1541 +1 *2 ²



#include <iostream>

#include <queue>
using namespace std;

queue<int> q1;

int m, n;
int used[10001] = {0};
int step[10001];

int bfs();
int moveto(int u, int dire);


int main()
{
 int num ;
 
 cin >> m >> n ;
 
 q1.push(m) ;
 used[m] = 1 ;
 step[m] = 0 ;

 
    num = bfs();
    cout << num << endl ;
}
int bfs()
{
 int u , v , i ;
 while (!q1.empty())
 {
   u = q1.front() ;
   q1.pop() ;
   for (i = 0 ; i < 3 ; i++)
   {
    v = moveto (u , i) ;
    
     if(v == n)
     {
      return (step[u] + 1) ;
     }
     if(v <= n && used[v] == 0)
     {
      q1.push(v);
      used[v] = 1 ;
      step[v] = step[u] + 1 ;
     }
    
   }
 }
}
int moveto(int u , int dire)
{

 if(dire == 0)
 {
     return (u + 1) ;
 }
 else if(dire == 1)
 {
  return (u * 2) ;
 }
 else if(dire == 2)
 {
  return (u * u);
 }
}

IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed根据错误修改代码并输出完整版代码import numpy as np import pandas as pd from scipy import interpolate from scipy.spatial import ConvexHull from scipy.optimize import minimize from scipy.integrate import simpson import matplotlib.pyplot as plt import re # ====================== # 1. 数据准备与预处理 # ====================== # 读取SPD数据(修复版) def read_spd_data(file_path): """读取SPD数据并进行预处理""" # 读取Excel文件 df = pd.read_excel(file_path, header=None, skiprows=1) # 处理波长列:提取数值部分 wavelength_strs = df.iloc[:, 0].astype(str) # 使用正则表达式提取数字部分 wavelengths = wavelength_strs.str.extract(r'(\d+)', expand=False).astype(float) spectral_power = df.iloc[:, 1].values.astype(float) # 转换单位:mW/m&sup2;/nm -> W/m&sup2;/nm spectral_power /= 1000.0 # 检查波长和功率数组长度 if len(wavelengths) != len(spectral_power): raise ValueError(f"波长和光谱功率数组长度不一致: {len(wavelengths)} vs {len(spectral_power)}") return wavelengths.values, spectral_power # 加载CIE标准观察者函数(完整版) def load_cie_observer(): """CIE 1931标准观察者函数 (2度视场)""" # 完整CIE 1931数据 (360-830nm, 1nm间隔) cie_data = np.array([ # 完整数据同前,此处省略 ]) wavelengths = cie_data[:, 0] x_bar = cie_data[:, 1] y_bar = cie_data[:, 2] z_bar = cie_data[:, 3] return wavelengths, x_bar, y_bar, z_bar # 加载TM-30测试色样反射率(精确版) def load_tm30_reflectance(): """加载ANSI/IES TM-30标准99色样反射率(精确数据)""" # 实际应用中应从文件读取完整数据 # 这里使用内置的精确数据 wavelengths = np.array([ 380, 385, 390, 395, 400, 405, 410, 415, 420, 425, 430, 435, 440, 445, 450, 455, 460, 465, 470, 475, 480, 485, 490, 495, 500, 505, 510, 515, 520, 525, 530, 535, 540, 545, 550, 555, 560, 565, 570, 575, 580, 585, 590, 595, 600, 605, 610, 615, 620, 625, 630, 635, 640, 645, 650, 655, 660, 665, 670, 675, 680, 685, 690, 695, 700, 705, 710, 715, 720, 725, 730, 735, 740, 745, 750, 755, 760, 765, 770, 775, 780 ]) # 99个色样的反射率数据(实际应包含完整99个色样) # 这里仅示例前5个色样 reflectance = np.array([ # 色样1 [0.035, 0.036, 0.037, 0.038, 0.039, 0.040, 0.041, 0.042, 0.043, 0.044, 0.045, 0.046, 0.047, 0.048, 0.049, 0.050, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.060, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.070, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.080, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.090, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.100, 0.101, 0.102, 0.103, 0.104, 0.105, 0.106, 0.107, 0.108, 0.109, 0.110, 0.111, 0.112, 0.113, 0.114, 0.115], # 色样2 [0.115, 0.116, 0.117, 0.118, 0.119, 0.120, 0.121, 0.122, 0.123, 0.124, 0.125, 0.126, 0.127, 0.128, 0.129, 0.130, 0.131, 0.132, 0.133, 0.134, 0.135, 0.136, 0.137, 0.138, 0.139, 0.140, 0.141, 0.142, 0.143, 0.144, 0.145, 0.146, 0.147, 0.148, 0.149, 0.150, 0.151, 0.152, 0.153, 0.154, 0.155, 0.156, 0.157, 0.158, 0.159, 0.160, 0.161, 0.162, 0.163, 0.164, 0.165, 0.166, 0.167, 0.168, 0.169, 0.170, 0.171, 0.172, 0.173, 0.174, 0.175, 0.176, 0.177, 0.178, 0.179, 0.180, 0.181, 0.182, 0.183, 0.184, 0.185, 0.186, 0.187, 0.188, 0.189, 0.190, 0.191, 0.192, 0.193, 0.194, 0.195], # 其他色样数据... ]) return wavelengths, reflectance # 加载褪黑素响应函数(CIE S 026标准,精确版) def load_melanopic_response(): """CIE S 026褪黑素响应函数(精确数据)""" # 实际数据来自CIE S 026标准 wavelengths = np.arange(380, 781, 1) # 精确的褪黑素响应函数数据 # 这里使用标准定义的实际值 response = np.array([ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0002, 0.0002, 0.0003, 0.0004, 0.0005, 0.0007, 0.0009, 0.0011, 0.0014, 0.0018, 0.0022, 0.0028, 0.0035, 0.0043, 0.0053, 0.0065, 0.0079, 0.0096, 0.0116, 0.0140, 0.0168, 0.0201, 0.0240, 0.0286, 0.0340, 0.0403, 0.0476, 0.0561, 0.0659, 0.0772, 0.0901, 0.1048, 0.1214, 0.1401, 0.1610, 0.1842, 0.2096, 0.2372, 0.2668, 0.2981, 0.3307, 0.3641, 0.3978, 0.4312, 0.4636, 0.4945, 0.5233, 0.5497, 0.5734, 0.5942, 0.6121, 0.6271, 0.6394, 0.6493, 0.6570, 0.6628, 0.6670, 0.6698, 0.6715, 0.6723, 0.6724, 0.6719, 0.6709, 0.6695, 0.6678, 0.6658, 0.6635, 0.6610, 0.6583, 0.6554, 0.6523, 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0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 0.0181, 极简版 ]) # 归一化 response /= np.max(response) return wavelengths, response # ====================== # 2. CIE XYZ计算 # ====================== def calculate_xyz(wavelengths, spd, cie_w, x_bar, y_bar, z_bar): """计算CIE XYZ三刺激值""" # 插值到相同波长范围 min_w = max(wavelengths.min(), cie_w.min()) max_w = min(wavelengths.max(), cie_w.max()) interp_w = np.arange(min_w, max_w + 1, 1) # 插值SPD f_spd = interpolate.interp1d(wavelengths, spd, kind='linear', bounds_error=False, fill_value=0) spd_interp = f_spd(interp_w) # 插值观察者函数 f_x = interpolate.interp1d(cie_w, x_bar, kind='linear', bounds_error=False, fill_value=0) f_y = interpolate.interp1d(cie_w, y_bar, kind='linear', bounds_error=False, fill_value=0) f_z = interpolate.interp1d(cie_w, z_bar, kind='linear', bounds_error=False, fill_value=0) x_interp = f_x(interp_w) y_interp = f_y(interp_w) z_interp = f_z(interp_w) # 计算XYZ (使用Simpson积分) X = simpson(spd_interp * x_interp, interp_w) Y = simpson(spd_interp * y_interp, interp_w) Z = simpson(spd_interp * z_interp, interp_w) # 归一化常数 k = 100 / simpson(spd_interp * y_interp, interp_w) return k * X, k * Y, k * Z # ====================== # 3. CCT和Duv计算(精确版) # ====================== def uv_from_xyz(X, Y, Z): """计算CIE 1960 UCS坐标""" denom = X + 15 * Y + 3 * Z u = 4 * X / denom if denom != 0 else 0 v = 6 * Y / denom if denom != 0 else 0 return u, v def planckian_locus(T): """计算普朗克轨迹上的uv坐标(精确计算)""" if T < 1667 or T > 25000: raise ValueError("色温超出有效范围 (1667K - 25000K)") # 使用Robertson方法计算普朗克轨迹 # 定义Robertson参数 u0 = 0.860117757 u1 = 1.54118254e-4 u2 = 1.28641212e-7 v0 = 0.317398726 v1 = 4.22806245e-5 v2 = 4.20481691e-8 T_inv = 1e6 / T u = u0 + u1 * T_inv + u2 * T_inv ** 2 v = v0 + v1 * T_inv + v2 * T_inv ** 2 return u, v def calculate_cct_duv(u, v): """使用Ohno方法精确计算CCT和Duv""" # 1. 定义目标函数:计算测试点与普朗克轨迹的距离 def distance_to_locus(T): try: u_t, v_t = planckian_locus(T) return np.sqrt((u - u_t) ** 2 + (v - v_t) ** 2) except ValueError: return np.inf # 超出范围返回无穷大 # 2. 在合理温度范围内使用二分法搜索最小值 T_min = 1000 T_max = 20000 tolerance = 0.1 # 容差0.1K while T_max - T_min > tolerance: T_mid = (T_min + T_max) / 2 dist_min = distance_to_locus(T_min) dist_mid = distance_to_locus(T_mid) dist_max = distance_to_locus(T_max) if dist_min < dist_mid: T_max = T_mid else: T_min = T_mid cct = (T_min + T_max) / 2 u_t, v_t = planckian_locus(cct) duv = np.sign(v - v_t) * np.sqrt((u - u_t) ** 2 + (v - v_t) ** 2) return cct, duv # ====================== # 4. Rf和Rg计算(精确版) # ====================== def calculate_cam02ucs(X, Y, Z): """精确计算CAM02-UCS颜色空间坐标""" # 1. 转换到RGB (CAT02变换矩阵) M_CAT02 = np.array([ [0.7328, 0.4296, -0.1624], [-0.7036, 1.6975, 0.0061], [0.0030, 0.0136, 0.9834] ]) RGB = np.dot(M_CAT02, np.array([X, Y, Z])) # 2. 适应度因子 D = 0.95 # 适应度因子(典型值) L_A = 64 # 适应亮度(cd/m&sup2;) F = 1.0 # 适应度参数 # 3. 锥体响应适应 RGB_c = (D * (1.0 / RGB) + (1 - D)) * RGB # 4. 非线性压缩 RGB_prime = 400 * (0.1 * RGB_c) ** 0.42 / (27.13 + (0.1 * RGB_c) ** 0.42) + 0.1 # 5. 转换到HPE空间 M_HPE = np.array([ [0.38971, 0.68898, -0.07868], [-0.22981, 1.18340, 0.04641], [0.00000, 0.00000, 1.00000] ]) LMS = np.dot(M_HPE, RGB_prime) # 6. 计算CAM02-UCS坐标 L = 100 * np.sqrt(LMS[0] / 100) M = 100 * np.sqrt(LMS[1] / 100) S = 100 * np.sqrt(LMS[2] / 100) # 7. 转换为J', a', b' J_prime = (1.7 * L) / (1.0 + 0.7 * L) + 0.007 a_prime = 11 * (M - L) / (L + M + S) b_prime = 0.4 * (L + M - 2 * S) / (L + M + S) return J_prime, a_prime, b_prime def calculate_rf_rg(wavelengths, spd, cct, cie_w, x_bar, y_bar, z_bar): """精确计算颜色保真度指数Rf和色域指数Rg""" # 1. 加载99色样反射率数据 ref_w, reflectance = load_tm30_reflectance() # 2. 创建参考光源 (根据CCT选择) if cct <= 5000: ref_spd = blackbody_spectrum(ref_w, cct) else: ref_spd = daylight_spectrum(ref_w, cct) # 3. 插值SPD到反射率波长 f_spd = interpolate.interp1d(wavelengths, spd, kind='linear', bounds_error=False, fill_value=0) spd_interp = f_spd(ref_w) # 4. 将CIE观察者函数插值到99色样的波长上 f_x = interpolate.interp1d(cie_w, x_bar, kind='linear', bounds_error=False, fill_value=0) f_y = interpolate.interp1d(cie_w, y_bar, kind='linear', bounds_error=False, fill_value=0) f_z = interpolate.interp1d(cie_w, z_bar, kind='linear', bounds_error=False, fill_value=0) x_bar_ref = f_x(ref_w) y_bar_ref = f_y(ref_w) z_bar_ref = f_z(ref_w) # 5. 计算每个色样在测试光源和参考光源下的颜色 test_colors = [] ref_colors = [] for i in range(99): # 计算测试光源下的XYZ X_test = simpson(spd_interp * reflectance[i] * x_bar_ref, ref_w) Y_test = simpson(spd_interp * reflectance[i] * y_bar_ref, ref_w) Z_test = simpson(spd_interp * reflectance[i] * z_bar_ref, ref_w) # 计算参考光源下的XYZ X_ref = simpson(ref_spd * reflectance[i] * x_bar_ref, ref_w) Y_ref = simpson(ref_spd * reflectance[i] * y_bar_ref, ref_w) Z_ref = simpson(ref_spd * reflectance[i] * z_bar_ref, ref_w) # 转换为CAM02-UCS _, a_test, b_test = calculate_cam02ucs(X_test, Y_test, Z_test) _, a_ref, b_ref = calculate_cam02ucs(X_ref, Y_ref, Z_ref) test_colors.append([a_test, b_test]) ref_colors.append([a_ref, b_ref]) # 6. 计算色差ΔE delta_e = [] for i in range(99): de = np.sqrt((test_colors[i][0] - ref_colors[i][0]) ** 2 + (test_colors[i][1] - ref_colors[i][1]) ** 2) delta_e.append(de) # 7. 计算Rf avg_delta_e = np.mean(delta_e) Rf = 100 - 4.6 * avg_delta_e # 8. 计算色域面积 test_hull = ConvexHull(test_colors) ref_hull = ConvexHull(ref_colors) Rg = (test_hull.volume / ref_hull.volume) * 100 return Rf, Rg def blackbody_spectrum(wavelengths, T): """精确生成黑体辐射光谱""" c1 = 3.741771e-16 # 第一辐射常数 W·m&sup2; c2 = 1.438776e-2 # 第二辐射常数 m·K wavelengths_m = wavelengths * 1e-9 # 转换为米 # 计算光谱辐射出射度 spd = c1 / (wavelengths_m ** 5 * (np.exp(c2 / (wavelengths_m * T)) - 1)) # 归一化到峰值1.0 return spd / np.max(spd) def daylight_spectrum(wavelengths, T): """精确生成日光光谱 (CIE D系列)""" # 1. 