EvgenyS/Facies_classification_ES.ipynb

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    "## Facies classification using ensemble classifiers \n",
    "\n",
    "\n",
    "#### by: <a href=\"https://ca.linkedin.com/in/evgeny-sorkin-509532b\">Evgeny Sorkin</a> [SJ Geophysics](https://www.sjgeophysics.com/)\n",
    "\n",
    "####  [Original contest notebook](https://github.com/seg/2016-ml-contest/blob/master/Facies_classification.ipynb) by Brendon Hall, [Enthought](https://www.enthought.com/)\n",
    "\n",
    "This notebook demonstrates how to train a machine learning algorithm to predict facies from well log data.  The dataset we will use comes from a class excercise from The University of Kansas on [Neural Networks and Fuzzy Systems](http://www.people.ku.edu/~gbohling/EECS833/).  This exercise is based on a consortium project to use machine learning techniques to create a reservoir model of the largest gas fields in North America, the Hugoton and Panoma Fields. For more info on the origin of the data, see [Bohling and Dubois (2003)](http://www.kgs.ku.edu/PRS/publication/2003/ofr2003-50.pdf) and [Dubois et al. (2007)](http://dx.doi.org/10.1016/j.cageo.2006.08.011). \n",
    "\n",
    "The dataset we will use is log data from nine wells that have been labeled with a facies type based on oberservation of core.  We will use this log data to train a majority-vote classifier comprised of support vector machine, random forest and XGBoosted tree.  We will use the classifiers implementation in [scikit-learn](http://scikit-learn.org) and [XGBoost](https://github.com/dmlc/xgboost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Exploring the dataset\n",
    "\n",
    "First, we will examine the data set we will use to train the classifier.  The training data is contained in the file `training_data.csv`.  The dataset consists of 5 wireline log measurements, two indicator variables and a facies label at half foot intervals.  In machine learning terminology, each log measurement is a feature vector that maps a set of 'features' (the log measurements) to a class (the facies type).  We will use the pandas library to load the data into a dataframe, which provides a convenient data structure to work with well log data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Facies</th>\n",
       "      <th>Formation</th>\n",
       "      <th>Well Name</th>\n",
       "      <th>Depth</th>\n",
       "      <th>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2793.0</td>\n",
       "      <td>77.450</td>\n",
       "      <td>0.664</td>\n",
       "      <td>9.900</td>\n",
       "      <td>11.915</td>\n",
       "      <td>4.600</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2793.5</td>\n",
       "      <td>78.260</td>\n",
       "      <td>0.661</td>\n",
       "      <td>14.200</td>\n",
       "      <td>12.565</td>\n",
       "      <td>4.100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>79.050</td>\n",
       "      <td>0.658</td>\n",
       "      <td>14.800</td>\n",
       "      <td>13.050</td>\n",
       "      <td>3.600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2794.5</td>\n",
       "      <td>86.100</td>\n",
       "      <td>0.655</td>\n",
       "      <td>13.900</td>\n",
       "      <td>13.115</td>\n",
       "      <td>3.500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2795.0</td>\n",
       "      <td>74.580</td>\n",
       "      <td>0.647</td>\n",
       "      <td>13.500</td>\n",
       "      <td>13.300</td>\n",
       "      <td>3.400</td>\n",
       "      <td>1</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2795.5</td>\n",
       "      <td>73.970</td>\n",
       "      <td>0.636</td>\n",
       "      <td>14.000</td>\n",
       "      <td>13.385</td>\n",
       "      <td>3.600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2796.0</td>\n",
       "      <td>73.720</td>\n",
       "      <td>0.630</td>\n",
       "      <td>15.600</td>\n",
       "      <td>13.930</td>\n",
       "      <td>3.700</td>\n",
       "      <td>1</td>\n",
       "      <td>0.872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2796.5</td>\n",
       "      <td>75.650</td>\n",
       "      <td>0.625</td>\n",
       "      <td>16.500</td>\n",
       "      <td>13.920</td>\n",
       "      <td>3.500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2797.0</td>\n",
       "      <td>73.790</td>\n",
       "      <td>0.624</td>\n",
       "      <td>16.200</td>\n",
       "      <td>13.980</td>\n",
       "      <td>3.400</td>\n",
       "      <td>1</td>\n",
       "      <td>0.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2797.5</td>\n",
       "      <td>76.890</td>\n",
       "      <td>0.615</td>\n",
       "      <td>16.900</td>\n",
       "      <td>14.220</td>\n",
       "      <td>3.500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3222</th>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3118.0</td>\n",
       "      <td>55.563</td>\n",
       "      <td>1.052</td>\n",
       "      <td>4.296</td>\n",
       "      <td>7.325</td>\n",
       "      <td>3.805</td>\n",
       "      <td>2</td>\n",
       "      <td>0.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3223</th>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3118.5</td>\n",
       "      <td>58.313</td>\n",
       "      <td>1.034</td>\n",
       "      <td>3.863</td>\n",
       "      <td>7.465</td>\n",
       "      <td>3.584</td>\n",
       "      <td>2</td>\n",
       "      <td>0.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3224</th>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>55.344</td>\n",
       "      <td>1.003</td>\n",
       "      <td>2.225</td>\n",
       "      <td>7.541</td>\n",
       "      <td>3.645</td>\n",
       "      <td>2</td>\n",
       "      <td>0.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3225</th>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3119.5</td>\n",
       "      <td>53.313</td>\n",
       "      <td>0.972</td>\n",
       "      <td>1.640</td>\n",
       "      <td>7.295</td>\n",
       "      <td>3.629</td>\n",
       "      <td>2</td>\n",
       "      <td>0.702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3226</th>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3120.0</td>\n",
       "      <td>49.594</td>\n",
       "      <td>0.954</td>\n",
       "      <td>1.494</td>\n",
       "      <td>7.149</td>\n",
       "      <td>3.727</td>\n",
       "      <td>2</td>\n",
       "      <td>0.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3227</th>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3120.5</td>\n",
       "      <td>46.719</td>\n",
       "      <td>0.947</td>\n",
       "      <td>1.828</td>\n",
       "      <td>7.254</td>\n",
       "      <td>3.617</td>\n",
       "      <td>2</td>\n",
       "      <td>0.685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3228</th>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3121.0</td>\n",
       "      <td>44.563</td>\n",
       "      <td>0.953</td>\n",
       "      <td>2.241</td>\n",
       "      <td>8.013</td>\n",
       "      <td>3.344</td>\n",
       "      <td>2</td>\n",
       "      <td>0.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3229</th>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3121.5</td>\n",
       "      <td>49.719</td>\n",
       "      <td>0.964</td>\n",
       "      <td>2.925</td>\n",
       "      <td>8.013</td>\n",
       "      <td>3.190</td>\n",
       "      <td>2</td>\n",
       "      <td>0.669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3122.0</td>\n",
       "      <td>51.469</td>\n",
       "      <td>0.965</td>\n",
       "      <td>3.083</td>\n",
       "      <td>7.708</td>\n",
       "      <td>3.152</td>\n",
       "      <td>2</td>\n",
       "      <td>0.661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3231</th>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3122.5</td>\n",
       "      <td>50.031</td>\n",
       "      <td>0.970</td>\n",
       "      <td>2.609</td>\n",
       "      <td>6.668</td>\n",
       "      <td>3.295</td>\n",
       "      <td>2</td>\n",
       "      <td>0.653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3232 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Facies Formation        Well Name   Depth      GR  ILD_log10  DeltaPHI  \\\n",
       "0          3     A1 SH        SHRIMPLIN  2793.0  77.450      0.664     9.900   \n",
       "1          3     A1 SH        SHRIMPLIN  2793.5  78.260      0.661    14.200   \n",
       "2          3     A1 SH        SHRIMPLIN  2794.0  79.050      0.658    14.800   \n",
       "3          3     A1 SH        SHRIMPLIN  2794.5  86.100      0.655    13.900   \n",
       "4          3     A1 SH        SHRIMPLIN  2795.0  74.580      0.647    13.500   \n",
       "5          3     A1 SH        SHRIMPLIN  2795.5  73.970      0.636    14.000   \n",
       "6          3     A1 SH        SHRIMPLIN  2796.0  73.720      0.630    15.600   \n",
       "7          3     A1 SH        SHRIMPLIN  2796.5  75.650      0.625    16.500   \n",
       "8          3     A1 SH        SHRIMPLIN  2797.0  73.790      0.624    16.200   \n",
       "9          3     A1 SH        SHRIMPLIN  2797.5  76.890      0.615    16.900   \n",
       "...      ...       ...              ...     ...     ...        ...       ...   \n",
       "3222       5      C LM  CHURCHMAN BIBLE  3118.0  55.563      1.052     4.296   \n",
       "3223       5      C LM  CHURCHMAN BIBLE  3118.5  58.313      1.034     3.863   \n",
       "3224       5      C LM  CHURCHMAN BIBLE  3119.0  55.344      1.003     2.225   \n",
