initial_value=value, dtype=tf.float32, name=f'W_{i + 1}') 37 | self.polynomial_weights.append(var) 38 | 39 | self._trainable_weights.extend([kernel for kernel in self.polynomial_weights if kernel.trainable]) 40 | 41 | def call(self, inputs): 42 | result = self.bias 43 | input_degree = tf.ones_like(inputs[:, 0:1]) 44 | 45 | for i in range(self.polynomial_order): 46 | input_degree = tf.einsum('bi,bj->bij', inputs, input_degree) 47 | input_degree = tf.reshape(input_degree, [-1, self.polynomial_dimensions[i]]) 48 | result = result + tf.matmul(input_degree, self.polynomial_weights[i]) 49 | 50 | return result 51 | 52 | def get_config(self): 53 | config = super().get_config() 54 | config.update({ 55 | "polynomial_order": self.polynomial_order, 56 | "output_dimension": self.output_dimension, 57 | "initial_weights": self.initial_weights, 58 | }) 59 | return config 60 | -------------------------------------------------------------------------------- /farodit/window_slider.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | from pandas import DataFrame 4 | 5 | 6 | class WindowSlider(object): 7 | 8 | def __init__(self, window_size: int = 5, response_size: int = 1): 9 | ''' 10 | window_size - number of time steps to look back 11 | response_size - number of time steps to predict 12 | slide_length - maximum length to slide - (#observation - w) 13 | p: final predictors - (#predictors * w) 14 | ''' 15 | self.window_size = window_size 16 | self.response_size = response_size 17 | self.slide_length = 0 18 | self.predictor_count = 0 19 | self.names = [] 20 | 21 | def collect_windows(self, df: DataFrame, window_size: int = 5, previous_y: bool = False): 22 | ''' 23 | Input: df датафрейм, первая колонка -- дельта по времени, затем предикторы и цель в последней колонке 24 | ''' 25 | self.window_size = window_size 26 | 27 | column_count = len(df.columns) 28 | row_count = len(df.index) 29 | 30 | self.slide_length = row_count - (self.window_size + self.response_size) + 1 31 | 32 | if previous_y: 33 | self.predictor_count = column_count * (self.window_size) 34 | X = df 35 | else: 36 | self.predictor_count = (column_count - 1) * (self.window_size) 37 | X = df.iloc[:, :-1] 38 | # Составляем колонки для предикторов 39 | columns = X.columns.values 40 | for i in range(self.window_size): 41 | for column in columns: 42 | name = f'{column}({i + 1})' 43 | self.names.append(name) 44 | 45 | # Составляем колонки для целей 46 | predictor_name = df.columns[-1] 47 | for i in range(self.response_size): 48 | name = f'{predictor_name}_resp({i + 1})' 49 | self.names.append(name) 50 | 51 | # Инициализация фрейма с окнами 52 | new_df = pd.DataFrame(np.zeros(shape=(self.slide_length, (self.predictor_count + self.response_size))), 53 | columns=self.names) 54 | 55 | # Заполнение фрейма с окнами 56 | original_frame_predictor_count = len(X.columns) 57 | if self.response_size > 0: 58 | for i in range(self.slide_length): 59 | # Значения предикторов (окно назад) 60 | predictor_values = X.values[i:i + self.window_size, :original_frame_predictor_count] 61 | 62 | # Целевые значения (окно вперёд) 63 | y_row = self.window_size + i 64 | target_values = df.values[y_row:y_row + self.response_size, -1] 65 | 66 | # Построчное заполнение фрейма 67 | new_df.iloc[i, :] = np.concatenate([predictor_values.flatten(), target_values.flatten()]) 68 | else: 69 | for i in range(self.slide_length): 70 | # Значения предикторов (окно назад) 71 | predictor_values = X.values[i:i + self.window_size, :original_frame_predictor_count] 72 | 73 | new_df.iloc[i, :] = predictor_values.flatten() 74 | 75 | return new_df 76 | 77 | def collect_windows_for_prediction(self, df: DataFrame, window_size: int = 5, previous_y: bool = False): 78 | old_size = self.response_size 79 | self.response_size = 0 80 | 81 | new_df = self.collect_windows(df, window_size=window_size, previous_y=previous_y) 82 | 83 | self.response_size = old_size 84 | 85 | return new_df 86 | -------------------------------------------------------------------------------- /farodit/predictor.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from pandas import DataFrame 3 | from typing import List 4 | from farodit.window_slider import WindowSlider 5 | 6 | delta_t_name = '∆t' 7 | 8 | 9 | class Predictor: 10 | _date_column = 'DATE' 11 | 12 | def __init__(self, model, target: str, columns: List[str], is_using_previous_y: bool): 13 | self._upper_percentile = 100 14 | self._lower_persentile = 0 15 | self._lower_bound = -1000000 16 | self._mean_window_size = 5 17 | self._sliding_window_size = 2 18 | self._future_points_count = 48 19 | self._max_train_points_count = 3000 20 | self._is_using_previous_y = is_using_previous_y 21 | 22 | self._target = target 23 | self._columns = columns 24 | self._model = model 25 | 26 | def set_using_previous_y(self, is_using_previous_y: bool): 27 | self._is_using_previous_y = is_using_previous_y 28 | 29 | def set_future_points_count(self, count: int): 30 | self._future_points_count = count 31 | 32 | def set_max_train_points_count(self, maxcount: int): 33 | self._max_train_points_count = maxcount 34 | 35 | def set_sliding_window_size(self, size: int): 36 | self._sliding_window_size = size 37 | 38 | def set_outliers_filter_settings(self, upper_percentile: float, lower_persentile: float, lower_bound: float): 39 | self._upper_percentile = upper_percentile 40 | self._lower_persentile = lower_persentile 41 | self._lower_bound = lower_bound 42 | 43 | def set_mean_filter_settings(self, mean_window_size: int): 44 | self._mean_window_size = mean_window_size 45 | 46 | def fit(self, df: DataFrame): 47 | # make copy 48 | df = df.copy() 49 | 50 | # filter by columns and move target to the end 51 | df = df[[self._date_column] + self._columns + [self._target]] 52 | 53 | # remove na values 54 | df.dropna(inplace=True) 55 | 56 | # filter outliers 57 | df = self._filter_outliers(df) 58 | 59 | # construct deltaT 60 | df = self._add_delta_t(df) 61 | 62 | # filter rollilng mean 63 | self._filter_rolling_mean(df) 64 | 65 | # construct windows 66 | window_constructor = WindowSlider(response_size=self._future_points_count) 67 | df = window_constructor.collect_windows(df, 68 | window_size=self._sliding_window_size, 69 | previous_y=self._is_using_previous_y) 70 | 71 | # filter windows 72 | self._filter_windows(df) 73 | df = self._remove_delta_t_columns(df) 74 | 75 | df = df.tail(self._max_train_points_count) 76 | 77 | # fit 78 | train_x = df.iloc[:, :-self._future_points_count] 79 | train_y = df.iloc[:, -self._future_points_count:] 80 | 81 | self._model.fit(train_x, train_y) 82 | 83 | def predict(self, df: DataFrame): 84 | # make copy 85 | df = df.copy() 86 | 87 | # filter by columns and move target to the end 88 | df = df[[self._date_column] + self._columns + [self._target]] 89 | 90 | # construct deltaT 91 | df = self._add_delta_t(df) 92 | 93 | # construct windows 94 | window_constructor = WindowSlider(response_size=0) 95 | df = window_constructor.collect_windows_for_prediction(df, 96 | window_size=self._sliding_window_size, 97 | previous_y=self._is_using_previous_y) 98 | 99 | df = self._remove_delta_t_columns(df) 100 | prediction = self._model.predict(df) 101 | 102 | return prediction 103 | 104 | def _filter_outliers(self, df: DataFrame): 105 | upper_limit = np.percentile(df[self._target].values, self._upper_percentile) 106 | lower_limit = max(np.percentile(df[self._target].values, self._lower_persentile), self._lower_bound) 107 | return df[(df[self._target] < upper_limit) & (df[self._target] > lower_limit)] 108 | 109 | def _add_delta_t(self, df: DataFrame): 110 | if len(df.index) > 1: 111 | dates = df[self._date_column] 112 | deltaT = np.array( 113 | [(dates.values[i + 1] - dates.values[i]) / np.timedelta64(1, 's') for i in range(len(df) - 1)]) 114 | deltaT[0] = 300 115 | deltaT = np.concatenate((np.array([300]), deltaT)) 116 | else: 117 | deltaT = np.array([300]) 118 | 119 | df.insert(1, delta_t_name, deltaT) 120 | needed_columns = [name for name in df.columns if name != self._date_column] 121 | return df[needed_columns] 122 | 123 | def _filter_rolling_mean(self, df: DataFrame): 124 | header = df.columns 125 | df[header[1:]] = df[header[1:]].rolling(self._mean_window_size).mean() 126 | df.dropna(inplace=True) 127 | df.reset_index(inplace=True, drop=True) 128 | 129 | def _filter_windows(self, windowed_dataframe: DataFrame): 130 | for name in windowed_dataframe.columns: 131 | if delta_t_name in name: 132 | windowed_dataframe.loc[np.abs(windowed_dataframe[name] - 300) > 20, name] = np.nan 133 | 134 | windowed_dataframe.dropna(inplace=True) 135 | windowed_dataframe.reset_index(inplace=True, drop=True) 136 | 137 | def _remove_delta_t_columns(self, windowed_dataframe: DataFrame): 138 | columns_without_delta_t = [] 139 | for name in windowed_dataframe.columns: 140 | if delta_t_name not in name: 141 | columns_without_delta_t.append(name) 142 | 143 | return windowed_dataframe[columns_without_delta_t] 144 | -------------------------------------------------------------------------------- /docs.md: -------------------------------------------------------------------------------- 1 | # Краткое описание 2 | Мы стремились создать платформу, которая поможет экспертам в области машинного обучения быстро и легко разрабатывать и тестировать модели предиктивной аналитики на основе табличных данных. Фреймворк предоставляет удобный доступ к данным, набор инструментов для их предварительной обработки, а также возможности для выбора алгоритмов машинного обучения и настройки параметров модели для достижения максимальной точности прогнозирования. 3 | #### Необходимые библиотеки: 4 | * numpy ~1.23.5 5 | * tensorflow ~2.15.0 6 | * matplotlib ~3.4.3 7 | * scikit-learn ~1.2.1 8 | * pandas ~1.5.3 9 | * filterpy ~1.4.5 10 | # Структура 11 | Основой предиктивной модели является нейронная сеть представленная классом `PolynomialModel`, которая состоит из слоёв - экземпляров класса `PolynomialNeuralLayer`. 12 | Предиктивная модель также может работать в режиме "скользящего окна" эта возможность реализована в классе `WindowSlider`. 13 | Также реализована модель фильтра Калмана. 14 | ### Пользовательские данные 15 | В этом разделе описаны задаваемые пользователем переменные, с которыми работает фреймворк. 