import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import CubicSpline from scipy.stats import pearsonr from sklearn.metrics import mean_squared_error import warnings from sklearn.metrics.pairwise import cosine_similarity from org.chatgpt2.q1_2 import wl_max, wl_min warnings.filterwarnings('ignore') # ----------------------------- # 1. 通用工具函数（保持不变） # ----------------------------- def make_strictly_increasing(wl, n, k): unique_wl, indices = np.unique(wl, return_index=True) if len(unique_wl) != len(wl): print(f"Removed {len(wl) - len(unique_wl)} duplicate wavelength points") wl, n, k = wl[indices], n[indices], k[indices] is_increasing = np.diff(wl) > 0 if not all(is_increasing): valid_indices = np.concatenate([[True], is_increasing]) wl, n, k = wl[valid_indices], n[valid_indices], k[valid_indices] return wl, n, k def read_split_data(file_path): with open(file_path, 'r', encoding='utf-8') as f: lines = [line.strip() for line in f if line.strip() and not line.startswith('#')] split_idx = None for i, line in enumerate(lines): if line == 'wl k': split_idx = i break n_lines = lines[1:split_idx] wl_n, n_list = [], [] for line in n_lines: wl, n_val = line.split() wl_n.append(float(wl)), n_list.append(float(n_val)) k_lines = lines[split_idx + 1:] wl_k, k_list = [], [] for line in k_lines: wl, k_val = line.split() wl_k.append(float(wl)), k_list.append(float(k_val)) wl_n, n_list = np.array(wl_n), np.array(n_list) wl_k, k_list = np.array(wl_k), np.array(k_list) assert np.allclose(wl_n, wl_k), "n和k的波长列表不一致！" sorted_idx = np.argsort(wl_n) return wl_n[sorted_idx], n_list[sorted_idx], k_list[sorted_idx] def fresnel_reflectance(n1, k1, n2, k2): m1 = n1 + 1j * k1 m2 = n2 + 1j * k2 return np.abs((m1 - m2) / (m1 + m2)) ** 2 def thin_film_emissivity(n_film, k_film, d, wl, n_air=1.0, k_air=0.0, n_sub=1.5, k_sub=0.0, r=0.0): m_air = n_air + 1j * k_air m_film = n_film + 1j * k_film m_sub = n_sub + 1j * k_sub R12 = np.abs((m_air - m_film) / (m_air + m_film)) ** 2 R23 = np.abs((m_film - m_sub) / (m_film + m_sub)) ** 2 beta = 2 * np.pi * m_film / wl delta_complex = beta * d alpha = 2 * np.imag(delta_complex) sqrt_term = np.sqrt(R12 * R23 * np.exp(-alpha)) cos_term = np.cos(2 * np.real(delta_complex)) R_specular = (R12 + R23 * np.exp(-alpha) + 2 * sqrt_term * cos_term) / ( 1 + R12 * R23 * np.exp(-alpha) + 2 * sqrt_term * cos_term) R_diffuse = 0.05 * r R_total = (1 - r) * R_specular + r * R_diffuse T_total = (1 - R12) * (1 - R23) * np.exp(-alpha) / (1 + R12 * R23 * np.exp(-alpha) + 2 * sqrt_term * cos_term) return np.clip(1 - R_total - T_total, 0, 1) # ----------------------------- # 2. 