Code/Preparing_training_and_test_datasets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our training (or test) dataset includes synthesized facies models, corresponding global features, well facies dataset, and probability maps. \n",
    "\n",
    "#### In our study, facies models (2D, 64x64) are generated using the object-based modeling method, in Petrel software. \n",
    "Synthesized facies models are exported from Petrel as model properties with \"Gslib\" format into one file. \n",
    "\n",
    "\n",
    "First lines of the exported file are like:\n",
    "\n",
    "PETREL: Properties\n",
    "\n",
    "17820  % Number of synthesized facies models\n",
    "\n",
    "\n",
    "Facies unit1 scale1  \n",
    "\n",
    "Facies unit1 scale1\n",
    "\n",
    "...\n",
    "\n",
    "Facies unit1 scale1\n",
    "\n",
    "% Totally, there are 64x64 lines, corresponding to 64x64 pixels in each facies model; each line has 17820 numbers splitted by space, corresponding to 17820 facies code values of 17820 generated facies realizations at each pixel. 0-background mud faceis, 1-channel sand facies, 2-channel bank facies. \n",
    "\n",
    "0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 1.000000 2.000000\n",
    "\n",
    "0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000\n",
    "\n",
    "...\n",
    "\n",
    "0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000\n",
    "\n",
    "\n",
    "\n",
    "The 17820 facies models in this file will be enlarged (by reversing the 17820 facies models) and arranged into (35640, 1, 64, 64) in the following code.\n",
    "\n",
    "Other software, like SGeMS, can also be used, as long as the final generated facies models are arranged into (N, 1, 64, 64).\n",
    "\n",
    "\n",
    "#### Original global features (as labels in this file) include channel orientation, channel width, channel wavelength, and channel amplitude, which are corresponding input parameters when synthesizing facies models with object-based method. We further add background mud proportion and channel sinuosity index global features. \n",
    "\n",
    "#### Probability maps are produced from the synthesized facies models using Gaussian kernel smoothing method.\n",
    "\n",
    "\n",
    "#### Well facies data are sampled from the probability data . \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 Write paths\n",
    "#### Filepath: path of synthesized facies models\n",
    "#### labels_path: path of global features (labels) corresponding to facies models\n",
    "#### tfrecord_dir_training: directory to save training dataset\n",
    "#### tfrecord_dir_test: directory to save test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Filepath = '/home/users/suihong/training_data/TrainingData(MultiChannels_Version4)/2D_AllPro_Gslib_fromPetrel(version4)' \n",
    "labels_path = '/home/users/suihong/training_data/TrainingData(MultiChannels_Version4)/labels(version4).txt' \n",
    "tfrecord_dir_training = '/scratch/users/suihong/DataSets(MultiChannels_Version4_Consistency)/TrainingData'\n",
    "tfrecord_dir_test = '/scratch/users/suihong/DataSets(MultiChannels_Version4_Consistency)/TestData'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 Load facies models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "allele=[]  \n",
    "# stores all facies code values of the file. includes 64x64 lists, and each list includes code values of all realizations at each pixel.\n",
    "\n",
    "with open (Filepath) as fl:\n",
    "    for line in fl:\n",
    "        eles = line.strip().split(' ')\n",
    "       \n",
    "        if len(eles)>=5:    # filter the lines with property name \"Facies unit1 scale1\"        \n",
    "            allele.append(eles)\n",
    "            \n",
    "rlz_no = len(allele[0]) # number of realizations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "partimgs = np.array(allele, dtype = float).reshape((64, 64, rlz_no)).astype(int)\n",
    "partimgs = np.where(partimgs == 1, 4, partimgs)/2 * 127.5 # originally, 1 for channel sand facies, and 2 for channel bank; here exchange their codes.\n",
    "# The orientation of the original simulated facies models is only from 0-90, thus we enlarge the facies model dataset by reversing it vertically to add facies models with orientation from -90 to 0.  \n",