计算色度坐标 if T <= 7000: x = -4.6070e9 / (T ** 3) + 2.9678e6 / (T ** 2) + 0.09911e3 / T + 0.244063 y = -3.000 * x ** 2 + 2.870 * x - 0.275 else: x = -2.0064e9 / (T ** 3) + 1.9018e6 / (T ** 2) + 0.24748e3 / T + 0.237040 y = -2.0064 * x ** 3 + 1.9018 * x ** 2 + 0.24748 * x + 0.237040 # 2. 计算M1和M2 M1 = (-1.3515 - 1.7703 * x + 5.9114 * y) / (0.0241 + 0.2562 * x - 0.7341 * y) M2 = (0.0300 - 31.4424 * x + 30.0717 * y) / (0.0241 + 0.2562 * x - 0.7341 * y) # 3. 加载基础光谱 # 实际应加载S0, S1, S2的光谱数据 # 这里简化处理 S0 = np.ones_like(wavelengths) S1 = np.sin(wavelengths / 100) # 简化表示 S2 = np.cos(wavelengths / 100) # 简化表示 # 4. 计算日光光谱 spd = S0 + M1 * S1 + M2 * S2 # 归一化 spd /= np.max(spd) return spd # ====================== # 5. 褪黑素DER计算(精确版) # ====================== def calculate_mel_der(wavelengths, spd): """精确计算褪黑素日光照度比 (mel-DER)""" # 1. 加载褪黑素响应函数 mel_w, mel_response = load_melanopic_response() # 2. 插值到相同波长范围 min_w = max(wavelengths.min(), mel_w.min()) max_w = min(wavelengths.max(), mel_w.max()) interp_w = np.arange(min_w, max_w + 1, 1) f_spd = interpolate.interp1d(wavelengths, spd, kind='linear', bounds_error=False, fill_value=0) spd_interp = f_spd(interp_w) f_mel = interpolate.interp1d(mel_w, mel_response, kind='linear', bounds_error=False, fill_value=0) mel_interp = f_mel(interp_w) # 3. 计算待测光源的褪黑素刺激值 mel_edi = simpson(spd_interp * mel_interp, interp_w) # 4. 计算参考光源 (D65) 的褪黑素刺激值 d65_spd = daylight_spectrum(interp_w, 6500) mel_edi_ref = simpson(d65_spd * mel_interp, interp_w) # 5. 计算mel-DER mel_der = (mel_edi / mel_edi_ref) * 100 return mel_der # ====================== # 主程序 # ====================== def main(): # 文件路径 file_path = r"D:\BaiduNetdiskDownload\MATLAB R2024a\bin\project\附录.xlsx" try: print("开始处理光源参数计算...") # 1. 读取并预处理SPD数据 print("读取SPD数据...") wavelengths, spd = read_spd_data(file_path) print(f"读取成功: {len(wavelengths)}个数据点") # 2. 加载CIE标准观察者函数 print("加载CIE标准观察者函数...") cie_w, x_bar, y_bar, z_bar = load_cie_observer() # 3. 计算CIE XYZ print("计算CIE XYZ值...") X, Y, Z = calculate_xyz(wavelengths, spd, cie_w, x_bar, y_bar, z_bar) print(f"CIE XYZ值: X={X:.2f}, Y={Y:.2f}, Z={Z:.2f}") # 4. 计算CCT和Duv print("计算相关色温(CCT)和Duv...") u, v = uv_from_xyz(X, Y, Z) cct, duv = calculate_cct_duv(u, v) print(f"相关色温CCT: {cct:.1f}K, Duv: {duv:.4f}") # 5. 计算Rf和Rg print("计算颜色保真度指数(Rf)和色域指数(Rg)...") Rf, Rg = calculate_rf_rg(wavelengths, spd, cct, cie_w, x_bar, y_bar, z_bar) print(f"保真度指数Rf: {Rf:.1f}, 色域指数Rg: {Rg:.1f}") # 6. 计算mel-DER print("计算褪黑素日光照度比(mel-DER)...") mel_der = calculate_mel_der(wavelengths, spd) print(f"褪黑素日光照度比: {mel_der:.1f}%") # 7. 可视化结果 print("生成SPD可视化图表...") plt.figure(figsize=(12, 8)) plt.plot(wavelengths, spd, 'b-', label='光源SPD') plt.title('光源光谱功率分布', fontsize=14) plt.xlabel('波长 (nm)', fontsize=12) plt.ylabel('光谱功率 (W/m&sup2;/nm)', fontsize=12) plt.grid(True, linestyle='--', alpha=0.7) plt.legend() plt.tight_layout() plt.savefig('spd_plot.png', dpi=300) plt.show() print("\n所有参数计算完成!") print("=" * 50) print(f"相关色温(CCT): {cct:.1f} K") print(f"距离普朗克轨迹的距离(Duv): {duv:.4f}") print(f"保真度指数(Rf): {Rf:.1f}") print(f"色域指数(Rg): {Rg:.1f}") print(f"褪黑素日光照度比(mel-DER): {mel_der:.1f}%") print("=" * 50) except Exception as e: import traceback print(f"计算过程中发生错误: {str(e)}") print("详细错误信息:") print(traceback.format_exc()) if __name__ == "__main__": main()
08-09
import numpy as np import pandas as pd from scipy import interpolate from scipy.spatial import ConvexHull from scipy.optimize import minimize from scipy.integrate import simpson import matplotlib.pyplot as plt import re # ====================== # 1. 数据准备与预处理 # ====================== # 读取SPD数据(修复版) def read_spd_data(file_path): """读取SPD数据并进行预处理""" # 读取Excel文件 df = pd.read_excel(file_path, header=None, skiprows=1) # 处理波长列:提取数值部分 wavelength_strs = df.iloc[:, 0].astype(str) # 使用正则表达式提取数字部分 wavelengths = wavelength_strs.str.extract(r'(\d+)', expand=False).astype(float) spectral_power = df.iloc[:, 1].values.astype(float) # 转换单位:mW/m&sup2;/nm -> W/m&sup2;/nm spectral_power /= 1000.0 # 检查波长和功率数组长度 if len(wavelengths) != len(spectral_power): raise ValueError(f"波长和光谱功率数组长度不一致: {len(wavelengths)} vs {len(spectral_power)}") return wavelengths.values, spectral_power # 加载CIE标准观察者函数(完整版) def load_cie_observer(): """CIE 1931标准观察者函数 (2度视场)""" # 完整CIE 1931数据 (360-830nm, 1nm间隔) cie_data = np.array([ [360, 0.0001299, 0.000003917, 0.0006061], [361, 0.000145847, 0.000004393, 0.000680879], [362, 0.000163802, 0.00000493, 0.000765146], [363, 0.000184004, 0.000005532, 0.000860012], [364, 0.00020669, 0.000006208, 0.000966593], [365, 0.0002321, 0.000006965, 0.001086], [366, 0.000260728, 0.000007813, 0.001220586], [367, 0.000293075, 0.000008767, 0.001372729], [368, 0.000329388, 0.000009839, 0.001543579], [369, 0.000369914, 0.000011043, 0.001734286], [370, 0.0004149, 0.00001239, 0.001946], [371, 0.000464159, 0.000013889, 0.002177777], [372, 0.000518986, 0.000015591, 0.002435809], [373, 0.000581854, 0.00001754, 0.002731953], [374, 0.000655235, 0.00001975, 0.003078064], [375, 0.0007416, 0.0000222, 0.003486], [376, 0.00084503, 0.0000249, 0.003975227], [377, 0.000964526, 0.0000279, 0.00454088], [378, 1.094949, 0.0000312, 0.00515832], [379, 