16 | Данные для обучения: 17 | * `X_data` - объясняющие факторы (предикторы) 18 | * `Y_data` - прогнозируемые факторы 19 | Размерность: 20 | * `output_dimension` - размер выходных значений, может быть задан только для последнего слоя сети и для каждого слоя по отдельности 21 | * `polynomial_order` - порядок полинома 22 | # Сценарии использования 23 | 1. Использование нейронной сети для предсказания 24 | 2. Использование нейронной сети в режиме "скользящего окна" 25 | Оба сценария использования представлены как примеры в папке `examples` 26 | # Основные пользовательские методы 27 | ## `PolynomialModel` 28 | `(self, order, output_dim)` 29 | Модель нейронной сети. При инициализации необходимо выбрать порядок полинома и размерность выходных данных. Размерность выходных данных должна совпадать с размерностью элементов `Y_data`. 30 | * `fit` 31 | `(self, X_train, Y_train, num_epoch, batch_size)` 32 | Обучение сети. Необходимо указать данные, на которых будет происходить обучение сети в пакетном режиме. Возможна настройка параметров количества эпох (по умолчанию 10000) и размера пакетов (по умолчанию 500). 33 | Ничего не возвращает. По окончании выполнения печатает полученные веса и время, затраченное на обучение. 34 | * `validate` 35 | `(X_data, Y_data)` 36 | Метод, вызываемый после обучения для оценки точности прогноза. 37 | Вычисляет среднеквадратическую, среднюю абсолютную и среднюю абсолютную процентную ошибки. Строит точечный график для сравнения оригинальных значений с прогнозными. 38 | * `predict` 39 | `(X_data)` 40 | Метод, вызываемый после обучения для предсказания. 41 | Возвращает прогнозное значение. 42 | ## `PolynomialNeuralLayer` 43 | `(self, output_dimension, polynomial_order, initial_weights, **kwargs)` 44 | * `get_config` 45 | `(self)` 46 | Метод позволяет получить данные о конкретном слое. Возвращает словарь с порядком полинома, размерностью вывода и весом для каждого нейрона. 47 | * `call` 48 | `(self, inputs)` 49 | Может быть использован при послойном запуске сети после обучения, когда требуются выходные значения с каждого слоя. Размерность `inputs` должна совпадать с размерностью входа слоя. 50 | ## `Predictor` 51 | `(self, model, target, columns, is_using_previous_y)` 52 | Используется для предсказания в режиме скользящего окна. 53 | Методы для настройки параметров: 54 | * `set_using_previous_y` 55 | `(self, is_using_previous_y: bool)` 56 | * `set_future_points_count` 57 | `(self, count: int)` 58 | * `set_max_train_points_count` 59 | `(self, maxcount: int)` 60 | * `set_sliding_window_size` 61 | `(self, size: int)` 62 | * `set_outliers_filter_settings` 63 | `(self, upper_percentile: float, lower_persentile: float, lower_bound: float)` 64 | * `set_mean_filter_settings` 65 | `(self, mean_window_size: int)` 66 | Основные методы: 67 | * `fit` 68 | `(self, df)` 69 | Обучение модели. Необходимо указать данные наблюдений. 70 | Ничего не возвращает. 71 | * `predict` 72 | `(self, df)` 73 | Метод, вызываемый после обучения для предсказания. 74 | Возвращает прогноз в заданном формате. 75 | ## `WindowSlider` 76 | `(self, window_size, response_size)` 77 | * `collect_windows_for_prediction` 78 | `(self, df, window_size, previous_y)` 79 | Метод для конструкции окна. Возвращает преобразованный объект DataFrame. 80 | `df` - объект DataFrame, первый столбец - значения $\Delta t$, затем предикторы и цель в последнем столбце 81 | `window_size` - количество временных шагов, на основе которых будет производиться предсказание (по умолчанию 5) 82 | `previous_y` - показывает, будут ли использоваться значения прошлых наблюдений как предикторы (по умолчанию False) 83 | ## `KalmanModel` 84 | `(self, dim_x, dim_z, transition_matrix, observation_matrix, process_covariance, observation_covariance, initial_state, initial_covariance)` 85 | Класс, реализующий фильтр Калмана. 86 | * `dim_x` - размерность состояния 87 | * `dim_z` - размерность наблюдения 88 | * `transition_matrix` - матрица перехода 89 | * `observation_matrix` - матрица наблюдения 90 | * `process_covariance` - ковариационная матрица шума процесса 91 | * `observation_covariance` - ковариационная матрица шума наблюдения 92 | * `initial_state` - начальное состояние 93 | * `initial_covariance` - начальная ковариационная матрица 94 | * `fit` 95 | `(self, dim_x, dim_z, transition_matrix, observation_matrix, process_covariance, observation_covariance, initial_state, initial_covariance)` 96 | Метод инициализации модели. 97 | Ничего не возвращает. 98 | * `predict` 99 | `(self, X_data)` 100 | Метод предсказания состояния. 101 | Возвращает отфильтрованное состояние и историю ковариационной матрицы состояния. 102 | * `validate` 103 | `(X_data, Y_data)` 104 | Метод проверки точности фильтра Калмана. 105 | Вычисляет среднеквадратическую, среднюю абсолютную и среднюю абсолютную процентную ошибки. Строит точечный график для сравнения оригинальных значений с прогнозными. 106 | # Популярные проблемы 107 | 108 | - Если вы используете сборку python из стороннего репозитория под Linux (например, распространённый ppa:deadsnakes/ppa), для установки может понадобиться явно установить соответствующий distutils 109 | - Для запуска примеров требуется графический backend для matplotlib (например, если вы используете Windows, то требуется иметь установленный Qt 5.9+ или при установке python поставить галочку для установки "tcl/tk and IDLE") 110 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | "f","alpha","c","U_infinity","delta","SSPL" 2 | 800,0,0.3048,71.3,0.00266337,126.201 3 | 1000,0,0.3048,71.3,0.00266337,125.201 4 | 1250,0,0.3048,71.3,0.00266337,125.951 5 | 1600,0,0.3048,71.3,0.00266337,127.591 6 | 2000,0,0.3048,71.3,0.00266337,127.461 7 | 2500,0,0.3048,71.3,0.00266337,125.571 8 | 3150,0,0.3048,71.3,0.00266337,125.201 9 | 4000,0,0.3048,71.3,0.00266337,123.061 10 | 5000,0,0.3048,71.3,0.00266337,121.301 11 | 6300,0,0.3048,71.3,0.00266337,119.541 12 | 8000,0,0.3048,71.3,0.00266337,117.151 13 | 10000,0,0.3048,71.3,0.00266337,115.391 14 | 12500,0,0.3048,71.3,0.00266337,112.241 15 | 16000,0,0.3048,71.3,0.00266337,108.721 16 | 500,0,0.3048,55.5,0.00283081,126.416 17 | 630,0,0.3048,55.5,0.00283081,127.696 18 | 800,0,0.3048,55.5,0.00283081,128.086 19 | 1000,0,0.3048,55.5,0.00283081,126.966 20 | 1250,0,0.3048,55.5,0.00283081,126.086 21 | 1600,0,0.3048,55.5,0.00283081,126.986 22 | 2000,0,0.3048,55.5,0.00283081,126.616 23 | 2500,0,0.3048,55.5,0.00283081,124.106 24 | 3150,0,0.3048,55.5,0.00283081,123.236 25 | 4000,0,0.3048,55.5,0.00283081,121.106 26 | 5000,0,0.3048,55.5,0.00283081,119.606 27 | 6300,0,0.3048,55.5,0.00283081,117.976 28 | 8000,0,0.3048,55.5,0.00283081,116.476 29 | 10000,0,0.3048,55.5,0.00283081,113.076 30 | 12500,0,0.3048,55.5,0.00283081,111.076 31 | 200,0,0.3048,39.6,0.00310138,118.129 32 | 250,0,0.3048,39.6,0.00310138,119.319 33 | 315,0,0.3048,39.6,0.00310138,122.779 34 | 400,0,0.3048,39.6,0.00310138,124.809 35 | 500,0,0.3048,39.6,0.00310138,126.959 36 | 630,0,0.3048,39.6,0.00310138,128.629 37 | 800,0,0.3048,39.6,0.00310138,129.099 38 | 1000,0,0.3048,39.6,0.00310138,127.899 39 | 1250,0,0.3048,39.6,0.00310138,125.499 40 | 1600,0,0.3048,39.6,0.00310138,124.049 41 | 2000,0,0.3048,39.6,0.00310138,123.689 42 | 2500,0,0.3048,39.6,0.00310138,121.399 43 | 3150,0,0.3048,39.6,0.00310138,120.319 44 | 4000,0,0.3048,39.6,0.00310138,119.229 45 | 5000,0,0.3048,39.6,0.00310138,117.789 46 | 6300,0,0.3048,39.6,0.00310138,116.229 47 | 8000,0,0.3048,39.6,0.00310138,114.779 48 | 10000,0,0.3048,39.6,0.00310138,112.139 49 | 12500,0,0.3048,39.6,0.00310138,109.619 50 | 200,0,0.3048,31.7,0.00331266,117.195 51 | 250,0,0.3048,31.7,0.00331266,118.595 52 | 315,0,0.3048,31.7,0.00331266,122.765 53 | 400,0,0.3048,31.7,0.00331266,125.045 54 | 500,0,0.3048,31.7,0.00331266,127.315 55 | 630,0,0.3048,31.7,0.00331266,129.095 56 | 800,0,0.3048,31.7,0.00331266,129.235 57 | 1000,0,0.3048,31.7,0.00331266,127.365 58 | 1250,0,0.3048,31.7,0.00331266,124.355 59 | 1600,0,0.3048,31.7,0.00331266,122.365 60 | 2000,0,0.3048,31.7,0.00331266,122.375 61 | 2500,0,0.3048,31.7,0.00331266,120.755 62 | 3150,0,0.3048,31.7,0.00331266,119.135 63 | 4000,0,0.3048,31.7,0.00331266,118.145 64 | 5000,0,0.3048,31.7,0.00331266,115.645 65 | 6300,0,0.3048,31.7,0.00331266,113.775 66 | 8000,0,0.3048,31.7,0.00331266,110.515 67 | 10000,0,0.3048,31.7,0.00331266,108.265 68 | 800,1.5,0.3048,71.3,0.00336729,127.122 69 | 1000,1.5,0.3048,71.3,0.00336729,125.992 70 | 1250,1.5,0.3048,71.3,0.00336729,125.872 71 | 1600,1.5,0.3048,71.3,0.00336729,126.632 72 | 2000,1.5,0.3048,71.3,0.00336729,126.642 73 | 2500,1.5,0.3048,71.3,0.00336729,124.512 74 | 3150,1.5,0.3048,71.3,0.00336729,123.392 75 | 4000,1.5,0.3048,71.3,0.00336729,121.762 76 | 5000,1.5,0.3048,71.3,0.00336729,119.632 77 | 6300,1.5,0.3048,71.3,0.00336729,118.122 78 | 8000,1.5,0.3048,71.3,0.00336729,115.372 79 | 10000,1.5,0.3048,71.3,0.00336729,113.492 80 | 12500,1.5,0.3048,71.3,0.00336729,109.222 81 | 16000,1.5,0.3048,71.3,0.00336729,106.582 82 | 315,1.5,0.3048,39.6,0.00392107,121.851 83 | 400,1.5,0.3048,39.6,0.00392107,124.001 84 | 500,1.5,0.3048,39.6,0.00392107,126.661 85 | 630,1.5,0.3048,39.6,0.00392107,128.311 86 | 800,1.5,0.3048,39.6,0.00392107,128.831 87 | 1000,1.5,0.3048,39.6,0.00392107,127.581 88 | 1250,1.5,0.3048,39.6,0.00392107,125.211 89 | 1600,1.5,0.3048,39.6,0.00392107,122.211 90 | 2000,1.5,0.3048,39.6,0.00392107,122.101 91 | 2500,1.5,0.3048,39.6,0.00392107,120.981 92 | 3150,1.5,0.3048,39.6,0.00392107,119.111 93 | 4000,1.5,0.3048,39.6,0.00392107,117.741 94 | 5000,1.5,0.3048,39.6,0.00392107,116.241 95 | 6300,1.5,0.3048,39.6,0.00392107,114.751 96 | 8000,1.5,0.3048,39.6,0.00392107,112.251 97 | 10000,1.5,0.3048,39.6,0.00392107,108.991 98 | 12500,1.5,0.3048,39.6,0.00392107,106.111 99 | 400,3,0.3048,71.3,0.00425727,127.564 100 | 500,3,0.3048,71.3,0.00425727,128.454 101 | 630,3,0.3048,71.3,0.00425727,129.354 102 | 800,3,0.3048,71.3,0.00425727,129.494 103 | 1000,3,0.3048,71.3,0.00425727,129.004 104 | 1250,3,0.3048,71.3,0.00425727,127.634 105 | 1600,3,0.3048,71.3,0.00425727,126.514 106 | 2000,3,0.3048,71.3,0.00425727,125.524 107 | 2500,3,0.3048,71.3,0.00425727,124.024 108 | 3150,3,0.3048,71.3,0.00425727,121.514 109 | 4000,3,0.3048,71.3,0.00425727,120.264 110 | 5000,3,0.3048,71.3,0.00425727,118.134 111 | 6300,3,0.3048,71.3,0.00425727,116.134 112 | 8000,3,0.3048,71.3,0.00425727,114.634 113 | 10000,3,0.3048,71.3,0.00425727,110.224 114 | 400,3,0.3048,55.5,0.00452492,126.159 115 | 500,3,0.3048,55.5,0.00452492,128.179 