├── README.md └── SI_for_GitHub ├── M序列发生器.ipynb ├── RGLS_SI.ipynb ├── RML_SI.ipynb └── data ├── data1.csv ├── data2.csv └── data3.csv /README.md: -------------------------------------------------------------------------------- 1 | # System Identification 2 | System Identification Test Python Codes 3 | 基于Python语言的系统辨识测试程序，可以用来辨识SISO系统。 4 | 5 | 系统辨识算法包括： 6 | 1、RLS（经典算法，用于ARX模型，建议优先使用）； 7 | 2、RELS（用于ARMAX模型）； 8 | 3、RIV（包括两种：纯滞后算法和滤波算法）； 9 | 4、RCLS； 10 | 5、RML； 11 | 6、RGLS。 12 | 13 | 模型预测控制算法包括： 14 | SISO-DMC算法（动态矩阵控制。与辨识配合使用。先辨识、再控制）。 15 | 16 | PID控制方法包括： 17 | 增量控制算法，lambda整定模拟。 18 | 19 | 未来计划： 20 | 1、基于Hammerstein、Wiener模型的非线性系统辨识； 21 | 2、基于状态空间方程的MPC。 22 | -------------------------------------------------------------------------------- /SI_for_GitHub/M序列发生器.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 11, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "[0.]\n", 13 | "[0.]\n", 14 | "[0.]\n", 15 | "[0.]\n", 16 | "[0.]\n", 17 | "[1.]\n", 18 | "[0.]\n", 19 | "[0.]\n", 20 | "[0.]\n", 21 | "[0.]\n", 22 | "[1.]\n", 23 | "[1.]\n", 24 | "[0.]\n", 25 | "[0.]\n", 26 | "[0.]\n", 27 | "[1.]\n", 28 | "[0.]\n", 29 | "[1.]\n", 30 | "[0.]\n", 31 | "[0.]\n", 32 | "6\n", 33 | "5\n", 34 | "4\n", 35 | "3\n", 36 | "2\n", 37 | "1\n" 38 | ] 39 | } 40 | ], 41 | "source": [ 42 | "# M序列发生器，s7+s6+1\n", 43 | "import numpy as np\n", 44 | "Morder = 6\n", 45 | "Mamp = 1.0\n", 46 | "M = 1.0*np.ones((Morder + 1, 1))\n", 47 | "\n", 48 | "for i in range(20):\n", 49 | " M[0] = M[5] + M[6]\n", 50 | " if M[0] == 2:\n", 51 | " M[0] = 0\n", 52 | " for i in range(6, 0, -1):\n", 53 | " M[i] = M[i-1]\n", 54 | " print(M[0])\n", 55 | " \n", 56 | "\n", 57 | "for i in range(6, 0, -1):\n", 58 | " print(i)" 59 | ] 60 | } 61 | ], 62 | "metadata": { 63 | "kernelspec": { 64 | "display_name": "Python 3", 65 | "language": "python", 66 | "name": "python3" 67 | }, 68 | "language_info": { 69 | "codemirror_mode": { 70 | "name": "ipython", 71 | "version": 3 72 | }, 73 | "file_extension": ".py", 74 | "mimetype": "text/x-python", 75 | "name": "python", 76 | "nbconvert_exporter": "python", 77 | "pygments_lexer": "ipython3", 78 | "version": "3.6.5" 79 | }, 80 | "varInspector": { 81 | "cols": { 82 | "lenName": 16, 83 | "lenType": 16, 84 | "lenVar": 40 85 | }, 86 | "kernels_config": { 87 | "python": { 88 | "delete_cmd_postfix": "", 89 | "delete_cmd_prefix": "del ", 90 | "library": "var_list.py", 91 | "varRefreshCmd": "print(var_dic_list())" 92 | }, 93 | "r": { 94 | "delete_cmd_postfix": ") ", 95 | "delete_cmd_prefix": "rm(", 96 | "library": "var_list.r", 97 | "varRefreshCmd": "cat(var_dic_list()) " 98 | } 99 | }, 100 | "position": { 101 | "height": "446px", 102 | "left": "652px", 103 | "right": "20px", 104 | "top": "100px", 105 | "width": "467px" 106 | }, 107 | "types_to_exclude": [ 108 | "module", 109 | "function", 110 | "builtin_function_or_method", 111 | "instance", 112 | "_Feature" 113 | ], 114 | "window_display": false 115 | } 116 | }, 117 | "nbformat": 4, 118 | "nbformat_minor": 2 119 | } 120 | -------------------------------------------------------------------------------- /SI_for_GitHub/RGLS_SI.