    "allimgs = np.concatenate((partimgs, partimgs[::-1,:,:]),2)  # partimgs[::-1,:,:] to reverse partimgs vertically: original partimgs \n",
    "allimgs = np.transpose(allimgs, (2, 0, 1))  # transpose into (?, 64, 64) dimension\n",
    "allimgs = allimgs.reshape(-1, 1, 64, 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35640, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "print(allimgs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del(allele)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 Generate probability maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[35640, 8, 1, 64, 64]\n"
     ]
    }
   ],
   "source": [
    "print(([allimgs.shape[0]]+[8]+[*allimgs.shape[1:]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define Guassian kernel\n",
    "from scipy.stats import multivariate_normal\n",
    "def norm_kernel(size = 9, sigma = 2):\n",
    "    sig = sigma * np.eye(2)\n",
    "    mean = np.zeros(2,)\n",
    "    size_min = -(size - 1)/2\n",
    "    size_max = (size - 1)/2\n",
    "    x = np.linspace(size_min, size_max, size)\n",
    "    y = np.linspace(size_min, size_max, size)\n",
    "    xk, yk = np.meshgrid(x, y)\n",
    "    kernel = np.zeros((size,size), dtype=np.float32)\n",
    "    for i in range(size):\n",
    "        for j in range(size):\n",
    "            coord = np.squeeze([xk[i,j], yk[i,j]])\n",
    "            kernel[i,j] = multivariate_normal.pdf(coord, mean=mean, cov=sig)\n",
    "    kernel_sum = np.sum(kernel)\n",
    "    kernel = kernel / kernel_sum\n",
    "    return kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Using Gaussian kernels with various size to smooth the indicator of channel complex to form the probability maps of channel complex with various blurriness. \n",
    "\n",
    "import scipy.signal as sig\n",
    "prob_rlzs = np.zeros(([allimgs.shape[0]]+[8]+[*allimgs.shape[1:]]), dtype=np.float32) # dimension = [35640, 8, 1, 64, 64]  \n",
    "count = 0\n",
    "for k in range(13,28,2):\n",
    "    kernel = norm_kernel(size = k, sigma = k)  # size should be set to be odd\n",
    "    for num in range(allimgs.shape[0]):\n",
    "        cur_image = allimgs[num,0]/255.0\n",
    "        padvalue = np.mean(cur_image)\n",
    "        prob_image = sig.convolve2d(cur_image, kernel, fillvalue = padvalue, mode='same') \n",
    "        prob_rlzs[num, count, 0] = prob_image \n",
    "            \n",
    "    count = count + 1    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(prob_rlzs.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test synthesized probability maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f478dc23390>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fkv0ocDQi9mX3v0En+V/Pyneyr7O9nhwRuyJiKiKmlrK8ipjNrIQi12c/IemIpIsi4hCd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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(allimgs[652,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xiGOMjMcAwUPmI2PvRvaMpMchQoWSKPpqGuMR60HjvrPfV6zFPSBtvKfPz7wU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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 756x756 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 4, sharex='col', sharey='row')\n",
    "fig.set_size_inches(10.5, 10.5, forward=True)\n",
    "  \n",
    "for i in range (2):\n",
    "  for j in range(4):\n",
    "    ax[i, j].imshow(prob_rlzs[652, i*4 + j,0,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prob_rlzs = prob_rlzs.reshape(-1,1, 64, 64) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(285120, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "print(prob_rlzs.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 Generate well facies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Generate random well points\n",
    "\n",
    "well_points = np.zeros(prob_rlzs.shape, dtype = int)\n",
    "for i in range(prob_rlzs.shape[0]):\n",
    "    well_points_num = np.random.choice(np.arange(1, 21), 1)  # Random choose the expected total number of well points\n",
    "    xs = np.random.choice(64, well_points_num)\n",
    "    ys = np.random.choice(64, well_points_num)\n",
    "    well_points[i, 0, xs, ys] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Prob_rlzs_max = np.max(prob_rlzs, axis = (2, 3), keepdims = True)\n",
    "Prob_rlzs_min = np.min(prob_rlzs, axis = (2, 3), keepdims = True)\n",