新增：局部相似度计算函数（滑动窗口法） # ----------------------------- def calculate_local_similarity(eps1, eps2, wl_common, window_size=0.5): """ 计算逐波长的局部相似度（滑动窗口法） 输入： eps1/eps2: 两个发射率数组 wl_common: 统一波长数组 window_size: 滑动窗口宽度（μm），默认0.5μm（覆盖5个采样点，保证平滑） 输出： local_corr: 逐波长皮尔逊相关系数（局部相似度） local_cos_sim: 逐波长余弦相似度 local_mae: 逐波长局部平均绝对误差（相似度的互补指标） """ n_points = len(wl_common) local_corr = np.zeros(n_points) local_cos_sim = np.zeros(n_points) local_mae = np.zeros(n_points) # 滑动窗口计算局部相似度（窗口中心为每个波长点） for i in range(n_points): # 确定当前窗口的波长范围 wl_center = wl_common[i] window_mask = (wl_common >= wl_center - window_size / 2) & (wl_common <= wl_center + window_size / 2) if np.sum(window_mask) < 3: # 窗口内至少3个点才计算（避免统计无意义） local_corr[i] = np.nan local_cos_sim[i] = np.nan local_mae[i] = np.nan continue # 提取窗口内的发射率数据 eps1_window = eps1[window_mask] eps2_window = eps2[window_mask] # 计算窗口内的相似度指标 corr, _ = pearsonr(eps1_window, eps2_window) cos_sim = cosine_similarity(eps1_window.reshape(1, -1), eps2_window.reshape(1, -1))[0][0] mae = np.mean(np.abs(eps1_window - eps2_window)) # 存储结果（相关系数和余弦相似度归一化到0-1范围） local_corr[i] = (corr + 1) / 2 # 原始相关系数-1~1 → 映射为0~1 local_cos_sim[i] = cos_sim local_mae[i] = mae return local_corr, local_cos_sim, local_mae # ----------------------------- # 3. 核心相似性分析函数（新增局部相似度计算） # ----------------------------- def analyze_spectral_similarity(file1, file2, thicknesses=[1.0], n_sub=1.5, k_sub=0.0, r=0.0, window_size=0.5): # Step 1: 数据读取与预处理 wl1, n1, k1 = read_split_data(file1) wl2, n2, k2 = read_split_data(file2) wl1, n1, k1 = make_strictly_increasing(wl1, n1, k1) wl2, n2, k2 = make_strictly_increasing(wl2, n2, k2) # Step 2: 统一波长范围 wl_min = max(wl1.min(), wl2.min()) wl_max = min(wl1.max(), wl2.max()) if wl_min >= wl_max: raise ValueError("两个文件的波长范围无交集，无法进行相似性分析！") wl_common = np.linspace(wl_min, wl_max, 1000) # Step 3: 插值得到统一波长下的n和k cs_n1 = CubicSpline(wl1, n1) cs_k1 = CubicSpline(wl1, k1) cs_n2 = CubicSpline(wl2, n2) cs_k2 = CubicSpline(wl2, k2) n1_common = cs_n1(wl_common) k1_common = cs_k1(wl_common) n2_common = cs_n2(wl_common) k2_common = cs_k2(wl_common) # Step 4: 计算发射率和局部相似度 emissivity_dict = {} local_similarity_dict = {} for d in thicknesses: eps1 = thin_film_emissivity(n1_common, k1_common, d, wl_common, n_sub=n_sub, k_sub=k_sub, r=r) eps2 = thin_film_emissivity(n2_common, k2_common, d, wl_common, n_sub=n_sub, k_sub=k_sub, r=r) emissivity_dict[d] = (eps1, eps2) # 计算局部相似度 local_corr, local_cos_sim, local_mae = calculate_local_similarity(eps1, eps2, wl_common, window_size) local_similarity_dict[d] = (local_corr, local_cos_sim, local_mae) # Step 5: 全局相似性指标计算 similarity_results = {} for d in thicknesses: eps1, eps2 = emissivity_dict[d] pearson_corr, _ = pearsonr(eps1, eps2) cos_sim = cosine_similarity(eps1.reshape(1, -1), eps2.reshape(1, -1))[0][0] mse = mean_squared_error(eps1, eps2) norm_mse = mse / (np.max([np.var(eps1), np.var(eps2)]) + 1e-8) mae = np.mean(np.abs(eps1 - eps2)) # 大气窗口指标 window_corr, window_mae = None, None if wl_min <= 