       "3225       5      C LM  CHURCHMAN BIBLE  3119.5  53.313      0.972     1.640   \n",
       "3226       5      C LM  CHURCHMAN BIBLE  3120.0  49.594      0.954     1.494   \n",
       "3227       5      C LM  CHURCHMAN BIBLE  3120.5  46.719      0.947     1.828   \n",
       "3228       5      C LM  CHURCHMAN BIBLE  3121.0  44.563      0.953     2.241   \n",
       "3229       5      C LM  CHURCHMAN BIBLE  3121.5  49.719      0.964     2.925   \n",
       "3230       5      C LM  CHURCHMAN BIBLE  3122.0  51.469      0.965     3.083   \n",
       "3231       5      C LM  CHURCHMAN BIBLE  3122.5  50.031      0.970     2.609   \n",
       "\n",
       "       PHIND     PE  NM_M  RELPOS  \n",
       "0     11.915  4.600     1   1.000  \n",
       "1     12.565  4.100     1   0.979  \n",
       "2     13.050  3.600     1   0.957  \n",
       "3     13.115  3.500     1   0.936  \n",
       "4     13.300  3.400     1   0.915  \n",
       "5     13.385  3.600     1   0.894  \n",
       "6     13.930  3.700     1   0.872  \n",
       "7     13.920  3.500     1   0.830  \n",
       "8     13.980  3.400     1   0.809  \n",
       "9     14.220  3.500     1   0.787  \n",
       "...      ...    ...   ...     ...  \n",
       "3222   7.325  3.805     2   0.726  \n",
       "3223   7.465  3.584     2   0.718  \n",
       "3224   7.541  3.645     2   0.710  \n",
       "3225   7.295  3.629     2   0.702  \n",
       "3226   7.149  3.727     2   0.694  \n",
       "3227   7.254  3.617     2   0.685  \n",
       "3228   8.013  3.344     2   0.677  \n",
       "3229   8.013  3.190     2   0.669  \n",
       "3230   7.708  3.152     2   0.661  \n",
       "3231   6.668  3.295     2   0.653  \n",
       "\n",
       "[3232 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "from pandas import set_option\n",
    "set_option(\"display.max_rows\", 20)\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "filename = '../training_data.csv'\n",
    "training_data = pd.read_csv(filename)\n",
    "training_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data is from the Council Grove gas reservoir in Southwest Kansas.  The Panoma Council Grove Field is predominantly a carbonate gas reservoir encompassing 2700 square miles in Southwestern Kansas.  This dataset is from nine wells (with 4149 examples), consisting of a set of seven predictor variables and a rock facies (class) for each example vector and validation (test) data (830 examples from two wells) having the same seven predictor variables in the feature vector.  Facies are based on examination of cores from nine wells taken vertically at half-foot intervals. Predictor variables include five from wireline log measurements and two geologic constraining variables that are derived from geologic knowledge. These are essentially continuous variables sampled at a half-foot sample rate. \n",
    "\n",
    "The seven predictor variables are:\n",
    "* Five wire line log curves include [gamma ray](http://petrowiki.org/Gamma_ray_logs) (GR), [resistivity logging](http://petrowiki.org/Resistivity_and_spontaneous_%28SP%29_logging) (ILD_log10),\n",
    "[photoelectric effect](http://www.glossary.oilfield.slb.com/en/Terms/p/photoelectric_effect.aspx) (PE), [neutron-density porosity difference and average neutron-density porosity](http://petrowiki.org/Neutron_porosity_logs) (DeltaPHI and PHIND). Note, some wells do not have PE.\n",
    "* Two geologic constraining variables: nonmarine-marine indicator (NM_M) and relative position (RELPOS)\n",
    "\n",
    "The nine discrete facies (classes of rocks) are: \n",
    "1. Nonmarine sandstone\n",
    "2. Nonmarine coarse siltstone \n",
    "3. Nonmarine fine siltstone \n",
    "4. Marine siltstone and shale \n",
    "5. Mudstone (limestone)\n",
    "6. Wackestone (limestone)\n",
    "7. Dolomite\n",
    "8. Packstone-grainstone (limestone)\n",
    "9. Phylloid-algal bafflestone (limestone)\n",
    "\n",
    "These facies aren't discrete, and gradually blend into one another. Some have neighboring facies that are rather close.  Mislabeling within these neighboring facies can be expected to occur.  The following table lists the facies, their abbreviated labels and their approximate neighbors.\n",
    "\n",
    "Facies |Label| Adjacent Facies\n",
    ":---: | :---: |:--:\n",
    "1 |SS| 2\n",
    "2 |CSiS| 1,3\n",
    "3 |FSiS| 2\n",
    "4 |SiSh| 5\n",
    "5 |MS| 4,6\n",
    "6 |WS| 5,7\n",
    "7 |D| 6,8\n",
    "8 |PS| 6,7,9\n",
    "9 |BS| 7,8\n",
    "\n",
    "Let's clean up this dataset.  The 'Well Name' and 'Formation' columns can be turned into a categorical data type.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[SHRIMPLIN, SHANKLE, LUKE G U, CROSS H CATTLE, NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]\n",
       "Categories (8, object): [SHRIMPLIN, SHANKLE, LUKE G U, CROSS H CATTLE, NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data['Well Name'] = training_data['Well Name'].astype('category')\n",
    "training_data['Formation'] = training_data['Formation'].astype('category')\n",
    "training_data['Well Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'NM_M', 'RELPOS'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.columns[4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Facies</th>\n",
       "      <th>Depth</th>\n",
       "      <th>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.422030</td>\n",
       "      <td>2875.824567</td>\n",
       "      <td>66.135769</td>\n",
       "      <td>0.642719</td>\n",
       "      <td>3.559642</td>\n",
       "      <td>13.483213</td>\n",
       "      <td>3.725014</td>\n",
       "      <td>1.498453</td>\n",
       "      <td>0.520287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.504243</td>\n",
       "      <td>131.006274</td>\n",
       "      <td>30.854826</td>\n",
       "      <td>0.241845</td>\n",
       "      <td>5.228948</td>\n",
       "      <td>7.698980</td>\n",
       "      <td>0.896152</td>\n",
       "      <td>0.500075</td>\n",
       "      <td>0.286792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2573.500000</td>\n",
       "      <td>13.250000</td>\n",
       "      <td>-0.025949</td>\n",
       "      <td>-21.832000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2791.000000</td>\n",
       "      <td>46.918750</td>\n",
       "      <td>0.492750</td>\n",
       "      <td>1.163750</td>\n",
       "      <td>8.346750</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.273000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2893.500000</td>\n",
       "      <td>65.721500</td>\n",
       "      <td>0.624437</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>12.150000</td>\n",
       "      <td>3.551500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.526000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>2980.000000</td>\n",
       "      <td>79.626250</td>\n",
       "      <td>0.812735</td>\n",
       "      <td>6.432500</td>\n",
       "      <td>16.453750</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.767250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3122.500000</td>\n",
       "      <td>361.150000</td>\n",
       "      <td>1.480000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>84.400000</td>\n",
       "      <td>8.094000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Facies        Depth           GR    ILD_log10     DeltaPHI  \\\n",
       "count  3232.000000  3232.000000  3232.000000  3232.000000  3232.000000   \n",
       "mean      4.422030  2875.824567    66.135769     0.642719     3.559642   \n",
       "std       2.504243   131.006274    30.854826     0.241845     5.228948   \n",
       "min       1.000000  2573.500000    13.250000    -0.025949   -21.832000   \n",
       "25%       2.000000  2791.000000    46.918750     0.492750     1.163750   \n",
       "50%       4.000000  2893.500000    65.721500     0.624437     3.500000   \n",
       "75%       6.000000  2980.000000    79.626250     0.812735     6.432500   \n",
       "max       9.000000  3122.500000   361.150000     1.480000    18.600000   \n",
       "\n",
       "             PHIND           PE         NM_M       RELPOS  \n",
       "count  3232.000000  3232.000000  3232.000000  3232.000000  \n",
       "mean     13.483213     3.725014     1.498453     0.520287  \n",
       "std       7.698980     0.896152     0.500075     0.286792  \n",
       "min       0.550000     0.200000     1.000000     0.010000  \n",
       "25%       8.346750     3.100000     1.000000     0.273000  \n",
       "50%      12.150000     3.551500     1.000000     0.526000  \n",
       "75%      16.453750     4.300000     2.000000     0.767250  \n",
       "max      84.400000     8.094000     2.000000     1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a quick view of the statistical distribution of the input variables.  Looking at the `count` values, there are 3232 feature vectors in the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 1=sandstone  2=c_siltstone   3=f_siltstone \n",
    "# 4=marine_silt_shale 5=mudstone 6=wackestone 7=dolomite\n",
    "# 8=packstone 9=bafflestone\n",
    "facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00',\n",
    "       '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']\n",
    "\n",
    "facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS',\n",
    "                 'WS', 'D','PS', 'BS']\n",
    "#facies_color_map is a dictionary that maps facies labels\n",
    "#to their respective colors\n",
    "facies_color_map = {}\n",
    "for ind, label in enumerate(facies_labels):\n",
