main_004.ipynb

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      "/public/home/wangzitao/miniconda3/envs/work/lib/python3.10/site-packages/pyDOE2/doe_factorial.py:16: DeprecationWarning: the imp module is deprecated in favour of importlib and slated for removal in Python 3.12; see the module's documentation for alternative uses\n",
      "  import imp\n",
      "/public/home/wangzitao/miniconda3/envs/work/lib/python3.10/site-packages/matplotlib_inline/config.py:68: DeprecationWarning: InlineBackend._figure_format_changed is deprecated in traitlets 4.1: use @observe and @unobserve instead.\n",
      "  def _figure_format_changed(self, name, old, new):\n"
     ]
    }
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   "source": [
    "import os\n",
    "import time\n",
    "# import flopy\n",
    "import numpy as np\n",
    "# import torch\n",
    "# import pandas as pd\n",
    "# import gstools as gs\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "# from multiprocessing import Pool\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from methods.ES import es\n",
    "from methods.ILUES import ilues\n",
    "from methods.MCMCmodel import metropolis_hastings\n",
    "from sko.GA import GA\n",
    "\n",
    "from models.modflow_model import forwardmodel as forwardmodel_true\n",
    "from models.surrogate_model import forwardmodel\n",
    "# from models.DNN_surrogate_model import forwardmodel\n",
    "from utils.utils import genex\n",
    "\n",
    "config = {'font.family': 'Times New Roman',\n",
    "          'font.size': 13,\n",
    "          'xtick.direction': 'in',\n",
    "          'ytick.direction': 'in',\n",
    "          'mathtext.fontset': 'stix',\n",
    "          'savefig.dpi': 300\n",
    "         }\n",
    "plt.rcParams.update(config)\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n",
    "# torch.multiprocessing.set_start_method('spawn')\n",
    "\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "parameter_number = 34\n",
    "\n",
    "nx, ny, mean_logk, lamda_xy, fn_x, fn_y, kesi_real = np.load('./data/KLE{}.npy'.format(parameter_number), allow_pickle=True)\n",
    "\n",
    "obs = forwardmodel_true(kesi_real)"
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   "source": [
    "# indices = indices = np.array([45, 43, 40, 37, 46, 33, 31, 39, 42, 34, 36, 27, 28, 30, 19, 25, 10,\n",
    "#         15, 16, 22, 13, 18,  4,  3, 21, 24,  7,  6,  9, 12,  1,  0, 47, 41,\n",
    "#         44, 35, 38, 29, 32, 26, 20, 17, 23,  8,  5, 11, 14,  2])\n",
    "\n",
    "# indices = indices[:parameter_number]\n",
    "\n",
    "# kesi_real_30 = np.random.uniform(low=-2.5, high=2.5, size=parameter_number)\n",
    "# kesi_real = kesi_real.reshape(-1)\n",
    "# for i in indices:\n",
    "    # kesi_real_30[i] = kesi_real[i]\n",
    "\n",
    "# real = [np.nan] * parameter_number\n",
    "# for j in range(parameter_number):\n",
    "    # real[j] = kesi_real[indices[j]]\n",
    "\n",
    "# real = np.array(real)\n",
    "\n",
    "# plt.figure(figsize=(6, 4))\n",
    "# plt.subplot(221)\n",
    "# plt.plot(kesi_real.reshape(-1))\n",
    "# plt.subplot(222)\n",
    "# plt.plot(obs)\n",
    "# plt.subplot(223)\n",
    "# # 任意随机数输入时的比较\n",
    "# obsfake = forwardmodel(kesi_real_30) #  np.random.randn(parameter_number))\n",
    "# # x_test = (x_test - x_test.min()) / (x_test.max() - x_test.min())\n",
    "# plt.scatter(obs, obsfake, s=5)\n",
    "\n",
    "# print(\"输入任意随机数时 R2: \", r2_score(obsfake, obs))"
   ]
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     "text": [
      "输入任意随机数时 R2:  0.9814489897464275\n"
     ]
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   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "plt.subplot(221)\n",
    "plt.plot(kesi_real.reshape(-1))\n",
    "plt.subplot(222)\n",
    "plt.plot(obs)\n",
    "plt.subplot(223)\n",
    "# 任意随机数输入时的比较\n",