116 | 630,3,0.3048,55.5,0.00452492,129.569 117 | 800,3,0.3048,55.5,0.00452492,129.949 118 | 1000,3,0.3048,55.5,0.00452492,129.329 119 | 1250,3,0.3048,55.5,0.00452492,127.329 120 | 1600,3,0.3048,55.5,0.00452492,124.439 121 | 2000,3,0.3048,55.5,0.00452492,123.069 122 | 2500,3,0.3048,55.5,0.00452492,122.439 123 | 3150,3,0.3048,55.5,0.00452492,120.189 124 | 4000,3,0.3048,55.5,0.00452492,118.689 125 | 5000,3,0.3048,55.5,0.00452492,117.309 126 | 6300,3,0.3048,55.5,0.00452492,115.679 127 | 8000,3,0.3048,55.5,0.00452492,113.799 128 | 10000,3,0.3048,55.5,0.00452492,112.169 129 | 315,3,0.3048,39.6,0.00495741,123.312 130 | 400,3,0.3048,39.6,0.00495741,125.472 131 | 500,3,0.3048,39.6,0.00495741,127.632 132 | 630,3,0.3048,39.6,0.00495741,129.292 133 | 800,3,0.3048,39.6,0.00495741,129.552 134 | 1000,3,0.3048,39.6,0.00495741,128.312 135 | 1250,3,0.3048,39.6,0.00495741,125.802 136 | 1600,3,0.3048,39.6,0.00495741,122.782 137 | 2000,3,0.3048,39.6,0.00495741,120.532 138 | 2500,3,0.3048,39.6,0.00495741,120.162 139 | 3150,3,0.3048,39.6,0.00495741,118.922 140 | 4000,3,0.3048,39.6,0.00495741,116.792 141 | 5000,3,0.3048,39.6,0.00495741,115.792 142 | 6300,3,0.3048,39.6,0.00495741,114.042 143 | 8000,3,0.3048,39.6,0.00495741,110.652 144 | 315,3,0.3048,31.7,0.00529514,123.118 145 | 400,3,0.3048,31.7,0.00529514,125.398 146 | 500,3,0.3048,31.7,0.00529514,127.548 147 | 630,3,0.3048,31.7,0.00529514,128.698 148 | 800,3,0.3048,31.7,0.00529514,128.708 149 | 1000,3,0.3048,31.7,0.00529514,126.838 150 | 1250,3,0.3048,31.7,0.00529514,124.838 151 | 1600,3,0.3048,31.7,0.00529514,122.088 152 | 2000,3,0.3048,31.7,0.00529514,120.088 153 | 2500,3,0.3048,31.7,0.00529514,119.598 154 | 3150,3,0.3048,31.7,0.00529514,118.108 155 | 4000,3,0.3048,31.7,0.00529514,115.608 156 | 5000,3,0.3048,31.7,0.00529514,113.858 157 | 6300,3,0.3048,31.7,0.00529514,109.718 158 | 250,4,0.3048,71.3,0.00497773,126.395 159 | 315,4,0.3048,71.3,0.00497773,128.175 160 | 400,4,0.3048,71.3,0.00497773,129.575 161 | 500,4,0.3048,71.3,0.00497773,130.715 162 | 630,4,0.3048,71.3,0.00497773,131.615 163 | 800,4,0.3048,71.3,0.00497773,131.755 164 | 1000,4,0.3048,71.3,0.00497773,131.015 165 | 1250,4,0.3048,71.3,0.00497773,129.395 166 | 1600,4,0.3048,71.3,0.00497773,126.645 167 | 2000,4,0.3048,71.3,0.00497773,124.395 168 | 2500,4,0.3048,71.3,0.00497773,123.775 169 | 3150,4,0.3048,71.3,0.00497773,121.775 170 | 4000,4,0.3048,71.3,0.00497773,119.535 171 | 5000,4,0.3048,71.3,0.00497773,117.785 172 | 6300,4,0.3048,71.3,0.00497773,116.165 173 | 8000,4,0.3048,71.3,0.00497773,113.665 174 | 10000,4,0.3048,71.3,0.00497773,110.905 175 | 12500,4,0.3048,71.3,0.00497773,107.405 176 | 250,4,0.3048,39.6,0.00579636,123.543 177 | 315,4,0.3048,39.6,0.00579636,126.843 178 | 400,4,0.3048,39.6,0.00579636,128.633 179 | 500,4,0.3048,39.6,0.00579636,130.173 180 | 630,4,0.3048,39.6,0.00579636,131.073 181 | 800,4,0.3048,39.6,0.00579636,130.723 182 | 1000,4,0.3048,39.6,0.00579636,128.723 183 | 1250,4,0.3048,39.6,0.00579636,126.343 184 | 1600,4,0.3048,39.6,0.00579636,123.213 185 | 2000,4,0.3048,39.6,0.00579636,120.963 186 | 2500,4,0.3048,39.6,0.00579636,120.233 187 | 3150,4,0.3048,39.6,0.00579636,118.743 188 | 4000,4,0.3048,39.6,0.00579636,115.863 189 | 5000,4,0.3048,39.6,0.00579636,113.733 190 | 1250,0,0.2286,71.3,0.00214345,128.144 191 | 1600,0,0.2286,71.3,0.00214345,129.134 192 | 2000,0,0.2286,71.3,0.00214345,128.244 193 | 2500,0,0.2286,71.3,0.00214345,128.354 194 | 3150,0,0.2286,71.3,0.00214345,127.834 195 | 4000,0,0.2286,71.3,0.00214345,125.824 196 | 5000,0,0.2286,71.3,0.00214345,124.304 197 | 6300,0,0.2286,71.3,0.00214345,122.044 198 | 8000,0,0.2286,71.3,0.00214345,118.024 199 | 10000,0,0.2286,71.3,0.00214345,118.134 200 | 12500,0,0.2286,71.3,0.00214345,117.624 201 | 16000,0,0.2286,71.3,0.00214345,114.984 202 | 20000,0,0.2286,71.3,0.00214345,114.474 203 | 315,0,0.2286,55.5,0.00229336,119.54 204 | 400,0,0.2286,55.5,0.00229336,121.66 205 | 500,0,0.2286,55.5,0.00229336,123.78 206 | 630,0,0.2286,55.5,0.00229336,126.16 207 | 800,0,0.2286,55.5,0.00229336,127.53 208 | 1000,0,0.2286,55.5,0.00229336,128.29 209 | 1250,0,0.2286,55.5,0.00229336,127.91 210 | 1600,0,0.2286,55.5,0.00229336,126.79 211 | 2000,0,0.2286,55.5,0.00229336,126.54 212 | 2500,0,0.2286,55.5,0.00229336,126.54 213 | 3150,0,0.2286,55.5,0.00229336,125.16 214 | 4000,0,0.2286,55.5,0.00229336,123.41 215 | 5000,0,0.2286,55.5,0.00229336,122.41 216 | 6300,0,0.2286,55.5,0.00229336,118.41 217 | 315,0,0.2286,39.6,0.00253511,121.055 218 | 400,0,0.2286,39.6,0.00253511,123.565 219 | 500,0,0.2286,39.6,0.00253511,126.195 220 | 630,0,0.2286,39.6,0.00253511,128.705 221 | 800,0,0.2286,39.6,0.00253511,130.205 222 | 1000,0,0.2286,39.6,0.00253511,130.435 223 | 1250,0,0.2286,39.6,0.00253511,129.395 224 | 1600,0,0.2286,39.6,0.00253511,127.095 225 | 2000,0,0.2286,39.6,0.00253511,125.305 226 | 2500,0,0.2286,39.6,0.00253511,125.025 227 | 3150,0,0.2286,39.6,0.00253511,124.625 228 | 4000,0,0.2286,39.6,0.00253511,123.465 229 | 5000,0,0.2286,39.6,0.00253511,122.175 230 | 6300,0,0.2286,39.6,0.00253511,117.465 231 | 315,0,0.2286,31.7,0.0027238,120.595 232 | 400,0,0.2286,31.7,0.0027238,123.635 233 | 500,0,0.2286,31.7,0.0027238,126.675 234 | 630,0,0.2286,31.7,0.0027238,129.465 235 | 800,0,0.2286,31.7,0.0027238,130.725 236 | 1000,0,0.2286,31.7,0.0027238,130.595 237 | 1250,0,0.2286,31.7,0.0027238,128.805 238 | 1600,0,0.2286,31.7,0.0027238,125.625 239 | 2000,0,0.2286,31.7,0.0027238,123.455 240 | 2500,0,0.2286,31.7,0.0027238,123.445 241 | 3150,0,0.2286,31.7,0.0027238,123.445 242 | 4000,0,0.2286,31.7,0.0027238,122.035 243 | 5000,0,0.2286,31.7,0.0027238,120.505 244 | 6300,0,0.2286,31.7,0.0027238,116.815 245 | 400,2,0.2286,71.3,0.00293031,125.116 246 | 500,2,0.2286,71.3,0.00293031,126.486 247 | 630,2,0.2286,71.3,0.00293031,127.356 248 | 800,2,0.2286,71.3,0.00293031,128.216 249 | 1000,2,0.2286,71.3,0.00293031,128.956 250 | 1250,2,0.2286,71.3,0.00293031,128.816 251 | 1600,2,0.2286,71.3,0.00293031,127.796 252 | 2000,2,0.2286,71.3,0.00293031,126.896 253 | 2500,2,0.2286,71.3,0.00293031,127.006 254 | 3150,2,0.2286,71.3,0.00293031,126.116 255 | 4000,2,0.2286,71.3,0.00293031,124.086 256 | 5000,2,0.2286,71.3,0.00293031,122.816 257 | 6300,2,0.2286,71.3,0.00293031,120.786 258 | 8000,2,0.2286,71.3,0.00293031,115.996 259 | 10000,2,0.2286,71.3,0.00293031,113.086 260 | 400,2,0.2286,55.5,0.00313525,122.292 261 | 500,2,0.2286,55.5,0.00313525,124.692 262 | 630,2,0.2286,55.5,0.00313525,126.842 263 | 800,2,0.2286,55.5,0.00313525,128.492 264 | 1000,2,0.2286,55.5,0.00313525,129.002 265 | 1250,2,0.2286,55.5,0.00313525,128.762 266 | 1600,2,0.2286,55.5,0.00313525,126.752 267 | 2000,2,0.2286,55.5,0.00313525,124.612 268 | 2500,2,0.2286,55.5,0.00313525,123.862 269 | 3150,2,0.2286,55.5,0.00313525,123.742 270 | 4000,2,0.2286,55.5,0.00313525,122.232 271 | 5000,2,0.2286,55.5,0.00313525,120.472 272 | 6300,2,0.2286,55.5,0.00313525,118.712 273 | 315,2,0.2286,39.6,0.00346574,120.137 274 | 400,2,0.2286,39.6,0.00346574,122.147 275 | 500,2,0.2286,39.6,0.00346574,125.157 276 | 630,2,0.2286,39.6,0.00346574,127.417 277 | 800,2,0.2286,39.6,0.00346574,129.037 278 | 1000,2,0.2286,39.6,0.00346574,129.147 279 | 1250,2,0.2286,39.6,0.00346574,128.257 280 | 1600,2,0.2286,39.6,0.00346574,125.837 281 | 2000,2,0.2286,39.6,0.00346574,122.797 282 | 2500,2,0.2286,39.6,0.00346574,121.397 283 | 3150,2,0.2286,39.6,0.00346574,121.627 284 | 4000,2,0.2286,39.6,0.00346574,120.227 285 | 5000,2,0.2286,39.6,0.00346574,118.827 286 | 6300,2,0.2286,39.6,0.00346574,116.417 287 | 315,2,0.2286,31.7,0.00372371,120.147 288 | 400,2,0.2286,31.7,0.00372371,123.417 289 | 500,2,0.2286,31.7,0.00372371,126.677 290 | 630,2,0.2286,31.7,0.00372371,129.057 291 | 800,2,0.2286,31.7,0.00372371,130.307 292 | 1000,2,0.2286,31.7,0.00372371,130.307 293 | 1250,2,0.2286,31.7,0.00372371,128.677 294 | 1600,2,0.2286,31.7,0.00372371,125.797 295 | 2000,2,0.2286,31.7,0.00372371,123.037 296 | 2500,2,0.2286,31.7,0.00372371,121.407 297 | 3150,2,0.2286,31.7,0.00372371,121.527 298 | 4000,2,0.2286,31.7,0.00372371,120.527 299 | 5000,2,0.2286,31.7,0.00372371,118.267 300 | 6300,2,0.2286,31.7,0.00372371,115.137 301 | 500,4,0.2286,71.3,0.00400603,126.758 302 | 630,4,0.2286,71.3,0.00400603,129.038 303 | 800,4,0.2286,71.3,0.00400603,130.688 304 | 1000,4,0.2286,71.3,0.00400603,131.708 305 | 1250,4,0.2286,71.3,0.00400603,131.718 306 | 1600,4,0.2286,71.3,0.00400603,129.468 307 | 2000,4,0.2286,71.3,0.00400603,126.218 308 | 2500,4,0.2286,71.3,0.00400603,124.338 309 | 3150,4,0.2286,71.3,0.00400603,124.108 310 | 4000,4,0.2286,71.3,0.00400603,121.728 311 | 5000,4,0.2286,71.3,0.00400603,121.118 312 | 6300,4,0.2286,71.3,0.00400603,118.618 313 | 8000,4,0.2286,71.3,0.00400603,112.848 314 | 10000,4,0.2286,71.3,0.00400603,113.108 315 | 12500,4,0.2286,71.3,0.00400603,114.258 316 | 16000,4,0.2286,71.3,0.00400603,112.768 317 | 20000,4,0.2286,71.3,0.00400603,109.638 318 | 400,4,0.2286,55.5,0.0042862,123.274 319 | 500,4,0.2286,55.5,0.0042862,127.314 320 | 630,4,0.2286,55.5,0.0042862,129.964 321 | 800,4,0.2286,55.5,0.0042862,131.864 322 | 1000,4,0.2286,55.5,0.0042862,132.134 323 | 1250,4,0.2286,55.5,0.0042862,131.264 324 | 1600,4,0.2286,55.5,0.0042862,128.264 325 | 2000,4,0.2286,55.5,0.0042862,124.254 326 | 2500,4,0.2286,55.5,0.0042862,122.384 327 | 3150,4,0.2286,55.5,0.0042862,122.394 328 | 4000,4,0.2286,55.5,0.0042862,120.654 329 | 5000,4,0.2286,55.5,0.0042862,120.034 330 | 6300,4,0.2286,55.5,0.0042862,117.154 331 | 8000,4,0.2286,55.5,0.0042862,112.524 332 | 315,4,0.2286,39.6,0.00473801,122.229 333 | 400,4,0.2286,39.6,0.00473801,123.879 334 | 500,4,0.2286,39.6,0.00473801,127.039 335 | 630,4,0.2286,39.6,0.00473801,129.579 336 | 800,4,0.2286,39.6,0.00473801,130.469 337 | 1000,4,0.2286,39.6,0.00473801,129.969 338 | 1250,4,0.2286,39.6,0.00473801,128.339 339 | 1600,4,0.2286,39.6,0.00473801,125.319 340 | 2000,4,0.2286,39.6,0.00473801,121.659 341 | 2500,4,0.2286,39.6,0.00473801,119.649 342 | 3150,4,0.2286,39.6,0.00473801,120.419 343 | 4000,4,0.2286,39.6,0.00473801,119.159 344 | 5000,4,0.2286,39.6,0.00473801,117.649 345 | 6300,4,0.2286,39.6,0.00473801,114.249 346 | 8000,4,0.2286,39.6,0.00473801,113.129 347 | 250,4,0.2286,31.7,0.00509068,120.189 348 | 315,4,0.2286,31.7,0.00509068,123.609 349 | 400,4,0.2286,31.7,0.00509068,126.149 350 | 500,4,0.2286,31.7,0.00509068,128.939 351 | 630,4,0.2286,31.7,0.00509068,130.349 352 | 800,4,0.2286,31.7,0.00509068,130.869 