    "    facies_color_map[label] = facies_colors[ind]\n",
    "\n",
    "def label_facies(row, labels):\n",
    "    return labels[ row['Facies'] -1]\n",
    "\n",
    "def make_facies_log_plot(logs, facies_colors):\n",
    "    #make sure logs are sorted by depth\n",
    "    logs = logs.sort_values(by='Depth')\n",
    "    cmap_facies = colors.ListedColormap(\n",
    "            facies_colors[0:len(facies_colors)], 'indexed')\n",
    "    \n",
    "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
    "    \n",
    "    cluster=np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
    "    \n",
    "    f, ax = plt.subplots(nrows=1, ncols=6, figsize=(8, 12))\n",
    "    ax[0].plot(logs.GR, logs.Depth, '-g')\n",
    "    ax[1].plot(logs.ILD_log10, logs.Depth, '-')\n",
    "    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')\n",
    "    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')\n",
    "    ax[4].plot(logs.PE, logs.Depth, '-', color='black')\n",
    "    im=ax[5].imshow(cluster, interpolation='none', aspect='auto',\n",
    "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    \n",
    "    divider = make_axes_locatable(ax[5])\n",
    "    cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
    "    cbar=plt.colorbar(im, cax=cax)\n",
    "    cbar.set_label((17*' ').join([' SS ', 'CSiS', 'FSiS', \n",
    "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
    "                                ' PS ', ' BS ']))\n",
    "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
    "    \n",
    "    for i in range(len(ax)-1):\n",
    "        ax[i].set_ylim(ztop,zbot)\n",
    "        ax[i].invert_yaxis()\n",
    "        ax[i].grid()\n",
    "        ax[i].locator_params(axis='x', nbins=3)\n",
    "    \n",
    "    ax[0].set_xlabel(\"GR\")\n",
    "    ax[0].set_xlim(logs.GR.min(),logs.GR.max())\n",
    "    ax[1].set_xlabel(\"ILD_log10\")\n",
    "    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())\n",
    "    ax[2].set_xlabel(\"DeltaPHI\")\n",
    "    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())\n",
    "    ax[3].set_xlabel(\"PHIND\")\n",
    "    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())\n",
    "    ax[4].set_xlabel(\"PE\")\n",
    "    ax[4].set_xlim(logs.PE.min(),logs.PE.max())\n",
    "    ax[5].set_xlabel('Facies')\n",
    "    \n",
    "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])\n",
    "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([])\n",
    "    ax[5].set_xticklabels([])\n",
    "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)\n",
    "def compare_facies_plot(logs, compadre, facies_colors):\n",
    "    #make sure logs are sorted by depth\n",
    "    logs = logs.sort_values(by='Depth')\n",
    "    cmap_facies = colors.ListedColormap(\n",
    "            facies_colors[0:len(facies_colors)], 'indexed')\n",
    "    \n",
    "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
    "    \n",
    "    cluster1 = np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
    "    cluster2 = np.repeat(np.expand_dims(logs[compadre].values,1), 100, 1)\n",
    "    \n",
    "    f, ax = plt.subplots(nrows=1, ncols=7, figsize=(9, 12))\n",
    "    ax[0].plot(logs.GR, logs.Depth, '-g')\n",
    "    ax[1].plot(logs.ILD_log10, logs.Depth, '-')\n",
    "    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')\n",
    "    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')\n",
    "    ax[4].plot(logs.PE, logs.Depth, '-', color='black')\n",
    "    im1 = ax[5].imshow(cluster1, interpolation='none', aspect='auto',\n",
    "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    im2 = ax[6].imshow(cluster2, interpolation='none', aspect='auto',\n",
    "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    \n",
    "    divider = make_axes_locatable(ax[6])\n",
    "    cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
    "    cbar=plt.colorbar(im2, cax=cax)\n",
    "    cbar.set_label((17*' ').join([' SS ', 'CSiS', 'FSiS', \n",
    "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
    "                                ' PS ', ' BS ']))\n",
    "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
    "    \n",
    "    for i in range(len(ax)-2):\n",
    "        ax[i].set_ylim(ztop,zbot)\n",
    "        ax[i].invert_yaxis()\n",
    "        ax[i].grid()\n",
    "        ax[i].locator_params(axis='x', nbins=3)\n",
    "    \n",
    "    ax[0].set_xlabel(\"GR\")\n",
    "    ax[0].set_xlim(logs.GR.min(),logs.GR.max())\n",
    "    ax[1].set_xlabel(\"ILD_log10\")\n",
    "    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())\n",
    "    ax[2].set_xlabel(\"DeltaPHI\")\n",
    "    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())\n",
    "    ax[3].set_xlabel(\"PHIND\")\n",
    "    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())\n",
    "    ax[4].set_xlabel(\"PE\")\n",
    "    ax[4].set_xlim(logs.PE.min(),logs.PE.max())\n",
    "    ax[5].set_xlabel('Facies')\n",
    "    ax[6].set_xlabel(compadre)\n",
    "    \n",
    "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])\n",
    "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([])\n",
    "    ax[5].set_xticklabels([])\n",
    "    ax[6].set_xticklabels([])\n",
    "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1a8aqVavKfaprW8i51Y5BKdQNrmG7YkO5KNG7Sin1CTASiAQuKqVq5zyVqmna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\n/GsaxMcX7Au8bx/s3q33FX7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ls2fDs8/q84kPHqwPYda9u9HRVWryqYIQ5cOuuzrEnYvj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dSqVQud3GAokTwR6fg+QmO3xtRyrsXEnROl46sCHh5iQWjUKAgPF6vBPXsKJ7hKFbXV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b8AVQv9SFWWlspqLoGByrQmHTooU5cYSIXPvwrUyLmbmxvvv/8+7733HuHh4cydO5dx48ZR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\n/BpS8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83e99184e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_facies_log_plot(\n",
    "    training_data[training_data['Well Name'] == 'SHRIMPLIN'],\n",
    "    facies_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next: make labels and features vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "feat_labels =training_data.columns[4:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Feature engineering "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def add_del(df,feat_names):\n",
    "    \"\"\"\"\"\"\n",
    "    for fn in feat_names:\n",
    "        df[\"del_\"+fn] = np.gradient(df[fn] )\n",
    "    return df\n",
    "training_data.columns[4:]\n",
    "training_data = add_del(training_data,[fn for fn in training_data.columns[4:] if fn != 'NM_M'])\n",
    "training_data = add_del(training_data,[fn for fn in training_data.columns[4:] if fn != 'NM_M'])\n",
    "training_data.columns[4:]\n",
    "feat_labels =training_data.columns[4:] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'NM_M', 'RELPOS',\n",
       "       'del_GR', 'del_ILD_log10', 'del_DeltaPHI', 'del_PHIND', 'del_PE',\n",
       "       'del_RELPOS', 'del_del_GR', 'del_del_ILD_log10', 'del_del_DeltaPHI',\n",
       "       'del_del_PHIND', 'del_del_PE', 'del_del_RELPOS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import and initilize a few classifires that we play with "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = training_data['Facies'].values\n",
    "X = training_data.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1).values\n",
    "feat_labels =training_data.columns[4:]\n",
    "label_encoded_y  = np.unique(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## import and initilize a few classifires that we play with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def randomize(dataset, labels):\n",
    "  permutation = np.random.permutation(labels.shape[0])\n",
    "  shuffled_dataset = dataset[permutation,:]\n",
    "  shuffled_labels = labels[permutation]\n",
    "  return shuffled_dataset, shuffled_labels\n",
    "X, y = randomize(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn import __version__ as sklearn_version\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "tree = DecisionTreeClassifier(criterion = 'entropy', max_depth = 1)\n",
    "forest = RandomForestClassifier( n_estimators = 10000, random_state=50, n_jobs=-1)\n",
    "bag = BaggingClassifier(base_estimator = tree, n_estimators=400,random_state=0)\n",
    "knn = KNeighborsClassifier(n_neighbors = 5, p=2, metric = 'minkowski')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import svm\n",
    "SVC_classifier = svm.SVC(kernel = 'rbf', random_state=0, gamma=0.01)\n",
    "pipe_svm = Pipeline([('scl',StandardScaler()),('clf',SVC_classifier)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/evgeny/.conda/envs/py3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import xgboost\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the training data into training and test sets. Let's use 10% of the data for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X,y, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# the scores \n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, make_scorer\n",
    "pre_scorer= make_scorer(score_func=f1_score, greater_is_better=True,average = 'micro')\n",
    "#the scorer is f1_scrore with micro averaging, i.e.\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "kfold = StratifiedKFold(y_train, n_folds=10, shuffle=True, random_state=7)\n",
    "# stratified kfold cross-validation keeps the classes balanced in each fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## check feature importance with random forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1) NM_M                           0.098258\n",