    "obsfake = forwardmodel(kesi_real) #  np.random.randn(parameter_number))\n",
    "plt.scatter(obs, obsfake, s=5)\n",
    "\n",
    "print(\"输入任意随机数时 R2: \", r2_score(obs[:], obsfake[:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.0230400808579607, 1.5996952280370067)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(kesi_real.min(), kesi_real.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ES & ILUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前第 0 次迭代\n",
      "当前第 1 次迭代\n",
      "当前第 2 次迭代\n",
      "当前第 3 次迭代\n",
      "当前第 4 次迭代\n",
      "当前第 5 次迭代\n",
      "当前第 6 次迭代\n",
      "当前第 7 次迭代\n",
      "当前第 8 次迭代\n",
      "当前第 9 次迭代\n",
      "Elapsed time: 1.189 min\n"
     ]
    }
   ],
   "source": [
    "N1 = 20000 # 采样数, 莫低于 20 否则易报错\n",
    "Npar = parameter_number # 未知参数数量\n",
    "range_ = np.tile(np.array([-2.5, 2.5]), (Npar, 1))\n",
    "\n",
    "Nobs = obs.shape[0]\n",
    "# obs = [8.47, ]\n",
    "sd = np.array([0.01, ])\n",
    "x1 = genex(range_, N1)\n",
    "y1 = np.empty((Nobs, N1))\n",
    "y1[:] = np.nan\n",
    "for i in range(N1):\n",
    "    y1[:,i] = 1666\n",
    "\n",
    "q = 10 # 迭代次数\n",
    "\n",
    "start_time = time.time()\n",
    "X, Y = es(x1, y1, obs, sd, q, range_, forwardmodel)\n",
    "# X, Y = ilues(x1, y1, obs, sd, q, range_, forwardmodel, alpha=0.1)\n",
    "end_time = time.time()\n",
    "print(f\"Elapsed time: {((end_time - start_time)/60):.3f} min\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        -0.49153527,  0.12930581, -2.02304008, -1.97253238,  0.0355577 ,\n",
       "        -0.25578848,  0.46206778,  0.21601572,  1.3042008 ,  1.34579188,\n",
       "         0.97573824,  0.88057423, -0.19495903,  0.18768442, -0.49742327,\n",
       "         1.15384367, -1.03706619, -0.43852673,  1.44600226, -0.15753793,\n",
       "        -0.24283745, -0.18996387,  1.59969523,  0.64235502, -0.97305024,\n",
       "        -0.47505204,  1.31421505,  0.27342882,  1.29805817]),\n",
       " array([ 0.54267328,  0.66974235,  0.89546207,  0.2245534 ,  0.08774859,\n",
       "         0.30810407, -0.80789641, -0.9448785 , -2.08991055,  0.04154551,\n",
       "        -0.36394351,  0.58036753,  0.40398322,  1.19091128,  1.71446025,\n",
       "         1.16113149,  0.96717508, -1.0306949 , -0.17519522,  0.21880092,\n",
       "         1.15336798, -0.73943212, -0.34571226,  1.70048498,  0.31991572,\n",
       "        -0.04913315, -0.33066122,  1.07530164,  0.2745059 , -1.15436478,\n",
       "        -0.95628187,  0.82402227,  1.35183895,  1.700857  ]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real[:], ES_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参数 R2:  0.671091008441068\n",
      "结果 R2:  0.9999006731877828\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.42984005,  0.63787829,  1.0156999 ,  0.12945908,  0.08517267,\n",
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       "       -0.53108903,  0.55789612,  0.29566519,  1.35520402,  1.93714881,\n",
       "        1.1148541 ,  0.85657078, -1.1924809 , -0.22451963,  0.25826674,\n",
       "        1.13304363, -0.88511175, -0.3029888 ,  1.73951588,  0.15834182,\n",
       "        0.01424221, -0.35378266,  1.12012308,  0.12971245, -1.09268435,\n",
       "       -1.23266001,  0.70731824,  1.42135345,  1.74524786])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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       "<Figure size 1000x800 with 4 Axes>"
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   ],
   "source": [
    "real = kesi_real.reshape(-1)\n",
    "ES_mean = X[:, -N1:].mean(axis=1)\n",
    "Y_mean = forwardmodel(ES_mean)\n",
    "\n",
    "#################################\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.scatter(real[:], ES_mean)\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(ES_mean)\n",