353 | 1000,4,0.2286,31.7,0.00509068,129.869 354 | 1250,4,0.2286,31.7,0.00509068,128.119 355 | 1600,4,0.2286,31.7,0.00509068,125.229 356 | 2000,4,0.2286,31.7,0.00509068,122.089 357 | 2500,4,0.2286,31.7,0.00509068,120.209 358 | 3150,4,0.2286,31.7,0.00509068,120.229 359 | 4000,4,0.2286,31.7,0.00509068,118.859 360 | 5000,4,0.2286,31.7,0.00509068,115.969 361 | 6300,4,0.2286,31.7,0.00509068,112.699 362 | 400,5.3,0.2286,71.3,0.0051942,127.7 363 | 500,5.3,0.2286,71.3,0.0051942,129.88 364 | 630,5.3,0.2286,71.3,0.0051942,131.8 365 | 800,5.3,0.2286,71.3,0.0051942,133.48 366 | 1000,5.3,0.2286,71.3,0.0051942,134 367 | 1250,5.3,0.2286,71.3,0.0051942,133.38 368 | 1600,5.3,0.2286,71.3,0.0051942,130.46 369 | 2000,5.3,0.2286,71.3,0.0051942,125.89 370 | 2500,5.3,0.2286,71.3,0.0051942,123.74 371 | 3150,5.3,0.2286,71.3,0.0051942,123.12 372 | 4000,5.3,0.2286,71.3,0.0051942,120.33 373 | 5000,5.3,0.2286,71.3,0.0051942,118.05 374 | 6300,5.3,0.2286,71.3,0.0051942,116.92 375 | 8000,5.3,0.2286,71.3,0.0051942,114.9 376 | 10000,5.3,0.2286,71.3,0.0051942,111.35 377 | 250,5.3,0.2286,39.6,0.00614329,127.011 378 | 315,5.3,0.2286,39.6,0.00614329,129.691 379 | 400,5.3,0.2286,39.6,0.00614329,131.221 380 | 500,5.3,0.2286,39.6,0.00614329,132.251 381 | 630,5.3,0.2286,39.6,0.00614329,132.011 382 | 800,5.3,0.2286,39.6,0.00614329,129.491 383 | 1000,5.3,0.2286,39.6,0.00614329,125.581 384 | 1250,5.3,0.2286,39.6,0.00614329,125.721 385 | 1600,5.3,0.2286,39.6,0.00614329,123.081 386 | 2000,5.3,0.2286,39.6,0.00614329,117.911 387 | 2500,5.3,0.2286,39.6,0.00614329,116.151 388 | 3150,5.3,0.2286,39.6,0.00614329,118.441 389 | 4000,5.3,0.2286,39.6,0.00614329,115.801 390 | 5000,5.3,0.2286,39.6,0.00614329,115.311 391 | 6300,5.3,0.2286,39.6,0.00614329,112.541 392 | 200,7.3,0.2286,71.3,0.0104404,138.758 393 | 250,7.3,0.2286,71.3,0.0104404,139.918 394 | 315,7.3,0.2286,71.3,0.0104404,139.808 395 | 400,7.3,0.2286,71.3,0.0104404,139.438 396 | 500,7.3,0.2286,71.3,0.0104404,136.798 397 | 630,7.3,0.2286,71.3,0.0104404,133.768 398 | 800,7.3,0.2286,71.3,0.0104404,130.748 399 | 1000,7.3,0.2286,71.3,0.0104404,126.838 400 | 1250,7.3,0.2286,71.3,0.0104404,127.358 401 | 1600,7.3,0.2286,71.3,0.0104404,125.728 402 | 2000,7.3,0.2286,71.3,0.0104404,122.708 403 | 2500,7.3,0.2286,71.3,0.0104404,122.088 404 | 3150,7.3,0.2286,71.3,0.0104404,120.458 405 | 4000,7.3,0.2286,71.3,0.0104404,119.208 406 | 5000,7.3,0.2286,71.3,0.0104404,115.298 407 | 6300,7.3,0.2286,71.3,0.0104404,115.818 408 | 200,7.3,0.2286,55.5,0.0111706,135.234 409 | 250,7.3,0.2286,55.5,0.0111706,136.384 410 | 315,7.3,0.2286,55.5,0.0111706,136.284 411 | 400,7.3,0.2286,55.5,0.0111706,135.924 412 | 500,7.3,0.2286,55.5,0.0111706,133.174 413 | 630,7.3,0.2286,55.5,0.0111706,130.934 414 | 800,7.3,0.2286,55.5,0.0111706,128.444 415 | 1000,7.3,0.2286,55.5,0.0111706,125.194 416 | 1250,7.3,0.2286,55.5,0.0111706,125.724 417 | 1600,7.3,0.2286,55.5,0.0111706,123.354 418 | 2000,7.3,0.2286,55.5,0.0111706,120.354 419 | 2500,7.3,0.2286,55.5,0.0111706,118.994 420 | 3150,7.3,0.2286,55.5,0.0111706,117.134 421 | 4000,7.3,0.2286,55.5,0.0111706,117.284 422 | 5000,7.3,0.2286,55.5,0.0111706,113.144 423 | 6300,7.3,0.2286,55.5,0.0111706,111.534 424 | 200,7.3,0.2286,39.6,0.0123481,130.989 425 | 250,7.3,0.2286,39.6,0.0123481,131.889 426 | 315,7.3,0.2286,39.6,0.0123481,132.149 427 | 400,7.3,0.2286,39.6,0.0123481,132.039 428 | 500,7.3,0.2286,39.6,0.0123481,130.299 429 | 630,7.3,0.2286,39.6,0.0123481,128.929 430 | 800,7.3,0.2286,39.6,0.0123481,126.299 431 | 1000,7.3,0.2286,39.6,0.0123481,122.539 432 | 1250,7.3,0.2286,39.6,0.0123481,123.189 433 | 1600,7.3,0.2286,39.6,0.0123481,121.059 434 | 2000,7.3,0.2286,39.6,0.0123481,117.809 435 | 2500,7.3,0.2286,39.6,0.0123481,116.559 436 | 3150,7.3,0.2286,39.6,0.0123481,114.309 437 | 4000,7.3,0.2286,39.6,0.0123481,114.079 438 | 5000,7.3,0.2286,39.6,0.0123481,111.959 439 | 6300,7.3,0.2286,39.6,0.0123481,110.839 440 | 200,7.3,0.2286,31.7,0.0132672,128.679 441 | 250,7.3,0.2286,31.7,0.0132672,130.089 442 | 315,7.3,0.2286,31.7,0.0132672,130.239 443 | 400,7.3,0.2286,31.7,0.0132672,130.269 444 | 500,7.3,0.2286,31.7,0.0132672,128.169 445 | 630,7.3,0.2286,31.7,0.0132672,126.189 446 | 800,7.3,0.2286,31.7,0.0132672,123.209 447 | 1000,7.3,0.2286,31.7,0.0132672,119.099 448 | 1250,7.3,0.2286,31.7,0.0132672,120.509 449 | 1600,7.3,0.2286,31.7,0.0132672,119.039 450 | 2000,7.3,0.2286,31.7,0.0132672,115.309 451 | 2500,7.3,0.2286,31.7,0.0132672,114.709 452 | 3150,7.3,0.2286,31.7,0.0132672,113.229 453 | 4000,7.3,0.2286,31.7,0.0132672,112.639 454 | 5000,7.3,0.2286,31.7,0.0132672,111.029 455 | 6300,7.3,0.2286,31.7,0.0132672,110.689 456 | 800,0,0.1524,71.3,0.0015988,125.817 457 | 1000,0,0.1524,71.3,0.0015988,127.307 458 | 1250,0,0.1524,71.3,0.0015988,128.927 459 | 1600,0,0.1524,71.3,0.0015988,129.667 460 | 2000,0,0.1524,71.3,0.0015988,128.647 461 | 2500,0,0.1524,71.3,0.0015988,128.127 462 | 3150,0,0.1524,71.3,0.0015988,129.377 463 | 4000,0,0.1524,71.3,0.0015988,128.857 464 | 5000,0,0.1524,71.3,0.0015988,126.457 465 | 6300,0,0.1524,71.3,0.0015988,125.427 466 | 8000,0,0.1524,71.3,0.0015988,122.527 467 | 10000,0,0.1524,71.3,0.0015988,120.247 468 | 12500,0,0.1524,71.3,0.0015988,117.087 469 | 16000,0,0.1524,71.3,0.0015988,113.297 470 | 500,0,0.1524,55.5,0.00172668,120.573 471 | 630,0,0.1524,55.5,0.00172668,123.583 472 | 800,0,0.1524,55.5,0.00172668,126.713 473 | 1000,0,0.1524,55.5,0.00172668,128.583 474 | 1250,0,0.1524,55.5,0.00172668,129.953 475 | 1600,0,0.1524,55.5,0.00172668,130.183 476 | 2000,0,0.1524,55.5,0.00172668,129.673 477 | 2500,0,0.1524,55.5,0.00172668,127.763 478 | 3150,0,0.1524,55.5,0.00172668,127.753 479 | 4000,0,0.1524,55.5,0.00172668,127.233 480 | 5000,0,0.1524,55.5,0.00172668,125.203 481 | 6300,0,0.1524,55.5,0.00172668,123.303 482 | 8000,0,0.1524,55.5,0.00172668,121.903 483 | 10000,0,0.1524,55.5,0.00172668,119.253 484 | 12500,0,0.1524,55.5,0.00172668,117.093 485 | 16000,0,0.1524,55.5,0.00172668,112.803 486 | 500,0,0.1524,39.6,0.00193287,119.513 487 | 630,0,0.1524,39.6,0.00193287,124.403 488 | 800,0,0.1524,39.6,0.00193287,127.903 489 | 1000,0,0.1524,39.6,0.00193287,130.033 490 | 1250,0,0.1524,39.6,0.00193287,131.023 491 | 1600,0,0.1524,39.6,0.00193287,131.013 492 | 2000,0,0.1524,39.6,0.00193287,129.633 493 | 2500,0,0.1524,39.6,0.00193287,126.863 494 | 3150,0,0.1524,39.6,0.00193287,125.603 495 | 4000,0,0.1524,39.6,0.00193287,125.343 496 | 5000,0,0.1524,39.6,0.00193287,123.453 497 | 6300,0,0.1524,39.6,0.00193287,121.313 498 | 8000,0,0.1524,39.6,0.00193287,120.553 499 | 10000,0,0.1524,39.6,0.00193287,115.413 500 | 500,0,0.1524,31.7,0.00209405,121.617 501 | 630,0,0.1524,31.7,0.00209405,125.997 502 | 800,0,0.1524,31.7,0.00209405,129.117 503 | 1000,0,0.1524,31.7,0.00209405,130.987 504 | 1250,0,0.1524,31.7,0.00209405,131.467 505 | 1600,0,0.1524,31.7,0.00209405,130.817 506 | 2000,0,0.1524,31.7,0.00209405,128.907 507 | 2500,0,0.1524,31.7,0.00209405,125.867 508 | 3150,0,0.1524,31.7,0.00209405,124.207 509 | 4000,0,0.1524,31.7,0.00209405,123.807 510 | 5000,0,0.1524,31.7,0.00209405,122.397 511 | 6300,0,0.1524,31.7,0.00209405,119.737 512 | 8000,0,0.1524,31.7,0.00209405,117.957 513 | 630,2.7,0.1524,71.3,0.00243851,127.404 514 | 800,2.7,0.1524,71.3,0.00243851,127.394 515 | 1000,2.7,0.1524,71.3,0.00243851,128.774 516 | 1250,2.7,0.1524,71.3,0.00243851,130.144 517 | 1600,2.7,0.1524,71.3,0.00243851,130.644 518 | 2000,2.7,0.1524,71.3,0.00243851,130.114 519 | 2500,2.7,0.1524,71.3,0.00243851,128.334 520 | 3150,2.7,0.1524,71.3,0.00243851,127.054 521 | 4000,2.7,0.1524,71.3,0.00243851,126.534 522 | 5000,2.7,0.1524,71.3,0.00243851,124.364 523 | 6300,2.7,0.1524,71.3,0.00243851,121.944 524 | 8000,2.7,0.1524,71.3,0.00243851,120.534 525 | 10000,2.7,0.1524,71.3,0.00243851,116.724 526 | 12500,2.7,0.1524,71.3,0.00243851,113.034 527 | 16000,2.7,0.1524,71.3,0.00243851,110.364 528 | 500,2.7,0.1524,39.6,0.00294804,121.009 529 | 630,2.7,0.1524,39.6,0.00294804,125.809 530 | 800,2.7,0.1524,39.6,0.00294804,128.829 531 | 1000,2.7,0.1524,39.6,0.00294804,130.589 532 | 1250,2.7,0.1524,39.6,0.00294804,130.829 533 | 1600,2.7,0.1524,39.6,0.00294804,130.049 534 | 2000,2.7,0.1524,39.6,0.00294804,128.139 535 | 2500,2.7,0.1524,39.6,0.00294804,125.589 536 | 3150,2.7,0.1524,39.6,0.00294804,122.919 537 | 4000,2.7,0.1524,39.6,0.00294804,121.889 538 | 5000,2.7,0.1524,39.6,0.00294804,121.499 539 | 6300,2.7,0.1524,39.6,0.00294804,119.209 540 | 8000,2.7,0.1524,39.6,0.00294804,116.659 541 | 10000,2.7,0.1524,39.6,0.00294804,112.589 542 | 12500,2.7,0.1524,39.6,0.00294804,108.649 543 | 400,5.4,0.1524,71.3,0.00401199,124.121 544 | 500,5.4,0.1524,71.3,0.00401199,126.291 545 | 630,5.4,0.1524,71.3,0.00401199,128.971 546 | 800,5.4,0.1524,71.3,0.00401199,131.281 547 | 1000,5.4,0.1524,71.3,0.00401199,133.201 548 | 1250,5.4,0.1524,71.3,0.00401199,134.111 549 | 1600,5.4,0.1524,71.3,0.00401199,133.241 550 | 2000,5.4,0.1524,71.3,0.00401199,131.111 551 | 2500,5.4,0.1524,71.3,0.00401199,127.591 552 | 3150,5.4,0.1524,71.3,0.00401199,123.311 553 | 4000,5.4,0.1524,71.3,0.00401199,121.431 554 | 5000,5.4,0.1524,71.3,0.00401199,120.061 555 | 6300,5.4,0.1524,71.3,0.00401199,116.411 556 | 400,5.4,0.1524,55.5,0.00433288,126.807 557 | 500,5.4,0.1524,55.5,0.00433288,129.367 558 | 630,5.4,0.1524,55.5,0.00433288,131.807 559 | 800,5.4,0.1524,55.5,0.00433288,133.097 560 | 1000,5.4,0.1524,55.5,0.00433288,132.127 561 | 1250,5.4,0.1524,55.5,0.00433288,130.777 562 | 1600,5.4,0.1524,55.5,0.00433288,130.567 563 | 2000,5.4,0.1524,55.5,0.00433288,128.707 564 | 2500,5.4,0.1524,55.5,0.00433288,124.077 565 | 3150,5.4,0.1524,55.5,0.00433288,121.587 566 | 4000,5.4,0.1524,55.5,0.00433288,119.737 567 | 5000,5.4,0.1524,55.5,0.00433288,118.757 568 | 6300,5.4,0.1524,55.5,0.00433288,117.287 569 | 8000,5.4,0.1524,55.5,0.00433288,114.927 570 | 315,5.4,0.1524,39.6,0.00485029,125.347 571 | 400,5.4,0.1524,39.6,0.00485029,127.637 572 | 500,5.4,0.1524,39.6,0.00485029,129.937 573 | 630,5.4,0.1524,39.6,0.00485029,132.357 574 | 800,5.4,0.1524,39.6,0.00485029,132.757 575 | 1000,5.4,0.1524,39.6,0.00485029,130.507 576 | 1250,5.4,0.1524,39.6,0.00485029,127.117 577 | 1600,5.4,0.1524,39.6,0.00485029,126.267 578 | 2000,5.4,0.1524,39.6,0.00485029,124.647 579 | 2500,5.4,0.1524,39.6,0.00485029,120.497 580 | 3150,5.4,0.1524,39.6,0.00485029,119.137 581 | 4000,5.4,0.1524,39.6,0.00485029,117.137 582 | 5000,5.4,0.1524,39.6,0.00485029,117.037 583 | 6300,5.4,0.1524,39.6,0.00485029,116.677 