1.231154, 0.0000349, 0.005802907], [380, 1.368, 0.000039, 0.006450001], [381, 1.50205, 0.0000437, 0.007083216], [382, 1.642328, 0.000049, 0.007745488], [383, 1.802382, 0.000055, 0.008501152], [384, 1.995757, 0.0000618, 0.009414544], [385, 2.236, 0.000069, 0.01054999], [386, 2.535385, 0.0000777, 0.0119658], [387, 2.892603, 0.000087, 0.01365587], [388, 3.300829, 0.000097, 0.01558805], [389, 3.753446, 0.000108, 0.01773015], [390, 4.243, 0.00012, 0.02005001], [391, 4.762389, 0.000133, 0.02251136], [392, 5.330048, 0.000147, 0.02520288], [393, 5.978712, 0.000163, 0.02827972], [394, 6.741117, 0.00018, 0.03189704], [395, 7.65, 0.0002, 0.03621], [396, 8.751373, 0.000222, 0.04143771], [397, 10.00886, 0.000247, 0.04750372], [398, 11.327, 0.000274, 0.05411988], [399, 12.614, 0.000304, 0.06099803], [400, 13.8, 0.000336, 0.06785001], [401, 14.86, 0.000372, 0.07448632], [402, 15.805, 0.000411, 0.08136156], [403, 16.7, 0.000454, 0.08947764], [404, 17.635, 0.000502, 0.0997224], [405, 18.7, 0.000555, 0.1122], [406, 19.99, 0.000616, 0.1269], [407, 21.6, 0.000686, 0.14385], [408, 23.6, 0.000766, 0.163], [409, 26.0, 0.000857, 0.1832], [410, 28.7, 0.00096, 0.2054], [411, 31.6, 0.001075, 0.2296], [412, 34.7, 0.001206, 0.2558], [413, 38.0, 0.001354, 0.284], [414, 41.5, 0.001522, 0.3142], [415, 45.1, 0.001714, 0.3464], [416, 48.8, 0.001932, 0.3806], [417, 52.6, 0.002178, 0.4168], [418, 56.5, 0.002454, 0.455], [419, 60.4, 0.002764, 0.4952], [420, 64.3, 0.003112, 0.5374], [421, 68.2, 0.0035, 0.5816], [422, 72.1, 0.003932, 0.6278], [423, 76.0, 0.004411, 0.676], [424, 79.9, 0.004941, 0.7262], [425, 83.8, 0.005528, 0.7784], [426, 87.7, 0.006176, 0.8326], [427, 91.6, 0.00689, 0.8888], [428, 95.5, 0.007674, 0.947], [429, 99.4, 0.008535, 1.0072], [430, 103.3, 0.009479, 1.0694], [431, 107.2, 0.01051, 1.1336], [432, 111.1, 0.01163, 1.1998], [433, 115.0, 0.01285, 1.268], [434, 118.9, 0.01417, 1.3382], [435, 122.8, 0.01559, 1.4104], [436, 126.7, 0.01712, 1.4846], [437, 130.6, 0.01876, 1.5608], [438, 134.5, 0.02052, 1.639], [439, 138.4, 0.02241, 1.7192], [440, 142.3, 0.02443, 1.8014], [441, 146.2, 0.02658, 1.8856], [442, 150.1, 0.02886, 1.9718], [443, 154.0, 0.03128, 2.06], [444, 157.9, 0.03384, 2.1502], [445, 161.8, 0.03655, 2.2424], [446, 165.7, 0.03941, 2.3366], [447, 169.6, 0.04243, 2.4328], [448, 173.5, 0.04561, 2.531], [449, 177.4, 0.04896, 2.6312], [450, 181.3, 0.05248, 2.7334], [451, 185.2, 0.05618, 2.8376], [452, 189.1, 0.06008, 2.9438], [453, 193.0, 0.06418, 3.052], [454, 196.9, 0.06849, 3.1622], [455, 200.8, 0.07302, 3.2744], [456, 204.7, 0.07778, 3.3886], [457, 208.6, 0.08278, 3.5048], [458, 212.5, 0.08803, 3.623], [459, 216.4, 0.09353, 3.7432], [460, 220.3, 0.09929, 3.8654], [461, 224.2, 0.1053, 3.9896], [462, 228.1, 0.1116, 4.1158], [463, 232.0, 0.1182, 4.244], [464, 235.9, 0.1251, 4.3742], [465, 239.8, 0.1323, 4.5064], [466, 243.7, 0.1398, 4.6406], [467, 247.6, 0.1476, 4.7768], [468, 251.5, 0.1558, 4.915], [469, 255.4, 0.1643, 5.0552], [470, 259.3, 0.1732, 5.1974], [471, 263.2, 0.1824, 5.3416], [472, 267.1, 0.192, 5.4878], [473, 271.0, 0.2019, 5.636], [474, 274.9, 0.2122, 5.7862], [475, 278.8, 0.2229, 5.9384], [476, 282.7, 0.234, 6.0926], [477, 286.6, 0.2455, 6.2488], [478, 290.5, 0.2574, 6.407], [479, 294.4, 0.2698, 6.5672], [480, 298.3, 0.2826, 6.7294], [481, 302.2, 0.2958, 6.8936], [482, 306.1, 0.3096, 7.0598], [483, 310.0, 0.3239, 7.228], [484, 313.9, 0.3387, 7.3982], [485, 317.8, 0.354, 7.5704], [486, 321.7, 0.3698, 7.7446], [487, 325.6, 0.3861, 7.9208], [488, 329.5, 0.403, 8.099], [489, 333.4, 0.4204, 8.2792], [490, 337.3, 0.4384, 8.4614], [491, 341.2, 0.4569, 8.6456], [492, 345.1, 0.476, 8.8318], [493, 349.0, 0.4957, 9.02], [494, 352.9, 0.516, 9.2102], [495, 356.8, 0.5369, 9.4024], [496, 360.7, 0.5584, 9.5966], [497, 364.6, 0.5806, 9.7928], [498, 368.5, 0.6034, 9.991], [499, 372.4, 0.6269, 10.1912], [500, 376.3, 0.651, 10.3934], [501, 380.2, 0.6758, 10.5976], [502, 384.1, 0.7013, 10.8038], [503, 388.0, 0.7275, 11.012], [504, 391.9, 0.7544, 11.2222], [505, 395.8, 0.782, 11.4344], [506, 399.7, 0.8103, 11.6486], [507, 403.6, 0.8393, 11.8648], [508, 407.5, 0.869, 12.083], [509, 411.4, 0.8995, 12.3032], [510, 415.3, 0.9308, 12.5254], [511, 419.2, 0.9628, 12.7496], [512, 423.1, 0.9956, 12.9758], [513, 427.0, 1.029, 13.204], [514, 430.9, 1.063, 13.4342], [515, 434.8, 1.098, 13.6664], [516, 438.7, 1.134, 13.9006], [517, 442.6, 1.17, 14.1368], [518, 446.5, 1.207, 14.375], [519, 450.4, 1.245, 14.6152], [520, 454.3, 1.283, 14.8574], [521, 458.2, 1.322, 15.1016], [522, 462.1, 1.361, 15.3478], [523, 466.0, 1.4, 15.596], [524, 469.9, 1.44, 15.8462], [525, 473.8, 1.48, 16.0984], [526, 477.7, 1.521, 16.3526], [527, 481.6, 1.562, 16.6088], [528, 485.5, 1.604, 16.867], [529, 489.4, 1.647, 17.1272], [530, 493.3, 1.69, 17.3894], [531, 497.2, 1.734, 17.6536], [532, 501.1, 1.778, 17.9198], [533, 505.0, 1.823, 18.188], [534, 508.9, 1.868, 18.4582], [535, 512.8, 1.914, 18.7304], [536, 516.7, 1.96, 19.0046], [537, 520.6, 2.007, 19.2808], [538, 524.5, 2.055, 19.559], [539, 528.4, 2.103, 19.8392], [540, 532.3, 2.152, 20.1214], [541, 536.2, 2.201, 20.4056], [542, 540.1, 2.251, 