    "plt.plot(real)\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.scatter(np.arange(obs.shape[0]), Y_mean, s=5, c=\"red\")\n",
    "plt.plot(np.arange(obs.shape[0]), obs, c=\"black\")\n",
    "colors = ['red', 'green', 'blue']\n",
    "for i in range(0, obs.shape[0], 10):\n",
    "    plt.axvspan(i, i+10, facecolor=colors[i//10 % len(colors)], alpha=0.2)\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "# sns.kdeplot(X[:, -1], c=\"red\")\n",
    "# sns.kdeplot(real, c=\"green\")\n",
    "plt.scatter(obs, Y[:, -1], s=5, c=\"red\")\n",
    "\n",
    "print(\"参数 R2: \", r2_score(ES_mean, real))\n",
    "print(\"结果 R2: \", r2_score(obs, Y_mean))\n",
    "ES_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1200x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "for i in range(4):\n",
    "    plt.subplot(4, 4, i+1)\n",
    "    plt.scatter(np.arange(X[i, N1:].shape[0]), X[i, N1:], s=5)\n",
    "    plt.axhline(y=real[i], color='black', linestyle='--')\n",
    "for j in range(4):\n",
    "    num = np.random.randint(1, 20)\n",
    "    plt.subplot(4, 4, j+5)\n",
    "    plt.scatter(np.arange(Y[j+num, N1:].shape[0]), Y[j+num, N1:], s=5, color=\"red\")\n",
    "    plt.axhline(y=obs[j+num], color='black', linestyle='--')\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "for i in range(4):\n",
    "    plt.subplot(4, 4, i+1)\n",
    "    sns.kdeplot(X[i, N1:], c=\"red\")\n",
    "    plt.axvline(x=real[i], color='black', linestyle='--')\n",
    "\n",
    "for j in range(4):\n",
    "    num = np.random.randint(1, 20)\n",
    "    plt.subplot(4, 4, j+5)\n",
    "    sns.kdeplot(Y[j+num, N1:])\n",
    "    plt.axvline(x=obs[j+num], color='black', linestyle='--')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2b066fb07610>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"705.912085pt\" height=\"190.543153pt\" viewBox=\"0 0 705.912085 190.543153\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
       " <metadata>\n",
       "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2023-11-20T18:35:26.384130</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
       "    <dc:creator>\n",
       "     <cc:Agent>\n",
       "      <dc:title>Matplotlib v3.8.0, https://matplotlib.org/</dc:title>\n",
       "     </cc:Agent>\n",
       "    </dc:creator>\n",
       "   </cc:Work>\n",
       "  </rdf:RDF>\n",
       " </metadata>\n",
       " <defs>\n",
       "  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
       " </defs>\n",
       " <g id=\"figure_1\">\n",
       "  <g id=\"patch_1\">\n",
       "   <path d=\"M 0 190.543153 \n",
       "L 705.912085 190.543153 \n",
       "L 705.912085 0 \n",
       "L 0 0 \n",
       "z\n",
       "\" style=\"fill: #ffffff\"/>\n",
       "  </g>\n",
       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
       "    <path d=\"M 23.7 168.035496 \n",
       "L 181.252941 168.035496 \n",
       "L 181.252941 10.482555 \n",
       "L 23.7 10.482555 \n",
       "z\n",
       "\" style=\"fill: #ffffff\"/>\n",
       "   </g>\n",
       "   <g clip-path=\"url(#p04ea8f58d7)\">\n",
       "    <image xlink:href=\"data:image/png;base64,\n",
       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   "source": [
    "def calculate_logk(nx, ny, mean_logk, lamda_xy, fn_x, fn_y, kesi):\n",
    "    \n",
    "    kesi = kesi.reshape(1, -1) # 增维, 必须\n",
    "    logk = np.zeros((nx, ny))\n",
    "    # 由随机数计算渗透率场\n",
    "    for i_x in range(nx):\n",
    "        for i_y in range(ny):\n",
    "            logk[i_y, i_x] = mean_logk + np.sum(np.sqrt(lamda_xy) * fn_x[i_x][0] * \\\n",
    "                                    fn_y[i_y][0] * kesi.transpose())\n",
    "    return logk\n",
    "\n",
    "suijishu = X[:, -1]\n",
    "\n",
    "logk11 = calculate_logk(nx, ny, mean_logk, lamda_xy, fn_x, fn_y, suijishu)\n",
    "\n",
    "logk11_real = calculate_logk(nx, ny, mean_logk, lamda_xy, fn_x, fn_y, kesi_real)\n",