      " 2) GR                             0.097813\n",
      " 3) del_RELPOS                     0.095153\n",
      " 4) PHIND                          0.084868\n",
      " 5) ILD_log10                      0.084802\n",
      " 6) PE                             0.083189\n",
      " 7) RELPOS                         0.057806\n",
      " 8) DeltaPHI                       0.055448\n",
      " 9) del_ILD_log10                  0.036323\n",
      "10) del_del_PHIND                  0.035217\n",
      "11) del_del_ILD_log10              0.034313\n",
      "12) del_PHIND                      0.033774\n",
      "13) del_GR                         0.033501\n",
      "14) del_del_GR                     0.033481\n",
      "15) del_del_PE                     0.030326\n",
      "16) del_del_DeltaPHI               0.029371\n",
      "17) del_DeltaPHI                   0.029149\n",
      "18) del_PE                         0.028322\n",
      "19) del_del_RELPOS                 0.018885\n"
     ]
    }
   ],
   "source": [
    "forest.fit(X, y)\n",
    "importances = forest.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "for f in range(X_train.shape[1]):\n",
    "    print(\"%2d) %-*s %f\" % (f+1 , 30, \n",
    "                            feat_labels[indices[f]], \n",
    "                            importances[indices[f]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like not all features are nearly important, e.g. may consider dropping those with relative importance below 2.3%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if sklearn_version < '0.18':\n",
    "    X_selected = forest.transform(X, threshold=0.023)\n",
    "else:\n",
    "    from sklearn.feature_selection import SelectFromModel\n",
    "    sfm = SelectFromModel(forest, threshold=0.023, prefit=True)\n",
    "    X_selected = sfm.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_selected = X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X_selected,y, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score: 0.7932\n"
     ]
    }
   ],
   "source": [
    "#tuned forest\n",
    "forest =  RandomForestClassifier( \n",
    "    n_estimators = 303,\n",
    "    min_samples_leaf = 1,\n",
    "    oob_score = True,\n",
    "    random_state=50, n_jobs=-1)\n",
    "forest.fit(X_train, y_train)\n",
    "y_pred = forest.predict(X_test)\n",
    "print (\"F1-score: %.4g\" % f1_score(y_test,y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score: 0.7809\n"
     ]
    }
   ],
   "source": [
    "#tuned xgb \n",
    "xg = XGBClassifier(learning_rate =0.005,\n",
    " n_estimators=1888,\n",
    " max_depth=10,\n",
    " min_child_weight=1,\n",
    " gamma=0.2,\n",
    " subsample=0.9,\n",
    " colsample_bytree=0.7,\n",
    " reg_alpha=0,\n",
    " nthread=-1,\n",
    " objective='multi:softprob',\n",
    " scale_pos_weight=1,\n",
    " seed=43)\n",
    "\n",
    "xg.fit(X_train, y_train,eval_metric=pre_scorer)\n",
    "y_pred = xg.predict(X_test)\n",
    "print (\"F1-score: %.4g\" % f1_score(y_test,y_pred, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted F1-Score: 0.7808641975308642\n"
     ]
    }
   ],
   "source": [
    "print('Predicted F1-Score: {}'.format(xg.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#tuned SVM\n",
    "pipe_svm = Pipeline([('scl',StandardScaler()),('clf',svm.SVC(kernel = 'rbf', random_state=0, gamma=0.1, C=100))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The combined majority vote classifier is constructed out of 3-individual classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "mv = VotingClassifier(estimators=[('forest',forest),('XGBoost',xg),('svn',pipe_svm)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-fold cross validation:\n",
      "\n",
      "F1-score: 0.77 (+/- 0.02) [Random Forest]\n",
      "F1-score: 0.78 (+/- 0.02) [XGBoost]\n",
      "F1-score: 0.70 (+/- 0.03) [SVN]\n",
      "F1-score: 0.78 (+/- 0.02) [Majority-Vote]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "clf_labels = [ 'Random Forest' ,'XGBoost','SVN', 'Majority-Vote']\n",
    "\n",
    "print('10-fold cross validation:\\n')\n",
    "for clf, label in zip([forest,xg,pipe_svm,mv], clf_labels):\n",
    "    scores = cross_val_score(estimator=clf,\n",
    "                             X=X_train,\n",
    "                             y=y_train,\n",
    "                             cv=kfold,\n",
    "                             scoring=pre_scorer)\n",
    "    print(\"F1-score: %0.2f (+/- %0.2f) [%s]\"\n",
    "          % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted F1-Score: 0.7870370370370371\n"
     ]
    }
   ],
   "source": [
    "mv.fit(X_train,y_train)\n",
    "print('Predicted F1-Score: {}'.format(mv.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted F1-Score: 0.7870370370370371\n"
     ]
    }
   ],
   "source": [
    "y_pred=mv.predict(X_test)\n",
    "print('Predicted F1-Score: {}'.format(f1_score(y_true=y_test,y_pred=y_pred, average='micro')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some more detailes metrics to evaluate how good our classifier is doing.  A [confusion matrix](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) is a table that can be used to describe the performance of a classification model.  [Scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) allows us to easily create a confusion matrix by supplying the actual and predicted facies labels.\n",
    "\n",
    "The confusion matrix is simply a 2D array.  The entries of confusion matrix `C[i][j]` are equal to the number of observations predicted to have facies `j`, but are known to have facies `i`.  \n",
    "\n",
    "To simplify reading the confusion matrix, a function has been written to display the matrix along with facies labels and various error metrics.  See the file `classification_utilities.py` in this repo for the `display_cm()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predicted_labels=mv.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from classification_utilities import display_cm, display_adj_cm\n",
    "facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS',\n",
    "                 'WS', 'D','PS', 'BS'];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
      "     True\n",
      "       SS    22           1                                        23\n",
      "     CSiS     1    59     3                                        63\n",
      "     FSiS          15    43                                        58\n",
      "     SiSh                 1    17     1     2           1          22\n",
      "       MS                 2     3    14     7     1     7          34\n",
      "       WS                       5          36           9          50\n",
      "        D                                         8                 8\n",
      "       PS                 2     1           6          39          48\n",
      "       BS                                   1                17    18\n",
      "\n",
      "Precision  0.96  0.80  0.83  0.65  0.93  0.69  0.89  0.70  1.00  0.80\n",
      "   Recall  0.96  0.94  0.74  0.77  0.41  0.72  1.00  0.81  0.94  0.79\n",
      "       F1  0.96  0.86  0.78  0.71  0.57  0.71  0.94  0.75  0.97  0.78\n"
     ]
    }
   ],
   "source": [
    "conf = confusion_matrix(y_test, predicted_labels)\n",
    "display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "The entries along the diagonal are the facies that have been correctly classified.  Below we define two functions that will give an overall value for how the algorithm is performing.  The accuracy is defined as the number of correct classifications divided by the total number of classifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def accuracy(conf):\n",
    "    total_correct = 0.\n",
    "    nb_classes = conf.shape[0]\n",
    "    for i in np.arange(0,nb_classes):\n",
    "        total_correct += conf[i][i]\n",
    "    acc = total_correct/sum(sum(conf))\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As noted above, the boundaries between the facies classes are not all sharp, and some of them blend into one another.  The error within these 'adjacent facies' can also be calculated.  We define an array to represent the facies adjacent to each other.  For facies label `i`, `adjacent_facies[i]` is an array of the adjacent facies labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "adjacent_facies = np.array([[1], [0,2], [1], [4], [3,5], [4,6,7], [5,7], [5,6,8], [6,7]])\n",
    "\n",
    "def accuracy_adjacent(conf, adjacent_facies):\n",
    "    nb_classes = conf.shape[0]\n",
    "    total_correct = 0.\n",
    "    for i in np.arange(0,nb_classes):\n",
    "        total_correct += conf[i][i]\n",
    "        for j in adjacent_facies[i]:\n",
    "            total_correct += conf[i][j]\n",
    "    return total_correct / sum(sum(conf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Facies classification accuracy = 0.787037\n",
      "Adjacent facies classification accuracy = 0.925926\n"
     ]
    }
   ],
   "source": [
    "print('Facies classification accuracy = %f' % accuracy(conf))\n",
    "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf, adjacent_facies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
      "     True\n",
      "       SS    22           1                                        23\n",
      "     CSiS          63                                              63\n",
      "     FSiS                58                                        58\n",
      "     SiSh                 1    18           2           1          22\n",