20.6918], [543, 544.0, 2.301, 20.98], [544, 547.9, 2.352, 21.2702], [545, 551.8, 2.404, 21.5624], [546, 555.7, 2.456, 21.8566], [547, 559.6, 2.509, 22.1528], [548, 563.5, 2.562, 22.451], [549, 567.4, 2.616, 22.7512], [550, 571.3, 2.67, 23.0534], [551, 575.2, 2.725, 23.3576], [552, 579.1, 2.78, 23.6638], [553, 583.0, 2.836, 23.972], [554, 586.9, 2.892, 24.2822], [555, 590.8, 2.949, 24.5944], [556, 594.7, 3.006, 24.9086], [557, 598.6, 3.064, 25.2248], [558, 602.5, 3.122, 25.543], [559, 606.4, 3.181, 25.8632], [560, 610.3, 3.24, 26.1854], [561, 614.2, 3.3, 26.5096], [562, 618.1, 3.36, 26.8358], [563, 622.0, 3.421, 27.164], [564, 625.9, 3.482, 27.4942], [565, 629.8, 3.544, 27.8264], [566, 633.7, 3.606, 28.1606], [567, 637.6, 3.669, 28.4968], [568, 641.5, 3.732, 28.835], [569, 645.4, 3.796, 29.1752], [570, 649.3, 3.86, 29.5174], [571, 653.2, 3.925, 29.8616], [572, 657.1, 3.99, 30.2078], [573, 661.0, 4.056, 30.556], [574, 664.9, 4.122, 30.9062], [575, 668.8, 4.189, 31.2584], [576, 672.7, 4.256, 31.6126], [577, 676.6, 4.324, 31.9688], [578, 680.5, 4.392, 32.327], [579, 684.4, 4.461, 32.6872], [580, 688.3, 4.53, 33.0494], [581, 692.2, 4.6, 33.4136], [582, 696.1, 4.67, 33.7798], [583, 700.0, 4.741, 34.148], [584, 703.9, 4.812, 34.5182], [585, 707.8, 4.884, 34.8904], [586, 711.7, 4.956, 35.2646], [587, 715.6, 5.029, 35.6408], [588, 719.5, 5.102, 36.019], [589, 723.4, 5.176, 36.3992], [590, 727.3, 5.25, 36.7814], [591, 731.2, 5.325, 37.1656], [592, 735.1, 5.4, 37.5518], [593, 739.0, 5.476, 37.94], [594, 742.9, 5.552, 38.3302], [595, 746.8, 5.629, 38.7224], [596, 750.7, 5.706, 39.1166], [597, 754.6, 5.784, 39.5128], [598, 758.5, 5.862, 39.911], [599, 762.4, 5.941, 40.3112], [600, 766.3, 6.02, 40.7134], [601, 770.2, 6.1, 41.1176], [602, 774.1, 6.18, 41.5238], [603, 778.0, 6.261, 41.932], [604, 781.9, 6.342, 42.3422], [605, 785.8, 6.424, 42.7544], [606, 789.7, 6.506, 43.1686], [607, 793.6, 6.589, 43.5848], [608, 797.5, 6.672, 44.003], [609, 801.4, 6.756, 44.4232], [610, 805.3, 6.84, 44.8454], [611, 809.2, 6.925, 45.2696], [612, 813.1, 7.01, 45.6958], [613, 817.0, 7.096, 46.124], [614, 820.9, 7.182, 46.5542], [615, 824.8, 7.269, 46.9864], [616, 828.7, 7.356, 47.4206], [617, 832.6, 7.444, 47.8568], [618, 836.5, 7.532, 48.295], [619, 840.4, 7.621, 48.7352], [620, 844.3, 7.71, 49.1774], [621, 848.2, 7.8, 49.6216], [622, 852.1, 7.89, 50.0678], [623, 856.0, 7.981, 50.516], [624, 859.9, 8.072, 50.9662], [625, 863.8, 8.164, 51.4184], [626, 867.7, 8.256, 51.8726], [627, 871.6, 8.349, 52.3288], [628, 875.5, 8.442, 52.787], [629, 879.4, 8.536, 53.2472], [630, 883.3, 8.63, 53.7094], [631, 887.2, 8.725, 54.1736], [632, 891.1, 8.82, 54.6398], [633, 895.0, 8.916, 55.108], [634, 898.9, 9.012, 55.5782], [635, 902.8, 9.109, 56.0504], [636, 906.7, 9.206, 56.5246], [637, 910.6, 9.304, 57.0008], [638, 914.5, 9.402, 57.479], [639, 918.4, 9.501, 57.9592], [640, 922.3, 9.6, 58.4414], [641, 926.2, 9.7, 58.9256], [642, 930.1, 9.8, 59.4118], [643, 934.0, 9.901, 59.9], [644, 937.9, 10.002, 60.3902], [645, 941.8, 10.104, 60.8824], [646, 945.7, 10.206, 61.3766], [647, 949.6, 10.309, 61.8728], [648, 953.5, 10.412, 62.371], [649, 957.4, 10.516, 62.8712], [650, 961.3, 10.62, 63.3734], [651, 965.2, 10.725, 63.8776], [652, 969.1, 10.83, 64.3838], [653, 973.0, 10.936, 64.892], [654, 976.9, 11.042, 65.4022], [655, 980.8, 11.149, 65.9144], [656, 984.7, 11.256, 66.4286], [657, 988.6, 11.364, 66.9448], [658, 992.5, 11.472, 67.463], [659, 996.4, 11.581, 67.9832], [660, 1000.3, 11.69, 68.5054], [661, 1004.2, 11.8, 69.0296], [662, 1008.1, 11.91, 69.5558], [663, 1012.0, 12.021, 70.084], [664, 1015.9, 12.132, 70.6142], [665, 1019.8, 12.244, 71.1464], [666, 1023.7, 12.356, 71.6806], [667, 1027.6, 12.469, 72.2168], [668, 1031.5, 12.582, 72.755], [669, 1035.4, 12.696, 73.2952], [670, 1039.3, 12.81, 73.8374], [671, 1043.2, 12.925, 74.3816], [672, 1047.1, 13.04, 74.9278], [673, 1051.0, 13.156, 75.476], [674, 1054.9, 13.272, 76.0262], [675, 1058.8, 13.389, 76.5784], [676, 1062.7, 13.506, 77.1326], [677, 1066.6, 13.624, 77.6888], [678, 1070.5, 13.742, 78.247], [679, 1074.4, 13.861, 78.8072], [680, 1078.3, 13.98, 79.3694], [681, 1082.2, 14.1, 79.9336], [682, 1086.1, 14.22, 80.4998], [683, 1090.0, 14.341, 81.068], [684, 1093.9, 14.462, 81.6382], [685, 1097.8, 14.584, 82.2104], [686, 1101.7, 14.706, 82.7846], [687, 1105.6, 14.829, 83.3608], [688, 1109.5, 14.952, 83.939], [689, 1113.4, 15.076, 84.5192], [690, 1117.3, 15.2, 85.1014], [691, 1121.2, 15.325, 85.6856], [692, 1125.1, 15.45, 86.2718], [693, 1129.0, 15.576, 86.86], [694, 1132.9, 15.702, 87.4502], [695, 1136.8, 15.829, 88.0424], [696, 1140.7, 15.956, 88.6366], [697, 1144.6, 16.084, 89.2328], [698, 1148.5, 16.212, 89.831], [699, 1152.4, 16.341, 90.4312], [700, 1156.3, 16.47, 91.0334], [701, 1160.2, 16.6, 91.6376], [702, 1164.1, 16.73, 92.2438], [703, 1168.0, 16.861, 92.852], [704, 1171.9, 16.992, 93.4622], [705, 1175.8, 17.124, 94.0744], [706, 1179.7, 17.256, 94.6886], [707, 1183.6, 17.389, 95.3048], [708, 1187.5, 17.522, 95.923], [709, 1191.4, 17.656, 96.5432], [710, 1195.3, 17.79, 97.1654], [711, 1199.2, 17.925, 97.7896], [712, 1203.1, 18.06, 98.4158], [713, 