    "\n",
    "fig = plt.figure(figsize=(12, 4))\n",
    "ax = fig.add_subplot(1, 3, 1)\n",
    "plt.imshow(logk11, cmap=\"jet\")\n",
    "plt.colorbar(shrink=0.71)\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "plt.imshow(logk11_real, cmap=\"jet\")\n",
    "plt.colorbar(shrink=0.71)\n",
    "ax = fig.add_subplot(1, 3, 3)\n",
    "plt.imshow(logk11 - logk11_real, cmap=\"jet\")\n",
    "plt.colorbar(shrink=0.71)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(x):\n",
    "    y = forwardmodel(x)\n",
    "    result = 1 - r2_score(y, obs)\n",
    "    return result\n",
    "\n",
    "ga = GA(func=func,\n",
    "        n_dim=parameter_number,\n",
    "        size_pop=800,\n",
    "        max_iter=1000,\n",
    "        lb=np.ones(parameter_number) * (-2.5),\n",
    "        ub=np.ones(parameter_number) * 2.5\n",
    "       )\n",
    "\n",
    "start_time = time.time()\n",
    "best_x, best_y = ga.run()\n",
    "print('best_x:', best_x, '\\n', 'best_y:', best_y)\n",
    "end_time = time.time()\n",
    "print(f\"Elapsed time: {((end_time - start_time)/60):.3f} min\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(kesi_real, best_x)\n",
    "r2_score(best_x.reshape(-1), kesi_real.reshape(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(obs, forwardmodel(best_x))\n",
    "r2_score(obs, forwardmodel(best_x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_logk(nx, ny, mean_logk, lamda_xy, fn_x, fn_y, kesi):\n",
    "    \n",
    "    kesi = kesi.reshape(1, -1) # 增维, 必须\n",
    "    logk = np.zeros((nx, ny))\n",
    "    # 由随机数计算渗透率场\n",
    "    for i_x in range(nx):\n",
    "        for i_y in range(ny):\n",
    "            logk[i_y, i_x] = mean_logk + np.sum(np.sqrt(lamda_xy) * fn_x[i_x][0] * \\\n",
    "                                    fn_y[i_y][0] * kesi.transpose())\n",
    "    return logk\n",
    "\n",
    "suijishu = best_x\n",
    "\n",
    "logk11 = calculate_logk(nx, ny, mean_logk, lamda_xy, fn_x, fn_y, suijishu)\n",
    "\n",
    "logk11_real = calculate_logk(nx, ny, mean_logk, lamda_xy, fn_x, fn_y, kesi_real)\n",
    "\n",
    "fig = plt.figure(figsize=(12, 4))\n",
    "ax = fig.add_subplot(1, 3, 1)\n",
    "plt.imshow(logk11, cmap=\"jet\")\n",
    "plt.colorbar()\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "plt.imshow(logk11_real, cmap=\"jet\")\n",
    "plt.colorbar()\n",
    "ax = fig.add_subplot(1, 3, 3)\n",
    "plt.imshow(logk11 - logk11_real, cmap=\"jet\")\n",
    "plt.colorbar(shrink=0.73)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MCMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "N1 = 1 # 采样数, 莫低于 20 否则易报错\n",
    "Npar = parameter_number # 未知参数数量\n",
    "range_ = np.tile(np.array([-2.5, 2.5]), (Npar, 1))\n",
    "\n",
    "sd = np.ones(parameter_number) * 0.01\n",
    "x1 = genex(range_, N1).reshape(-1)\n",
    "\n",
    "q = 80000 # 迭代次数\n",
    "\n",
    "start_time = time.time()\n",
    "accepted, rejected, scores = metropolis_hastings(x1, obs, sd, q, range_, forwardmodel)\n",
    "end_time = time.time()\n",
    "print(f\"Elapsed time: {((end_time - start_time)/60):.3f} min\")\n",
    "print(\"接受了 {} 个样品\".format(accepted.shape[0]))\n",
    "\n",
    "MCMC_mean = accepted[-1000:, :].mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = forwardmodel(accepted[-1000:, :].mean(axis=0))\n",
    "r2_score(obs, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(121)\n",
    "plt.plot(scores)\n",
    "plt.subplot(122)\n",
    "plt.scatter(MCMC_mean, kesi_real[0, :])\n",
    "print(\"参数 R2: \", r2_score(accepted[-1000:, :].mean(axis=0).reshape(-1), kesi_real.reshape(-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accepted[-1000:, :].mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_logk(nx, ny, mean_logk, lamda_xy, fn_x, fn_y, kesi):\n",
    "    \n",
    "    kesi = kesi.reshape(1, -1) # 增维, 必须\n",
