Generate_Conditional_Sampling-45.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from HistoryMatching.ReadFiles import ReadObsFile\n",
    "from HistoryMatching.Utils import GerenateObsFile,Contitional_ES_MDA,Plot_Result\n",
    "from HistoryMatching.ModelDL import *\n",
    "from Model.Utils import MPS_Generator\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Ensemble Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = MPS_Generator('DataSet/MPS45/test_val.tfrecords',200)\n",
    "Facies = generator.get_numpy_batch()\n",
    "Facies = np.argmax(Facies,axis=-1).reshape((200,45*45)).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 0, 1, 1, 1, 0, 1]\n"
     ]
    }
   ],
   "source": [
    "GenerateMeasurement=True\n",
    "import scipy.io as sio\n",
    "True45 = sio.loadmat('DataSet/True45.mat')\n",
    "True45 = True45['True']\n",
    "if GenerateMeasurement:\n",
    "    \"\"\n",
    "    \"\"\n",
    "    xp = [2,37,7,33]      # produtor well\n",
    "    yp = [22,4,42,41]     # produtor well\n",
    "    xi = [7,24,42]        # injetor well\n",
    "    yi = [4,14,26]        # injetor well\n",
    "    position=np.array([yp+yi] + [xp+xi]).T\n",
    "    obs=GerenateObsFile(position,True45)\n",
    "    print(obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test #1\n",
    "<ul>\n",
    "    <li> File utilizado: 'Obs.dat' </li>\n",
    "        <ul>\n",
    "            <li> xp = [2,37,7,33] - Poço produtor </li>\n",
    "            <li> yp = [22,4,42,41] - Poço produtor </li>\n",
    "            <li> xi = [7,24,42] - Poço injetor </li>\n",
    "            <li> yi = [4,14,26] - Poço injetor </li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li> position = np.array([yp+yi] + [xp+xi]).T </li>\n",
    "</ul>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Observation File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs,R,t_acu,act,produccion = ReadObsFile('Obs.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /work/Smith/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "model = {'redePath':'Model/TrainModel/CVAE45',\n",
    "        'PCA' : False,\n",
    "        'IsGans' : False,\n",
    "        'Model_Dim' : (45,45,1),\n",
    "        'toCategorical' : True,\n",
    "        'isTanh' : False,\n",
    "        'NumFacies' :2}\n",
    "network = ModelDL(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate parameterized Facies data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_x  = network.predict(Facies, is_update=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reconstucted Initial Facies data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_f = network.predict(m_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute ES-MDA for generate Contidional Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erro ite_ 0  :  800.0\n",
      "Erro ite_ 1  :  394.0\n",
      "Erro ite_ 2  :  165.0\n",
      "Erro ite_ 3  :  78.0\n",
      "Erro ite_ 4  :  42.0\n",
      "Erro ite_ 5  :  34.0\n",
      "Erro ite_ 6  :  17.0\n",
      "Erro ite_ 7  :  9.0\n",
      "Erro ite_ 8  :  6.0\n",
      "Erro ite_ 9  :  3.0\n",
      "Erro End:  3.0\n"
     ]
    }
   ],
   "source": [
    "m_f_a=Contitional_ES_MDA(10,[],position,obs,R,m_x,m_f,[45,45],redeVAE=network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot True Model and Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib notebook\n",
    "plt.imshow(True45.reshape((45,45)),cmap='jet')\n",
    "plt.plot(xp,yp,'^',color='yellow')\n",
    "plt.plot(xi,yi,'o',color='green')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_Result(Facies,m_f_a,xp,yp,xi,yi,initial_data=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Teste 2\n",
    "\n",
    "<ul>\n",
    "    <li> Arquivo utilizado: 'Obs2.dat' </li>\n",
    "    <li> Pontos utilizados:\n",
    "        <ul>\n",
    "            <li> xe = [2,8,18,33,41,44,2,10,26,38,44] </li>\n",
    "            <li> ye = [40,34,27,23,19,7,15,10,1,40,36] </li>\n",
    "            <li> xi = [] </li>\n",