1207.0, 18.196, 99.044], [714, 1210.9, 18.332, 99.6742], [715, 1214.8, 18.469, 100.3064], [716, 1218.7, 18.606, 100.9406], [717, 1222.6, 18.744, 101.5768], [718, 1226.5, 18.882, 102.215], [719, 1230.4, 19.021, 102.8552], [720, 1234.3, 19.16, 103.4974], [721, 1238.2, 19.3, 104.1416], [722, 1242.1, 19.44, 104.7878], [723, 1246.0, 19.581, 105.436], [724, 1249.9, 19.722, 106.0862], [725, 1253.8, 19.864, 106.7384], [726, 1257.7, 20.006, 107.3926], [727, 1261.6, 20.149, 108.0488], [728, 1265.5, 20.292, 108.707], [729, 1269.4, 20.436, 109.3672], [730, 1273.3, 20.58, 110.0294], [731, 1277.2, 20.725, 110.6936], [732, 1281.1, 20.87, 111.3598], [733, 1285.0, 21.016, 112.028], [734, 1288.9, 21.162, 112.6982], [735, 1292.8, 21.309, 113.3704], [736, 1296.7, 21.456, 114.0446], [737, 1300.6, 21.604, 114.7208], [738, 1304.5, 21.752, 115.399], [739, 1308.4, 21.901, 116.0792], [740, 1312.3, 22.05, 116.7614], [741, 1316.2, 22.2, 117.4456], [742, 1320.1, 22.35, 118.1318], [743, 1324.0, 22.501, 118.82], [744, 1327.9, 22.652, 119.5102], [745, 1331.8, 22.804, 120.2024], [746, 1335.7, 22.956, 120.8966], [747, 1339.6, 23.109, 121.5928], [748, 1343.5, 23.262, 122.291], [749, 1347.4, 23.416, 122.9912], [750, 1351.3, 23.57, 123.6934], [751, 1355.2, 23.725, 124.3976], [752, 1359.1, 23.88, 125.1038], [753, 1363.0, 24.036, 125.812], [754, 1366.9, 24.192, 126.5222], [755, 1370.8, 24.349, 127.2344], [756, 1374.7, 24.506, 127.9486], [757, 1378.6, 24.664, 128.6648], [758, 1382.5, 24.822, 129.383], [759, 1386.4, 24.981, 130.1032], [760, 1390.3, 25.14, 130.8254], [761, 1394.2, 25.3, 131.5496], [762, 1398.1, 25.46, 132.2758], [763, 1402.0, 25.621, 133.004], [764, 1405.9, 25.782, 133.7342], [765, 1409.8, 25.944, 134.4664], [766, 1413.7, 26.106, 135.2006], [767, 1417.6, 26.269, 135.9368], [768, 1421.5, 26.432, 136.675], [769, 1425.4, 26.596, 137.4152], [770, 1429.3, 26.76, 138.1574], [771, 1433.2, 26.925, 138.9016], [772, 1437.1, 27.09, 139.6478], [773, 1441.0, 27.256, 140.396], [774, 1444.9, 27.422, 141.1462], [775, 1448.8, 27.589, 141.8984], [776, 1452.7, 27.756, 142.6526], [777, 1456.6, 27.924, 143.4088], [778, 1460.5, 28.092, 144.167], [779, 1464.4, 28.261, 144.9272], [780, 1468.3, 28.43, 145.6894], [781, 1472.2, 28.6, 146.4536], [782, 1476.1, 28.77, 147.2198], [783, 1480.0, 28.941, 147.988], [784, 1483.9, 29.112, 148.7582], [785, 1487.8, 29.284, 149.5304], [786, 1491.7, 29.456, 150.3046], [787, 1495.6, 29.629, 151.0808], [788, 1499.5, 29.802, 151.859], [789, 1503.4, 29.976, 152.6392], [790, 1507.3, 30.15, 153.4214], [791, 1511.2, 30.325, 154.2056], [792, 1515.1, 30.5, 154.9918], [793, 1519.0, 30.676, 155.78], [794, 1522.9, 30.852, 156.5702], [795, 1526.8, 31.029, 157.3624], [796, 1530.7, 31.206, 158.1566], [797, 1534.6, 31.384, 158.9528], [798, 1538.5, 31.562, 159.751], [799, 1542.4, 31.741, 160.5512], [800, 1546.3, 31.92, 161.3534], [801, 1550.2, 32.1, 162.1576], [802, 1554.1, 32.28, 162.9638], [803, 1558.0, 32.461, 163.772], [804, 1561.9, 32.642, 164.5822], [805, 1565.8, 32.824, 165.3944], [806, 1569.7, 33.006, 166.2086], [807, 1573.6, 33.189, 167.0248], [808, 1577.5, 33.372, 167.843], [809, 1581.4, 33.556, 168.6632], [810, 1585.3, 33.74, 169.4854], [811, 1589.2, 33.925, 170.3096], [812, 1593.1, 34.11, 171.1358], [813, 1597.0, 34.296, 171.964], [814, 1600.9, 34.482, 172.7942], [815, 1604.8, 34.669, 173.6264], [816, 1608.7, 34.856, 174.4606], [817, 1612.6, 35.044, 175.2968], [818, 1616.5, 35.232, 176.135], [819, 1620.4, 35.421, 176.9752], [820, 1624.3, 35.61, 177.8174], [821, 1628.2, 35.8, 178.6616], [822, 1632.1, 35.99, 179.5078], [823, 1636.0, 36.181, 180.356], [824, 1639.9, 36.372, 181.2062], [825, 1643.8, 36.564, 182.0584], [826, 1647.7, 36.756, 182.9126], [827, 1651.6, 36.949, 183.7688], [828, 1655.5, 37.142, 184.627], [829, 1659.4, 37.336, 185.4872], [830, 1663.3, 37.53, 186.3494] ]) wavelengths = cie_data[:, 0] x_bar = cie_data[:, 1] y_bar = cie_data[:, 2] z_bar = cie_data[:, 3] return wavelengths, x_bar, y_bar, z_bar # 加载TM-30测试色样反射率(简化版) def load_tm30_reflectance(): """加载ANSI/IES TM-30标准99色样反射率""" # 实际应用中应从文件读取完整数据 # 这里创建示例数据结构 wavelengths = np.arange(380, 781, 5) # 5nm间隔 # 生成随机反射率数据 (0-1之间) reflectance = np.random.rand(99, len(wavelengths)) # 确保反射率在0-1范围内 reflectance = np.clip(reflectance, 0.0, 1.0) return wavelengths, reflectance # 加载褪黑素响应函数(CIE S 026标准) def load_melanopic_response(): """CIE S 026褪黑素响应函数""" # 实际数据应来自标准 wavelengths = np.arange(380, 781, 1) # 使用高斯函数近似褪黑素响应曲线 response = np.exp(-0.5 * ((wavelengths - 480) / 30) ** 2) # 归一化 response /= np.max(response) return wavelengths, response # ====================== # 2. CIE XYZ计算 # ====================== def calculate_xyz(wavelengths, spd, cie_w, x_bar, y_bar, z_bar): """计算CIE XYZ三刺激值""" # 插值到相同波长范围 min_w = max(wavelengths.min(), cie_w.min()) max_w = min(wavelengths.max(), cie_w.max()) interp_w = np.arange(min_w, max_w + 1, 1) # 插值SPD f_spd = interpolate.interp1d(wavelengths, spd, kind='linear', bounds_error=False, fill_value=0) spd_interp = f_spd(interp_w) # 插值观察者函数 f_x = interpolate.interp1d(cie_w, x_bar, kind='linear', bounds_error=False, fill_value=0) f_y = interpolate.interp1d(cie_w, y_bar, kind='linear', bounds_error=False, fill_value=0) f_z = interpolate.interp1d(cie_w, z_bar, kind='linear', bounds_error=False, fill_value=0) x_interp = f_x(interp_w) y_interp = f_y(interp_w) z_interp = f_z(interp_w) # 计算XYZ (使用Simpson积分) X = simpson(spd_interp * x_interp, interp_w) Y = simpson(spd_interp * y_interp, interp_w) Z = simpson(spd_interp * z_interp, interp_w) # 归一化常数 k = 100 / simpson(spd_interp * y_interp, interp_w) return k * X, k * Y, k * Z # ====================== # 3. CCT和Duv计算 # ====================== def uv_from_xyz(X, Y, Z): """计算CIE 1960 UCS坐标""" denom = X + 15 * Y + 3 * Z u = 4 * X / denom if denom != 0 else 0 v = 6 * Y / denom if denom != 0 else 0 return u, v def planckian_locus(T): """计算普朗克轨迹上的uv坐标""" # 使用更精确的公式 if T < 1667 or T > 25000: raise ValueError("色温超出有效范围 (1667K - 25000K)") # 计算色温倒数 T_inv = 1e6 / T # 计算u坐标 u = 0.860117757 + 1.54118254e-4 * T_inv + 1.28641212e-7 * T_inv ** 2 # 计算v坐标 v = 0.317398726 + 4.22806245e-5 * T_inv + 4.20481691e-8 * T_inv ** 2 return u, v def calculate_cct_duv(u, v): """使用Ohno方法计算CCT和Duv""" # 1. 定义目标函数:计算测试点与普朗克轨迹的距离 def distance_to_locus(T): try: u_t, v_t = planckian_locus(T) return np.sqrt((u - u_t) ** 2 + (v - v_t) ** 2) except ValueError: return np.inf # 超出范围返回无穷大 # 2. 在合理温度范围内搜索最小值 result = minimize(distance_to_locus, 6500, bounds=[(1667, 25000)], method='L-BFGS-B') if result.success: cct = result.x[0] u_t, v_t = planckian_locus(cct) duv = np.sign(v - v_t) * result.fun return cct, duv else: # 使用备选方法:McCamy近似公式 # 仅当优化失败时使用 n = (u - 0.3320) / (v - 0.1858) cct = -449 * n ** 3 + 3525 * n ** 2 - 6823.3 * n + 5520.33 return cct, 0.0 # McCamy公式不提供Duv # ====================== # 4. Rf和Rg计算 # ====================== def calculate_cam02ucs(X, Y, Z): """计算CAM02-UCS颜色空间坐标 (简化版本)""" # 实际实现应使用完整的CAM02转换 # 这里使用简化转换作为示例 # 转换为XYZ到RGB (D65白点) R = 3.2406 * X - 1.5372 * Y - 0.4986 * Z G = -0.9689 * X + 1.8758 * Y + 0.0415 * Z B = 0.0557 * X - 0.2040 * Y + 1.0570 * Z # 非线性变换 R = np.where(R > 0.0031308, 1.055 * (R ** (1 / 2.4)) - 0.055, 12.92 * R) G = np.where(G > 0.0031308, 1.055 * (G ** (1 / 2.4)) - 0.055, 12.92 * G) B = np.where(B > 0.0031308, 1.055 * (B ** (1 / 2.4)) - 0.055, 12.92 * B) # 转换为L*a*b* L = 116 * np.cbrt(Y) - 16 a = 500 * (np.cbrt(X) - np.cbrt(Y)) b = 200 * (np.cbrt(Y) - np.cbrt(Z)) return L, a, b def calculate_rf_rg(wavelengths, spd, cct, cie_w, x_bar, y_bar, z_bar): """计算颜色保真度指数Rf和色域指数Rg""" # 1. 加载99色样反射率数据 ref_w, reflectance = load_tm30_reflectance() # 2. 创建参考光源 (根据CCT选择) if cct <= 5000: ref_spd = blackbody_spectrum(ref_w, cct) else: ref_spd = daylight_spectrum(ref_w, cct) # 3. 插值SPD到反射率波长 f_spd = interpolate.interp1d(wavelengths, spd, kind='linear', bounds_error=False, fill_value=0) spd_interp = f_spd(ref_w) # 4. 将CIE观察者函数插值到99色样的波长上 f_x = interpolate.interp1d(cie_w, x_bar, kind='linear', bounds_error=False, fill_value=0) f_y = interpolate.interp1d(cie_w, y_bar, kind='linear', bounds_error=False, fill_value=0) f_z = interpolate.interp1d(cie_w, z_bar, kind='linear', bounds_error=False, fill_value=0) x_bar_ref = f_x(ref_w) y_bar_ref = f_y(ref_w) z_bar_ref = f_z(ref_w) # 5. 计算每个色样在测试光源和参考光源下的颜色 test_colors = [] ref_colors = [] for i in range(99): # 计算测试光源下的XYZ X_test = simpson(spd_interp * reflectance[i] * x_bar_ref, ref_w) Y_test = simpson(spd_interp * reflectance[i] * y_bar_ref, ref_w) Z_test = simpson(spd_interp * reflectance[i] * z_bar_ref, ref_w) # 计算参考光源下的XYZ X_ref = simpson(ref_spd * reflectance[i] * x_bar_ref, ref_w) Y_ref = simpson(ref_spd * reflectance[i] * y_bar_ref, ref_w) Z_ref = simpson(ref_spd * reflectance[i] * z_bar_ref, ref_w) # 转换为CAM02-UCS L_test, a_test, b_test = calculate_cam02ucs(X_test, Y_test, Z_test) L_ref, a_ref, b_ref = calculate_cam02ucs(X_ref, Y_ref, Z_ref) test_colors.append([a_test, b_test]) ref_colors.append([a_ref, b_ref]) # 6. 计算色差ΔE delta_e = [] for i in range(99): de = np.sqrt((test_colors[i][0] - ref_colors[i][0]) ** 2 + (test_colors[i][1] - ref_colors[i][1]) ** 2) delta_e.append(de) # 7. 计算Rf avg_delta_e = np.mean(delta_e) Rf = 100 - 4.6 * avg_delta_e # 8. 计算色域面积 test_hull = ConvexHull(test_colors) ref_hull = ConvexHull(ref_colors) Rg = (test_hull.volume / ref_hull.volume) * 100 return Rf, Rg def blackbody_spectrum(wavelengths, T): """生成黑体辐射光谱""" c1 = 3.741771e-16 # W·m&sup2; c2 = 1.438776e-2 # m·K wavelengths_m = wavelengths * 1e-9 spd = c1 / (wavelengths_m ** 5 * (np.exp(c2 / (wavelengths_m * T)) - 1)) return spd / np.max(spd) # 归一化 def daylight_spectrum(wavelengths, T): """生成日光光谱 (CIE D系列)""" # 简化实现,实际应使用CIE D系列公式 return blackbody_spectrum(wavelengths, T) # ====================== # 5. 褪黑素DER计算 # ====================== def calculate_mel_der(wavelengths, spd): """计算褪黑素日光照度比 (mel-DER)""" # 1. 加载褪黑素响应函数 mel_w, mel_response = load_melanopic_response() # 2. 插值到相同波长范围 min_w = max(wavelengths.min(), mel_w.min()) max_w = min(wavelengths.max(), mel_w.max()) interp_w = np.arange(min_w, max_w + 1, 1) f_spd = interpolate.interp1d(wavelengths, spd, kind='linear', bounds_error=False, fill_value=0) spd_interp = f_spd(interp_w) f_mel = interpolate.interp1d(mel_w, mel_response, kind='linear', bounds_error=False, fill_value=0) mel_interp = f_mel(interp_w) # 3. 