13 and wl_max >= 8: window_mask = (wl_common >= 8) & (wl_common <= 13) eps1_window = eps1[window_mask] eps2_window = eps2[window_mask] window_corr, _ = pearsonr(eps1_window, eps2_window) window_mae = np.mean(np.abs(eps1_window - eps2_window)) similarity_results[d] = { "pearson_correlation": pearson_corr, "cosine_similarity": cos_sim, "normalized_mse": norm_mse, "mae": mae, "window_pearson_correlation": window_corr, "window_mae": window_mae } # Step 6: 可视化（新增相似度曲线） plot_spectral_comparison(wl_common, emissivity_dict, local_similarity_dict, thicknesses, wl_min, wl_max) return similarity_results, wl_common, emissivity_dict, local_similarity_dict # ----------------------------- # 4. 可视化函数（新增相似度曲线子图） # ----------------------------- def plot_spectral_comparison(wl_common, emissivity_dict, local_similarity_dict, thicknesses, wl_min, wl_max): n_plots = len(thicknesses) fig, axes = plt.subplots(n_plots, 3, figsize=(18, 5 * n_plots)) # 新增1列用于相似度曲线 plt.rcParams['font.sans-serif'] = ['Arial'] for idx, d in enumerate(thicknesses): eps1, eps2 = emissivity_dict[d] local_corr, local_cos_sim, local_mae = local_similarity_dict[d] diff = np.abs(eps1 - eps2) # 子图1：发射率光谱对比 ax1 = axes[idx, 0] if n_plots > 1 else axes[0] ax1.plot(wl_common, eps1, linewidth=2, label='data.txt', color='darkblue') ax1.plot(wl_common, eps2, linewidth=2, label='data2.txt', color='darkred', linestyle='--') if wl_min <= 13 and wl_max >= 8: ax1.axvspan(8, 13, alpha=0.15, color='orange', label='Atmospheric Window (8-13 μm)') ax1.set_xlabel('Wavelength (μm)', fontsize=12) ax1.set_ylabel('Emissivity ε(λ)', fontsize=12) ax1.set_title(f'Emissivity Spectrum Comparison (Thickness = {d} μm)', fontsize=14, fontweight='bold') ax1.grid(True, alpha=0.3) ax1.legend(fontsize=10) ax1.set_ylim(0, 1.05) # 子图2：发射率差异 ax2 = axes[idx, 1] if n_plots > 1 else axes[1] ax2.plot(wl_common, diff, linewidth=2, color='darkgreen', label='Absolute Difference |ε1 - ε2|') ax2.fill_between(wl_common, 0, diff, alpha=0.3, color='darkgreen') if wl_min <= 13 and wl_max >= 8: ax2.axvspan(8, 13, alpha=0.15, color='orange', label='Atmospheric Window (8-13 μm)') ax2.set_xlabel('Wavelength (μm)', fontsize=12) ax2.set_ylabel('Absolute Difference', fontsize=12) ax2.set_title(f'Emissivity Difference (Thickness = {d} μm)', fontsize=14, fontweight='bold') ax2.grid(True, alpha=0.3) ax2.legend(fontsize=10) ax2.set_ylim(0, np.nanmax(diff) * 1.2) # 子图3：相似度曲线（核心新增） ax3 = axes[idx, 2] if n_plots > 1 else axes[2] # 绘制局部皮尔逊相关系数（归一化到0-1） ax3.plot(wl_common, local_corr, linewidth=2.5, color='#4B0082', label='Local Pearson Correlation (0-1)') # 绘制局部余弦相似度（0-1） ax3.plot(wl_common, local_cos_sim, linewidth=2.5, color='orange', label='Local Cosine Similarity (0-1)', linestyle='--') # 标注相似度阈值线（0.9为高相似，0.8为中等相似） ax3.axhline(y=0.9, color='red', linestyle=':', linewidth=1.5, label='High