584 | 315,5.4,0.1524,31.7,0.00525474,125.741 585 | 400,5.4,0.1524,31.7,0.00525474,127.781 586 | 500,5.4,0.1524,31.7,0.00525474,129.681 587 | 630,5.4,0.1524,31.7,0.00525474,131.471 588 | 800,5.4,0.1524,31.7,0.00525474,131.491 589 | 1000,5.4,0.1524,31.7,0.00525474,128.241 590 | 1250,5.4,0.1524,31.7,0.00525474,123.991 591 | 1600,5.4,0.1524,31.7,0.00525474,123.761 592 | 2000,5.4,0.1524,31.7,0.00525474,122.771 593 | 2500,5.4,0.1524,31.7,0.00525474,119.151 594 | 3150,5.4,0.1524,31.7,0.00525474,118.291 595 | 4000,5.4,0.1524,31.7,0.00525474,116.181 596 | 5000,5.4,0.1524,31.7,0.00525474,115.691 597 | 6300,5.4,0.1524,31.7,0.00525474,115.591 598 | 315,7.2,0.1524,71.3,0.00752039,128.713 599 | 400,7.2,0.1524,71.3,0.00752039,130.123 600 | 500,7.2,0.1524,71.3,0.00752039,132.043 601 | 630,7.2,0.1524,71.3,0.00752039,134.853 602 | 800,7.2,0.1524,71.3,0.00752039,136.023 603 | 1000,7.2,0.1524,71.3,0.00752039,134.273 604 | 1250,7.2,0.1524,71.3,0.00752039,132.513 605 | 1600,7.2,0.1524,71.3,0.00752039,130.893 606 | 2000,7.2,0.1524,71.3,0.00752039,128.643 607 | 2500,7.2,0.1524,71.3,0.00752039,124.353 608 | 3150,7.2,0.1524,71.3,0.00752039,116.783 609 | 4000,7.2,0.1524,71.3,0.00752039,119.343 610 | 5000,7.2,0.1524,71.3,0.00752039,118.343 611 | 6300,7.2,0.1524,71.3,0.00752039,116.603 612 | 8000,7.2,0.1524,71.3,0.00752039,113.333 613 | 10000,7.2,0.1524,71.3,0.00752039,110.313 614 | 250,7.2,0.1524,39.6,0.00909175,127.488 615 | 315,7.2,0.1524,39.6,0.00909175,130.558 616 | 400,7.2,0.1524,39.6,0.00909175,132.118 617 | 500,7.2,0.1524,39.6,0.00909175,132.658 618 | 630,7.2,0.1524,39.6,0.00909175,133.198 619 | 800,7.2,0.1524,39.6,0.00909175,132.358 620 | 1000,7.2,0.1524,39.6,0.00909175,128.338 621 | 1250,7.2,0.1524,39.6,0.00909175,122.428 622 | 1600,7.2,0.1524,39.6,0.00909175,120.058 623 | 2000,7.2,0.1524,39.6,0.00909175,120.228 624 | 2500,7.2,0.1524,39.6,0.00909175,117.478 625 | 3150,7.2,0.1524,39.6,0.00909175,111.818 626 | 4000,7.2,0.1524,39.6,0.00909175,114.258 627 | 5000,7.2,0.1524,39.6,0.00909175,113.288 628 | 6300,7.2,0.1524,39.6,0.00909175,112.688 629 | 8000,7.2,0.1524,39.6,0.00909175,111.588 630 | 10000,7.2,0.1524,39.6,0.00909175,110.868 631 | 200,9.9,0.1524,71.3,0.0193001,134.319 632 | 250,9.9,0.1524,71.3,0.0193001,135.329 633 | 315,9.9,0.1524,71.3,0.0193001,135.459 634 | 400,9.9,0.1524,71.3,0.0193001,135.079 635 | 500,9.9,0.1524,71.3,0.0193001,131.279 636 | 630,9.9,0.1524,71.3,0.0193001,129.889 637 | 800,9.9,0.1524,71.3,0.0193001,128.879 638 | 1000,9.9,0.1524,71.3,0.0193001,126.349 639 | 1250,9.9,0.1524,71.3,0.0193001,122.679 640 | 1600,9.9,0.1524,71.3,0.0193001,121.789 641 | 2000,9.9,0.1524,71.3,0.0193001,120.779 642 | 2500,9.9,0.1524,71.3,0.0193001,119.639 643 | 3150,9.9,0.1524,71.3,0.0193001,116.849 644 | 4000,9.9,0.1524,71.3,0.0193001,115.079 645 | 5000,9.9,0.1524,71.3,0.0193001,114.569 646 | 6300,9.9,0.1524,71.3,0.0193001,112.039 647 | 200,9.9,0.1524,55.5,0.0208438,131.955 648 | 250,9.9,0.1524,55.5,0.0208438,133.235 649 | 315,9.9,0.1524,55.5,0.0208438,132.355 650 | 400,9.9,0.1524,55.5,0.0208438,131.605 651 | 500,9.9,0.1524,55.5,0.0208438,127.815 652 | 630,9.9,0.1524,55.5,0.0208438,127.315 653 | 800,9.9,0.1524,55.5,0.0208438,126.565 654 | 1000,9.9,0.1524,55.5,0.0208438,124.665 655 | 1250,9.9,0.1524,55.5,0.0208438,121.635 656 | 1600,9.9,0.1524,55.5,0.0208438,119.875 657 | 2000,9.9,0.1524,55.5,0.0208438,119.505 658 | 2500,9.9,0.1524,55.5,0.0208438,118.365 659 | 3150,9.9,0.1524,55.5,0.0208438,115.085 660 | 4000,9.9,0.1524,55.5,0.0208438,112.945 661 | 5000,9.9,0.1524,55.5,0.0208438,112.065 662 | 6300,9.9,0.1524,55.5,0.0208438,110.555 663 | 200,9.9,0.1524,39.6,0.0233328,127.315 664 | 250,9.9,0.1524,39.6,0.0233328,128.335 665 | 315,9.9,0.1524,39.6,0.0233328,128.595 666 | 400,9.9,0.1524,39.6,0.0233328,128.345 667 | 500,9.9,0.1524,39.6,0.0233328,126.835 668 | 630,9.9,0.1524,39.6,0.0233328,126.465 669 | 800,9.9,0.1524,39.6,0.0233328,126.345 670 | 1000,9.9,0.1524,39.6,0.0233328,123.835 671 | 1250,9.9,0.1524,39.6,0.0233328,120.555 672 | 1600,9.9,0.1524,39.6,0.0233328,118.545 673 | 2000,9.9,0.1524,39.6,0.0233328,117.925 674 | 2500,9.9,0.1524,39.6,0.0233328,116.295 675 | 3150,9.9,0.1524,39.6,0.0233328,113.525 676 | 4000,9.9,0.1524,39.6,0.0233328,112.265 677 | 5000,9.9,0.1524,39.6,0.0233328,111.135 678 | 6300,9.9,0.1524,39.6,0.0233328,109.885 679 | 200,9.9,0.1524,31.7,0.0252785,127.299 680 | 250,9.9,0.1524,31.7,0.0252785,128.559 681 | 315,9.9,0.1524,31.7,0.0252785,128.809 682 | 400,9.9,0.1524,31.7,0.0252785,128.939 683 | 500,9.9,0.1524,31.7,0.0252785,127.179 684 | 630,9.9,0.1524,31.7,0.0252785,126.049 685 | 800,9.9,0.1524,31.7,0.0252785,125.539 686 | 1000,9.9,0.1524,31.7,0.0252785,122.149 687 | 1250,9.9,0.1524,31.7,0.0252785,118.619 688 | 1600,9.9,0.1524,31.7,0.0252785,117.119 689 | 2000,9.9,0.1524,31.7,0.0252785,116.859 690 | 2500,9.9,0.1524,31.7,0.0252785,114.729 691 | 3150,9.9,0.1524,31.7,0.0252785,112.209 692 | 4000,9.9,0.1524,31.7,0.0252785,111.459 693 | 5000,9.9,0.1524,31.7,0.0252785,109.949 694 | 6300,9.9,0.1524,31.7,0.0252785,108.689 695 | 200,12.6,0.1524,71.3,0.0483159,128.354 696 | 250,12.6,0.1524,71.3,0.0483159,129.744 697 | 315,12.6,0.1524,71.3,0.0483159,128.484 698 | 400,12.6,0.1524,71.3,0.0483159,127.094 699 | 500,12.6,0.1524,71.3,0.0483159,121.664 700 | 630,12.6,0.1524,71.3,0.0483159,123.304 701 | 800,12.6,0.1524,71.3,0.0483159,123.054 702 | 1000,12.6,0.1524,71.3,0.0483159,122.044 703 | 1250,12.6,0.1524,71.3,0.0483159,120.154 704 | 1600,12.6,0.1524,71.3,0.0483159,120.534 705 | 2000,12.6,0.1524,71.3,0.0483159,117.504 706 | 2500,12.6,0.1524,71.3,0.0483159,115.234 707 | 3150,12.6,0.1524,71.3,0.0483159,113.334 708 | 4000,12.6,0.1524,71.3,0.0483159,108.034 709 | 5000,12.6,0.1524,71.3,0.0483159,108.034 710 | 6300,12.6,0.1524,71.3,0.0483159,107.284 711 | 200,12.6,0.1524,39.6,0.0584113,114.75 712 | 250,12.6,0.1524,39.6,0.0584113,115.89 713 | 315,12.6,0.1524,39.6,0.0584113,116.02 714 | 400,12.6,0.1524,39.6,0.0584113,115.91 715 | 500,12.6,0.1524,39.6,0.0584113,114.9 716 | 630,12.6,0.1524,39.6,0.0584113,116.55 717 | 800,12.6,0.1524,39.6,0.0584113,116.56 718 | 1000,12.6,0.1524,39.6,0.0584113,114.67 719 | 1250,12.6,0.1524,39.6,0.0584113,112.16 720 | 1600,12.6,0.1524,39.6,0.0584113,110.78 721 | 2000,12.6,0.1524,39.6,0.0584113,109.52 722 | 2500,12.6,0.1524,39.6,0.0584113,106.88 723 | 3150,12.6,0.1524,39.6,0.0584113,106.26 724 | 4000,12.6,0.1524,39.6,0.0584113,104.5 725 | 5000,12.6,0.1524,39.6,0.0584113,104.13 726 | 6300,12.6,0.1524,39.6,0.0584113,103.38 727 | 800,0,0.0508,71.3,0.000740478,130.96 728 | 1000,0,0.0508,71.3,0.000740478,129.45 729 | 1250,0,0.0508,71.3,0.000740478,128.56 730 | 1600,0,0.0508,71.3,0.000740478,129.68 731 | 2000,0,0.0508,71.3,0.000740478,131.06 732 | 2500,0,0.0508,71.3,0.000740478,131.31 733 | 3150,0,0.0508,71.3,0.000740478,135.07 734 | 4000,0,0.0508,71.3,0.000740478,134.43 735 | 5000,0,0.0508,71.3,0.000740478,134.43 736 | 6300,0,0.0508,71.3,0.000740478,133.04 737 | 8000,0,0.0508,71.3,0.000740478,130.89 738 | 10000,0,0.0508,71.3,0.000740478,128.74 739 | 12500,0,0.0508,71.3,0.000740478,125.22 740 | 800,0,0.0508,55.5,0.00076193,124.336 741 | 1000,0,0.0508,55.5,0.00076193,125.586 742 | 1250,0,0.0508,55.5,0.00076193,127.076 743 | 1600,0,0.0508,55.5,0.00076193,128.576 744 | 2000,0,0.0508,55.5,0.00076193,131.456 745 | 2500,0,0.0508,55.5,0.00076193,133.956 746 | 3150,0,0.0508,55.5,0.00076193,134.826 747 | 4000,0,0.0508,55.5,0.00076193,134.946 748 | 5000,0,0.0508,55.5,0.00076193,134.556 749 | 6300,0,0.0508,55.5,0.00076193,132.796 750 | 8000,0,0.0508,55.5,0.00076193,130.156 751 | 10000,0,0.0508,55.5,0.00076193,127.636 752 | 12500,0,0.0508,55.5,0.00076193,125.376 753 | 800,0,0.0508,39.6,0.000791822,126.508 754 | 1000,0,0.0508,39.6,0.000791822,127.638 755 | 1250,0,0.0508,39.6,0.000791822,129.148 756 | 1600,0,0.0508,39.6,0.000791822,130.908 757 | 2000,0,0.0508,39.6,0.000791822,132.918 758 | 2500,0,0.0508,39.6,0.000791822,134.938 759 | 3150,0,0.0508,39.6,0.000791822,135.938 760 | 4000,0,0.0508,39.6,0.000791822,135.308 761 | 5000,0,0.0508,39.6,0.000791822,134.308 762 | 6300,0,0.0508,39.6,0.000791822,131.918 763 | 8000,0,0.0508,39.6,0.000791822,128.518 764 | 10000,0,0.0508,39.6,0.000791822,125.998 765 | 12500,0,0.0508,39.6,0.000791822,123.988 766 | 800,0,0.0508,31.7,0.000812164,122.79 767 | 1000,0,0.0508,31.7,0.000812164,126.78 768 | 1250,0,0.0508,31.7,0.000812164,129.27 769 | 1600,0,0.0508,31.7,0.000812164,131.01 770 | 2000,0,0.0508,31.7,0.000812164,133.01 771 | 2500,0,0.0508,31.7,0.000812164,134.87 772 | 3150,0,0.0508,31.7,0.000812164,135.49 773 | 4000,0,0.0508,31.7,0.000812164,134.11 774 | 5000,0,0.0508,31.7,0.000812164,133.23 775 | 6300,0,0.0508,31.7,0.000812164,130.34 776 | 8000,0,0.0508,31.7,0.000812164,126.59 777 | 10000,0,0.0508,31.7,0.000812164,122.45 778 | 12500,0,0.0508,31.7,0.000812164,119.07 779 | 1600,4.2,0.0508,71.3,0.00142788,124.318 780 | 2000,4.2,0.0508,71.3,0.00142788,129.848 781 | 2500,4.2,0.0508,71.3,0.00142788,131.978 782 | 3150,4.2,0.0508,71.3,0.00142788,133.728 783 | 4000,4.2,0.0508,71.3,0.00142788,133.598 784 | 5000,4.2,0.0508,71.3,0.00142788,132.828 785 | 6300,4.2,0.0508,71.3,0.00142788,129.308 786 | 8000,4.2,0.0508,71.3,0.00142788,125.268 787 | 10000,4.2,0.0508,71.3,0.00142788,121.238 788 | 12500,4.2,0.0508,71.3,0.00142788,117.328 789 | 1000,4.2,0.0508,39.6,0.00152689,125.647 790 | 1250,4.2,0.0508,39.6,0.00152689,128.427 791 | 1600,4.2,0.0508,39.6,0.00152689,130.197 792 | 2000,4.2,0.0508,39.6,0.00152689,132.587 793 | 2500,4.2,0.0508,39.6,0.00152689,133.847 794 | 3150,4.2,0.0508,39.6,0.00152689,133.587 795 | 4000,4.2,0.0508,39.6,0.00152689,131.807 796 | 5000,4.2,0.0508,39.6,0.00152689,129.777 797 | 6300,4.2,0.0508,39.6,0.00152689,125.717 798 | 8000,4.2,0.0508,39.6,0.00152689,120.397 799 | 10000,4.2,0.0508,39.6,0.00152689,116.967 800 | 800,8.4,0.0508,71.3,0.00529514,127.556 801 | 1000,8.4,0.0508,71.3,0.00529514,129.946 802 | 1250,8.4,0.0508,71.3,0.00529514,132.086 803 | 1600,8.4,0.0508,71.3,0.00529514,133.846 804 | 2000,8.4,0.0508,71.3,0.00529514,134.476 805 | 2500,8.4,0.0508,71.3,0.00529514,134.226 806 | 3150,8.4,0.0508,71.3,0.00529514,131.966 807 | 4000,8.4,0.0508,71.3,0.00529514,126.926 808 | 5000,8.4,0.0508,71.3,0.00529514,121.146 809 | 400,8.4,0.0508,55.5,0.00544854,121.582 810 | 500,8.4,0.0508,55.5,0.00544854,123.742 811 | 630,8.4,0.0508,55.5,0.00544854,126.152 812 | 800,8.4,0.0508,55.5,0.00544854,128.562 813 | 