program

Signed-off-by: 葛峻恺 <202115006@mail.sdu.edu.cn>

6_extract_impulse.py

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+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)