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import matplotlib.pyplot as plt\n", 10 | "%matplotlib inline\n", 11 | "import numpy as np\n", 12 | "\n", 13 | "np.set_printoptions(suppress=True)\n", 14 | "np.set_printoptions(precision=2) #设精度为3\n", 15 | "plt.rcParams['font.sans-serif'] = ['SimHei']\n", 16 | "plt.rcParams['axes.unicode_minus'] = False" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 2, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "data = np.loadtxt(\"./data/data2.csv\", delimiter=\",\", skiprows=1)\n", 26 | "# print(data[0:10,0])\n", 27 | "# print(data[0:10,1])" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 5, 33 | "metadata": {}, 34 | "outputs": [ 35 | { 36 | "name": "stdout", 37 | "output_type": "stream", 38 | "text": [ 39 | "1.0621092063234407\n" 40 | ] 41 | }, 42 | { 43 | "data": { 44 | "image/png": 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\n", 45 | "text/plain": [ 46 | "
" 47 | ] 48 | }, 49 | "metadata": {}, 50 | "output_type": "display_data" 51 | } 52 | ], 53 | "source": [ 54 | "# RGLS\n", 55 | "# 参数初始化\n", 56 | "na = 2\n", 57 | "nb = 2\n", 58 | "nc = 2\n", 59 | "nMax = max(na, nb, nc)\n", 60 | "L = 1700 # 数据长度\n", 61 | "a = 100\n", 62 | "\n", 63 | "# 序列初始化\n", 64 | "e1 = np.zeros(L + nMax) # 误差 数 残差\n", 65 | "h = np.zeros((L + nMax, na + nb, 1))\n", 66 | "hf = np.zeros((L + nMax, na + nb, 1)) # 滤波数据向量\n", 67 | "he = np.zeros((L + nMax, nc, 1)) # 噪声数据向量\n", 68 | "Theta = np.zeros((L + nMax, na + nb, 1)) # [a1,a2,b1,b2].T\n", 69 | "P = np.array([a**2 * np.eye((na + nb))] * (L + nMax))\n", 70 | "K = np.zeros((L + nMax, na + nb, 1)) # 算法增益\n", 71 | "Inn = np.zeros(L + nMax) # 数 新息\n", 72 | "s = np.zeros(L + nMax) # 数\n", 73 | "\n", 74 | "Thetae = np.zeros((L + nMax, nc, 1)) # 噪声估计向量\n", 75 | "Pe = np.array([a**2 * np.eye((nc))] * (L + nMax))\n", 76 | "Ke = np.zeros((L + nMax, nc, 1)) # 噪声算法增益\n", 77 | "Inne = np.zeros(L + nMax) # 数 噪声新息\n", 78 | "se = np.zeros(L + nMax) # 数\n", 79 | "# 损失函数\n", 80 | "J = np.zeros(L + nMax) # 数\n", 81 | "Je = np.zeros(L + nMax) # 数\n", 82 | "\n", 83 | "u = data[:, 0]\n", 84 | "z = data[:, 1]\n", 85 | "uf = np.zeros(len(u))\n", 86 | "zf = np.zeros(len(z))\n", 87 | "v1 = np.zeros(L + nMax)\n", 88 | "z_pre = np.ones((len(z), 1))\n", 89 | "k = 0\n", 90 | "for k in range(nMax, L + nMax):\n", 91 | " zf[k] = z[k]\n", 92 | " uf[k] = u[k]\n", 93 | " for i in range(nc): \n", 94 | " zf[k] += Thetae[k-1][i] * z[k-i-1]\n", 