    "well_facies = well_points * (prob_rlzs - Prob_rlzs_min)/(Prob_rlzs_max - Prob_rlzs_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Sample well facies according to calculated probability maps.\n",
    "well_facies = np.where(well_facies<=0.4, 0, well_facies)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f20703816a0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZU9a0JJ0MHGf7uXL/7cC1wGZgHfCJ8udtU/8TImLQFYNLuze61PYWYMu4c9e07L91OuVV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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(well_facies[664,0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "well_facies_random = np.random.uniform(0,1,well_facies.shape)\n",
    "well_facies = np.where(well_facies_random<well_facies, 1, 0) + well_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test synthesized well facies data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f20702b6208>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ma5PQM7nZjGyw9sl8yR1bXgpiVFPwtJe+ZZOwdIPFu4cNxT1CL2k3DzntGQsV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\nILBR+P94voct/iMnFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(prob_rlzs[28994,0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f2070261438>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(well_facies[28994,0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 Load labels (Global features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels_list=[]  # store all labels of the file. \n",
    "with open (labels_path) as lb:  # read label file\n",
    "    for line in lb:\n",
    "        labels_list.append(line.strip().split('  '))\n",
    "\n",
    "ori_labels_no = len(labels_list) # number of labels in the opened file\n",
    "ori_labels_arr=np.array(labels_list, dtype=np.float).reshape(ori_labels_no, 5).astype(np.float)\n",
    "\n",
    "all_labels_arr = np.empty((ori_labels_no*6,5), dtype=np.float)\n",
    "\n",
    "# Every label corresponds to 3 consecutive facies models, thus labels are mutiplied by 3.\n",
    "all_labels_arr[:ori_labels_no*3:3,:]=ori_labels_arr\n",
    "all_labels_arr[1:ori_labels_no*3:3,:]=ori_labels_arr\n",
    "all_labels_arr[2:ori_labels_no*3:3,:]=ori_labels_arr\n",
    "\n",
    "# When facies models are enlarged by reversing vertically; the orientation of reversed facies models is from -90 to 0 degree. \n",
    "all_labels_arr[:,1]=all_labels_arr[:,1]*(-1) # orientation is negativized. \n",
    "\n",
    "# Other labels for the reversed facies models remain the same. \n",
    "all_labels_arr[ori_labels_no*3:ori_labels_no*6:3,:]=ori_labels_arr\n",
    "all_labels_arr[ori_labels_no*3+1:ori_labels_no*6:3,:]=ori_labels_arr\n",
    "all_labels_arr[ori_labels_no*3+2:ori_labels_no*6:3,:]=ori_labels_arr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35640, 5)\n"
     ]
    }
   ],
   "source": [
    "print(all_labels_arr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels_cor = np.empty((all_labels_arr.shape[0], 4), dtype=np.float)  \n",
    "orit = all_labels_arr[:, 1]   # Orientation is placed in the first column\n",
    "\n",
    "# labels_cor include orientation of channels, inter-channel mud facies ratio, width of channel sand, and sinuosity index (amplitude/wavelength) of channel sand\n",
    "# at the range of -1 to 1\n",
    "labels_cor[:,0] = ((orit - np.min(orit))/(np.max(orit) - np.min(orit)) - 0.5) * 2  \n",
    "\n",
    "back_ratio = 1-np.count_nonzero(allimgs, (1,2,3))/(64*64)\n",
    "labels_cor[:,1] = ((back_ratio - np.min(back_ratio))/(np.max(back_ratio) - np.min(back_ratio)) - 0.5) * 2\n",
    "\n",
    "width = all_labels_arr[:, 2]\n",
    "labels_cor[:,2] = ((width - np.min(width ))/(np.max(width ) - np.min(width )) - 0.5) * 2\n",
    "    \n",
    "amwv_ratio = all_labels_arr[:, 4]/all_labels_arr[:, 3]  #amplitude to wavelength ratio\n",
    "### Important: set amplitude/wavelength to be 0.55 when it is larger than 0.55, because the sinuosity level of channels can not be more apparent even when that ratio is larger.\n",
    "amwv_ratio = np.where(amwv_ratio> 0.55, 0.55, amwv_ratio)\n",
    "labels_cor[:,3] = ((amwv_ratio  - np.min(amwv_ratio ))/(np.max(amwv_ratio ) - np.min(amwv_ratio )) - 0.5) * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6 Generate training and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32640, 1, 64, 64)\n",