1000,8.4,0.0508,55.5,0.00544854,130.722 814 | 1250,8.4,0.0508,55.5,0.00544854,132.252 815 | 1600,8.4,0.0508,55.5,0.00544854,133.032 816 | 2000,8.4,0.0508,55.5,0.00544854,133.042 817 | 2500,8.4,0.0508,55.5,0.00544854,131.542 818 | 3150,8.4,0.0508,55.5,0.00544854,128.402 819 | 4000,8.4,0.0508,55.5,0.00544854,122.612 820 | 5000,8.4,0.0508,55.5,0.00544854,115.812 821 | 400,8.4,0.0508,39.6,0.00566229,120.015 822 | 500,8.4,0.0508,39.6,0.00566229,122.905 823 | 630,8.4,0.0508,39.6,0.00566229,126.045 824 | 800,8.4,0.0508,39.6,0.00566229,128.435 825 | 1000,8.4,0.0508,39.6,0.00566229,130.195 826 | 1250,8.4,0.0508,39.6,0.00566229,131.205 827 | 1600,8.4,0.0508,39.6,0.00566229,130.965 828 | 2000,8.4,0.0508,39.6,0.00566229,129.965 829 | 2500,8.4,0.0508,39.6,0.00566229,127.465 830 | 3150,8.4,0.0508,39.6,0.00566229,123.965 831 | 4000,8.4,0.0508,39.6,0.00566229,118.955 832 | 400,8.4,0.0508,31.7,0.00580776,120.076 833 | 500,8.4,0.0508,31.7,0.00580776,122.966 834 | 630,8.4,0.0508,31.7,0.00580776,125.856 835 | 800,8.4,0.0508,31.7,0.00580776,128.246 836 | 1000,8.4,0.0508,31.7,0.00580776,129.516 837 | 1250,8.4,0.0508,31.7,0.00580776,130.156 838 | 1600,8.4,0.0508,31.7,0.00580776,129.296 839 | 2000,8.4,0.0508,31.7,0.00580776,127.686 840 | 2500,8.4,0.0508,31.7,0.00580776,125.576 841 | 3150,8.4,0.0508,31.7,0.00580776,122.086 842 | 4000,8.4,0.0508,31.7,0.00580776,118.106 843 | 200,11.2,0.0508,71.3,0.014072,125.941 844 | 250,11.2,0.0508,71.3,0.014072,127.101 845 | 315,11.2,0.0508,71.3,0.014072,128.381 846 | 400,11.2,0.0508,71.3,0.014072,129.281 847 | 500,11.2,0.0508,71.3,0.014072,130.311 848 | 630,11.2,0.0508,71.3,0.014072,133.611 849 | 800,11.2,0.0508,71.3,0.014072,136.031 850 | 1000,11.2,0.0508,71.3,0.014072,136.941 851 | 1250,11.2,0.0508,71.3,0.014072,136.191 852 | 1600,11.2,0.0508,71.3,0.014072,135.191 853 | 2000,11.2,0.0508,71.3,0.014072,133.311 854 | 2500,11.2,0.0508,71.3,0.014072,130.541 855 | 3150,11.2,0.0508,71.3,0.014072,127.141 856 | 4000,11.2,0.0508,71.3,0.014072,122.471 857 | 200,11.2,0.0508,39.6,0.0150478,125.01 858 | 250,11.2,0.0508,39.6,0.0150478,126.43 859 | 315,11.2,0.0508,39.6,0.0150478,128.99 860 | 400,11.2,0.0508,39.6,0.0150478,130.67 861 | 500,11.2,0.0508,39.6,0.0150478,131.96 862 | 630,11.2,0.0508,39.6,0.0150478,133.13 863 | 800,11.2,0.0508,39.6,0.0150478,133.79 864 | 1000,11.2,0.0508,39.6,0.0150478,132.43 865 | 1250,11.2,0.0508,39.6,0.0150478,130.05 866 | 1600,11.2,0.0508,39.6,0.0150478,126.54 867 | 2000,11.2,0.0508,39.6,0.0150478,124.42 868 | 2500,11.2,0.0508,39.6,0.0150478,122.17 869 | 3150,11.2,0.0508,39.6,0.0150478,119.67 870 | 4000,11.2,0.0508,39.6,0.0150478,115.52 871 | 200,15.4,0.0508,71.3,0.0264269,123.595 872 | 250,15.4,0.0508,71.3,0.0264269,124.835 873 | 315,15.4,0.0508,71.3,0.0264269,126.195 874 | 400,15.4,0.0508,71.3,0.0264269,126.805 875 | 500,15.4,0.0508,71.3,0.0264269,127.285 876 | 630,15.4,0.0508,71.3,0.0264269,129.645 877 | 800,15.4,0.0508,71.3,0.0264269,131.515 878 | 1000,15.4,0.0508,71.3,0.0264269,131.865 879 | 1250,15.4,0.0508,71.3,0.0264269,130.845 880 | 1600,15.4,0.0508,71.3,0.0264269,130.065 881 | 2000,15.4,0.0508,71.3,0.0264269,129.285 882 | 2500,15.4,0.0508,71.3,0.0264269,127.625 883 | 3150,15.4,0.0508,71.3,0.0264269,125.715 884 | 4000,15.4,0.0508,71.3,0.0264269,122.675 885 | 5000,15.4,0.0508,71.3,0.0264269,119.135 886 | 6300,15.4,0.0508,71.3,0.0264269,115.215 887 | 8000,15.4,0.0508,71.3,0.0264269,112.675 888 | 200,15.4,0.0508,55.5,0.0271925,122.94 889 | 250,15.4,0.0508,55.5,0.0271925,124.17 890 | 315,15.4,0.0508,55.5,0.0271925,125.39 891 | 400,15.4,0.0508,55.5,0.0271925,126.5 892 | 500,15.4,0.0508,55.5,0.0271925,127.22 893 | 630,15.4,0.0508,55.5,0.0271925,129.33 894 | 800,15.4,0.0508,55.5,0.0271925,130.43 895 | 1000,15.4,0.0508,55.5,0.0271925,130.4 896 | 1250,15.4,0.0508,55.5,0.0271925,130 897 | 1600,15.4,0.0508,55.5,0.0271925,128.2 898 | 2000,15.4,0.0508,55.5,0.0271925,127.04 899 | 2500,15.4,0.0508,55.5,0.0271925,125.63 900 | 3150,15.4,0.0508,55.5,0.0271925,123.46 901 | 4000,15.4,0.0508,55.5,0.0271925,120.92 902 | 5000,15.4,0.0508,55.5,0.0271925,117.11 903 | 6300,15.4,0.0508,55.5,0.0271925,112.93 904 | 200,15.4,0.0508,39.6,0.0282593,121.783 905 | 250,15.4,0.0508,39.6,0.0282593,122.893 906 | 315,15.4,0.0508,39.6,0.0282593,124.493 907 | 400,15.4,0.0508,39.6,0.0282593,125.353 908 | 500,15.4,0.0508,39.6,0.0282593,125.963 909 | 630,15.4,0.0508,39.6,0.0282593,127.443 910 | 800,15.4,0.0508,39.6,0.0282593,128.423 911 | 1000,15.4,0.0508,39.6,0.0282593,127.893 912 | 1250,15.4,0.0508,39.6,0.0282593,126.743 913 | 1600,15.4,0.0508,39.6,0.0282593,124.843 914 | 2000,15.4,0.0508,39.6,0.0282593,123.443 915 | 2500,15.4,0.0508,39.6,0.0282593,122.413 916 | 3150,15.4,0.0508,39.6,0.0282593,120.513 917 | 4000,15.4,0.0508,39.6,0.0282593,118.113 918 | 5000,15.4,0.0508,39.6,0.0282593,114.453 919 | 6300,15.4,0.0508,39.6,0.0282593,109.663 920 | 200,15.4,0.0508,31.7,0.0289853,119.975 921 | 250,15.4,0.0508,31.7,0.0289853,121.225 922 | 315,15.4,0.0508,31.7,0.0289853,122.845 923 | 400,15.4,0.0508,31.7,0.0289853,123.705 924 | 500,15.4,0.0508,31.7,0.0289853,123.695 925 | 630,15.4,0.0508,31.7,0.0289853,124.685 926 | 800,15.4,0.0508,31.7,0.0289853,125.555 927 | 1000,15.4,0.0508,31.7,0.0289853,124.525 928 | 1250,15.4,0.0508,31.7,0.0289853,123.255 929 | 1600,15.4,0.0508,31.7,0.0289853,121.485 930 | 2000,15.4,0.0508,31.7,0.0289853,120.835 931 | 2500,15.4,0.0508,31.7,0.0289853,119.945 932 | 3150,15.4,0.0508,31.7,0.0289853,118.045 933 | 4000,15.4,0.0508,31.7,0.0289853,115.635 934 | 5000,15.4,0.0508,31.7,0.0289853,112.355 935 | 6300,15.4,0.0508,31.7,0.0289853,108.185 936 | 200,19.7,0.0508,71.3,0.0341183,118.005 937 | 250,19.7,0.0508,71.3,0.0341183,119.115 938 | 315,19.7,0.0508,71.3,0.0341183,121.235 939 | 400,19.7,0.0508,71.3,0.0341183,123.865 940 | 500,19.7,0.0508,71.3,0.0341183,126.995 941 | 630,19.7,0.0508,71.3,0.0341183,128.365 942 | 800,19.7,0.0508,71.3,0.0341183,124.555 943 | 1000,19.7,0.0508,71.3,0.0341183,121.885 944 | 1250,19.7,0.0508,71.3,0.0341183,121.485 945 | 1600,19.7,0.0508,71.3,0.0341183,120.575 946 | 2000,19.7,0.0508,71.3,0.0341183,120.055 947 | 2500,19.7,0.0508,71.3,0.0341183,118.385 948 | 3150,19.7,0.0508,71.3,0.0341183,116.225 949 | 4000,19.7,0.0508,71.3,0.0341183,113.045 950 | 200,19.7,0.0508,39.6,0.036484,125.974 951 | 250,19.7,0.0508,39.6,0.036484,127.224 952 | 315,19.7,0.0508,39.6,0.036484,129.864 953 | 400,19.7,0.0508,39.6,0.036484,130.614 954 | 500,19.7,0.0508,39.6,0.036484,128.444 955 | 630,19.7,0.0508,39.6,0.036484,120.324 956 | 800,19.7,0.0508,39.6,0.036484,119.174 957 | 1000,19.7,0.0508,39.6,0.036484,118.904 958 | 1250,19.7,0.0508,39.6,0.036484,118.634 959 | 1600,19.7,0.0508,39.6,0.036484,117.604 960 | 2000,19.7,0.0508,39.6,0.036484,117.724 961 | 2500,19.7,0.0508,39.6,0.036484,116.184 962 | 3150,19.7,0.0508,39.6,0.036484,113.004 963 | 4000,19.7,0.0508,39.6,0.036484,108.684 964 | 2500,0,0.0254,71.3,0.000400682,133.707 965 | 3150,0,0.0254,71.3,0.000400682,137.007 966 | 4000,0,0.0254,71.3,0.000400682,138.557 967 | 5000,0,0.0254,71.3,0.000400682,136.837 968 | 6300,0,0.0254,71.3,0.000400682,134.987 969 | 8000,0,0.0254,71.3,0.000400682,129.867 970 | 10000,0,0.0254,71.3,0.000400682,130.787 971 | 12500,0,0.0254,71.3,0.000400682,133.207 972 | 16000,0,0.0254,71.3,0.000400682,130.477 973 | 20000,0,0.0254,71.3,0.000400682,123.217 974 | 2000,0,0.0254,55.5,0.00041229,127.623 975 | 2500,0,0.0254,55.5,0.00041229,130.073 976 | 3150,0,0.0254,55.5,0.00041229,130.503 977 | 4000,0,0.0254,55.5,0.00041229,133.223 978 | 5000,0,0.0254,55.5,0.00041229,135.803 979 | 6300,0,0.0254,55.5,0.00041229,136.103 980 | 8000,0,0.0254,55.5,0.00041229,136.163 981 | 10000,0,0.0254,55.5,0.00041229,134.563 982 | 12500,0,0.0254,55.5,0.00041229,131.453 983 | 16000,0,0.0254,55.5,0.00041229,125.683 984 | 20000,0,0.0254,55.5,0.00041229,121.933 985 | 1600,0,0.0254,39.6,0.000428464,124.156 986 | 2000,0,0.0254,39.6,0.000428464,130.026 987 | 2500,0,0.0254,39.6,0.000428464,131.836 988 | 3150,0,0.0254,39.6,0.000428464,133.276 989 | 4000,0,0.0254,39.6,0.000428464,135.346 990 | 5000,0,0.0254,39.6,0.000428464,136.536 991 | 6300,0,0.0254,39.6,0.000428464,136.826 992 | 8000,0,0.0254,39.6,0.000428464,135.866 993 | 10000,0,0.0254,39.6,0.000428464,133.376 994 | 12500,0,0.0254,39.6,0.000428464,129.116 995 | 16000,0,0.0254,39.6,0.000428464,124.986 996 | 1000,0,0.0254,31.7,0.000439472,125.127 997 | 1250,0,0.0254,31.7,0.000439472,127.947 998 | 1600,0,0.0254,31.7,0.000439472,129.267 999 | 2000,0,0.0254,31.7,0.000439472,130.697 1000 | 2500,0,0.0254,31.7,0.000439472,132.897 1001 | 3150,0,0.0254,31.7,0.000439472,135.227 1002 | 4000,0,0.0254,31.7,0.000439472,137.047 1003 | 5000,0,0.0254,31.7,0.000439472,138.607 1004 | 6300,0,0.0254,31.7,0.000439472,138.537 1005 | 8000,0,0.0254,31.7,0.000439472,137.207 1006 | 10000,0,0.0254,31.7,0.000439472,134.227 1007 | 12500,0,0.0254,31.7,0.000439472,128.977 1008 | 16000,0,0.0254,31.7,0.000439472,125.627 1009 | 2000,4.8,0.0254,71.3,0.000848633,128.398 1010 | 2500,4.8,0.0254,71.3,0.000848633,130.828 1011 | 3150,4.8,0.0254,71.3,0.000848633,133.378 1012 | 4000,4.8,0.0254,71.3,0.000848633,134.928 1013 | 5000,4.8,0.0254,71.3,0.000848633,135.468 1014 | 6300,4.8,0.0254,71.3,0.000848633,134.498 1015 | 8000,4.8,0.0254,71.3,0.000848633,131.518 1016 | 10000,4.8,0.0254,71.3,0.000848633,127.398 1017 | 12500,4.8,0.0254,71.3,0.000848633,127.688 1018 | 16000,4.8,0.0254,71.3,0.000848633,124.208 1019 | 20000,4.8,0.0254,71.3,0.000848633,119.708 1020 | 1600,4.8,0.0254,55.5,0.000873218,121.474 1021 | 2000,4.8,0.0254,55.5,0.000873218,125.054 1022 | 2500,4.8,0.0254,55.5,0.000873218,129.144 1023 | 3150,4.8,0.0254,55.5,0.000873218,132.354 1024 | 4000,4.8,0.0254,55.5,0.000873218,133.924 1025 | 5000,4.8,0.0254,55.5,0.000873218,135.484 1026 | 6300,4.8,0.0254,55.5,0.000873218,135.164 1027 | 8000,4.8,0.0254,55.5,0.000873218,132.184 1028 | 10000,4.8,0.0254,55.5,0.000873218,126.944 1029 | 12500,4.8,0.0254,55.5,0.000873218,125.094 1030 | 16000,4.8,0.0254,55.5,0.000873218,124.394 1031 | 20000,4.8,0.0254,55.5,0.000873218,121.284 1032 | 500,4.8,0.0254,39.6,0.000907475,116.366 1033 | 630,4.8,0.0254,39.6,0.000907475,118.696 1034 | 800,4.8,0.0254,39.6,0.000907475,120.766 1035 | 1000,4.8,0.0254,39.6,0.000907475,122.956 1036 | 1250,4.8,0.0254,39.6,0.000907475,125.026 1037 | 1600,4.8,0.0254,39.6,0.000907475,125.966 1038 | 2000,4.8,0.0254,39.6,0.000907475,128.916 1039 | 2500,4.8,0.0254,39.6,0.000907475,131.236 