95 | " uf[k] += Thetae[k-1][i] * u[k-i-1]\n", 96 | " for i in range(na):\n", 97 | " hf[k][i] = -1 * zf[k - i - 1]\n", 98 | " h[k][i] = -1 * z[k - i - 1]\n", 99 | " for j in range(nb):\n", 100 | " hf[k][na + j] = uf[k - j - 1]\n", 101 | " h[k][na + j] = u[k - j - 1]\n", 102 | " for i in range(nc): \n", 103 | " he[k][i] = -1 * e1[k - i - 1]\n", 104 | " \n", 105 | " s[k] = hf[k].T @ P[k - 1] @ hf[k] + 1.0\n", 106 | " Inn[k] = zf[k] - hf[k].T @ Theta[k - 1]\n", 107 | " K[k] = P[k - 1] @ hf[k] / s[k]\n", 108 | " P[k] = P[k - 1] - K[k] @ K[k].T * s[k]\n", 109 | " Theta[k] = Theta[k - 1] + K[k] * Inn[k]\n", 110 | " \n", 111 | " se[k] = he[k].T @ Pe[k-1] @ he[k] + 1.0\n", 112 | " e1[k] = z[k] - h[k].T @ Theta[k]\n", 113 | " Inne[k] = e1[k] - he[k].T @ Thetae[k-1]\n", 114 | " Ke[k] = Pe[k-1] @ he[k] / se[k]\n", 115 | " Pe[k] = Pe[k-1] - Ke[k] @ Ke[k].T * se[k]\n", 116 | " Thetae[k] = Thetae[k-1] + Ke[k] * Inne[k]\n", 117 | " v1[k] = e1[k] - he[k].T @ Thetae[k]\n", 118 | " \n", 119 | " J[k] = J[k-1] + Inn[k]**2 / s[k]\n", 120 | " Je[k] = Je[k-1] + Inne[k]**2 / se[k]\n", 121 | " if k >= nMax:\n", 122 | " z_pre[k] = h[k].T @ Theta[k] + e1[k]\n", 123 | " \n", 124 | "ax3 = plt.subplot(2,1,1)\n", 125 | "ax3.plot(range(len(Inn)), Inn)\n", 126 | "plt.ylim([-5, 5]) # y轴边界\n", 127 | "ax4 = plt.subplot(2,1,2)\n", 128 | "ax4.plot(range(L + nMax),Theta[:,0],\n", 129 | " range(L + nMax),Theta[:,1],\n", 130 | " range(L + nMax),Theta[:,2],\n", 131 | " range(L + nMax),Theta[:,3],)\n", 132 | "ax4.legend([\"a1\", \"a2\", \"b1\", \"b2\"])\n", 133 | "print(np.sqrt(J[L + nMax - 1]/L))" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": 6, 139 | "metadata": {}, 140 | "outputs": [ 141 | { 142 | "data": { 143 | "text/plain": [ 144 | "array([[-1.5],\n", 145 | " [ 0.7],\n", 146 | " [ 1. ],\n", 147 | " [ 0.5]])" 148 | ] 149 | }, 150 | "execution_count": 6, 151 | "metadata": {}, 152 | "output_type": "execute_result" 153 | } 154 | ], 155 | "source": [ 156 | "Theta[k]" 157 | ] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": 7, 162 | "metadata": {}, 163 | "outputs": [ 164 | { 165 | "data": { 166 | "text/plain": [ 167 | "array([[0.94],\n", 168 | " [0.42]])" 169 | ] 170 | }, 171 | "execution_count": 7, 172 | "metadata": {}, 173 | "output_type": "execute_result" 174 | } 175 | ], 176 | "source": [ 177 | "Thetae[k]" 178 | ] 179 | }, 180 | { 181 | "cell_type": "code", 182 | "execution_count": null, 183 | "metadata": {}, 184 | "outputs": [], 185 | "source": [ 186 | "# ============================残差白噪声检验============================================" 187 | ] 188 | }, 189 | { 190 | "cell_type": "code", 191 | "execution_count": 8, 192 | "metadata": {}, 193 | "outputs": [ 194 | { 195 | "name": "stdout", 196 | "output_type": "stream", 197 | "text": [ 198 | "[-0.13 0. 0.02 0.15 0. 0.04 -0.03 0. -0. 0.03 -0.01 0.\n", 199 | " 0.04 -0.02 0.03 -0.01 0.01 0.05 0. 0. 0.03 0.01 0.04 0.02\n", 200 | " 0.01 0.02 -0.02 0.07 -0. -0. ]\n", 201 | "100.06472189259786 ? 42.78084504248447\n" 202 | ] 203 | }, 204 | { 205 | "data": { 206 | "image/png": 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\n", 207 | "text/plain": [ 208 | "