      "(3000, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "# Randomly split facies models into training and test dataset\n",
    "randseq=np.random.RandomState(232).permutation(allimgs.shape[0])\n",
    "allimgs_training = allimgs[randseq[:32640]]\n",
    "allimgs_test = allimgs[randseq[32640:]]\n",
    "print(allimgs_training.shape)\n",
    "print(allimgs_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32640, 4)\n",
      "(3000, 4)\n"
     ]
    }
   ],
   "source": [
    "# Corresponding split global features into training and test dataset\n",
    "labels_cor_training = labels_cor[randseq[:32640]]\n",
    "labels_cor_test = labels_cor[randseq[32640:]]\n",
    "print(labels_cor_training.shape)\n",
    "print(labels_cor_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Option 1 Building consistent training dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### This block is used when building consistent training dataset, where facies models, global features, well facies, and probmaps are consistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32640, 1, 64, 64)\n",
      "(32640, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Randomly split probability maps into training and test dataset\n",
    "prob_rlzs=prob_rlzs.reshape(-1,8, 64, 64) \n",
    "prob_rlzs_training = prob_rlzs[randseq[:32640]].reshape(-1,1, 64, 64)\n",
    "prob_rlzs_test = prob_rlzs[randseq[32640:]].reshape(-1,1, 64, 64)\n",
    "\n",
    "# Randomly split well facies data into training and test dataset\n",
    "well_facies=well_facies.reshape(-1, 8, 64, 64)\n",
    "well_facies_training = well_facies[randseq[:32640]].reshape(-1,1, 64, 64)\n",
    "well_facies_test = well_facies[randseq[32640:]].reshape(-1,1, 64, 64)\n",
    "\n",
    "# Keep random one of the 8 probmaps and one of the 8 well facies data in training dataset. Test dataset still have 8 probmaps and 8 well facies\n",
    "proborder = np.arange(32640) * 8 + np.random.RandomState(32).randint(0, 8, size=32640)\n",
    "prob_rlzs_training = prob_rlzs_training[proborder]\n",
    "well_facies_training = well_facies_training[proborder]\n",
    "\n",
    "print(prob_rlzs_training.shape)\n",
    "print(well_facies_training.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f478d469e48>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kbQXeAK6vIlgzq0ehZI+IvcDYLHdV10ybWa1G7osweXWXwcNaZMBsvrxuvJl9wMlulggn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\nN0uEk90sEU52s0T8L4HfilubQacoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(allimgs_training[1225,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.35714286  0.60753341 -1.          1.        ]\n"
     ]
    }
   ],
   "source": [
    "print(labels_cor[randseq[1225]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.35714286  0.60753341 -1.          1.        ]\n"
     ]
    }
   ],
   "source": [
    "print(labels_cor_training[1225])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f206b48c630>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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syGY8pXBuB13iqS/xhANzgq00Pt+ne9G9HZkM0XIrnntssvyqNgRotekYz413\nnI7gnd1xOsJ6yz9JHIlcptFlTfwNMnM2srit19f7DfvRjKoHZsLCBk1moJHMfKDtnrARjyHlAHrj\n/GwsK22svjIzX8ZH6g+NNdWVQIUVryBhC9ajK/UxkmJxhVchM34yNvqIRQz77NHzt5Pp/fqUfckm\nPaCzSdmFtXp33OIXzSW4XKA2WfxM+ZvdcTqCd3bH6Qje2R2nI6zXZw/RD5mZ3E8+e2qy6foUJ+Fy\nt32jPT8cxGPUG9oXrzZJZICWsy0Tepuwz64dbvb/lAAiDFZcgVA+vAtgHAlBZcmZa1rF9aSgZ2yi\n71E64XLf+hDpMG6rJvp52aXnZW8Q/feJycwEPY6b5gQciuOxq8yMa3HmnfXnX2rXxZZC5+8v3OI4\nzg2Fd3bH6QhrNeNrCPbbGSrDRqtYJXZmAsGmDZvx232tPb+3Ec2hypjn5ZjMtFE0gVJTCTahjCgO\n1QBAuiD0NmM4KU003UYh8ytYwTAPxS1GXdMZYfe4yerGU1VXZcaPTQmmUTx+OTQVgOl5qYbaPRxu\nRdN9dzOGaveNjmKT711eHogOGQ9oIszpNIbhrLlvw9XMwbP003RxP/I3u+N0BO/sjtMRvLM7TkdY\nq8/ehASX2hTUnUynorI/Yv13FqDkif6n+lrmancj+kkXt43PXkRfPKF0yKTU/llCQgVJqccVtGAh\npWHaUFtDvpUJBWmhCw+9XQ1WLESF22zqMvnsMqEZkybtNSNfPN83M8q2aIxnX/vsk+14nN1JfP72\nB/rZYTFK63ufIT89p1TanVQLvAxkcVr2gfDlD2wRO8Lf7I7TEbyzO05HWG/oLchlM34r21DbVHgt\n0eEq1u/apnDEmZ42c0Zb0aSy+t77FWl/l3G/pFpixhu9MRY/yCueTWWztsjcSo2agM3wcg6N0pGf\nMeOj6S7j+FylQ2PGk4hJPtTvwJLM+mxbH34ypNJkRTzmuNbPDuvGW/GKM0k043eS6JrWpizzVmJS\n+4gD4Qxbc4HxN7vjdATv7I7TEdZqxldNgpdGmwB01hAA5DwCb6TftLBFNNPO5NqML0iIwprxFZnx\nEzXirvdLi8XZdekk7ptMSKtuohssPFkitRN+KINOzDafGLMaNoOuXhz9QEHPGZXz4pF5AEhH8R5m\nJksuo4w6zr6cHoeeqwmb8abCMGVL5la8IonP8dnUCuBFeiR8UpqJNi9VW