1040 | 3150,4.8,0.0254,39.6,0.000907475,133.436 1041 | 4000,4.8,0.0254,39.6,0.000907475,134.996 1042 | 5000,4.8,0.0254,39.6,0.000907475,135.426 1043 | 6300,4.8,0.0254,39.6,0.000907475,134.336 1044 | 8000,4.8,0.0254,39.6,0.000907475,131.346 1045 | 10000,4.8,0.0254,39.6,0.000907475,126.066 1046 | 500,4.8,0.0254,31.7,0.000930789,116.128 1047 | 630,4.8,0.0254,31.7,0.000930789,120.078 1048 | 800,4.8,0.0254,31.7,0.000930789,122.648 1049 | 1000,4.8,0.0254,31.7,0.000930789,125.348 1050 | 1250,4.8,0.0254,31.7,0.000930789,127.408 1051 | 1600,4.8,0.0254,31.7,0.000930789,128.718 1052 | 2000,4.8,0.0254,31.7,0.000930789,130.148 1053 | 2500,4.8,0.0254,31.7,0.000930789,132.588 1054 | 3150,4.8,0.0254,31.7,0.000930789,134.268 1055 | 4000,4.8,0.0254,31.7,0.000930789,135.328 1056 | 5000,4.8,0.0254,31.7,0.000930789,135.248 1057 | 6300,4.8,0.0254,31.7,0.000930789,132.898 1058 | 8000,4.8,0.0254,31.7,0.000930789,127.008 1059 | 630,9.5,0.0254,71.3,0.00420654,125.726 1060 | 800,9.5,0.0254,71.3,0.00420654,127.206 1061 | 1000,9.5,0.0254,71.3,0.00420654,129.556 1062 | 1250,9.5,0.0254,71.3,0.00420654,131.656 1063 | 1600,9.5,0.0254,71.3,0.00420654,133.756 1064 | 2000,9.5,0.0254,71.3,0.00420654,134.976 1065 | 2500,9.5,0.0254,71.3,0.00420654,135.956 1066 | 3150,9.5,0.0254,71.3,0.00420654,136.166 1067 | 4000,9.5,0.0254,71.3,0.00420654,134.236 1068 | 5000,9.5,0.0254,71.3,0.00420654,131.186 1069 | 6300,9.5,0.0254,71.3,0.00420654,127.246 1070 | 400,9.5,0.0254,55.5,0.0043284,120.952 1071 | 500,9.5,0.0254,55.5,0.0043284,123.082 1072 | 630,9.5,0.0254,55.5,0.0043284,125.452 1073 | 800,9.5,0.0254,55.5,0.0043284,128.082 1074 | 1000,9.5,0.0254,55.5,0.0043284,130.332 1075 | 1250,9.5,0.0254,55.5,0.0043284,132.202 1076 | 1600,9.5,0.0254,55.5,0.0043284,133.062 1077 | 2000,9.5,0.0254,55.5,0.0043284,134.052 1078 | 2500,9.5,0.0254,55.5,0.0043284,134.152 1079 | 3150,9.5,0.0254,55.5,0.0043284,133.252 1080 | 4000,9.5,0.0254,55.5,0.0043284,131.582 1081 | 5000,9.5,0.0254,55.5,0.0043284,128.412 1082 | 6300,9.5,0.0254,55.5,0.0043284,124.222 1083 | 200,9.5,0.0254,39.6,0.00449821,116.074 1084 | 250,9.5,0.0254,39.6,0.00449821,116.924 1085 | 315,9.5,0.0254,39.6,0.00449821,119.294 1086 | 400,9.5,0.0254,39.6,0.00449821,121.154 1087 | 500,9.5,0.0254,39.6,0.00449821,123.894 1088 | 630,9.5,0.0254,39.6,0.00449821,126.514 1089 | 800,9.5,0.0254,39.6,0.00449821,129.014 1090 | 1000,9.5,0.0254,39.6,0.00449821,130.374 1091 | 1250,9.5,0.0254,39.6,0.00449821,130.964 1092 | 1600,9.5,0.0254,39.6,0.00449821,131.184 1093 | 2000,9.5,0.0254,39.6,0.00449821,131.274 1094 | 2500,9.5,0.0254,39.6,0.00449821,131.234 1095 | 3150,9.5,0.0254,39.6,0.00449821,129.934 1096 | 4000,9.5,0.0254,39.6,0.00449821,127.864 1097 | 5000,9.5,0.0254,39.6,0.00449821,125.044 1098 | 6300,9.5,0.0254,39.6,0.00449821,120.324 1099 | 200,9.5,0.0254,31.7,0.00461377,119.146 1100 | 250,9.5,0.0254,31.7,0.00461377,120.136 1101 | 315,9.5,0.0254,31.7,0.00461377,122.766 1102 | 400,9.5,0.0254,31.7,0.00461377,124.756 1103 | 500,9.5,0.0254,31.7,0.00461377,126.886 1104 | 630,9.5,0.0254,31.7,0.00461377,129.006 1105 | 800,9.5,0.0254,31.7,0.00461377,130.746 1106 | 1000,9.5,0.0254,31.7,0.00461377,131.346 1107 | 1250,9.5,0.0254,31.7,0.00461377,131.446 1108 | 1600,9.5,0.0254,31.7,0.00461377,131.036 1109 | 2000,9.5,0.0254,31.7,0.00461377,130.496 1110 | 2500,9.5,0.0254,31.7,0.00461377,130.086 1111 | 3150,9.5,0.0254,31.7,0.00461377,128.536 1112 | 4000,9.5,0.0254,31.7,0.00461377,126.736 1113 | 5000,9.5,0.0254,31.7,0.00461377,124.426 1114 | 6300,9.5,0.0254,31.7,0.00461377,120.726 1115 | 250,12.7,0.0254,71.3,0.0121808,119.698 1116 | 315,12.7,0.0254,71.3,0.0121808,122.938 1117 | 400,12.7,0.0254,71.3,0.0121808,125.048 1118 | 500,12.7,0.0254,71.3,0.0121808,126.898 1119 | 630,12.7,0.0254,71.3,0.0121808,128.878 1120 | 800,12.7,0.0254,71.3,0.0121808,130.348 1121 | 1000,12.7,0.0254,71.3,0.0121808,131.698 1122 | 1250,12.7,0.0254,71.3,0.0121808,133.048 1123 | 1600,12.7,0.0254,71.3,0.0121808,134.528 1124 | 2000,12.7,0.0254,71.3,0.0121808,134.228 1125 | 2500,12.7,0.0254,71.3,0.0121808,134.058 1126 | 3150,12.7,0.0254,71.3,0.0121808,133.758 1127 | 4000,12.7,0.0254,71.3,0.0121808,131.808 1128 | 5000,12.7,0.0254,71.3,0.0121808,128.978 1129 | 6300,12.7,0.0254,71.3,0.0121808,125.398 1130 | 8000,12.7,0.0254,71.3,0.0121808,120.538 1131 | 10000,12.7,0.0254,71.3,0.0121808,114.418 1132 | 250,12.7,0.0254,39.6,0.0130253,121.547 1133 | 315,12.7,0.0254,39.6,0.0130253,123.537 1134 | 400,12.7,0.0254,39.6,0.0130253,125.527 1135 | 500,12.7,0.0254,39.6,0.0130253,127.127 1136 | 630,12.7,0.0254,39.6,0.0130253,128.867 1137 | 800,12.7,0.0254,39.6,0.0130253,130.217 1138 | 1000,12.7,0.0254,39.6,0.0130253,130.947 1139 | 1250,12.7,0.0254,39.6,0.0130253,130.777 1140 | 1600,12.7,0.0254,39.6,0.0130253,129.977 1141 | 2000,12.7,0.0254,39.6,0.0130253,129.567 1142 | 2500,12.7,0.0254,39.6,0.0130253,129.027 1143 | 3150,12.7,0.0254,39.6,0.0130253,127.847 1144 | 4000,12.7,0.0254,39.6,0.0130253,126.537 1145 | 5000,12.7,0.0254,39.6,0.0130253,125.107 1146 | 6300,12.7,0.0254,39.6,0.0130253,123.177 1147 | 8000,12.7,0.0254,39.6,0.0130253,120.607 1148 | 10000,12.7,0.0254,39.6,0.0130253,116.017 1149 | 200,17.4,0.0254,71.3,0.016104,112.506 1150 | 250,17.4,0.0254,71.3,0.016104,113.796 1151 | 315,17.4,0.0254,71.3,0.016104,115.846 1152 | 400,17.4,0.0254,71.3,0.016104,117.396 1153 | 500,17.4,0.0254,71.3,0.016104,119.806 1154 | 630,17.4,0.0254,71.3,0.016104,122.606 1155 | 800,17.4,0.0254,71.3,0.016104,124.276 1156 | 1000,17.4,0.0254,71.3,0.016104,125.816 1157 | 1250,17.4,0.0254,71.3,0.016104,126.356 1158 | 1600,17.4,0.0254,71.3,0.016104,126.406 1159 | 2000,17.4,0.0254,71.3,0.016104,126.826 1160 | 2500,17.4,0.0254,71.3,0.016104,126.746 1161 | 3150,17.4,0.0254,71.3,0.016104,126.536 1162 | 4000,17.4,0.0254,71.3,0.016104,125.586 1163 | 5000,17.4,0.0254,71.3,0.016104,123.126 1164 | 6300,17.4,0.0254,71.3,0.016104,119.916 1165 | 8000,17.4,0.0254,71.3,0.016104,115.466 1166 | 200,17.4,0.0254,55.5,0.0165706,109.951 1167 | 250,17.4,0.0254,55.5,0.0165706,110.491 1168 | 315,17.4,0.0254,55.5,0.0165706,111.911 1169 | 400,17.4,0.0254,55.5,0.0165706,115.461 1170 | 500,17.4,0.0254,55.5,0.0165706,119.621 1171 | 630,17.4,0.0254,55.5,0.0165706,122.411 1172 | 800,17.4,0.0254,55.5,0.0165706,123.091 1173 | 1000,17.4,0.0254,55.5,0.0165706,126.001 1174 | 1250,17.4,0.0254,55.5,0.0165706,129.301 1175 | 1600,17.4,0.0254,55.5,0.0165706,126.471 1176 | 2000,17.4,0.0254,55.5,0.0165706,125.261 1177 | 2500,17.4,0.0254,55.5,0.0165706,124.931 1178 | 3150,17.4,0.0254,55.5,0.0165706,124.101 1179 | 4000,17.4,0.0254,55.5,0.0165706,121.771 1180 | 5000,17.4,0.0254,55.5,0.0165706,118.941 1181 | 6300,17.4,0.0254,55.5,0.0165706,114.861 1182 | 200,17.4,0.0254,39.6,0.0172206,114.044 1183 | 250,17.4,0.0254,39.6,0.0172206,114.714 1184 | 315,17.4,0.0254,39.6,0.0172206,115.144 1185 | 400,17.4,0.0254,39.6,0.0172206,115.444 1186 | 500,17.4,0.0254,39.6,0.0172206,117.514 1187 | 630,17.4,0.0254,39.6,0.0172206,124.514 1188 | 800,17.4,0.0254,39.6,0.0172206,135.324 1189 | 1000,17.4,0.0254,39.6,0.0172206,138.274 1190 | 1250,17.4,0.0254,39.6,0.0172206,131.364 1191 | 1600,17.4,0.0254,39.6,0.0172206,127.614 1192 | 2000,17.4,0.0254,39.6,0.0172206,126.644 1193 | 2500,17.4,0.0254,39.6,0.0172206,124.154 1194 | 3150,17.4,0.0254,39.6,0.0172206,123.564 1195 | 4000,17.4,0.0254,39.6,0.0172206,122.724 1196 | 5000,17.4,0.0254,39.6,0.0172206,119.854 1197 | 200,17.4,0.0254,31.7,0.0176631,116.146 1198 | 250,17.4,0.0254,31.7,0.0176631,116.956 1199 | 315,17.4,0.0254,31.7,0.0176631,118.416 1200 | 400,17.4,0.0254,31.7,0.0176631,120.766 1201 | 500,17.4,0.0254,31.7,0.0176631,127.676 1202 | 630,17.4,0.0254,31.7,0.0176631,136.886 1203 | 800,17.4,0.0254,31.7,0.0176631,139.226 1204 | 1000,17.4,0.0254,31.7,0.0176631,131.796 1205 | 1250,17.4,0.0254,31.7,0.0176631,128.306 1206 | 1600,17.4,0.0254,31.7,0.0176631,126.846 1207 | 2000,17.4,0.0254,31.7,0.0176631,124.356 1208 | 2500,17.4,0.0254,31.7,0.0176631,124.166 1209 | 3150,17.4,0.0254,31.7,0.0176631,123.466 1210 | 4000,17.4,0.0254,31.7,0.0176631,121.996 1211 | 5000,17.4,0.0254,31.7,0.0176631,117.996 1212 | 315,22.2,0.0254,71.3,0.0214178,115.857 1213 | 400,22.2,0.0254,71.3,0.0214178,117.927 1214 | 500,22.2,0.0254,71.3,0.0214178,117.967 1215 | 630,22.2,0.0254,71.3,0.0214178,120.657 1216 | 800,22.2,0.0254,71.3,0.0214178,123.227 1217 | 1000,22.2,0.0254,71.3,0.0214178,134.247 1218 | 1250,22.2,0.0254,71.3,0.0214178,140.987 1219 | 1600,22.2,0.0254,71.3,0.0214178,131.817 1220 | 2000,22.2,0.0254,71.3,0.0214178,127.197 1221 | 2500,22.2,0.0254,71.3,0.0214178,126.097 1222 | 3150,22.2,0.0254,71.3,0.0214178,124.127 1223 | 4000,22.2,0.0254,71.3,0.0214178,123.917 1224 | 5000,22.2,0.0254,71.3,0.0214178,125.727 1225 | 6300,22.2,0.0254,71.3,0.0214178,123.127 1226 | 8000,22.2,0.0254,71.3,0.0214178,121.657 1227 | 200,22.2,0.0254,39.6,0.0229028,116.066 1228 | 250,22.2,0.0254,39.6,0.0229028,117.386 1229 | 315,22.2,0.0254,39.6,0.0229028,120.716 1230 | 400,22.2,0.0254,39.6,0.0229028,123.416 1231 | 500,22.2,0.0254,39.6,0.0229028,129.776 1232 | 630,22.2,0.0254,39.6,0.0229028,137.026 1233 | 800,22.2,0.0254,39.6,0.0229028,137.076 1234 | 1000,22.2,0.0254,39.6,0.0229028,128.416 1235 | 1250,22.2,0.0254,39.6,0.0229028,126.446 1236 | 1600,22.2,0.0254,39.6,0.0229028,122.216 1237 | 2000,22.2,0.0254,39.6,0.0229028,121.256 1238 | 2500,22.2,0.0254,39.6,0.0229028,121.306 1239 | 3150,22.2,0.0254,39.6,0.0229028,120.856 1240 | 4000,22.2,0.0254,39.6,0.0229028,119.646 1241 | 5000,22.2,0.0254,39.6,0.0229028,118.816 1242 | 630,0,0.1016,71.3,0.00121072,124.155 1243 | 800,0,0.1016,71.3,0.00121072,126.805 1244 | 1000,0,0.1016,71.3,0.00121072,128.825 1245 | 1250,0,0.1016,71.3,0.00121072,130.335 1246 | 1600,0,0.1016,71.3,0.00121072,131.725 1247 | 2000,0,0.1016,71.3,0.00121072,132.095 1248 | 2500,0,0.1016,71.3,0.00121072,132.595 1249 | 3150,0,0.1016,71.3,0.00121072,131.955 1250 | 4000,0,0.1016,71.3,0.00121072,130.935 1251 | 5000,0,0.1016,71.3,0.00121072,130.795 1252 | 6300,0,0.1016,71.3,0.00121072,129.395 1253 | 8000,0,0.1016,71.3,0.00121072,125.465 1254 | 10000,0,0.1016,71.3,0.00121072,123.305 1255 | 12500,0,0.1016,71.3,0.00121072,119.375 1256 | 630,0,0.1016,55.5,0.00131983,126.17 1257 | 800,0,0.1016,55.5,0.00131983,127.92 1258 | 1000,0,0.1016,55.5,0.00131983,129.8 1259 | 1250,0,0.1016,55.5,0.00131983,131.43 1260 | 1600,0,0.1016,55.5,0.00131983,132.05 1261 | 2000,0,0.1016,55.5,0.00131983,132.54 1262 | 