" 209 | ] 210 | }, 211 | "metadata": {}, 212 | "output_type": "display_data" 213 | } 214 | ], 215 | "source": [ 216 | "err = v1\n", 217 | "# 残差白噪声检验\n", 218 | "# \n", 219 | "Re_0 = 0.0\n", 220 | "for i in range(2*nMax+1, L + nMax+1):\n", 221 | " Re_0 += err[i-1]**2 / (L - nMax)\n", 222 | " \n", 223 | "Lr = 30\n", 224 | "lou_e = np.zeros(Lr)\n", 225 | "for r in range(1, Lr+1):\n", 226 | " for k in range(2*nMax+1, L + nMax -r+1):\n", 227 | " lou_e[r-1] = lou_e[r-1] + err[k-1] * err[k-1 + r]/(L - nMax -r)\n", 228 | " lou_e[r-1] = lou_e[r-1]/Re_0\n", 229 | "\n", 230 | "print(lou_e)\n", 231 | "plt.bar(range(1, Lr+1), height=lou_e, width=0.2, align='center',)\n", 232 | "plt.scatter(range(1, Lr+1), lou_e, )\n", 233 | "plot_scale = 1.0\n", 234 | "plt.plot([1,Lr], [1.98*plot_scale/np.sqrt(Lr), 1.98*plot_scale/np.sqrt(Lr)], 'r--')\n", 235 | "plt.plot([1,Lr], [-1.98*plot_scale/np.sqrt(Lr), -1.98*plot_scale/np.sqrt(Lr)], 'r--')\n", 236 | "\n", 237 | "# 计算残差估计值相关系数平方和\n", 238 | "lou_e_2 = 0\n", 239 | "for r in range(Lr):\n", 240 | " lou_e_2 += lou_e[r]**2\n", 241 | "lou_e_2 = (L - nMax)*lou_e_2\n", 242 | "lou_e_2_threshold = Lr + 1.65*np.sqrt(2*Lr)\n", 243 | "print(lou_e_2, \"?\",lou_e_2_threshold)" 244 | ] 245 | } 246 | ], 247 | "metadata": { 248 | "kernelspec": { 249 | "display_name": "Python 3", 250 | "language": "python", 251 | "name": "python3" 252 | }, 253 | "language_info": { 254 | "codemirror_mode": { 255 | "name": "ipython", 256 | "version": 3 257 | }, 258 | "file_extension": ".py", 259 | "mimetype": "text/x-python", 260 | "name": "python", 261 | "nbconvert_exporter": "python", 262 | "pygments_lexer": "ipython3", 263 | "version": "3.6.5" 264 | } 265 | }, 266 | "nbformat": 4, 267 | "nbformat_minor": 2 268 | } 269 | -------------------------------------------------------------------------------- /SI_for_GitHub/RML_SI.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "[ 5. -5. 5. 5. -5. 5. -5. -5. 5. -5.]\n", 13 | "[-6.92 -4.56 -4.23 -2.35 8.14 12.09 14.23 8.83 -2.59 -6.8 ]\n" 14 | ] 15 | } 16 | ], 17 | "source": [ 18 | "import matplotlib.pyplot as plt\n", 19 | "%matplotlib inline\n", 20 | "import numpy as np\n", 21 | "\n", 22 | "np.set_printoptions(suppress=True)\n", 23 | "np.set_printoptions(precision=2) #设精度为3\n", 24 | "plt.rcParams['font.sans-serif'] = ['SimHei']\n", 25 | "plt.rcParams['axes.unicode_minus'] = False\n", 26 | "\n", 27 | "data = np.loadtxt(\"./data/data2.csv\", delimiter=\",\", skiprows=1)\n", 28 | "print(data[0:10,0])\n", 29 | "print(data[0:10,1])" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 6, 35 | "metadata": { 36 | "scrolled": true 37 | }, 38 | "outputs": [ 39 | { 40 | "name": "stdout", 41 | "output_type": "stream", 42 | "text": [ 43 | "1.1391161981077362\n", 44 | "[-1.49 0.69 0.98 0.56 -0.51 -0.13]\n" 45 | ] 46 | }, 47 | { 48 | "data": { 49 | "image/png": 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\n", 50 | "text/plain": [ 51 | "
" 52 | ] 53 | }, 54 | "metadata": {}, 55 | "output_type": "display_data" 56 | } 57 | ], 58 | "source": [ 59 | "# RML\n", 60 | "# 参数初始化\n", 61 | "na = 2\n", 62 | "nb = 2\n", 63 | "nd = 2\n", 64 | "nMax = max(na, nb, nd)\n", 65 | "L = 80 # 数据长度\n", 66 | "a = 1\n", 67 | "\n", 68 | "u = data[:L + nMax, 0]\n", 69 | "z = data[:L + nMax, 1]\n", 70 | "uf = np.zeros(len(u))\n", 71 | "zf = np.zeros(len(z))\n", 72 | "z_pre = np.zeros((len(z), 1))\n", 73 | "\n", 74 | "v1 = np.zeros(len(u)) # 噪声估计值\n", 75 | "v1f = np.zeros(len(u)) # 噪声估计滤波值\n", 76 | "\n", 77 | "# 序列初始化\n", 78 | "h = np.zeros((L, na + nb + nd, 1))\n", 79 | "hf = np.zeros((L, na + nb + nd, 1))\n", 80 | "P = np.array([a**2 * np.eye((na + nb + nd))] * (L))\n", 81 | "s = np.zeros(L) # 数\n", 82 | "Inn = np.zeros(L) # 数\n", 83 | "K = np.zeros((L, na + nb + nd, 1))\n", 84 | "Theta = np.zeros((L, na + nb + nd, 1)) # [a1,a2,b1,b2].T\n", 85 | "J = np.zeros(L) # 数\n", 86 | "\n", 87 | "\n", 88 | "for k in range(nMax, L):\n", 89 | " for i in range(na):\n", 90 | " h[k][i] = -1 * z[k - i - 1]\n", 91 | " hf[k][i] = -1 * zf[k - i - 1]\n", 92 | " for j in range(nb):\n", 93 | " h[k][na + j] = u[k - j - 1]\n", 94 | " hf[k][na + j] = uf[k - j - 1]\n", 95 | " for i in range(nd): \n", 96 | " h[k][na + nb + i] = v1[k - i - 1]\n", 97 | " hf[k][na + nb + i] = v1f[k - i - 1]\n", 98 | " \n", 99 | " s[k] = hf[k].T @ P[k - 1] @ hf[k] + 1.0\n", 100 | " Inn[k] = z[k] - h[k].T @ Theta[k - 1]\n", 101 | " K[k] = P[k - 1] @ hf[k] / s[k]\n", 102 | " P[k] = P[k-1] - K[k] @ K[k].T * s[k]\n", 103 | " Theta[k] = Theta[k - 1] + K[k] * Inn[k]\n", 104 | " J[k] = J[k-1] + Inn[k]**2 / s[k]\n", 105 | " v1[k] = z[k] - h[k].T @ Theta[k]\n", 106 | " zf[k] = z[k]\n", 107 | " uf[k] = u[k]\n", 108 | " v1f[k] = v1[k]\n", 109 | " for i in range(nd):\n", 110 | " zf[k] = zf[k] - Theta[k][na + nb + i] * zf[k - i - 1]\n", 111 | " uf[k] = uf[k] - Theta[k][na + nb + i] * uf[k - i - 1]\n", 112 | " v1f[k] = v1f[k] - Theta[k][na + nb + i] * v1f[k - i - 1]\n", 113 | " \n", 114 | "\n", 115 | "plt.figure(1,figsize=(10,6))\n", 116 | "\n", 117 | "ax4 = plt.subplot(1,1,1)\n", 118 | "ax4.plot(range(L),Theta[:,0],\n", 119 | " range(L),Theta[:,1],\n", 120 | " range(L),Theta[:,2],\n", 121 | " range(L),Theta[:,3],)\n", 