+2x3Yx3nM7jnd1xOoJ3\ndsfpCOsNvTUJLo6mobc81b5FRj67zaDjcBuHFk6ZElKMndxf0/r5inz7Us9OSsgvT00dykxti5eO\n/T0AENKehxE7mBUldw6LytHSpP4AAA6ISURBVKibEa8g/7iYH4YDgGQcM96ykb5nGc2YzMb6uWJx\nyorGfyY29Ebv1RlxFvKzd2gcJzdjOgli+y+FPbXt5nx32r4ls0dXrc/+IwC7mOZ3ViGEu0TkLIAv\nArgdwI8AfCCE8PIqx3McZ/28EjP+F0IId4YQ7mrXPwrg0RDCHQAebdcdx7lGOYwZ/z4Ad7fLD2Ja\nA+4jy77QNILh/tRsvpBoc6NH5nlmsoAWhd62zeR+3maPwXBY7oXKZEtx6G1iQ29xORuxSW/M+NGS\nElKkey+pDsGE4JryKzFzbbgUl7nvlEGnwqBmklNCobik0NtS0pQ3yZ3KK6s5S9OEftmtTI2p3aP1\nzSS6lRn0MWrShD8l+tk/yK5LbLkxbuvCLZoA4C9F5Bsicn/72S0hhGfa5WcB3LLisRzHOQFWfbO/\nM4TwtIj8PQCPiMjf8MYQQhCRua+i9o/D/QCQ3nz6UI11HOfqWenNHkJ4uv3/PIA/xbRU83MicisA\ntP+fX/DdB0IId4UQ7kp3to6m1Y7jvGKu+GYXkS0ASQhht13+RQD/AcDDAO4F8PH2/4eueLZa0OxN\nfdg9U5Mqz8gvz7Qvu00ik6cQ42HLBPnstpR8maqhEIkpwXuJwmulDbPQejZiAUujPT+KfldamFAb\nz8JaWiOOfE+bHur+vIauhxW2UOW0WXDEXnuauWiFRpMqzF0GVLkDBCqjXJvQL/vsdbAhNV6m58qE\n3lKVcmva2D7fMzUMiFXM+FsA/KlMc7ozAP8thPDnIvJ1AF8SkfsA/BjAB1Y4luM4J8QVO3sI4UkA\nb57z+YsA3n0cjXIc5+hZb/mnRpDuTU2TKtUhqd08atJt5tr03evFUMjZPJrZVn97h1LedhKdXccm\nPuuBjSrdjkkRL0kx3lTbUgq3FTT7KRvry8ihuKTQWnusY456cZhEYUv3LhPA6LqJP+Py0LWj6xaM\nGc8CJIkp+8Xlna0MnLD4CS0XRvhkRHUS+PkDgDGZ9ZOwOMOypN9mj9G05v+yu++58Y7TEbyzO05H\n8M7uOB1hrT67NFHpo+npvzOTPulvG81tVQq3v7jJ7MPniXauUsr5Uefa0j71xUlcPz802t8kRMj1\nv4qRCZEon137f2kZjz9TXphhRRvrX9LyjP9O3+uk/25+syqt3SxJR244LGfqxdUcetNf43RZYTWk\nSvvUl+i+v1hr1cqLTXy+d5o41rRlwmu75LNfCno8ab89RrPk/e1vdsfpCN7ZHacjrN2MzxaY8RWJ\nRY4m2nzeJ4GJSRObXJu/VSzkdyrRyhM9Dr1R+O5iX+vXv7gVU3p3d7SwxZBCb+WIxStMiV8WuTAz\nqITCbYkxJSWh35PQMY3wZShiRqHNmFoalus4YUHJbQA6DGrKeXHoLS2D2RbvgJCQxXik7/tLk2h2\nP1OcUdv+LjsbjycvXF4eGPHIcYju4fPVKbXt5VZw0mbnqbYu3OI4zg2Fd3bH6QjrzaALcfK/FQEQ\nMlXLQjeL9bzYjG+MycKiADa7jtfrLJpbuz1txj8/2Lm8/NKWHvEc71AWFI3MJ1bkggQwEjPRRuro\nGuS1MSXZzMRqsElvv9fJTDtZ8cotmVwk5r5wxd7EmPE834onSpVGl/CFYXQP/25wk9rGmZ9liM+L\nLfc0buIxf1rqYzw7Od1+35QbI/zN7jgdwTu743QE7+yO0xHW77O3rjP7tQBQ0Xpd6r9BQyqxPKIa\nWuNgtLnJh7fa81wni8vins20/vbPDC5cXn5xU/vsu9vR375EobfS/BbtsxuhApoNxf47AGQL/Oil\nXqgJE/ERlP9uQ02rDgpc5wiFMIX9eVtnjzHXVKrFobd0Etc5q7Iy9eIu7MaxoZ/0tL/N2Z0Xq7jf\nZqrHY9gff7HQWXg/HU1DcUXjPrvjdB7v7I7TEdabQUdmvK2ClJDpWxVmcj+Fry4V0cy5WBkzO4/b\nzoSh2pZKNIl6oHI7RuTiZjLr//7Grtp2cTsef0zleUuT4VZQ+znDCgCSOu4rjQnL0YSIdEm2l5rQ\nYc1ztSMLN6wolHGjwZmIaTr/c4u93hx6K/S2jBI1M3rksl1Tj6AX7+1z2Y7eRhrzL2/EZ3qQLRay\n2DNly14eT5/NqvEMOsfpPN7ZHacjeGd3nI6w/tBbG8awvixHGWqTfsoziC5sRr/5eRN+uDmPvpBN\nNWyS6FBxWM7W3dqk753JtN//qsH+5eVLJHrxoinLXNLsp8T48/y7EyNwoCY5NYvTN4X9+SVpryx6\nIUtq393Q0LiFsM++LK3WzkYkMYt0op+XbEyht/h4IO8Z3fgsPiMj6BTt81Tq+dI4+uL9TN8zLrpU\nmmdn1I4hVbWH3hyn83hnd5yOsF4zHtFUXRZ6S0zZpZJ02S+Oovn8XF9P4OcSzqlR0B5nMVTGpnpp\ns/Do71/fCI7tZPF7pwYx5rK3ocMg461oSlUT81v4d86U/6UMQAo3JpUpL8x6aTbbS+3IoTfzd31Z\nyO5GYlHoLTXm7hLtPjbjk8KY8aO4nu/HYzamUnegdpS1fuYqcvV2B/GLez1zb8mMD42pJdCGe7kE\nlWWlN7uInBGRPxKRvxGRx0Xk7SJyVkQeEZEftv/fdOUjOY5zUqxqxn8awJ+HEN6IaSmoxwF8FMCj\nIYQ7ADzarjuOc42yShXX0wB+HsC/AIAQQgGgEJH3Abi73e1BAF8F8JGlxwpTHTpgtowOm/WJmVhS\nk/bb/pAykXp6NL6XLq6KOqQSUtskFsDVXQGt89WY2SIZjWj36VyDnvZJJmSK1RvaXKw24jHTTTMZ\niEz+lHTsEiPmATLrE5vttWD02cpRd9GMVxNhMnNNU3rv2ZF6JWyhnxcenc9pIkxI9XtUqAxVYkxt\nNuPrflxuciOLzcEEc/suP5qHNONfB+B5AP9VRL4pIv+lLd18SwjhmXafZzGt9uo4zjXKKp09A/Cz\nAD4TQngLgH0Ykz1MZTvnvipE5H4ROSci56rx/rxdHMdZA6t09qcAPBVCeKxd/yNMO/9zInIrALT/\nn5/35RDCAyGEu0IId2WDrXm7OI6zBlapz/6siPxERN4QQvg+pjXZv9f+uxfAx9v/H1rlhAc+e2JF\n/SizLDWht4bKK5W96A9fyPSst4QcGTv75xKJApzJY2acDa815FsNGx3yYuEMdd7E6Izncb024ZOm\nTyIdfeO7DegasG9f2Ew7EsBoFs9mYx16yUyoqSs+O8NhuEQ/H4GvT7r4HWhnIHIojsUrgp1VR18z\ncvBqjIr1TEJm+sGS3nrw6MuSyY2rxtl/A8DnRaQH4EkA/xJTq+BLInIfgB8D+MCKx3Ic5wRYqbOH\nEL4F4K45m959tM1xHOe4WH8G3UHozZoyFL0y0ltohhzSiE021j5eoKyiotI/7eIgmvE7eQzZWYGA\njOygyujSjyvSwqPlxgoGkCtgtd44fGKzrHi97rFpZyfTxIOIOUjCYSM2R614BWeJ3aga8sDiCS/W\nVOequZkx8dnknxG2oAqvpE/HmXXt1vgdk/2WkMZdTeHXmSw8enYWVXmy/Wp+CxzHuaHxzu44HcE7\nu+N0hLWLVxyILYhJ61NlcY0zHtjvoj9PlZmxVlRxv5dKHWraH8Qw2suU3trPdegtTxbHLmryxUcF\n6dcX2rlqqB3WP1PM+PPkr9FPq40QQtJnn32xvy10PCuA0XmfXVZ7xqbr8QObBqsOSdeYteYBIFWz\n5UwqLW3iNHLzeKtnYia0167aNFrG3+yO0xG8sztOR5CwRhNORJ7HNAHnZgAvrO3E87kW2gB4Oyze\nDs0rbcc/CCG8et6GtXb2yycVORdCmJek06k2eDu8Hetsh5vxjtMRvLM7Tkc4qc7+wAmdl7kW2gB4\nOyzeDs2RteNEfHbHcdaPm/GO0xHW2tlF5B4R+b6IPCEia1OjFZHPich5EfkOfbZ2KWwRea2IfEVE\nvici3xWRD59EW0RkICJfE5Fvt+343fbz14nIY+39+WKrX3DsiEja6ht++aTaISI/EpG/FpFvici5\n9rOTeEaOTbZ9bZ1dRFIA/xnAPwPwJgAfFJE3ren0vw/gHvPZSUhhVwB+K4TwJgBvA/Ch9hqsuy0T\nAO8KIbwZwJ0A7hGRtwH4BIBPhRBeD+BlAPcdczsO+DCm8uQHnFQ7fiGEcCeFuk7iGTk+2fYQwlr+\nAXg7gL+g9Y8B+Ngaz387gO/Q+vcB3Nou3wrg++tqC7XhIQDvOcm2ANgE8H8B/BymyRvZvPt1jOe/\nrX2A3wXgy5hmeZ9EO34E4Gbz2VrvC4DTAP4f2rG0o27HOs341wD4Ca0/1X52UpyoFLaI3A7gLQAe\nO4m2tKbztzAVCn0EwN8CuBBCOJiKsa7783sAfhu4LOD/qhNqRwDwlyLyDRG5v/1s3fflWGXbfYAO\ny6WwjwMR2QbwxwB+M4Rw6STaEkKoQwh3YvpmfSuANx73OS0i8ssAzocQvrHuc8/hnSGEn8XUzfyQ\niPw8b1zTfTmUbPuVWGdnfxrAa2n9tvazk2IlKeyjRkRyTDv650MIf3KSbQGAEMIFAF/B1Fw+IyIH\nEynXcX/eAeBXRORHAL6AqSn/6RNoB0IIT7f/nwfwp5j+AVz3fTmUbPuVWGdn/zqAO9qR1h6AXwXw\n8BrPb3kYUwls4BVIYR8GmdYf+iyAx0MInzyptojIq0XkTLu8gem4weOYdvr3r6sdIYSPhRBuCyHc\njunz8D9DCL++7naIyJaI7BwsA/hFAN/Bmu9LCOFZAD8RkTe0Hx3Ith9NO4574MMMNLwXwA8w9Q//\n3RrP+wcAngFQYvrX8z5MfcNHAfwQwP8AcHYN7XgnpibYXwH4VvvvvetuC4B/DOCbbTu+A+Dft5//\nQwBfA/AEgD8E0F/jPbobwJdPoh3t+b7d/vvuwbN5Qs/InQDOtffmvwO46aja4Rl0jtMRfIDOcTqC\nd3bH6Qje2R2nI3hnd5yO4J3dcTqCd3bH6Qje2R2nI3hnd5yO8P8BV8E8b1kfewQAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(prob_rlzs_training[1225,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f206b46f278>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD7CAYAAACscuKmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUiOMXWqEsUuN+D+OkjJKu26+GgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(well_facies_training[1225,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Option 2 Building non-consistent training dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### This block is used when building non-consistent training dataset, where only facies models and global features are consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(261120, 1, 64, 64)\n",
      "(24000, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "# Randomly split probability maps into training and test dataset\n",
    "# prob_rlzs=prob_rlzs.reshape(-1,8, 64, 64) \n",
    "# prob_rlzs_training = prob_rlzs[randseq[:32640]].reshape(-1,1, 64, 64)\n",
    "# prob_rlzs_test = prob_rlzs[randseq[32640:]].reshape(-1,1, 64, 64)\n",
    "# print(prob_rlzs_training.shape)\n",
    "# print(prob_rlzs_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(261120, 1, 64, 64)\n",
      "(24000, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "# shuffle probability maps again, there are 8 probability maps correpsonding to 1 facies models, and the 8 maps are in order\n",
    "# prob_randseq=np.random.permutation(prob_rlzs_training.shape[0])\n",
    "# prob_rlzs_training = prob_rlzs_training[prob_randseq] \n",
    "\n",
    "# Randomly split well facies data into training and test dataset\n",
    "# well_facies=well_facies.reshape(-1, 8, 64, 64)\n",
    "# well_facies_training = well_facies[randseq[:32640]].reshape(-1,1, 64, 64)\n",
    "# well_facies_test = well_facies[randseq[32640:]].reshape(-1,1, 64, 64)\n",
    "# print(well_facies_training.shape)\n",
    "# print(well_facies_test.shape)\n",
    "\n",
    "# well_facies_training = well_facies_training[prob_randseq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(261120, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "# print(well_facies_training.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Store training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "# copy from ProGAN github dataset_tools.py\n",
    "class TFRecordExporter:\n",
    "    def __init__(self, tfrecord_dir, expected_images, print_progress=True, progress_interval=10):\n",
    "        self.tfrecord_dir       = tfrecord_dir\n",
    "        self.tfr_prefix         = os.path.join(self.tfrecord_dir, os.path.basename(self.tfrecord_dir))\n",
    "        self.expected_images    = expected_images\n",
    "        self.cur_images         = 0\n",
    "        self.shape              = None\n",
    "        self.resolution_log2    = None\n",
    "        self.tfr_writers        = []\n",
    "        self.print_progress     = print_progress\n",
    "        self.progress_interval  = progress_interval\n",
    "        if self.print_progress:\n",
    "            print('Creating dataset \"%s\"' % tfrecord_dir)\n",
    "        if not os.path.isdir(self.tfrecord_dir):\n",
    "            os.makedirs(self.tfrecord_dir)\n",
    "        assert(os.path.isdir(self.tfrecord_dir))\n",
    "        \n",
    "    def close(self):\n",
    "        if self.print_progress:\n",
    "            print('%-40s\\r' % 'Flushing data...', end='', flush=True)\n",
    "        for tfr_writer in self.tfr_writers:\n",