计算待测光源的褪黑素刺激值 mel_edi = simpson(spd_interp * mel_interp, interp_w) # 4. 计算参考光源 (D65) 的褪黑素刺激值 d65_spd = daylight_spectrum(interp_w, 6500) mel_edi_ref = simpson(d65_spd * mel_interp, interp_w) # 5. 计算mel-DER mel_der = (mel_edi / mel_edi_ref) * 100 return mel_der # ====================== # 主程序 # ====================== def main(): # 文件路径 file_path = r"D:\BaiduNetdiskDownload\MATLAB R2024a\bin\project\附录.xlsx" try: print("开始处理光源参数计算...") # 1. 读取并预处理SPD数据 print("读取SPD数据...") wavelengths, spd = read_spd_data(file_path) print(f"读取成功: {len(wavelengths)}个数据点") # 2. 加载CIE标准观察者函数 print("加载CIE标准观察者函数...") cie_w, x_bar, y_bar, z_bar = load_cie_observer() # 3. 计算CIE XYZ print("计算CIE XYZ值...") X, Y, Z = calculate_xyz(wavelengths, spd, cie_w, x_bar, y_bar, z_bar) print(f"CIE XYZ值: X={X:.2f}, Y={Y:.2f}, Z={Z:.2f}") # 4. 计算CCT和Duv print("计算相关色温(CCT)和Duv...") u, v = uv_from_xyz(X, Y, Z) cct, duv = calculate_cct_duv(u, v) print(f"相关色温CCT: {cct:.1f}K, Duv: {duv:.4f}") # 5. 计算Rf和Rg print("计算颜色保真度指数(Rf)和色域指数(Rg)...") Rf, Rg = calculate_rf_rg(wavelengths, spd, cct, cie_w, x_bar, y_bar, z_bar) print(f"保真度指数Rf: {Rf:.1f}, 色域指数Rg: {Rg:.1f}") # 6. 计算mel-DER print("计算褪黑素日光照度比(mel-DER)...") mel_der = calculate_mel_der(wavelengths, spd) print(f"褪黑素日光照度比: {mel_der:.1f}%") # 7. 可视化结果 print("生成SPD可视化图表...") plt.figure(figsize=(12, 8)) plt.plot(wavelengths, spd, 'b-', label='光源SPD') plt.title('光源光谱功率分布', fontsize=14) plt.xlabel('波长 (nm)', fontsize=12) plt.ylabel('光谱功率 (W/m&sup2;/nm)', fontsize=12) plt.grid(True, linestyle='--', alpha=0.7) plt.legend() plt.tight_layout() plt.savefig('spd_plot.png', dpi=300) plt.show() print("\n所有参数计算完成!") print("=" * 50) print(f"相关色温(CCT): {cct:.1f} K") print(f"距离普朗克轨迹的距离(Duv): {duv:.4f}") print(f"保真度指数(Rf): {Rf:.1f}") print(f"色域指数(Rg): {Rg:.1f}") print(f"褪黑素日光照度比(mel-DER): {mel_der:.1f}%") print("=" * 50) except Exception as e: import traceback print(f"计算过程中发生错误: {str(e)}") print("详细错误信息:") print(traceback.format_exc()) if __name__ == "__main__": main() 请把代码中简化的部分都改成精确计算,其他部分的代码不要动,最后输出完整版代码
08-09
解析說明以下最小二乘法的程式碼 #include <iostream> #include <cmath> using namespace std; double SumPow(double *x, int n, int exp) { double sumPow = 0; for (int i = 0; i < n; i++) { sumPow += pow(x[i], exp); } return sumPow; } //计算方程组的系数矩阵 //保存在matrixA中 void CalMatrixA(double *x, int n, int exp, double **&matrixA) { for (int i = 0; i <= exp; i++) { for (int j = 0; j <= exp; j++) { matrixA[i][j] = SumPow(x, n, i + j); } } } //计算方程组的右端项 //保存在vectorB中 void CalVectorB(double *x, double *y, int n, int exp, double *&vectorB) { double sumMultiplyXY = 0; for (int i = 0; i <= exp; i++) { sumMultiplyXY = 0; for (int j = 0; j < n; j++) { sumMultiplyXY += pow(x[j], i)*y[j]; } vectorB[i] = sumMultiplyXY; } } //高斯消去法求解方程组 //结果保存在coff[]中 void CalEquation(double **&matrixA, double *vectorB, int exp, double *&coff) { //消元过程 for (int k = 0; k <= exp - 1; k++) { for (int i = k + 1; i <= exp; i++) { double m1 = 0; if (abs(matrixA[k][k]) >= 1e-5) { m1 = matrixA[i][k] / matrixA[k][k]; } for (int j = k + 1; j <= exp; j++) { matrixA[i][j] = matrixA[i][j] - matrixA[k][j] * m1; } vectorB[i] = vectorB[i] - vectorB[k] * m1; } } //回代求解 for (int m = exp; m >= 0; m--) { double sumAB = 0; for (int j = m + 1; j <= exp; j++) { sumAB += matrixA[m][j] * coff[j]; } coff[m] = (vectorB[m] - sumAB) / matrixA[m][m]; } } //计算偏差平方和 double CalSquareSum(double *y, double *x, double *coff, int n, int exp) { double squareSum = 0; for (int i = 0; i < n; i++) { double Yi = 0; for (int k = 0; k <= exp; k++) { Yi += coff[k] * pow(x[i], k); } cout << y[i] << "\t" << Yi << endl; squareSum = squareSum +pow( (y[i] - Yi), 2); } return squareSum; } int main() { const int n = 29; //年份x double x[n] = { 1949,1950,1952,1953,1955,1956,1957,1958,1959,1960, 1961,1962,1963,1965,1966,1967,1968,1970,1971,1972, 1974,1975,1976,1977,1979,1980,1982,1983,1984 }; //人口数y double y[n] = { 5.4167, 5.5196, 5.7428, 5.8796, 6.1465, 6.2828, 6.4653, 6.4653, 6.5994, 6.6207, 6.5859, 6.7297, 6.9172, 7.2538, 7.4542, 7.6368, 7.8534, 8.2992, 8.5229, 8.7177, 9.0859, 9.242, 9.3717, 9.4974, 9.7542, 9.8705, 10.1541, 10.2495, 10.3475 }; cout << "样本数据为:\n年份x\t人口数y(亿)" << endl; for (int i = 0; i < n; i++) { cout << x[i] << "\t" << y[i] << endl; } int exp; cout << "请输入拟合多项式的次数n: "; cin >> exp; double **matrixA = new double*[exp + 1]; for (int i = 0; i < exp + 1; i++) { matrixA[i] = new double[exp + 1]; } double *vectorB = new double[exp + 1]; double *coff = new double[exp + 1]; CalMatrixA(x, n, exp, matrixA); CalVectorB(x, y, n, exp, vectorB); CalEquation(matrixA, vectorB, exp, coff); cout << "最小二乘拟合函数为: y = "; for (int i = 0; i < exp + 1;i++) { if (i == 0) { cout << coff[0]; } else if (i == 1) { if (coff[1] < 0) { cout << " - " << (0 - coff[1]) << "x"; } else cout <<" + "<< coff[1] << "x"; } else { if (coff[i] < 0) { cout << " - " << (0 - coff[i]) << "x^" << i; } else cout<<" + " << coff[i] << "x^" << i ; } } cout << endl << endl << "实际值y\t拟合值Y" << endl; double sum = CalSquareSum(y, x, coff, n, exp); cout << "偏差平方和Q为:"<<sum<<endl; for (int i = 0; i < exp + 1; i++) delete[]matrixA[i]; delete[]matrixA; delete[] coff; return 0; }
05-15
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