122 | "ax4.legend([\"a1\", \"a2\", \"b1\", \"b2\"])\n", 123 | "\n", 124 | "print(np.sqrt(J[L - 1]/L))\n", 125 | "Theta_hat = np.sum(Theta[-21:-1][:,:,0], axis=0)/ 20\n", 126 | "print(Theta_hat.T)\n", 127 | "\n", 128 | "# # 计算残差\n", 129 | "# err = [] # 数 残差\n", 130 | "# for k in range(2*nMax, L + nMax - 1):\n", 131 | "# for i in range(na):\n", 132 | "# h[k][i] = -1 * z[k - i - 1]\n", 133 | "# for j in range(nb):\n", 134 | "# h[k][na + j] = u[k - j - 1]\n", 135 | "# for i in range(nd): \n", 136 | "# h[k][na + nb + i] = v1[k - i - 1]\n", 137 | " \n", 138 | "# err.append(z[k] - h[k].T @ Theta_hat)\n", 139 | "# z_pre[k] = h[k].T @ Theta_hat\n", 140 | "\n", 141 | "# err = np.array(err).reshape((-1,1)) \n", 142 | "# ax3 = plt.subplot(3,1,2)\n", 143 | "# ax3.plot(range(len(err)), err)\n", 144 | "\n", 145 | "# for k in range(2*nMax, L + nMax - 1):\n", 146 | "# for i in range(na):\n", 147 | "# h[k][i] = -1 * z_pre[k - i - 1]\n", 148 | "# for j in range(nb):\n", 149 | "# h[k][na + j] = u[k - j - 1]\n", 150 | "# for i in range(nd): \n", 151 | "# h[k][na + nb + i] = v1[k - i - 1]\n", 152 | " \n", 153 | "# z_pre[k] = h[k][0: na + nb].T @ Theta_hat[0: na + nb]\n", 154 | " \n", 155 | "# ax5 = plt.subplot(3,1,3)\n", 156 | "# ax5.plot(range(L), z[nMax:nMax+L],range(L), z_pre[nMax:nMax+L])\n", 157 | "# ax5.set_ylabel(\"$\"+\"z_pre\"+\"$\",fontsize=10)" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": 6, 163 | "metadata": {}, 164 | "outputs": [ 165 | { 166 | "data": { 167 | "text/plain": [ 168 | "array([ 0.00e+000, 0.00e+000, 1.00e+000, 6.17e+001, 7.22e+001,\n", 169 | " 5.93e+001, 2.26e+002, 8.90e+001, 1.18e+001, 3.01e+000,\n", 170 | " 3.12e+001, -6.62e+001, 2.53e+002, -6.08e+001, 6.71e+003,\n", 171 | " 4.76e+003, 1.07e+006, 5.62e+006, 1.38e+008, 1.16e+010,\n", 172 | " 8.60e+011, 2.09e+014, 3.52e+016, -1.82e+018, -3.28e+020,\n", 173 | " 9.90e+022, -1.58e+025, -7.41e+028, 4.22e+031, 3.19e+033,\n", 174 | " -3.66e+035, 1.23e+037, 1.79e+041, 3.00e+044, 4.77e+047,\n", 175 | " 3.86e+050, 1.90e+053, 5.68e+056, 4.46e+060, -3.59e+065,\n", 176 | " 1.53e+070, -3.52e+076, 2.74e+081, -7.20e+086, 2.08e+092,\n", 177 | " 1.82e+098, -4.68e+103, -2.92e+111, -3.22e+116, -5.48e+123,\n", 178 | " 7.41e+128, 4.20e+138, -1.39e+146, -2.50e+154, -4.83e+161,\n", 179 | " -2.56e+168, -2.15e+176, -7.32e+183, 1.31e+192, -1.02e+200,\n", 180 | " 5.62e+207, -4.26e+215, -3.21e+224, -2.38e+233, -3.08e+242,\n", 181 | " -5.37e+251, 1.20e+260, -2.83e+269, -6.60e+278, 3.35e+290,\n", 182 | " -4.23e+299, nan, nan, nan, nan,\n", 183 | " nan, nan, nan, nan, nan,\n", 184 | " nan, 0.00e+000])" 185 | ] 186 | }, 187 | "execution_count": 6, 188 | "metadata": {}, 189 | "output_type": "execute_result" 190 | } 191 | ], 192 | "source": [ 193 | "s" 194 | ] 195 | } 196 | ], 197 | "metadata": { 198 | "kernelspec": { 199 | "display_name": "Python 3", 200 | "language": "python", 201 | "name": "python3" 202 | }, 203 | "language_info": { 204 | "codemirror_mode": { 205 | "name": "ipython", 206 | "version": 3 207 | }, 208 | "file_extension": ".py", 209 | "mimetype": "text/x-python", 210 | "name": "python", 211 | "nbconvert_exporter": "python", 212 | "pygments_lexer": "ipython3", 213 | "version": "3.6.5" 214 | }, 215 | "varInspector": { 216 | "cols": { 217 | "lenName": 16, 218 | "lenType": 16, 219 | "lenVar": 40 220 | }, 221 | "kernels_config": { 222 | "python": { 223 | "delete_cmd_postfix": "", 224 | "delete_cmd_prefix": "del ", 225 | "library": "var_list.py", 226 | "varRefreshCmd": "print(var_dic_list())" 227 | }, 228 | "r": { 229 | "delete_cmd_postfix": ") ", 230 | "delete_cmd_prefix": "rm(", 231 | "library": "var_list.r", 232 | "varRefreshCmd": "cat(var_dic_list()) " 233 | } 234 | }, 235 | "position": { 236 | "height": "327px", 237 | "left": "820px", 238 | "right": "20px", 239 | "top": "75px", 240 | "width": "451px" 241 | }, 242 | "types_to_exclude": [ 243 | "module", 244 | "function", 245 | "builtin_function_or_method", 246 | "instance", 247 | "_Feature" 248 | ], 249 | "window_display": false 250 | } 251 | }, 252 | "nbformat": 4, 253 | "nbformat_minor": 2 254 | } 255 | -------------------------------------------------------------------------------- /SI_for_GitHub/data/data1.csv: -------------------------------------------------------------------------------- 1 | u,z 2 | 5,-8.95769992303852 3 | -5,-6.16020582377032 4 | 5,-3.63553750446361 5 | 5,2.58054615375077 6 | -5,13.6974024560949 7 | 5,15.8913207305153 8 | -5,16.2161109961965 9 | -5,11.4119969019108 10 | 5,-2.09961697122237 11 | -5,-8.78898687147584 12 | -5,-14.0393453861824 13 | -5,-20.0837667490722 14 | 5,-27.5998234442879 15 | -5,-26.2421758599973 16 | -5,-21.7946202126843 17 | 5,-23.465404257597 18 | 5,-17.9597809409787 19 | -5,-2.29219136285882 20 | 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5,7.68158070173126 1878 | 5,8.89459478893122 1879 | -5,12.8319422081627 1880 | -5,12.759229523507 1881 | -5,0.179692500693108 1882 | -5,-15.7711403433668 1883 | 5,-29.7167092290013 1884 | -5,-31.582540767962 1885 | 5,-30.13603867544 1886 | 5,-18.0155353999108 1887 | 5,-1.03021901301916 1888 | -5,20.8052157493439 1889 | -5,28.577615839909 1890 | -5,20.2810363458795 1891 | 5,3.87477776321223 1892 | 5,-4.47488508448358 1893 | -5,-5.72744578293871 1894 | 5,-4.96431844697074 1895 | 5,-2.45373873653268 1896 | -5,9.47792400581659 1897 | 5,8.98637069293785 1898 | -5,13.2637332517335 1899 | -5,8.85834062931225 1900 | 5,-3.67705826675142 1901 | -5,-7.85293390959426 1902 | -5,-13.3453172580569 1903 | -5,-20.3444729457996 1904 | 