    "            tfr_writer.close()\n",
    "        self.tfr_writers = []\n",
    "        if self.print_progress:\n",
    "            print('%-40s\\r' % '', end='', flush=True)\n",
    "            print('Added %d images.' % self.cur_images)\n",
    "\n",
    "    def choose_shuffled_order(self): # Note: Images and labels must be added in shuffled order.\n",
    "        order = np.arange(self.expected_images)\n",
    "        np.random.RandomState(123).shuffle(order)\n",
    "        return order\n",
    "\n",
    "    def add_real_image(self, real_img):\n",
    "        if self.print_progress and self.cur_images % self.progress_interval == 0:\n",
    "            print('%d / %d\\r' % (self.cur_images, self.expected_images), end='', flush=True)\n",
    "        if self.shape is None:\n",
    "            self.shape = real_img.shape\n",
    "            self.resolution_log2 = int(np.log2(self.shape[1]))\n",
    "            assert self.shape[0] in [1, 3]\n",
    "            assert self.shape[1] == self.shape[2]\n",
    "            assert self.shape[1] == 2**self.resolution_log2\n",
    "            tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)\n",
    "            for lod in range(self.resolution_log2 - 1):\n",
    "                tfr_file = self.tfr_prefix + '-1r%02d.tfrecords' % (self.resolution_log2 - lod)\n",
    "                self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))\n",
    "            self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))\n",
    "        assert real_img.shape == self.shape\n",
    "        for lod, tfr_writer in enumerate(self.tfr_writers[:-1]):\n",
    "            if lod:\n",
    "                real_img = real_img.astype(np.float32)\n",
    "                \n",
    "                # used to produce low-D with most frequent facies code\n",
    "                #real_img_t = np.expand_dims(real_img, axis = 3)\n",
    "                #real_img_t_c = np.concatenate((real_img_t[:, 0::2, 0::2], real_img_t[:, 0::2, 1::2], real_img_t[:, 1::2, 0::2], real_img_t[:, 1::2, 1::2]), axis = 3)                \n",
    "                #mode, _ = stats.mode(real_img_t_c, axis = 3)\n",
    "                #real_img = np.squeeze(mode, axis = 3)\n",
    "                \n",
    "                # used to produce low-D with averaging method\n",
    "                real_img = (real_img[:, 0::2, 0::2] + real_img[:, 0::2, 1::2] + real_img[:, 1::2, 0::2] + real_img[:, 1::2, 1::2]) * 0.25\n",
    "            \n",
    "            quant = np.rint(real_img).clip(0, 255).astype(np.uint8)\n",
    "            ex = tf.train.Example(features=tf.train.Features(feature={\n",
    "                'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)),\n",
    "                'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))}))\n",
    "            tfr_writer.write(ex.SerializeToString())\n",
    "        self.cur_images += 1\n",
    "        \n",
    "        \n",
    "    def add_prob_image(self, prob_image):\n",
    "        if self.print_progress and self.cur_images % self.progress_interval == 0:\n",
    "            print('%d / %d\\r' % (self.cur_images, self.expected_images), end='', flush=True)\n",
    "        if self.shape is None:\n",
    "            self.shape = prob_image.shape\n",
    "            tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)\n",
    "            # add probimages writer into self.tfr_writers    \n",
    "            tfr_file = self.tfr_prefix + '-2probimages.tfrecords' \n",
    "            self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))\n",
    "        # Writting prob_image into tfrecord file\n",
    "        quant = prob_image.clip(0, 1).astype(np.float16)\n",
    "        ex = tf.train.Example(features=tf.train.Features(feature={\n",
    "            'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)),\n",
    "            'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))}))\n",
    "        self.tfr_writers[0].write(ex.SerializeToString())\n",
    "        self.cur_images += 1           \n",
    "\n",
    "    def add_well_facies(self, well_facies):\n",
    "        if self.print_progress and self.cur_images % self.progress_interval == 0:\n",
    "            print('%d / %d\\r' % (self.cur_images, self.expected_images), end='', flush=True)\n",