      "       MS                 2          24           1     7          34\n",
      "       WS                       5          45                      50\n",
      "        D                                         8                 8\n",
      "       PS                 2     1                      45          48\n",
      "       BS                                   1                17    18\n",
      "\n",
      "Precision  1.00  1.00  0.91  0.75  1.00  0.94  0.89  0.85  1.00  0.93\n",
      "   Recall  0.96  1.00  1.00  0.82  0.71  0.90  1.00  0.94  0.94  0.93\n",
      "       F1  0.98  1.00  0.95  0.78  0.83  0.92  0.94  0.89  0.97  0.92\n"
     ]
    }
   ],
   "source": [
    "display_adj_cm(conf, facies_labels, adjacent_facies, \n",
    "           display_metrics=True, hide_zeros=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering adjacent facies, the `F1` scores for all facies types are above 0.9, except when classifying `SiSh` or marine siltstone and shale.  The classifier often misclassifies this facies (recall of 0.87), most often as wackestone. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we can train the classifier using the entire data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted F1-Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "mv.fit(X,y)\n",
    "print('Predicted F1-Score: {}'.format(mv.score(X,y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying the classification model to new data\n",
    "\n",
    "Now that we have a trained facies classification model we can use it to identify facies in wells that do not have core data.  In this case, we will apply the classifier to two wells, but we could use it on any number of wells for which we have the same set of well logs for input.\n",
    "\n",
    "This dataset is similar to the training data except it does not have facies labels.  It is loaded into a dataframe called `test_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "well_data = pd.read_csv('../validation_data_nofacies.csv')\n",
    "well_data['Well Name'] = well_data['Well Name'].astype('category')\n",
    "well_data.columns[4:]\n",
    "well_data= add_del(well_data,[fn for fn in well_data.columns[3:] if fn != 'NM_M'])\n",
    "well_data = add_del(well_data,[fn for fn in well_data.columns[3:] if fn != 'NM_M'])\n",
    "well_data.columns[3:]\n",
    "feat_labels =well_data.columns[3:] \n",
    "X_unknown = well_data.drop(['Formation', 'Well Name', 'Depth'], axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'NM_M', 'RELPOS',\n",
       "       'del_GR', 'del_ILD_log10', 'del_DeltaPHI', 'del_PHIND', 'del_PE',\n",
       "       'del_RELPOS', 'del_del_GR', 'del_del_ILD_log10', 'del_del_DeltaPHI',\n",
       "       'del_del_PHIND', 'del_del_PE', 'del_del_RELPOS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_data.columns[3:] \n",
    "\n",
    "#training_data.columns[4:] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we predict facies labels for the unknown data, and store the results in a `Facies` column of the `test_data` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Formation</th>\n",
       "      <th>Well Name</th>\n",
       "      <th>Depth</th>\n",
       "      <th>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "      <th>...</th>\n",
       "      <th>del_PHIND</th>\n",
       "      <th>del_PE</th>\n",
       "      <th>del_RELPOS</th>\n",
       "      <th>del_del_GR</th>\n",
       "      <th>del_del_ILD_log10</th>\n",
       "      <th>del_del_DeltaPHI</th>\n",
       "      <th>del_del_PHIND</th>\n",
       "      <th>del_del_PE</th>\n",
       "      <th>del_del_RELPOS</th>\n",
       "      <th>Facies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2808.0</td>\n",
       "      <td>66.276</td>\n",
       "      <td>0.630</td>\n",
       "      <td>3.300</td>\n",
       "      <td>10.650</td>\n",
       "      <td>3.591</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3000</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>-2.66450</td>\n",
       "      <td>0.01300</td>\n",
       "      <td>-0.15000</td>\n",
       "      <td>0.17500</td>\n",
       "      <td>-0.01350</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2808.5</td>\n",
       "      <td>77.252</td>\n",
       "      <td>0.585</td>\n",
       "      <td>6.500</td>\n",
       "      <td>11.950</td>\n",
       "      <td>3.341</td>\n",
       "      <td>1</td>\n",
       "      <td>0.978</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4750</td>\n",
       "      <td>-0.2635</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>-4.63325</td>\n",
       "      <td>0.02450</td>\n",
       "      <td>-0.85000</td>\n",
       "      <td>-0.32500</td>\n",
       "      <td>0.03400</td>\n",
       "      <td>-2.500000e-04</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2809.0</td>\n",
       "      <td>82.899</td>\n",
       "      <td>0.566</td>\n",
       "      <td>9.400</td>\n",
       "      <td>13.600</td>\n",
       "      <td>3.064</td>\n",
       "      <td>1</td>\n",
       "      <td>0.956</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6500</td>\n",
       "      <td>-0.1820</td>\n",
       "      <td>-0.0225</td>\n",
       "      <td>-5.88775</td>\n",
       "      <td>0.03400</td>\n",
       "      <td>-1.70000</td>\n",
       "      <td>-1.05000</td>\n",
       "      <td>0.12075</td>\n",
       "      <td>-2.500000e-04</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2809.5</td>\n",
       "      <td>80.671</td>\n",
       "      <td>0.593</td>\n",
       "      <td>9.500</td>\n",
       "      <td>13.250</td>\n",
       "      <td>2.977</td>\n",
       "      <td>1</td>\n",
       "      <td>0.933</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6250</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>-0.0225</td>\n",
       "      <td>-2.53375</td>\n",
       "      <td>0.01650</td>\n",
       "      <td>-1.40000</td>\n",
       "      <td>-0.57500</td>\n",
       "      <td>0.11825</td>\n",
       "      <td>2.500000e-04</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>75.971</td>\n",
       "      <td>0.638</td>\n",
       "      <td>8.700</td>\n",
       "      <td>12.350</td>\n",
       "      <td>3.020</td>\n",
       "      <td>1</td>\n",
       "      <td>0.911</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5000</td>\n",
       "      <td>0.0545</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>2.22975</td>\n",
       "      <td>-0.00900</td>\n",
       "      <td>-0.37500</td>\n",
       "      <td>0.33750</td>\n",
       "      <td>0.02900</td>\n",
       "      <td>2.500000e-04</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2810.5</td>\n",
       "      <td>73.955</td>\n",
       "      <td>0.667</td>\n",
       "      <td>6.900</td>\n",
       "      <td>12.250</td>\n",
       "      <td>3.086</td>\n",
       "      <td>1</td>\n",
       "      <td>0.889</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0360</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>4.16375</td>\n",
       "      <td>-0.01850</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.35000</td>\n",
       "      <td>-0.01800</td>\n",
       "      <td>-2.500000e-04</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2811.0</td>\n",
       "      <td>77.962</td>\n",
       "      <td>0.674</td>\n",
       "      <td>6.500</td>\n",
       "      <td>12.450</td>\n",
       "      <td>3.092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.867</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>-0.0225</td>\n",
       "      <td>1.11775</td>\n",
       "      <td>-0.01425</td>\n",
       "      <td>0.60000</td>\n",
       "      <td>0.12500</td>\n",
       "      <td>-0.01075</td>\n",
       "      <td>-2.500000e-04</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2811.5</td>\n",
       "      <td>83.894</td>\n",
       "      <td>0.667</td>\n",
       "      <td>6.300</td>\n",
       "      <td>12.650</td>\n",
       "      <td>3.123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.844</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>-0.0225</td>\n",
       "      <td>-2.66825</td>\n",
       "      <td>-0.00625</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>-0.02500</td>\n",
       "      <td>-0.00825</td>\n",
       "      <td>2.500000e-04</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2812.0</td>\n",
       "      <td>84.424</td>\n",
       "      <td>0.653</td>\n",
       "      <td>6.700</td>\n",
       "      <td>13.050</td>\n",
       "      <td>3.121</td>\n",
       "      <td>1</td>\n",
       "      <td>0.822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>-2.95575</td>\n",
       "      <td>0.00475</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>-0.40000</td>\n",
       "      <td>-0.00075</td>\n",
       "      <td>2.500000e-04</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2812.5</td>\n",
       "      <td>83.160</td>\n",
       "      <td>0.642</td>\n",
       "      <td>7.300</td>\n",
       "      <td>12.950</td>\n",
       "      <td>3.127</td>\n",
       "      <td>1</td>\n",
       "      <td>0.800</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5000</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>-3.35600</td>\n",
       "      <td>0.01500</td>\n",
       "      <td>-0.52500</td>\n",
       "      <td>-0.61250</td>\n",
       "      <td>-0.00875</td>\n",
       "      <td>-2.775558e-17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>C SH</td>\n",
       "      <td>CRAWFORD</td>\n",
       "      <td>3156.0</td>\n",