    "    logk = np.zeros((nx, ny))\n",
    "    # 由随机数计算渗透率场\n",
    "    for i_x in range(nx):\n",
    "        for i_y in range(ny):\n",
    "            logk[i_y, i_x] = mean_logk + np.sum(np.sqrt(lamda_xy) * fn_x[i_x][0] * \\\n",
    "                                    fn_y[i_y][0] * kesi.transpose())\n",
    "    return logk\n",
    "\n",
    "# suijishu = X[:, -1]\n",
    "suijishu = accepted[-1, :]\n",
    "\n",
    "logk11 = calculate_logk(nx, ny, mean_logk, lamda_xy, fn_x, fn_y, suijishu)\n",
    "\n",
    "logk11_real = calculate_logk(nx, ny, mean_logk, lamda_xy, fn_x, fn_y, kesi_real)\n",
    "\n",
    "fig = plt.figure(figsize=(12, 4))\n",
    "ax = fig.add_subplot(1, 3, 1)\n",
    "plt.imshow(logk11, cmap=\"jet\")\n",
    "plt.colorbar()\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "plt.imshow(logk11_real, cmap=\"jet\")\n",
    "plt.colorbar()\n",
    "ax = fig.add_subplot(1, 3, 3)\n",
    "plt.imshow(logk11 - logk11_real, cmap=\"jet\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ESMDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pyesmda import ESMDA, ESMDA_RS\n",
    "# # seed for the reproductibility\n",
    "# seed = 10\n",
    "# rng = np.random.default_rng(seed=seed)\n",
    "\n",
    "# obs = obs + rng.normal(0.0, 0.1, 360)\n",
    "# # Observation error covariance matrix\n",
    "# cov_obs = np.diag([0.1] * obs.shape[0])\n",
    "\n",
    "# n_ensemble = 700  # size of the ensemble 采样数\n",
    "\n",
    "# range_ = np.tile(np.array([-2.5, 2.5]), (71, 1))\n",
    "# x1 = genex(range_, n_ensemble).T\n",
    "# m_ensemble = x1\n",
    "\n",
    "# # Bounds on parameters (size m * 2)\n",
    "# m_bounds = range_\n",
    "\n",
    "# # Number of assimilations\n",
    "# n_assimilations = 3\n",
    "\n",
    "# # Use a geometric suite (see procedure un evensen 2018) to compte alphas.\n",
    "# # Also explained in Torrado 2021 (see her PhD manuscript.)\n",
    "# cov_obs_inflation_geo = 1.2\n",
    "# cov_obs_inflation_factors: list[float] = [1.1]\n",
    "# for l in range(1, n_assimilations):\n",
    "#     cov_obs_inflation_factors.append(\n",
    "#         cov_obs_inflation_factors[l - 1] / cov_obs_inflation_geo\n",
    "#     )\n",
    "# scaling_factor: float = np.sum(1 / np.array(cov_obs_inflation_factors))\n",
    "# cov_obs_inflation_factors = [\n",
    "#     alpha * scaling_factor for alpha in cov_obs_inflation_factors\n",
    "# ]\n",
    "\n",
    "# np.testing.assert_almost_equal(sum(1.0 / np.array(cov_obs_inflation_factors)), 1.0)\n",
    "\n",
    "# # This is just for the test\n",
    "# cov_mm_inflation_factors: list[float] = [1.2] * n_assimilations\n",
    "\n",
    "# # print(m_ensemble.shape)\n",
    "# x = m_ensemble\n",
    "# def total_forwardmodel(x):\n",
    "#     y = np.zeros([700, 360])\n",
    "#     # print(x.size)\n",
    "#     for i in range(x.shape[0]):\n",
    "#         y[i, :] = forwardmodel(x[i, :])\n",
    "#     return y\n",
    "\n",
    "# solver = ESMDA(\n",
    "#     obs,\n",
    "#     m_ensemble,\n",
    "#     cov_obs,\n",
    "#     total_forwardmodel,\n",
    "#     forward_model_args=(),\n",
    "#     forward_model_kwargs={},\n",
    "#     n_assimilations=n_assimilations,\n",
    "#     cov_obs_inflation_factors=cov_obs_inflation_factors,\n",
    "#     cov_mm_inflation_factors=cov_mm_inflation_factors,\n",
    "#     m_bounds=m_bounds,\n",
    "#     save_ensembles_history=True,\n",
    "#     seed=seed\n",
    "# )\n",
    "# # Call the ES-MDA solver\n",
    "# solver.solve()\n",
    "\n",
    "# plt.scatter(solver.m_prior.mean(axis=0), kesi_real)\n",
    "# canshu_esmda=np.array(solver.m_history)\n",
    "# canshu_esmda[2,].mean(axis=0)\n",
    "\n",
    "# canshu_esmda=np.array(solver.m_history)\n",
    "\n",
    "# plt.scatter(canshu_esmda[3, -1], kesi_real)"
   ]
  }
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