Similarity Threshold (0.9)') ax3.axhline(y=0.8, color='orange', linestyle=':', linewidth=1.5, label='Medium Similarity Threshold (0.8)') # 标注大气窗口 if wl_min <= 13 and wl_max >= 8: ax3.axvspan(8, 13, alpha=0.15, color='orange', label='Atmospheric Window (8-13 μm)') ax3.set_xlabel('Wavelength (μm)', fontsize=12) ax3.set_ylabel('Local Similarity (0-1)', fontsize=12) ax3.set_title(f'Wavelength-Dependent Similarity Curve (Thickness = {d} μm)', fontsize=14, fontweight='bold') ax3.grid(True, alpha=0.3) ax3.legend(fontsize=10) ax3.set_ylim(0, 1.05) # 相似度范围0-1 ax3.set_xlim(wl_min, wl_max) plt.tight_layout() plt.savefig('spectral_similarity_complete.png', dpi=300, bbox_inches='tight') plt.show() # ----------------------------- # 5. 主程序执行 # ----------------------------- if __name__ == "__main__": file1 = '/Users/spasolreisa/IdeaProjects/asiaMath/data.txt' file2 = '/Users/spasolreisa/IdeaProjects/asiaMath/data2.txt' target_thicknesses = [0.5, 1.0, 1.5, 2.0] base_params = { 'n_sub': 1.5, 'k_sub': 0.0, 'r': 0.05 } window_size = 0.5 # 滑动窗口宽度（可调整，建议0.3-0.8μm） print("=== 开始光谱相似性分析（含相似度曲线） ===") print(f"文件1: {file1}") print(f"文件2: {file2}") print(f"分析厚度: {target_thicknesses} μm") print(f"滑动窗口宽度: {window_size} μm") print("-" * 50) results, wl_common, emissivity_dict, local_similarity_dict = analyze_spectral_similarity( file1, file2, thicknesses=target_thicknesses, n_sub=base_params['n_sub'], k_sub=base_params['k_sub'], r=base_params['r'], window_size=window_size ) # 输出全局指标 print("\n=== 全局相似性指标 ===") for d in target_thicknesses: res = results[d] print(f"\n【厚度 {d} μm】") print(f"全局皮尔逊相关系数: {res['pearson_correlation']:.4f}") print(f"全局余弦相似度: {res['cosine_similarity']:.4f}") print(f"归一化均方误差: {res['normalized_mse']:.4f}") print(f"平均绝对误差: {res['mae']:.4f}") if res['window_pearson_correlation'] is not None: print(f"大气窗口全局相关系数: {res['window_pearson_correlation']:.4f}") # 输出局部相似度统计（大气窗口内） print("\n=== 大气窗口（8-13μm）局部相似度统计 ===") for d in target_thicknesses: local_corr, local_cos_sim, _ = local_similarity_dict[d] if wl_min <= 13 and wl_max >= 8: window_mask = (wl_common >= 8) & (wl_common <= 13) window_local_corr = local_corr[window_mask] window_local_cos = local_cos_sim[window_mask] # 过滤NaN值 window_local_corr = window_local_corr[~np.isnan(window_local_corr)] window_local_cos = window_local_cos[~np.isnan(window_local_cos)] if len(window_local_corr) > 0: print(f"\n【厚度 {d} μm】") print(f"大气窗口局部相关系数均值: {np.mean(window_local_corr):.4f}") print(f"大气窗口局部相关系数最小值: {np.min(window_local_corr):.4f}") print(f"大气窗口局部余弦相似度均值: {np.mean(window_local_cos):.4f}") # 结果解读 print("\n=== 结果解读 ===") print("1. 相似度曲线（子图3）：值越接近1，对应波长下的发射率越相似；") print("2. 高相似区域（≥0.9）：两文件在该波段的辐射冷却性能几乎一致；") print("3. 低相似区域（<0.8）：需关注该波段的材料差异对冷却效果的影响；") print("4. 大气窗口内相似度：优先关注该区域，直接决定辐射冷却核心性能是否一致。")