process/process_ETT.py

import pandas as pd
import os
from tqdm import tqdm
import argparse

def main():
    """
    Main function to process CSV data and generate input and gt data files for ETT forecasting.
    """
    # Set up argument parser
    parser = argparse.ArgumentParser(description="Process ETT forecasting data.")
    parser.add_argument("--data_path", type=str, required=True, help="Path to the ETTh1.csv file.")
    args = parser.parse_args()

    try:
        # Define the source folder containing the original files
        csv_file_path = args.data_path
        
        # Read the CSV file into a DataFrame
        df = pd.read_csv(csv_file_path)

        # Define target folders for input and ground truth data
        target_folder = "Energy-ETT-Transformer_sensor_signal-Forecasting"
        input_data_path = os.path.join(target_folder, "raw_input_data")
        gt_data_path = os.path.join(target_folder, "raw_gt_data")
        
        # Create directories if they don't exist
        os.makedirs(input_data_path, exist_ok=True)
        os.makedirs(gt_data_path, exist_ok=True)

        # Define sequence and prediction lengths
        seq_len_list = [96, 96]
        pred_len_list = [96, 720]
        label = 0  # No overloap
        
        # Specify the type of data to generate (e.g., "train", "val", "test")
        generate_data_type = "test"

        # Iterate over sequence and prediction lengths
        for seq_len, pred_len in zip(seq_len_list, pred_len_list):

            # Define start and end indices for different data types
            start_idx = {
                "train": 0,
                "val": 12 * 30 * 24 - pred_len,
                "test": (12 + 4) * 30 * 24 - pred_len,
            }

            end_idx = {
                "train": 12 * 30 * 24 - seq_len - pred_len,
                "val": (12 + 4) * 30 * 24 - seq_len - pred_len,
                "test": (12 + 8) * 30 * 24 - seq_len - pred_len,
            }

            # Iterate over the specified range of indices
            for i in tqdm(range(start_idx[generate_data_type], end_idx[generate_data_type] + 1), desc=f"Generating data: context_length: {seq_len}, prediction_length: {pred_len}"):
                # Extract input and gt data
                data_input = df.iloc[i : i + seq_len]
                data_gt = df.iloc[i + seq_len : i + seq_len + pred_len]
                
                # Save input and gt data to CSV files, select 'OT' column
                data_input[['OT']].to_csv(
                    os.path.join(input_data_path, f'seq{seq_len}_label{label}_pred{pred_len}_index{i}_input_ts_OT.csv'),
                    index=False,
                    header=False,
                    encoding='utf-8'
                )
                data_gt[['OT']].to_csv(
                    os.path.join(gt_data_path, f'seq{seq_len}_label{label}_pred{pred_len}_index{i}_target_ts_OT.csv'),
                    index=False,
                    header=False,
                    encoding='utf-8'
                )

    except FileNotFoundError:
        print(f"Error: File {csv_file_path} not found. Please check the path or filename.")
    except pd.errors.EmptyDataError:
        print(f"Error: File {csv_file_path} is empty.")
    except pd.errors.ParserError:
        print(f"Error: File {csv_file_path} is not a valid CSV file.")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")


if __name__ == "__main__":
    main()