diff --git a/6_extract_impulse.py b/6_extract_impulse.py
deleted file mode 100644
index b44ea6f..0000000
--- a/6_extract_impulse.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from scipy.interpolate import interp1d
-from config import Field_data_test_Config as cfg
-from config import Path_Config as pcfg
-
-def shift_data_to_end(data, n):
-    """
-    Shift the first n values of a 1D data array to the end.
-    
-    Parameters:
-        data (list or numpy array): 1D data array
-        n (int): Number of elements to shift
-        
-    Returns:
-        numpy array: Shifted data
-    """
-    if n < 0 or n > len(data):
-        raise ValueError("Shift length n must be between 0 and the data length.")
-    
-    return np.concatenate((data[n:], data[:n]))
-
-# Step 1: Read 1D data from CSV or TXT file
-def read_file(file_path, idx):
-    if file_path.endswith('.csv'):
-        data = pd.read_csv(file_path, header=None)  # Assuming no column names
-        one_d_data = data.iloc[1:, idx].values  # Read data from the idx-th column
-    elif file_path.endswith('.txt'):
-        data = np.loadtxt(file_path)  # Assuming TXT file contains only numeric values
-        one_d_data = data if data.ndim == 1 else data[:, 0]  # Ensure it's 1D
-    else:
-        raise ValueError("Unsupported file format. Please provide a .csv or .txt file.")
-    return one_d_data
-
-# Step 2: Normalize data by dividing by its maximum absolute value
-def normalize_data(data):
-    max_abs_value = np.max(np.abs(data))
-    return data if max_abs_value == 0 else data / max_abs_value  # Avoid division by zero
-
-# Step 3: Interpolate data to a length of 1000
-def interpolate_data(data, target_length=1000):
-    original_length = len(data)
-    x_original = np.linspace(0, 1, original_length)
-    x_target = np.linspace(0, 1, target_length)
-    interpolator = interp1d(x_original, data, kind='cubic')  # Cubic interpolation
-    return interpolator(x_target)
-
-# Step 4: Save processed data to a CSV file
-def save_to_csv(data, output_path):
-    pd.DataFrame(data).to_csv(output_path, index=False, header=False)
-
-# Step 5: Plot original and interpolated data
-def plot_data(original_data, interpolated_data):
-    plt.figure(figsize=(10, 5))
-    plt.plot(original_data, label='Original Data', marker='o', linestyle='--', alpha=0.7)
-    plt.plot(np.linspace(0, len(original_data)-1, len(interpolated_data)), interpolated_data, label='Interpolated Data', linestyle='-', linewidth=2)
-    plt.legend()
-    plt.xlabel('Index')
-    plt.ylabel('Value')
-    plt.title('Data Normalization and Interpolation')
-    plt.grid(alpha=0.3)
-    plt.show()
-
-# Main execution
-if __name__ == "__main__":
-    input_file = pcfg.PROCESSED_TEST_FILE  # Input file path
-    output_csv = pcfg.field_impulse  # Output file path   
-    idx = cfg.refer_wave_idx  # Column index to read data from
-    static_time=cfg.static_time
-    wavelet_range=cfg.wavelet_range
-    data = read_file(input_file, idx)
-    normalized_data = normalize_data(data)
-    
-    # Zero out unwanted data regions
-    normalized_data[wavelet_range[1]:] = 0  # Set values after the source wave to zero
-    normalized_data[0:wavelet_range[0]] = 0  # Set values before the source wave to zero
-    
-    interpolated_data = interpolate_data(normalized_data,cfg.time_window_length)
-    interpolated_data = shift_data_to_end(interpolated_data, static_time)
-    
-    save_to_csv(interpolated_data, output_csv)
-    plot_data(normalized_data, interpolated_data)
\ No newline at end of file