5,-29.5304131779075 1905 | -5,-27.3645946158648 1906 | -5,-22.7068847186207 1907 | 5,-22.3967591548009 1908 | 5,-16.2868272375874 1909 | -5,-1.34011112019912 1910 | -5,9.30138037145198 1911 | 5,3.82379380881579 1912 | -5,4.11770070037834 1913 | 5,0.308645180647476 1914 | -5,-0.262357616682051 1915 | 5,-3.23525680726475 1916 | -5,-1.16493135853471 1917 | -5,-2.4413025750936 1918 | -5,-11.468396398327 1919 | -5,-21.1673038044518 1920 | -5,-33.2940337485624 1921 | -5,-39.6285008198806 1922 | 5,-45.8918421120671 1923 | 5,-37.251750110689 1924 | 5,-17.629562169058 1925 | 5,8.35381920012876 1926 | 5,32.5715281112938 1927 | -5,48.7386277117638 1928 | 5,50.7731059551304 1929 | 5,42.9717295456121 1930 | 5,36.069404521702 1931 | 5,33.3736945041292 1932 | -5,28.8779624715686 1933 | -5,22.4954772859198 1934 | 5,0.68200197442953 1935 | 5,-8.41726628174002 1936 | 5,-6.50713894094119 1937 | -5,3.02956077211244 1938 | 5,7.5684548460012 1939 | -5,10.9699170918479 1940 | 5,8.80132733421001 1941 | 5,9.49019707018271 1942 | -5,14.8030750718964 1943 | -5,12.7980526875816 1944 | -5,1.23944236453533 1945 | -5,-15.1797854631524 1946 | 5,-29.6029520410742 1947 | -5,-33.868936996272 1948 | 5,-30.1479897784844 1949 | 5,-21.8851274442689 1950 | 5,-0.45256606211902 1951 | -5,18.9958165260643 1952 | -5,28.8599371479285 1953 | -5,20.596828849861 1954 | 5,4.16183578608107 1955 | 5,-5.25018270719272 1956 | -5,-4.07635685185937 1957 | 5,-3.93430981219524 1958 | 5,-3.08619678336899 1959 | -5,6.9268568299411 1960 | 5,11.6635495467313 1961 | -5,13.9188231348526 1962 | -5,8.91524124414205 1963 | 5,-4.31786119148749 1964 | -5,-8.46281945902496 1965 | -5,-12.4775859718388 1966 | -5,-22.9425751579586 1967 | 5,-29.3410505486298 1968 | -5,-28.4044525360397 1969 | -5,-23.3589763963078 1970 | 5,-23.9672903958897 1971 | 5,-14.903371092952 1972 | -5,-0.516856752072934 1973 | -5,7.142469330475 1974 | 5,4.14242072868774 1975 | -5,4.25842027192114 1976 | 5,0.914878113609991 1977 | -5,-0.775132575816596 1978 | 5,-2.01280505416576 1979 | -5,-2.24766638256695 1980 | -5,-2.67771959654174 1981 | -5,-12.0169846946421 1982 | -5,-20.7103356499407 1983 | -5,-31.9917022023935 1984 | -5,-41.0467965584238 1985 | 5,-46.4623993932698 1986 | 5,-39.3656120331332 1987 | 5,-17.8487375573657 1988 | 5,7.67242716600106 1989 | 5,31.1431799469397 1990 | -5,48.6736502623189 1991 | 5,49.7867989229628 1992 | 5,40.9288175172493 1993 | 5,36.0820962630372 1994 | 5,31.7652178701694 1995 | -5,30.8400452077057 1996 | -5,20.9694884876394 1997 | 5,0.417513735413192 1998 | 5,-7.39203833695511 1999 | 5,-5.19995392357404 2000 | -5,1.248772038416 2001 | 5,6.79066417911908 2002 | -5,9.51627565077671 2003 | 5,10.0465511489627 2004 | --------------------------------------------------------------------------------