       "      <td>69.445</td>\n",
       "      <td>0.617</td>\n",
       "      <td>3.390</td>\n",
       "      <td>11.540</td>\n",
       "      <td>3.820</td>\n",
       "      <td>1</td>\n",
       "      <td>0.778</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2100</td>\n",
       "      <td>-0.1455</td>\n",
       "      <td>-0.0280</td>\n",
       "      <td>-0.95725</td>\n",
       "      <td>0.00050</td>\n",
       "      <td>0.23875</td>\n",
       "      <td>0.41125</td>\n",
       "      <td>-0.07000</td>\n",
       "      <td>-2.500000e-04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>C SH</td>\n",
       "      <td>CRAWFORD</td>\n",
       "      <td>3156.5</td>\n",
       "      <td>73.389</td>\n",
       "      <td>0.608</td>\n",
       "      <td>3.625</td>\n",
       "      <td>12.775</td>\n",
       "      <td>3.620</td>\n",
       "      <td>1</td>\n",
       "      <td>0.750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9400</td>\n",
       "      <td>-0.1765</td>\n",
       "      <td>-0.0280</td>\n",
       "      <td>-0.29575</td>\n",
       "      <td>0.00125</td>\n",
       "      <td>0.27250</td>\n",
       "      <td>-0.34250</td>\n",
       "      <td>0.00775</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>C SH</td>\n",
       "      <td>CRAWFORD</td>\n",
       "      <td>3157.0</td>\n",
       "      <td>77.115</td>\n",
       "      <td>0.605</td>\n",
       "      <td>4.140</td>\n",
       "      <td>13.420</td>\n",
       "      <td>3.467</td>\n",
       "      <td>1</td>\n",
       "      <td>0.722</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5250</td>\n",
       "      <td>-0.1300</td>\n",
       "      <td>-0.0280</td>\n",
       "      <td>-0.54225</td>\n",
       "      <td>-0.00400</td>\n",
       "      <td>0.08625</td>\n",
       "      <td>-0.11375</td>\n",
       "      <td>0.02325</td>\n",
       "      <td>2.500000e-04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>C SH</td>\n",
       "      <td>CRAWFORD</td>\n",
       "      <td>3157.5</td>\n",
       "      <td>79.840</td>\n",
       "      <td>0.596</td>\n",
       "      <td>4.875</td>\n",
       "      <td>13.825</td>\n",
       "      <td>3.360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.694</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7125</td>\n",
       "      <td>-0.1300</td>\n",
       "      <td>-0.0275</td>\n",
       "      <td>-0.05325</td>\n",
       "      <td>-0.00750</td>\n",
       "      <td>-0.27125</td>\n",
       "      <td>0.31875</td>\n",
       "      <td>0.01525</td>\n",
       "      <td>2.500000e-04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>C SH</td>\n",
       "      <td>CRAWFORD</td>\n",
       "      <td>3158.0</td>\n",
       "      <td>82.616</td>\n",
       "      <td>0.577</td>\n",
       "      <td>5.235</td>\n",
       "      <td>14.845</td>\n",
       "      <td>3.207</td>\n",
       "      <td>1</td>\n",
       "      <td>0.667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1625</td>\n",
       "      <td>-0.0995</td>\n",
       "      <td>-0.0275</td>\n",
       "      <td>0.18450</td>\n",
       "      <td>-0.00250</td>\n",
       "      <td>-0.19250</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.04275</td>\n",
       "      <td>-2.500000e-04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>C SH</td>\n",
       "      <td>CRAWFORD</td>\n",
       "      <td>3158.5</td>\n",
       "      <td>86.078</td>\n",
       "      <td>0.554</td>\n",
       "      <td>5.040</td>\n",
       "      <td>16.150</td>\n",
       "      <td>3.161</td>\n",
       "      <td>1</td>\n",
       "      <td>0.639</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9525</td>\n",
       "      <td>-0.0445</td>\n",
       "      <td>-0.0280</td>\n",
       "      <td>-0.45650</td>\n",
       "      <td>0.00450</td>\n",
       "      <td>0.28875</td>\n",
       "      <td>-0.42375</td>\n",
       "      <td>0.05150</td>\n",
       "      <td>-2.500000e-04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>C SH</td>\n",
       "      <td>CRAWFORD</td>\n",
       "      <td>3159.0</td>\n",
       "      <td>88.855</td>\n",
       "      <td>0.539</td>\n",
       "      <td>5.560</td>\n",
       "      <td>16.750</td>\n",
       "      <td>3.118</td>\n",
       "      <td>1</td>\n",
       "      <td>0.611</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3150</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>-0.0280</td>\n",
       "      <td>-1.02975</td>\n",
       "      <td>0.00525</td>\n",
       "      <td>0.28750</td>\n",
       "      <td>-0.41500</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>2.500000e-04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>C SH</td>\n",
       "      <td>CRAWFORD</td>\n",
       "      <td>3159.5</td>\n",
       "      <td>90.490</td>\n",
       "      <td>0.530</td>\n",
       "      <td>6.360</td>\n",
       "      <td>16.780</td>\n",
       "      <td>3.168</td>\n",
       "      <td>1</td>\n",
       "      <td>0.583</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>-0.0275</td>\n",
       "      <td>-1.19850</td>\n",
       "      <td>0.00175</td>\n",
       "      <td>-0.04375</td>\n",
       "      <td>0.04625</td>\n",
       "      <td>-0.01250</td>\n",
       "      <td>2.500000e-04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>C SH</td>\n",
       "      <td>CRAWFORD</td>\n",
       "      <td>3160.0</td>\n",
       "      <td>90.975</td>\n",
       "      <td>0.522</td>\n",
       "      <td>7.035</td>\n",
       "      <td>16.995</td>\n",
       "      <td>3.154</td>\n",
       "      <td>1</td>\n",
       "      <td>0.556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4075</td>\n",
       "      <td>-0.0215</td>\n",
       "      <td>-0.0275</td>\n",
       "      <td>-0.96350</td>\n",
       "      <td>-0.00025</td>\n",
       "      <td>-0.13375</td>\n",
       "      <td>0.23875</td>\n",
       "      <td>-0.02350</td>\n",
       "      <td>-2.500000e-04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>C SH</td>\n",
       "      <td>CRAWFORD</td>\n",
       "      <td>3160.5</td>\n",
       "      <td>90.108</td>\n",
       "      <td>0.513</td>\n",
       "      <td>7.505</td>\n",
       "      <td>17.595</td>\n",
       "      <td>3.125</td>\n",
       "      <td>1</td>\n",
       "      <td>0.528</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>-0.0290</td>\n",
       "      <td>-0.0280</td>\n",
       "      <td>-0.67600</td>\n",
       "      <td>-0.00050</td>\n",
       "      <td>-0.10250</td>\n",
       "      <td>0.19250</td>\n",
       "      <td>-0.00750</td>\n",
       "      <td>-5.000000e-04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>830 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Formation Well Name   Depth      GR  ILD_log10  DeltaPHI   PHIND     PE  \\\n",
       "0       A1 SH    STUART  2808.0  66.276      0.630     3.300  10.650  3.591   \n",
       "1       A1 SH    STUART  2808.5  77.252      0.585     6.500  11.950  3.341   \n",
       "2       A1 SH    STUART  2809.0  82.899      0.566     9.400  13.600  3.064   \n",
       "3       A1 SH    STUART  2809.5  80.671      0.593     9.500  13.250  2.977   \n",
       "4       A1 SH    STUART  2810.0  75.971      0.638     8.700  12.350  3.020   \n",
       "5       A1 SH    STUART  2810.5  73.955      0.667     6.900  12.250  3.086   \n",
       "6       A1 SH    STUART  2811.0  77.962      0.674     6.500  12.450  3.092   \n",
       "7       A1 SH    STUART  2811.5  83.894      0.667     6.300  12.650  3.123   \n",
       "8       A1 SH    STUART  2812.0  84.424      0.653     6.700  13.050  3.121   \n",
       "9       A1 SH    STUART  2812.5  83.160      0.642     7.300  12.950  3.127   \n",
       "..        ...       ...     ...     ...        ...       ...     ...    ...   \n",
       "820      C SH  CRAWFORD  3156.0  69.445      0.617     3.390  11.540  3.820   \n",
       "821      C SH  CRAWFORD  3156.5  73.389      0.608     3.625  12.775  3.620   \n",
       "822      C SH  CRAWFORD  3157.0  77.115      0.605     4.140  13.420  3.467   \n",
       "823      C SH  CRAWFORD  3157.5  79.840      0.596     4.875  13.825  3.360   \n",
       "824      C SH  CRAWFORD  3158.0  82.616      0.577     5.235  14.845  3.207   \n",
       "825      C SH  CRAWFORD  3158.5  86.078      0.554     5.040  16.150  3.161   \n",
       "826      C SH  CRAWFORD  3159.0  88.855      0.539     5.560  16.750  3.118   \n",
       "827      C SH  CRAWFORD  3159.5  90.490      0.530     6.360  16.780  3.168   \n",
       "828      C SH  CRAWFORD  3160.0  90.975      0.522     7.035  16.995  3.154   \n",
       "829      C SH  CRAWFORD  3160.5  90.108      0.513     7.505  17.595  3.125   \n",
       "\n",
       "     NM_M  RELPOS   ...    del_PHIND  del_PE  del_RELPOS  del_del_GR  \\\n",
       "0       1   1.000   ...       1.3000 -0.2500     -0.0220    -2.66450   \n",
       "1       1   0.978   ...       1.4750 -0.2635     -0.0220    -4.63325   \n",
       "2       1   0.956   ...       0.6500 -0.1820     -0.0225    -5.88775   \n",
       "3       1   0.933   ...      -0.6250 -0.0220     -0.0225    -2.53375   \n",
       "4       1   0.911   ...      -0.5000  0.0545     -0.0220     2.22975   \n",
       "5       1   0.889   ...       0.0500  0.0360     -0.0220     4.16375   \n",
       "6       1   0.867   ...       0.2000  0.0185     -0.0225     1.11775   \n",
       "7       1   0.844   ...       0.3000  0.0145     -0.0225    -2.66825   \n",