2500,0,0.1016,55.5,0.00131983,133.04 1263 | 3150,0,0.1016,55.5,0.00131983,131.78 1264 | 4000,0,0.1016,55.5,0.00131983,129.5 1265 | 5000,0,0.1016,55.5,0.00131983,128.36 1266 | 6300,0,0.1016,55.5,0.00131983,127.73 1267 | 8000,0,0.1016,55.5,0.00131983,124.45 1268 | 10000,0,0.1016,55.5,0.00131983,121.93 1269 | 12500,0,0.1016,55.5,0.00131983,119.91 1270 | 630,0,0.1016,39.6,0.00146332,125.401 1271 | 800,0,0.1016,39.6,0.00146332,128.401 1272 | 1000,0,0.1016,39.6,0.00146332,130.781 1273 | 1250,0,0.1016,39.6,0.00146332,132.271 1274 | 1600,0,0.1016,39.6,0.00146332,133.261 1275 | 2000,0,0.1016,39.6,0.00146332,133.251 1276 | 2500,0,0.1016,39.6,0.00146332,132.611 1277 | 3150,0,0.1016,39.6,0.00146332,130.961 1278 | 4000,0,0.1016,39.6,0.00146332,127.801 1279 | 5000,0,0.1016,39.6,0.00146332,126.021 1280 | 6300,0,0.1016,39.6,0.00146332,125.631 1281 | 8000,0,0.1016,39.6,0.00146332,122.341 1282 | 10000,0,0.1016,39.6,0.00146332,119.561 1283 | 630,0,0.1016,31.7,0.00150092,126.413 1284 | 800,0,0.1016,31.7,0.00150092,129.053 1285 | 1000,0,0.1016,31.7,0.00150092,131.313 1286 | 1250,0,0.1016,31.7,0.00150092,133.063 1287 | 1600,0,0.1016,31.7,0.00150092,133.553 1288 | 2000,0,0.1016,31.7,0.00150092,133.153 1289 | 2500,0,0.1016,31.7,0.00150092,132.003 1290 | 3150,0,0.1016,31.7,0.00150092,129.973 1291 | 4000,0,0.1016,31.7,0.00150092,126.933 1292 | 5000,0,0.1016,31.7,0.00150092,124.393 1293 | 6300,0,0.1016,31.7,0.00150092,124.253 1294 | 8000,0,0.1016,31.7,0.00150092,120.193 1295 | 10000,0,0.1016,31.7,0.00150092,115.893 1296 | 800,3.3,0.1016,71.3,0.00202822,131.074 1297 | 1000,3.3,0.1016,71.3,0.00202822,131.434 1298 | 1250,3.3,0.1016,71.3,0.00202822,132.304 1299 | 1600,3.3,0.1016,71.3,0.00202822,133.664 1300 | 2000,3.3,0.1016,71.3,0.00202822,134.034 1301 | 2500,3.3,0.1016,71.3,0.00202822,133.894 1302 | 3150,3.3,0.1016,71.3,0.00202822,132.114 1303 | 4000,3.3,0.1016,71.3,0.00202822,128.704 1304 | 5000,3.3,0.1016,71.3,0.00202822,127.054 1305 | 6300,3.3,0.1016,71.3,0.00202822,124.904 1306 | 8000,3.3,0.1016,71.3,0.00202822,121.234 1307 | 10000,3.3,0.1016,71.3,0.00202822,116.694 1308 | 630,3.3,0.1016,55.5,0.002211,126.599 1309 | 800,3.3,0.1016,55.5,0.002211,129.119 1310 | 1000,3.3,0.1016,55.5,0.002211,131.129 1311 | 1250,3.3,0.1016,55.5,0.002211,132.769 1312 | 1600,3.3,0.1016,55.5,0.002211,133.649 1313 | 2000,3.3,0.1016,55.5,0.002211,133.649 1314 | 2500,3.3,0.1016,55.5,0.002211,132.889 1315 | 3150,3.3,0.1016,55.5,0.002211,130.629 1316 | 4000,3.3,0.1016,55.5,0.002211,127.229 1317 | 5000,3.3,0.1016,55.5,0.002211,124.839 1318 | 6300,3.3,0.1016,55.5,0.002211,123.839 1319 | 8000,3.3,0.1016,55.5,0.002211,120.569 1320 | 10000,3.3,0.1016,55.5,0.002211,115.659 1321 | 630,3.3,0.1016,39.6,0.00245138,127.251 1322 | 800,3.3,0.1016,39.6,0.00245138,129.991 1323 | 1000,3.3,0.1016,39.6,0.00245138,131.971 1324 | 1250,3.3,0.1016,39.6,0.00245138,133.211 1325 | 1600,3.3,0.1016,39.6,0.00245138,133.071 1326 | 2000,3.3,0.1016,39.6,0.00245138,132.301 1327 | 2500,3.3,0.1016,39.6,0.00245138,130.791 1328 | 3150,3.3,0.1016,39.6,0.00245138,128.401 1329 | 4000,3.3,0.1016,39.6,0.00245138,124.881 1330 | 5000,3.3,0.1016,39.6,0.00245138,122.371 1331 | 6300,3.3,0.1016,39.6,0.00245138,120.851 1332 | 8000,3.3,0.1016,39.6,0.00245138,118.091 1333 | 10000,3.3,0.1016,39.6,0.00245138,115.321 1334 | 630,3.3,0.1016,31.7,0.00251435,128.952 1335 | 800,3.3,0.1016,31.7,0.00251435,131.362 1336 | 1000,3.3,0.1016,31.7,0.00251435,133.012 1337 | 1250,3.3,0.1016,31.7,0.00251435,134.022 1338 | 1600,3.3,0.1016,31.7,0.00251435,133.402 1339 | 2000,3.3,0.1016,31.7,0.00251435,131.642 1340 | 2500,3.3,0.1016,31.7,0.00251435,130.392 1341 | 3150,3.3,0.1016,31.7,0.00251435,128.252 1342 | 4000,3.3,0.1016,31.7,0.00251435,124.852 1343 | 5000,3.3,0.1016,31.7,0.00251435,122.082 1344 | 6300,3.3,0.1016,31.7,0.00251435,120.702 1345 | 8000,3.3,0.1016,31.7,0.00251435,117.432 1346 | 630,6.7,0.1016,71.3,0.00478288,131.448 1347 | 800,6.7,0.1016,71.3,0.00478288,134.478 1348 | 1000,6.7,0.1016,71.3,0.00478288,136.758 1349 | 1250,6.7,0.1016,71.3,0.00478288,137.658 1350 | 1600,6.7,0.1016,71.3,0.00478288,136.678 1351 | 2000,6.7,0.1016,71.3,0.00478288,134.568 1352 | 2500,6.7,0.1016,71.3,0.00478288,131.458 1353 | 3150,6.7,0.1016,71.3,0.00478288,124.458 1354 | 500,6.7,0.1016,55.5,0.0052139,129.343 1355 | 630,6.7,0.1016,55.5,0.0052139,133.023 1356 | 800,6.7,0.1016,55.5,0.0052139,135.953 1357 | 1000,6.7,0.1016,55.5,0.0052139,137.233 1358 | 1250,6.7,0.1016,55.5,0.0052139,136.883 1359 | 1600,6.7,0.1016,55.5,0.0052139,133.653 1360 | 2000,6.7,0.1016,55.5,0.0052139,129.653 1361 | 2500,6.7,0.1016,55.5,0.0052139,124.273 1362 | 400,6.7,0.1016,39.6,0.00578076,128.295 1363 | 500,6.7,0.1016,39.6,0.00578076,130.955 1364 | 630,6.7,0.1016,39.6,0.00578076,133.355 1365 | 800,6.7,0.1016,39.6,0.00578076,134.625 1366 | 1000,6.7,0.1016,39.6,0.00578076,134.515 1367 | 1250,6.7,0.1016,39.6,0.00578076,132.395 1368 | 1600,6.7,0.1016,39.6,0.00578076,127.375 1369 | 2000,6.7,0.1016,39.6,0.00578076,122.235 1370 | 315,6.7,0.1016,31.7,0.00592927,126.266 1371 | 400,6.7,0.1016,31.7,0.00592927,128.296 1372 | 500,6.7,0.1016,31.7,0.00592927,130.206 1373 | 630,6.7,0.1016,31.7,0.00592927,132.116 1374 | 800,6.7,0.1016,31.7,0.00592927,132.886 1375 | 1000,6.7,0.1016,31.7,0.00592927,131.636 1376 | 1250,6.7,0.1016,31.7,0.00592927,129.256 1377 | 1600,6.7,0.1016,31.7,0.00592927,124.346 1378 | 2000,6.7,0.1016,31.7,0.00592927,120.446 1379 | 200,8.9,0.1016,71.3,0.0103088,133.503 1380 | 250,8.9,0.1016,71.3,0.0103088,134.533 1381 | 315,8.9,0.1016,71.3,0.0103088,136.583 1382 | 400,8.9,0.1016,71.3,0.0103088,138.123 1383 | 500,8.9,0.1016,71.3,0.0103088,138.523 1384 | 630,8.9,0.1016,71.3,0.0103088,138.423 1385 | 800,8.9,0.1016,71.3,0.0103088,137.813 1386 | 1000,8.9,0.1016,71.3,0.0103088,135.433 1387 | 1250,8.9,0.1016,71.3,0.0103088,132.793 1388 | 1600,8.9,0.1016,71.3,0.0103088,128.763 1389 | 2000,8.9,0.1016,71.3,0.0103088,124.233 1390 | 2500,8.9,0.1016,71.3,0.0103088,123.623 1391 | 3150,8.9,0.1016,71.3,0.0103088,123.263 1392 | 4000,8.9,0.1016,71.3,0.0103088,120.243 1393 | 5000,8.9,0.1016,71.3,0.0103088,116.723 1394 | 6300,8.9,0.1016,71.3,0.0103088,117.253 1395 | 200,8.9,0.1016,39.6,0.0124596,133.42 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1000,12.3,0.1016,71.3,0.0337792,127.928 1419 | 1250,12.3,0.1016,71.3,0.0337792,126.648 1420 | 1600,12.3,0.1016,71.3,0.0337792,124.748 1421 | 2000,12.3,0.1016,71.3,0.0337792,122.218 1422 | 2500,12.3,0.1016,71.3,0.0337792,121.318 1423 | 3150,12.3,0.1016,71.3,0.0337792,120.798 1424 | 4000,12.3,0.1016,71.3,0.0337792,118.018 1425 | 5000,12.3,0.1016,71.3,0.0337792,116.108 1426 | 6300,12.3,0.1016,71.3,0.0337792,113.958 1427 | 200,12.3,0.1016,55.5,0.0368233,132.304 1428 | 250,12.3,0.1016,55.5,0.0368233,133.294 1429 | 315,12.3,0.1016,55.5,0.0368233,135.674 1430 | 400,12.3,0.1016,55.5,0.0368233,136.414 1431 | 500,12.3,0.1016,55.5,0.0368233,133.774 1432 | 630,12.3,0.1016,55.5,0.0368233,124.244 1433 | 800,12.3,0.1016,55.5,0.0368233,125.114 1434 | 1000,12.3,0.1016,55.5,0.0368233,125.484 1435 | 1250,12.3,0.1016,55.5,0.0368233,124.214 1436 | 1600,12.3,0.1016,55.5,0.0368233,121.824 1437 | 2000,12.3,0.1016,55.5,0.0368233,118.564 1438 | 2500,12.3,0.1016,55.5,0.0368233,117.054 1439 | 3150,12.3,0.1016,55.5,0.0368233,116.914 1440 | 4000,12.3,0.1016,55.5,0.0368233,114.404 1441 | 5000,12.3,0.1016,55.5,0.0368233,112.014 1442 | 6300,12.3,0.1016,55.5,0.0368233,110.124 1443 | 200,12.3,0.1016,39.6,0.0408268,128.545 1444 | 250,12.3,0.1016,39.6,0.0408268,129.675 1445 | 315,12.3,0.1016,39.6,0.0408268,129.415 1446 | 400,12.3,0.1016,39.6,0.0408268,128.265 1447 | 500,12.3,0.1016,39.6,0.0408268,122.205 1448 | 630,12.3,0.1016,39.6,0.0408268,121.315 1449 | 800,12.3,0.1016,39.6,0.0408268,122.315 1450 | 1000,12.3,0.1016,39.6,0.0408268,122.435 1451 | 1250,12.3,0.1016,39.6,0.0408268,121.165 1452 | 1600,12.3,0.1016,39.6,0.0408268,117.875 1453 | 2000,12.3,0.1016,39.6,0.0408268,114.085 1454 | 2500,12.3,0.1016,39.6,0.0408268,113.315 1455 | 3150,12.3,0.1016,39.6,0.0408268,113.055 1456 | 4000,12.3,0.1016,39.6,0.0408268,110.905 1457 | 5000,12.3,0.1016,39.6,0.0408268,108.625 1458 | 6300,12.3,0.1016,39.6,0.0408268,107.985 1459 | 200,12.3,0.1016,31.7,0.0418756,124.987 1460 | 250,12.3,0.1016,31.7,0.0418756,125.857 1461 | 315,12.3,0.1016,31.7,0.0418756,124.717 1462 | 400,12.3,0.1016,31.7,0.0418756,123.207 1463 | 500,12.3,0.1016,31.7,0.0418756,118.667 1464 | 630,12.3,0.1016,31.7,0.0418756,119.287 1465 | 800,12.3,0.1016,31.7,0.0418756,120.037 1466 | 1000,12.3,0.1016,31.7,0.0418756,119.777 1467 | 1250,12.3,0.1016,31.7,0.0418756,118.767 1468 | 1600,12.3,0.1016,31.7,0.0418756,114.477 1469 | 2000,12.3,0.1016,31.7,0.0418756,110.447 1470 | 2500,12.3,0.1016,31.7,0.0418756,110.317 1471 | 3150,12.3,0.1016,31.7,0.0418756,110.307 1472 | 4000,12.3,0.1016,31.7,0.0418756,108.407 1473 | 5000,12.3,0.1016,31.7,0.0418756,107.147 1474 | 6300,12.3,0.1016,31.7,0.0418756,107.267 1475 | 200,15.6,0.1016,71.3,0.0437259,130.898 1476 | 250,15.6,0.1016,71.3,0.0437259,132.158 1477 | 315,15.6,0.1016,71.3,0.0437259,133.808 1478 | 400,15.6,0.1016,71.3,0.0437259,134.058 1479 | 500,15.6,0.1016,71.3,0.0437259,130.638 1480 | 630,15.6,0.1016,71.3,0.0437259,122.288 1481 | 800,15.6,0.1016,71.3,0.0437259,124.188 1482 | 1000,15.6,0.1016,71.3,0.0437259,124.438 1483 | 1250,15.6,0.1016,71.3,0.0437259,123.178 1484 | 1600,15.6,0.1016,71.3,0.0437259,121.528 1485 | 2000,15.6,0.1016,71.3,0.0437259,119.888 1486 | 2500,15.6,0.1016,71.3,0.0437259,118.998 1487 | 3150,15.6,0.1016,71.3,0.0437259,116.468 1488 | 4000,15.6,0.1016,71.3,0.0437259,113.298 1489 | 200,15.6,0.1016,39.6,0.0528487,123.514 1490 | 250,15.6,0.1016,39.6,0.0528487,124.644 1491 | 315,15.6,0.1016,39.6,0.0528487,122.754 1492 | 400,15.6,0.1016,39.6,0.0528487,120.484 1493 | 500,15.6,0.1016,39.6,0.0528487,115.304 1494 | 630,15.6,0.1016,39.6,0.0528487,118.084 1495 | 800,15.6,0.1016,39.6,0.0528487,118.964 1496 | 1000,15.6,0.1016,39.6,0.0528487,119.224 1497 | 1250,15.6,0.1016,39.6,0.0528487,118.214 1498 | 1600,15.6,0.1016,39.6,0.0528487,114.554 1499 | 2000,15.6,0.1016,39.6,0.0528487,110.894 1500 | 2500,15.6,0.1016,39.6,0.0528487,110.264 1501 | 3150,15.6,0.1016,39.6,0.0528487,109.254 1502 | 4000,15.6,0.1016,39.6,0.0528487,106.604 1503 | 5000,15.6,0.1016,39.6,0.0528487,106.224 1504 | 6300,15.6,0.1016,39.6,0.0528487,104.204 1505 | --------------------------------------------------------------------------------