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 # taper cut def apply_taper_window(data, start_idx, end_idx, left_frac=0.15, right_frac=0.15): """ left_frac/right_frac: taper ratio (0–1) """ n = len(data) start = int(max(0, min(n, start_idx))) end = int(max(0, min(n, end_idx))) if end <= start: raise ValueError("end_idx must be greater than start_idx.") N = end - start L = int(np.clip(int(N * float(left_frac)), 0, N)) R = int(np.clip(int(N * float(right_frac)), 0, N - L)) win = np.ones(N, dtype=float) if L > 0: win[:L] = 0.5 - 0.5 * np.cos(np.linspace(0, np.pi, L)) # 0 -> 1 if R > 0: win[-R:] = 0.5 - 0.5 * np.cos(np.linspace(np.pi, 0, R)) # 1 -> 0 mask = np.zeros(n, dtype=float) mask[start:end] = win return data * mask def shift_data_to_end(data, n): 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])) def read_file(file_path, idx): if file_path.endswith('.csv'): data = pd.read_csv(file_path, header=None) one_d_data = data.iloc[1:, idx].values elif file_path.endswith('.txt'): data = np.loadtxt(file_path) one_d_data = data if data.ndim == 1 else data[:, 0] else: raise ValueError("Unsupported file format. Please provide a .csv or .txt file.") return one_d_data def normalize_data(data): max_abs_value = np.max(np.abs(data)) return data if max_abs_value == 0 else data / max_abs_value 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') return interpolator(x_target) def save_to_csv(data, output_path): pd.DataFrame(data).to_csv(output_path, index=False, header=False) def plot_data(original_data, interpolated_data): plt.figure(figsize=(10, 5)) plt.plot(original_data, label='Original (after taper)', linestyle='-') plt.plot( np.linspace(0, len(original_data)-1, len(interpolated_data)), interpolated_data, label='Interpolated', linewidth=2 ) plt.legend(); plt.xlabel('Index'); plt.ylabel('Value') plt.title('Soft-gated (tapered) Window + Interpolation') plt.grid(alpha=0.3) plt.show() # —— Main pipeline update: use a soft taper instead of a hard cut —— if __name__ == "__main__": input_file = pcfg.PROCESSED_TEST_FILE output_csv = pcfg.field_impulse idx = cfg.refer_wave_idx static_time = cfg.static_time wavelet_range = cfg.wavelet_range # [start_idx, end_idx] # Optional: allow taper ratios to be configured in cfg; fall back to defaults here taper_left_frac = getattr(cfg, 'taper_left_frac', 0.15) # 15% on the left taper_right_frac = getattr(cfg, 'taper_right_frac', 0.15) # 15% on the right data = read_file(input_file, idx) normalized_data = -normalize_data(data) # Key step: apply a soft-gated window (taper at both ends) to the selected interval; samples outside become 0 tapered_data = apply_taper_window( normalized_data, wavelet_range[0], wavelet_range[1], left_frac=taper_left_frac, right_frac=taper_right_frac ) save_to_csv(tapered_data, './impulse/initial_impulse.csv') # Then interpolate to the target length (recommended: interpolate after tapering) interpolated_data = interpolate_data(tapered_data, cfg.time_window_length) interpolated_data = shift_data_to_end(interpolated_data, static_time) save_to_csv(interpolated_data, output_csv) plot_data(tapered_data, interpolated_data)