       "8       1   0.822   ...       0.1500  0.0020     -0.0220    -2.95575   \n",
       "9       1   0.800   ...      -0.5000  0.0130     -0.0220    -3.35600   \n",
       "..    ...     ...   ...          ...     ...         ...         ...   \n",
       "820     1   0.778   ...       1.2100 -0.1455     -0.0280    -0.95725   \n",
       "821     1   0.750   ...       0.9400 -0.1765     -0.0280    -0.29575   \n",
       "822     1   0.722   ...       0.5250 -0.1300     -0.0280    -0.54225   \n",
       "823     1   0.694   ...       0.7125 -0.1300     -0.0275    -0.05325   \n",
       "824     1   0.667   ...       1.1625 -0.0995     -0.0275     0.18450   \n",
       "825     1   0.639   ...       0.9525 -0.0445     -0.0280    -0.45650   \n",
       "826     1   0.611   ...       0.3150  0.0035     -0.0280    -1.02975   \n",
       "827     1   0.583   ...       0.1225  0.0180     -0.0275    -1.19850   \n",
       "828     1   0.556   ...       0.4075 -0.0215     -0.0275    -0.96350   \n",
       "829     1   0.528   ...       0.6000 -0.0290     -0.0280    -0.67600   \n",
       "\n",
       "     del_del_ILD_log10  del_del_DeltaPHI  del_del_PHIND  del_del_PE  \\\n",
       "0              0.01300          -0.15000        0.17500    -0.01350   \n",
       "1              0.02450          -0.85000       -0.32500     0.03400   \n",
       "2              0.03400          -1.70000       -1.05000     0.12075   \n",
       "3              0.01650          -1.40000       -0.57500     0.11825   \n",
       "4             -0.00900          -0.37500        0.33750     0.02900   \n",
       "5             -0.01850           0.50000        0.35000    -0.01800   \n",
       "6             -0.01425           0.60000        0.12500    -0.01075   \n",
       "7             -0.00625           0.40000       -0.02500    -0.00825   \n",
       "8              0.00475           0.10000       -0.40000    -0.00075   \n",
       "9              0.01500          -0.52500       -0.61250    -0.00875   \n",
       "..                 ...               ...            ...         ...   \n",
       "820            0.00050           0.23875        0.41125    -0.07000   \n",
       "821            0.00125           0.27250       -0.34250     0.00775   \n",
       "822           -0.00400           0.08625       -0.11375     0.02325   \n",
       "823           -0.00750          -0.27125        0.31875     0.01525   \n",
       "824           -0.00250          -0.19250        0.12000     0.04275   \n",
       "825            0.00450           0.28875       -0.42375     0.05150   \n",
       "826            0.00525           0.28750       -0.41500     0.03125   \n",
       "827            0.00175          -0.04375        0.04625    -0.01250   \n",
       "828           -0.00025          -0.13375        0.23875    -0.02350   \n",
       "829           -0.00050          -0.10250        0.19250    -0.00750   \n",
       "\n",
       "     del_del_RELPOS  Facies  \n",
       "0      0.000000e+00       2  \n",
       "1     -2.500000e-04       3  \n",
       "2     -2.500000e-04       3  \n",
       "3      2.500000e-04       3  \n",
       "4      2.500000e-04       3  \n",
       "5     -2.500000e-04       3  \n",
       "6     -2.500000e-04       3  \n",
       "7      2.500000e-04       3  \n",
       "8      2.500000e-04       3  \n",
       "9     -2.775558e-17       2  \n",
       "..              ...     ...  \n",
       "820   -2.500000e-04       2  \n",
       "821    0.000000e+00       2  \n",
       "822    2.500000e-04       2  \n",
       "823    2.500000e-04       2  \n",
       "824   -2.500000e-04       2  \n",
       "825   -2.500000e-04       2  \n",
       "826    2.500000e-04       2  \n",
       "827    2.500000e-04       2  \n",
       "828   -2.500000e-04       2  \n",
       "829   -5.000000e-04       2  \n",
       "\n",
       "[830 rows x 23 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predict facies of unclassified data\n",
    "y_unknown = mv.predict(X_unknown)\n",
    "well_data['Facies'] = y_unknown\n",
    "well_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[STUART, CRAWFORD]\n",
       "Categories (2, object): [STUART, CRAWFORD]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_data['Well Name'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the well log plot to view the classification results along with the well logs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Wv0S/Gv1oH3r3sp6DBg0yKbLMc6ZYM2PAAKhYUV+IfuKEffdl1RyawR65jIuL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\nFh1641nfbt26xerVq1m6dCnr1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Pq9kZgIr78O7duLHJxjx+DDDzNcu89aPLAWHXojvb49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rVJTk6Wi92epVw5aNECTp6Ee/fUVqNb0tLSePz4sVzgJpGYgAwVskCwTzCvl32dj7d8zM2H\nN9WWI/mbEydEU6haWSupLJFYnDt37nD16lUqVKigthRtsXo1zJkD48eDp6faanTL+++/z+3bt1m0\naJHaUiQS3SID3ywyqekkElMSCZgRwMZzG7O8n7ErcNVAT1oB/PxEY6j27bUziaQ3D7WMlrzMqhZF\nUciTJw/btm3j0qVLFlZlXYwaj+Rk+N//4M03oVUrUctXDR02QtmyZWnVqhUjR47M9gJKW/HNVs5D\noh4y8M0iZdzLcKz3MWoUrcEbC99g4G8D/0mByIxDhw5ZQZ150JNWgG7dRDviLVugRg1Rs1ftK8x6\n81DLaMnLrGpxd3enR48eGAwG5s+fz86dOy2szHpkezxu3xa9wr//Xsz0rlxplnwkLb0u1OC7777j\n5s2bTJgwIVv72YpvtnIeEvVwUFuAnvBw8WB9x/X8uO9HBvw2gOS0ZH5s9iOGTLojTJ061YoKTUNP\nWtNp0wYqV4auXeH116F6ddGwomVLdXJ+9eihVtGSl5lpSUhI4MqVK0RFRXHlyhWuX79OamoquXLl\nItGGupRlezw+/BDOnoVdu8yaj6Sl14UalC5dmipVqnDmzJls7WcrvtnKeUjUQwa+2cRgMPBRrY9w\ncnAiZH0IBfIUYEyDMWrLytGULi0WuG3bBmPHiquq/v7w6afw1lvqlTaT2C43b97k0KFDXLp0iZiY\nGEC0lfXx8cHf3x8fHx88PDywy6krLmfPhqVLRa9wmYRvVuLj4zl8+DCdOnVSW4pEoktk4GskPav3\n5HbibYZvHU4t71o0L9dcbUk5GoMBXntNbLt2iQC4Y0f44gv45BN45x1wdFRbpUTPJCcnc+LECQ4e\nPMjVq1dxdXWlXLlyBAcH4+Pjg5ubW6ZXf3IEaWkwahSMHg0ffAAdOqityOb49ttvURSF5s3lZ45E\nYgwy8DWB/wX9j/Ar4XRd3ZVTfU7h4eKhtiQJEBwsavru3w9ffilygUeOhA0bxII4iSS7HDhwgK1b\nt5KUlETp0qVp37495cqVw15eTvgXRYF334WFC2HcOLGoTWJWTpw4wddff82gQYMoWbKk2nIkEl2S\nQ6/DmQeDwcDsFrOJS4xj8/nNz31My5YtrazKePSkNSu8/LKoonT0KMTFibU1lsbWPFQTLXnZp08f\nkpKS+OCDD+jcuTMVK1bMcUHvC8dj2TIIDYVff4Vhw8RlGDV02Ch79uyhbt26lC1blk8//TTb+9uK\nb7ZyHhL1kIGviXi6elKuYDn2Xdv33Pv7mqF8j7XQk9bsEBAg6v3euWP557JVD9VAS1726dMHgLNn\nz6qsRD0yHY+UFBg0CBo0EHlFaumwUdasWUPDhg3x8/Njx44d5M2bN9vHsBXfbOU8JOohA18z4Ozo\nTHJa8nPva9y4sZXVGI+etGYXRbHYBNRT2LKH1kZtLxVF4cKFC4SGhnLp0iWcnZ0pVKiQqprUJNPx\nUBQoXx7CwuDjj01uS2y0DhsjNTWVTz/9lNatW9OsWTO2bNlCgQIFjDqWrfhmK+chUQ+Z42siiqJw\n7vY5Ovp3VFuKJAMUBWJjwcjPC0kO5MaNG6xatYqYmBiKFClCq1at8Pf3x8FB/st8Lo6O8Pvv8MMP\nYjXppk0wbRrUr6+2Mt0SGxvLO++8w9atWxk/fjxDhgyRiyclEjMgZ3xNxGAwULFwRVafXo2idvcE\nyXNZu1bk+AYHq61EogeioqKYN28eDg4OdOvWjZCQEKpUqSKD3hdhZyfSHQ4ehPz54dVXRT3By5fV\nVqY7Dh48SPXq1Tl8+DBbtmxh6NChMuiVSMyEDHzNwPiG49lzdQ+hx0L/c9/q1atVUGQcetKaVS5c\nEIvL69eHevUs/3y26KFamNtLRVF49OgRcXFxXLlyhTNnznDo0CF27tzJ5s2bWbVqFb/++isLFiyg\nSJEidO3aFV9fXwwGgxxXsjEelSpBeDgsWCBuK1SAhg1F7m///qLW4MyZsGqVqD149qxIwM/ixIEt\nj0VCQgJfffUVQUFBeHp6cvDgQV577TWzHNtWfLOV85Coh5zCMAMNSjag00ud+GD9B/h7+FOlSJV/\n7lu0aBGtW7dWUV3W0ZPWF5GaCj/+KK66enrC5MnWeV5b8lBtXuRleiAbHx9PQkIC8fHxT/38vNvU\n1NT/HMfJyQlnZ2dcXFxwcXEhMDCQunXr4vhE4Wc5rtn0wGCAzp2hVSuR/nDsGNy4AZGRcOuWaGec\n9kzLd0dHKFQIChcGD4+nb5/4edHMmbSuXx/c3KyTuG8FUlJS+Pnnnxk5ciQxMTH07duXr776ijx5\n8pjtOWzlNWyt84g9Gs+Nm/Iqbjqx0QkZ3leosgtepbO+4DL6grrvWxn4momZLWZyMuYkrRe35vxH\n53GwE9YuWbJEZWVZR09aMyMsDIYPh337oF8/UcvX1dU6z20rHmqB53mZlpZGaGgoMTExxMfHk/Zs\n8IQIZNODWGdnZwoUKPDU78/eZqW7mhxXIz3ImxdGjPjv31NTRf5RTIzYbt16+jYmBq5fF7UI0wPl\nv2eEl4BI2Hd0/DcgLl4cfHz+u3l5ab51Y2pqKi+//DL/Z+/O46qo/j+OvwZkE0EUV0DFFFHBNfcl\n9y2VSvPnmrmbppmWWmqllRVaZqllmprlnqX5VXMpt9TUchf3XVzAFRSQdX5/jBIqIMu998zMPc/H\ngwdwGWbe8xmRD/eeOefAgQN07dqVjz76iDJlylj8OGb5N2yW85DEkY2vhSQkJ3Az7iZ+nn44Kvr+\nj9astm/Xfsdu2QI1asC2bXJcr9kkJCRw9uxZgoKCKFmy5CNNrLu7O25ubva7TLCRODr+90xuVjxs\nlNM2xg8/vnYNLl2CrVu191FRjx7H1zf9prhkSa1hFvzMsYODA0lJSdSuXZuFCxfKsbySZGWy8c0l\nVVXZcGYD47eOJ+p+FFt7bZX/cdmYqkK/fjB3LlSpAr/9Bu3bm+ZVUCmNhz9bt2/fxtPTE2dnZ1xc\nXPDy8sLZ2VlwOslqstMoR0VpDfDFi0++7dwJ4eHavMMPeXhoU7G1bAlt2kCdOmDDGxkVRSE0NJS2\nbdsyduxYxo0bR968eW12fEmyN7LxzaG4xDgWHFrA1N1TOXr9KNWKVWNll5X4e/mLjmZ3fvlFa3q/\n+QYGDtRuLpfMydnZmebNm3Px4kWOHz/O33//nfq1AgUKUKRIEQoXLkyRIkUoUqQI3t7ecjYGe5M/\nv/YWHJz+15OTtWeJ0zbEBw5oN9x98on2vS1aaE1w69Y2idymTRvef/99PvvsMxYsWMCkSZPo3Lmz\nfBJFkqxAtgjZkJySzKZzmxi0ehAlvizBwNUDCSgYwJZXt7B3wF4a+zd+4nt69+5t+6A5ZKSsD6kq\nvPmmNpSveXPxTa8Ra6hX6dVSURTq169P165deeONN3j33Xfp168fISEhlC9fnqSkJA4cOMCvv/7K\nzJkz+eSTT1izZo1VstgbvdQg1zkeDn+oWxc6d4aRI7Wllq9dg927tZkntmyBvn217SZOtEjuzCiK\nwoQJEwgLC6N69ep07dqVpk2bcvHiRYsdQy/XL7fMch6SOLLxfYrklGQ2n9vM4DWD8ZniQ7Mfm7H2\n9Fp6V+3NyaEnWdllJY38G2X4l7mRVpkxUta0hg/XXrkMDISQEO3mNlFTKhu1hnqUlVo6Ozvj6+uL\nv78/3t7euLi4pM7c4OTkRNmyZSlbtqxNspidXmpglRyXLsH8+fDll9pLRzdugLs7tG0LDRta/ngZ\nKFu2LCtXrmTDhg2cOXOGypUrW+xmLr1cv9wyy3lI4sjXAB+ToqZwOOIw2y5sY9vFbWw5v4UbsTco\nmb8kr1R+hf8L+j9q+tTM8ktQXbsaZ0U3I2V9SFG0FVJffx0WLdJ+bzVtCv7+2g1u1ar991asmPXz\nGLGGevW0WsbGxnLw4EH27dvHjRs3UBQFX19fatasyTPPPIOfnx+OFrqjX15X/dQgSzlUFe7eTf9m\nuMcfi4zUZpBQFKhZUxsv1bKlNtbX2Rn27bP+ST2mRYsWHDx4kNdee40uXbowfvx4ypUrR0BAwCNv\nvr6+Wb6ZUy/XL7fMch6SOHbf+CYmJ7L/2n6t0b2wjb8u/sWd+3dwdnSmlm8tBlQfQEhgCLV8a8nx\nVjrm6gp9+kDv3vDnn7BmDezfD5Mm/XeTd/HijzbC1apB6dLyJjgjUVWVixcvsnfvXo4ePYqqqlSo\nUIGmTZtSunRpi857KulIZo1sRh8nJDy5H2/vR+cFfuYZ7eNKlaBZMyhY0PbnloECBQqwZMkSunTp\nwubNmzl16hQrV67k/Pnzqa9quLm5UaZMmSca4oCAAIoXLy5/Z+XCkdg3iLkbIDqGbpyLPQUMEh3D\nIuyy8b0cfZm1p9ay9vRa/jj7B/cS7uGWx416JeoxvM5wniv1HLV9a+Pm5CY6qpRNiqKN9W3eXPtc\nVeHcOa0Jfvg2d642lz5o97FUrfpoM1yhgk1v6payYd26dezZswfQxkUGBQVRvHhxkpOTiYyMxNPT\nEw8PD4s90ytZgapCbKw2nODmTe192rfHH3v4eXz8k/vKqJFNZ+ELvL0N94OtKAovvfQSL730Uupj\nCQkJnD9/nlOnTj3ytmzZMi5cuID6YJyXu7s7/v7+lCpVCn9//9S3h58XLlxYNsaSXTLW/wI5lJyS\nzO7Lu1lzcg1rTq3hYMRBHBQH6vrV5d0G79LEvwnP+jyLs6Plp0Pavn07DQwymayRsmaVomi/C595\nBjp2/O/xa9cebYb/