    "        if self.shape is None:\n",
    "            self.shape = well_facies.shape\n",
    "            tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)\n",
    "            # add well_facies writer into self.tfr_writers    \n",
    "            tfr_file = self.tfr_prefix + '-3wellfacies.tfrecords' \n",
    "            self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))\n",
    "        # Writting well_facies into tfrecord file\n",
    "        quant = well_facies.astype(np.uint8)\n",
    "        ex = tf.train.Example(features=tf.train.Features(feature={\n",
    "            'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)),\n",
    "            'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))}))\n",
    "        self.tfr_writers[0].write(ex.SerializeToString())\n",
    "        self.cur_images += 1           \n",
    "        \n",
    "        \n",
    "    def add_labels(self, labels):\n",
    "        if self.print_progress:\n",
    "            print('%-40s\\r' % 'Saving labels...', end='', flush=True)\n",
    "        assert labels.shape[0] == self.cur_images\n",
    "        with open(self.tfr_prefix + '-4rxx.labels', 'wb') as f:\n",
    "            np.save(f, labels.astype(np.float32))\n",
    "\n",
    "    def __enter__(self):\n",
    "        return self\n",
    "    \n",
    "    def __exit__(self, *args):\n",
    "        self.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating dataset \"/scratch/users/suihong/DataSets(MultiChannels_Version4_Consistency)/TrainingData\"\n",
      "Added 32640 images.                     \n"
     ]
    }
   ],
   "source": [
    "with TFRecordExporter(tfrecord_dir_training, allimgs_training.shape[0]) as tfr:\n",
    "    order = tfr.choose_shuffled_order()\n",
    "    for idx in range(order.size):\n",
    "        tfr.add_real_image(allimgs_training[order[idx]])\n",
    "    tfr.add_labels(labels_cor_training[order])       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating dataset \"/scratch/users/suihong/DataSets(MultiChannels_Version4_Consistency)/TrainingData\"\n",
      "Added 32640 images.                     \n"
     ]
    }
   ],
   "source": [
    "with TFRecordExporter(tfrecord_dir_training, prob_rlzs_training.shape[0]) as tfr:\n",
    "    for idx in range(prob_rlzs_training.shape[0]):\n",
    "        tfr.add_prob_image(prob_rlzs_training[idx])     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating dataset \"/scratch/users/suihong/DataSets(MultiChannels_Version4_Consistency)/TrainingData\"\n",
      "Added 32640 images.                     \n"
     ]
    }
   ],
   "source": [
    "with TFRecordExporter(tfrecord_dir_training, well_facies_training.shape[0]) as tfr:\n",
    "    for idx in range(well_facies_training.shape[0]):\n",
    "        tfr.add_well_facies(well_facies_training[idx])     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Store test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating dataset \"/scratch/users/suihong/DataSets(MultiChannels_Version4_Consistency)/TestData\"\n",
      "Added 3000 images.                      \n"
     ]
    }
   ],
   "source": [
    "with TFRecordExporter(tfrecord_dir_test, allimgs_test.shape[0]) as tfr:\n",
    "    for idx in range(allimgs_test.shape[0]):\n",
    "        tfr.add_real_image(allimgs_test[idx])\n",
    "    tfr.add_labels(labels_cor_test)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating dataset \"/scratch/users/suihong/DataSets(MultiChannels_Version4_Consistency)/TestData\"\n",
      "Added 24000 images.                     \n"
     ]
    }
   ],
   "source": [
    "with TFRecordExporter(tfrecord_dir_test, prob_rlzs_test.shape[0]) as tfr:\n",
    "    for idx in range(prob_rlzs_test.shape[0]):\n",
    "        tfr.add_prob_image(prob_rlzs_test[idx])     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating dataset \"/scratch/users/suihong/DataSets(MultiChannels_Version4_Consistency)/TestData\"\n",
      "Added 24000 images.                     \n"
     ]
    }
   ],
   "source": [
    "with TFRecordExporter(tfrecord_dir_test, well_facies_test.shape[0]) as tfr:\n",
    "    for idx in range(well_facies_test.shape[0]):\n",
    "        tfr.add_well_facies(well_facies_test[idx])     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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