    "            <li> yi = [] </li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li> position = np.array([ye] + [xe]).T </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "GenerateMeasurement=True\n",
    "import scipy.io as sio\n",
    "True45 = sio.loadmat('DataSet/True45.mat')\n",
    "True45 = True45['True']\n",
    "if GenerateMeasurement:\n",
    "    \"\"\n",
    "    \"\"\n",
    "    xe=[2,12,22,32,42,2,12,22,32,42,2,12,22,32,42,2,12,22,32,42]\n",
    "    ye=[3, 3 ,3,3,3,16,16,16,16,16,28,28,28,28,28,42,42,42,42,42]\n",
    "    #xe = [2, 8, 18,33,41,44,2 ,10,26,38,44]\n",
    "    #ye = [40,34,30,28,25,7 ,15,10,1 ,40,36]\n",
    "    position=np.array([ye] + [xe]).T\n",
    "    obs=GerenateObsFile(position,True45,filePath='Obs2.dat')\n",
    "    print(obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Observation File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs,R,t_acu,act,produccion = ReadObsFile('Obs2.dat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate parameterized Facies data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_x  = network.predict(Facies, is_update=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reconstucted Initial Facies data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_f = network.predict(m_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute ES-MDA for generate Contidional Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erro ite_ 0  :  1797.0\n",
      "Erro ite_ 1  :  1061.0\n",
      "Erro ite_ 2  :  561.0\n",
      "Erro ite_ 3  :  347.0\n",
      "Erro ite_ 4  :  195.0\n",
      "Erro ite_ 5  :  110.0\n",
      "Erro ite_ 6  :  82.0\n",
      "Erro ite_ 7  :  60.0\n",
      "Erro ite_ 8  :  48.0\n",
      "Erro ite_ 9  :  41.0\n",
      "Erro End:  40.0\n"
     ]
    }
   ],
   "source": [
    "m_f_a=Contitional_ES_MDA(10,[],position,obs,R,m_x,m_f,[45,45],redeVAE=network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot True Model and Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib notebook\n",
    "plt.imshow(True45.reshape((45,45)),cmap='jet')\n",
    "plt.plot(xe,ye,'o',color='green')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_Result(Facies,m_f_a,xe,ye,[],[],initial_data=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Random Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "z_0=np.random.normal(0,1,size=(100,500))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_0 = network.predict(z_0.T)\n",
    "z_i  = z_0\n",
    "Z    = [x_0]\n",
    "for i in range(100):  \n",
    "    dz   = np.random.normal(0,0.01,size=(100,500)) \n",
    "    z_i  = z_i + dz   \n",
    "    x_i  = network.predict(z_i.T)\n",
    "    Z.append(x_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Plot_Random(facies,m_f_a,initial_data=0,marked='r^',shape=[45,45],sampling=[0,20]):\n",
    "    plt.rcParams['figure.figsize'] = (15,8)\n",
    "    fig, axs = plt.subplots(2,5)\n",
    "    #plt.title(title)\n",
    "    for i in range(5):\n",
    "        axs[0,i].imshow(facies[:,i+initial_data].reshape(shape[0],shape[1]), cmap='jet')\n",
    "        axs[0,i].axis('off')\n",
    "        axs[0,i].set_title('Sampling: x_%i (%i)'%(sampling[0],i+1))\n",
    "        axs[1,i].imshow(m_f_a[:,i+initial_data].reshape(shape[0],shape[1]), cmap='jet')        \n",
    "        axs[1,i].axis('off')\n",
    "        axs[1,i].set_title('Sampling: x_%i (%i)'%(sampling[1],i+1)) \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_imagens(Z,index,num=5,shape=[45,45]):\n",
    "    plt.rcParams['figure.figsize'] = (15,8)\n",
    "    aux = Z[index]    \n",
    "    for i in range(num):\n",
    "        plt.imshow(aux[:,i].reshape(shape[0],shape[1]),cmap='jet')\n",
    "        plt.axis('off')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_Random(Z[0],Z[20],initial_data=0,sampling=[0,20])\n",
    "Plot_Random(Z[40],Z[60],initial_data=0,sampling=[40,60])\n",
    "Plot_Random(Z[80],Z[100],initial_data=0,sampling=[80,100]) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}