9z9tgSkAFxftCaCHjXD16trnWZlhyIw1FCW9WlapUgVXV1eio6O5e/cuERER\nnDx5koTHnt3Lly8fnp6e6b7lz58/282xvK6Z1CA2NmsNbNrH7t9/cj+urtrKbQ/fChfW/gpN+3nh\nwmwPD6dBq1aGbGQtwdnZmXLlylGuXLknvhYfH8/Zs2c5deoUp0+f5vz581y4cIHt27czb9484uLi\nUrd1c3PLtDEuWrSoLhtj2/4s6u/8pdwz9f8a0fHRzPx3JlN3TeXqvat4u3nTJqANo+uPplXZVhR0\ns/7LWpMmTTLML0wjZc2tYsW02YratPnvsagobaGoh83w7t0wb55245yTE/z4I3Tpkvl+7amG1pZe\nLX18fPDx8Xli2/j4eKKjo4mOjiYqKir147t373Lu3Dmio6OJT/OMoaIo9OjRg2eeeSbHWexNag0u\nXYI//tDe/vwTIiKe3NjZWWtUCxXSGtRChbS7Tx82sQ8fS/t53rxZGnc0KSSEBj17WuEMjc/FxYUK\nFSpQoUKFJ74WEhLC/PnzuXDhAufPn3/kbdeuXSxZsoQ7d+6kbu/h4UHDhg1p2rQpTZo0oUqVKrp4\nJUX+LEq5ZcrGNzImkq92fcWMf2YQmxjLq1VepU+1PtTyrYWjg21/cJcsWWLT4+WGkbJaQ/788Nxz\n2ttD9+/DkSPaWOHevSEgAJ59NuN92HsNLSk7tXRxcaFw4cIUfmyBA1VVuXbtGocPH+bgwYPExsbi\n5eVFcHAwvr6+VsliOnfuwObNLClWTGteT57UGtRnn9V+KIKCnmxy3d2tNnjerq9FLixZsiR1Ce+q\nVaumu01UVFRqY3zkyBG2bNnCe++9R1xcHF5eXjRq1Ci1EQ4KChKySqK8/lJuma7x3R2+m0brG6Gq\nKq/VeI3hdYbj65n1X3CWZqQVeIyU1VZcXbXZIT74QFsR7rXX4J9/Mt5e1tByclJLVVW5efMmFy5c\nSH2Ljo4mb968BAUFUalSJfz8/LL9Eq7dXteNG7VlEOPjyVu2rDZ4/pNPoEkTYTeC2e21yKWs1C1/\n/vxUrlyZypUrExISwpgxY4iPj2fPnj1s2rSJzZs3M3LkSBISEujQoQPLly+3+XAIef2l3DJd4zts\n3TCa12/Ogg4LbDKUQTK3uDgIDYXPPtOGR3z0kehEUlqqqnL9+nXOnz/PxYsXuXDhAvfu3UNRFIoX\nL05QUBDPPPMMpUuX1sXLtIZy6RJ07aq9BDJrljZHoGR3XFxcaNiwIQ0bNuSDDz4gLi6On376iYED\nB/Lrr7/SMe3NE5JkAKZrfBuUbMCKzitwyeMiOopkYDEx8P33MHmyNjvSyJEwZoz2Cq6kD+fPn2fD\nhg1cvXoVBwcHfHx8qFKlCv7+/pQoUQIXF/l/QK68/rr2D37RIm34giSh3RQ3YMAAVq9eTe/evTl0\n6BDDhg2joI6mgpOkzJhu5bZBNQbpqukdOXKk6AhZZqSs1nLnjrZCqb+/tjBGs2baGN+JE7PW9Moa\nWk5Gtbx58yZLly5l/vz5ODg40K1bN0aPHk3fvn1p3rw5ZcuWtXjTa3fX9fp1WLsW3nkntenVSw30\nksNoLF23H374gX79+jF58mRKlSrFO++8Q2RkpEWPkR55/aXcMt0zvk4OTqIjPKJkyZKiI2SZkbJa\nWmSkNp3ZjBnadKF9+2rP8mb31V17rqGlpVfLmJgYZs2ahaurKx06dCA4ONgmYwzt7rr+9RckJ2tj\neR/QSw30ksNoLFm3+Ph4zp8/T1BQEO3bt2fZsmWEhoaydu1aDh06ZLHjpEdefym3TNf4/nbiN15s\n+qLoGKmGDh0qOkKWGSmrpVy6BJ9/DrNng6MjDBoEI0bkfHlje6yhtaRXy7CwMJKSkhgwYADuNhx3\nYnfXtWFDyJdPm88vNBTQTw30ksNoclq3u3fvcujQIfbv38++ffvYv38/YWFhJCYmoigKgYGBdO3a\nlWrVqtGyZUsLp36SvP5Sbpmu8V0atpRh14ZRtVj607VIEmgzMoWGwk8/ab/fR4+GoUN1tWKplMbV\nq1f5999/OXz4MAEBATZteu1S4cLw5pvw6afayyFjxmhz+UmmlZSUxMmTJzl8+PAjb+fOnQO0hTOC\ng4N59tln6devH9WqVaNy5crky5dPcHJJyh7TNb7+Xv60XtCaHX12UKZgGdFxJJ05dEibjennn7WV\nTD/9FAYMAA8P0cmkxyUmJnL48GH27t3LlStX8PDwoF69etSqVUt0NPvw/vvaX4KTJ2urt3TuDOPG\nQcWKopNJuRQVFXty7ZIAACAASURBVMWuXbs4dOhQaoN77Nix1EVeihcvTuXKlenYsSOVKlWiSpUq\nVKhQAWdny69uKkm2ZrrGd1qbaQzYO4ARG0bwW5ffRMfh+PHjlC9fXnSMLDFS1py4e1ebc9/PTxvL\n26uXNk+vJZm9hrY0Z84cIiIiCAgIoEuXLgQEBAiZMB/s9Lo6OcHw4dr4n3nzOP7RR5T//XdtpTaB\nDZBdXgsLSFu39u3b89dff+Hu7k5wcDA1a9akT58+VKpUiUqVKuHt7S04bcbk9Zdyy3SzOhRwK0Bd\nv7pEx0eLjgLAqFGjREfIMiNlzYn797Xlh6dO1RaisHTTC+avoS3NmTOHUqVK0a1bNwIDA4U1vWDn\n19XVFQYNYlTx4tq0J4mJQuPY9bXIhbR1i4qKom/fvkRHR7Nr1y5mzZrF0KFDady4sa6bXpDXX8o9\n0zW+APld8rP/6n7WnV4nOgrTp08XHSHLjJQ1u2JjtdXXALy8rHccM9fQlqKiomjbtq1u5uKV1xWm\nv/aa9sGlS2JzyGuRI1OmTOHXX3+lbdu2HDlyBG9vb6F/TOaUvP5SbhnvX30WfNjkQ+qVqEebhW0Y\n++dYklOShWUx0tQrRsqaHXv2QLVq2g3qX3+tLURlLWatoS3dvHmTefPm4eXlRatWrUTHAeR1BSj5\nyivg4wMffyw2h7wW2XL27FlGjhxJ/fr16dixIzdu3ODbb7/lg4fPBBiMvP5SbplujC+Ad15vVndb\nTej2UMZtHkdep7yMfW6s6FiSADdvaotQVKgABw5AYKDoRNLT7Ny5k6ioKAICAlJvtpF0wNUVGjeG\nX36B+fO1+f8kXbt37x516tQhOTmZV155hb59+1KpUiXRsSRJqGw946soymuKohxUFCXqwdtORVFa\nP7bNh4qiXFEUJVZRlI2KopR97OsuiqLMUBTlhqIodxVFWa4oSpHHtimgKMrCB8e4rSjK94qiZGv+\nIgfFgXcbvstbdd9i4l8TOX/nfHa+XTKJadO0efjXrJFNr1G0aNGC5s2bExERwaxZs5g3bx7Hjh1D\nVVXR0ezbr79qyxdPnCibXoOYPXs2t2/fZt++fUydOlU2vZJE9oc6XAJGA9WBZ4FNwG+KolQAUBRl\nNDAEGADUAmKA9YqipL0FeCrQFugIPAf4AL88dpxFQAWg2YNtnwO+y2ZWAN5v9D4eLh5M3yNmXFDo\ng8nfjcBIWbNq/XqoX1+bltQWzFhDW3N1daV+/frEx8fz8ssvo6oqy5YtY+bMmcIaYLu/rvHxhPbs\nCS+9pM30IJDdX4ssCg8PZ+LEifTo0YNSpUqZpm5mOQ9JnGwNdVBVdc1jD41TFGUQUAc4BgwDPlJV\ndTWAoig9gQjgRWCZoiieQB+gi6qqWx9s0xs4pihKLVVV9zxoolsBz6qquv/BNkOBNYqivK2q6rXs\nZM7nnI/6Jepz4NqB7HybxcTGxgo5bk4YKWtWvfKKtjDFsWPacAdrM2MNRYmLiyMoKIigoCAuXbrE\nli1bWLZsGcWLF6dZs2aUKWO7ebrt/rouXUpsTIw28bUNlojOjN1fiyxISkqie/fuuLm58fnnnwPm\nqZtZzkMSJ8c3tymK4qAoShcgL7BTUZTSQDHgz4fbqKoaDewG6j54qAZas512mxPAxTTb1AFuP2x6\nH/gDUIHaOckaUDCAs7fP5uRbc23ChAlCjpsTRsqaVX37avP2DhsGtnii0Iw1FCVtLUuUKMErr7zC\nq6++SlxcHMuXLxeWxS7t3MmEIkXA3190EnktsuD06dNs27aN6tWr4/FgdR6z1M0s5yGJk+3GV1GU\nYEVR7gLxwDfASw+a12JozWnEY98S8eBrAEWBhAcNcUbbFAMi035RVdVk4FaabbIlMSUR1zxWmLRV\n0j0XF5g1CzZu1N5LxlawYEFiYmKoXr266Cj2ZcgQ7U7RL74QnUTKgvLlyzN//nzWrVtHixYtuHYt\nWy+USpKp5eQZ3+NAFbQxvN8CPyqKoptlVJ5//nlCQkJS39q0a8M3A7/B+dSjKw1t2LCBkJCQJ77/\n9ddfZ86cOY88tm/fPkJCQrhx48Yjj3/wwQdPjDe6ePEiISEhHD9+/JHHp02bxsiRIx95LDY2lpCQ\nELZv3/7I44sXL6Z3795PZOvcuTMrV660yXlMmzaNYsWK0bRp09RaDrfg2L7Hr1NISAh169a1yvnt\n3PkBZcqEsmDBf4/J65R1trxWGf1MtWnThtmzZzN9+nScnJxo0KCBoa7V4sWLadGixSPXylDXqVgx\nGDFCW7L4nXf4YNw4U/7fZ/jrlOb8evbsSffu3dm7dy9ly5Zl3Lhx3LlzxxTX6WFea//fJ5mTktsb\nRRRF2QicBiYBZ4CqqqoeSvP1LcB+VVWHK4rSBG3YQoG0z/oqinIe+FJV1a8ejPn9XFVV7zRfdwTu\nAy+rqpruOsSKolQH9u7duzf12aDklGTaLmrLnst72N1vNwHeAbk615y4ceMGhQoVsvlxc+JpWfft\n28ezzz4L2vjrfTk5RnrXydru34dixbThDtZ+lUwP19sS1wnEXKu0bty4QZ48edi6dSuHDh3C2dmZ\nWrVqUatWLdzdszXJi0WyWPq6Gu063YiMpNAPP8DYsVC9OixdKmTog61/xox2nR5369YtJk+ezNSp\nU3Fzc2P06NEMGzYMV2ssXWkDmV1/S/6O+vjlbylduFzOg5rMuesnGbd8EKSp7cNabf+iBtXKeGR5\nX/vP3KXBW/8+si9bssQCFg6Ai6qq54BraDMxAPDgZrbawM4HD+0Fkh7bJhAoCfz94KG/AS9FUaql\nOUYzQEEbL5xl606vY/2Z9SzuuFhI0wvQp08fIcfNCSNlzarISGjdGmJioFs36x/PjDUUpWfPnvzw\nww+cPn2a5s2bM3z4cJo0aWLzphfkdQXo068fjBoF27fD9etQpw7ss/nvLHktsqlgwYJ8+umnNGzY\nkG7dujFu3DiqVKnCli1bREfLEXn9pdzK7jy+nyiK0lBRlFIPxvp+CjQCHr6IPBVtpof2iqJUAn4E\nwoHfIPVmtznAFEVRGiuK8iwwF9ihquqeB9scB9YDsxVFqakoSn1gGrA4uzM6rDy+koCCAbQs0zI7\n32ZR48ePF3bs7DJS1qzYtw9q1NBmdNi0yTbz+JqthqIkJCRQtWpVUlJS6N+/P3Xr1sXZ2fnp32gl\n8rqmqUHt2rB7N5QsCY0awR9/iMkhZctnn33G9OnTOXjwIIULF6ZJkyb06dPHcLMkyOsv5VZ2n/Et\nAsxHG+f7B9pcvi1VVd0EoKrqJLQm9Tu0Z2fdgDaqqiak2cdwYDWwHNgCXEGb0zetbmmOsRrYBgzM\nZlZ2XNpBi2daoAicfsdIN+EYKevTrFunLU1crBjs3QsNG9rmuGaqoUhhYWG4uLjQvXt3PD09RceR\n15XHalC4sPbXZIMG0L49bNsmJoeUZQ/rVrFiRbZt28bQoUOZN28e//zzj+Bk2SOvv5Rb2Z3Ht18W\nthkPjM/k6/HA0AdvGW1zB+iRnWzpuRl3k2L5cjQRhGRgCxdCr17aEIelSyFvXtGJpOyKiIigYMGC\nFC1aVHQUKSP58sGKFdCunfa2bRtUrSo6lZQFSUlJrF+/nkaNGvHcc8+JjiNJNmWJMb66FZMQQz7n\nfKJjSDZ08CD07g09emi/k2XTa0znz5/Hx8dHdAzpaVxdtaWM4+O1HzjJEPbu3cvJkyeZOHGi0FdE\nJSk9iqLUVRSl3WOP9VQU5ZyiKJGKosxSFMUlp/s3deProDiQoqYIzfD4dC16ZqSs6UlM1J7pLV8e\nZs6EPNl6PcMyjF5DPYiMjCQiIoJjx46JjpJKXtdManDgACQkQKtWYnNImUpbt1OnTgFQsmRJUXFy\nTF5/u/A+EPTwkwf3jM1BG/76GdAeeDenOzd14+vl6kVEzOPradjWPgF3PeeUkbKm57fftN/Bc+Zo\nC1eIYPQa6sHhw4dxdXXlypUroqOkktc1kxr88QcUKaLN8iAyh5Spv/76i88//5zKlSvz6quvUqJE\nCQoXLiw6VrbJ628XqpJmhV+gC7BbVdX+qqpOAd4A/i+nOzd141vLtxa7wncJzTBjxgyhx88OI2VN\nz/z52g3nNWuKy2D0GoqmqiqHDx+mYsWKfPPNN6LjpJLXNZMaHD4MVaqAg21+nchrkT07d+6kdevW\n/PTTT4wbN44KFSqwevVqzpw5Y8i5fOX1twsFeHQV4EbA72k+/wcokdOdm7rxrVqsKsdvHH/6hpIp\n7NkDbdqITiHlRkpKCjExMXh5eYmOImXF9evaeuD16olOImUgNDSUsLAwZs6cybVr11i6dClt27bF\nyclJdDRJykgEUBpAURRnoDqQ9llMDyAxpzs3deNb0K0gt+/fJrer00nGIf8vNzZHR0fKlStHWFiY\n6ChSVoSGas/0Ds1wkh5JsJiYGOrXr0///v3lH5SSUawFPlMUpSHwKRAL/JXm65XRVgrOEVM3vn6e\nfiSlJHH57mXRUSQbKF4c9u8XnULKrQoVKhAREWG4ifXtjqrCTz9B//7g7f307SUhHk4NKEkG8h7a\nKr9bgf5A/8fWg+gDbMjpzk3d+NbwqQHAnst7hGUICQkRduzsMlLW9PTtCytXwtmz4jIYvYZ68PCG\nGz3VUk9ZRHmiBkeP/rcmuMgcUoaSkpI4ceIEFStWNE3dzHIeUsZUVb2hqupzaGN9C6iq+vhciZ2A\nCTndv6kbXx8PH0rlL8XW81uFZRgyZIiwY2eXkbKm55VXwNcX6ta16UJSjzB6DUWLiopi1apV5MmT\nh4EDs71Yo9XI65pODRYuBE9PqF9fbA4pQykpKTg7O3Pnzh3T1M0s5yE9naqqUaqqJqfz+K3HngHO\nFlM3vgCty7Zm7em1wsb5tmzZUshxc8JIWdPj5QX//AMVK0KzZjBxIty9a9sMRq+hSJGRkcyePZuY\nmBj69OlDp06dREdKJa/rYzW4fRtmzYI+fWy+Soy8Flnn7OxMu3btWLRokWnqZpbzkNKnKMqviqJ4\npvk4w7ecHsP0jW+nip04fes0q0+uFh1FsoHChWHDBnjzTZgwAUqWhHHjtJvPJX3bt28fDg4O9O/f\nn+LFi4uOI2Xk0iVo0ED7eNgwsVmkDKmqyjfffMOKFSsoUKCA6DiSlFVRgJrm48zecsT0jW/T0k1p\nWropo/8YTUxCjOg4kg04OcHkydpY3z59YOpUKFVKu/H8wgXR6aSMREZGkj9/flxErT4iZS45WRtE\nX68exMTAjh3g7y86lZSOhIQEunTpwuuvv86AAQPYtGmT6EiSlCWqqvZWVfVumo8zfMvpMUzf+CqK\nwpSWU7gUfYnWC1sTHR9t0+OvXLnSpsfLDSNlzQo/P/jiC63ZfecdWLwYypSBnj3BWrNlma2GtuTr\n68vly5eZOnUq27dvZ9myZaIjpbLr6xoTAzNmsNLPD156CcqWhZ07ITBQSBy7vhZZoKoq/fv3Z8WK\nFfz8889MmzYNFxcX09TNLOchZZ+iKI0URXleUZRcvYRh+sYXoEqxKmx8ZSOHIw7T/MfmNm1+Fy9e\nbLNj5ZaRsmaHtze8/77WAH/xBWzeDMHB0KGDNlTRksxaQ1to1qwZQ4YMITAwkC1btvDxxx+zceNG\noqJy/IqWxdjddVVVOHgQRo3Sxgu98QaLnZxg927tB8jHR1g0u7sW2XDz5k0GDx7Mjz/+yA8//MDL\nL7+c+jWz1M0s5yFlTFGU0YqifJTmc0VRlHXAZmA1cExRlKCc7j+PBTIaQh2/Omx6dRNN5zel47KO\nrOm2BmdHZ6sfd+nSpVY/hqUYKWtOuLtrQxIHDYJFi+Ctt6BFC21MsKWmuTR7Da2tYMGCtG/fnsaN\nG1O7dm327t3L33//TYUKFahduzYlSpRAURSb57Kb63runPbSyMKF2nRlhQrBq6/CG2+wVCfDGuzm\nWmTDrVu3mDJlCl9//TXJyclMnz6dbt26PbKNWepmq/P46vp2nBNP2eRYRpBwJ+LpG1lOZyA0zecv\nA88BDYFjwI/AB8D/5WTndvGM70PVi1dnZZeVbLuwjUGrB4mOIwni7Ay9esGmTXD+vNb8JiWJTiWl\n5eHhQYsWLRgxYgRt2rQhIiKCefPm8f333xMTI8fqW9zmzdoNa888o02HUrUqrFkDV67AlClyLK9O\nJSUl8dlnn+Hv78+XX37JoEGDOH/+PK+//rroaJKUG6WBQ2k+fx5YrqrqDlVVbwEfA3VzunO7anwB\nGvs35uvWXzP3wFwOXDsgOo4kUJUq0Ly5Nge/gCcRpSxwdnamZs2avP7663Tr1o0rV65w8uRJ0bHM\nIyICevSApk0hJQUWLNAeW7gQnn9ergGuY8eOHaNevXqMHTuWvn37cu7cOUJDQ1MXgJEkA8sDxKf5\nvC6wM83nV4BCOd253TW+AH2r96VMgTJM/Gui6CiSQJs2wbJlMHYsODqKTiNlRlEUAgICKFiwIAcO\nHODcuXOkpKSIjmVsf/6p3aC2bh3MnQvbt0P37pAvn+hkUiaio6OZMGEC1apVIzo6mp07d/Lll19S\npEgR0dEkyVLOoA1tQFGUkkA5IO2yVH7AzZzu3C4b3zwOeRjw7ADWnFxDQnKOF//Ikt69czzjhs0Z\nKWtupKTAZ59pQxyaNNGmPLMUe6mhLaRXy0aNGhEVFcWPP/7IlClTWL16tU2aYNNd19hYbY3vqlXh\nxAno3RscMv91oJca6CWHrcXExDBp0iRKly7Np59+yrBhw9i/fz+1a9fO0vebpW5mOQ8pUzOA6Yqi\nzAF+B/5WVfVomq83BfbndOd2c3Pb4xqVakRcUhz7r+6ntl/W/uPICSOtMmOkrDl1/z506QK//aY9\n0zt+POSx4E+BPdTQVtKrZeXKlalUqRKXL1/m6NGjHD16lL1795I3b15CQkIItNIUW6a7rtOmadOc\nLF+uTXuSBXqpgV5y2Mr9+/eZNWsWn3zyCbdu3aJ///6MGTMGX1/fbO3HLHUzy3lIGVNVdbaiKMlA\ne7Rneic8tokPMDen+7fbxvd6rLaUV37X/FY9TteuXa26f0syUtaciI+Hl1/WXuH93/+gXTvLH8Ps\nNbSljGqpKAp+fn74+fnRokULrly5wvr169myZYvVGl/TXdegIPDwgJAQ+PZbeOGFp36LXmqglxzW\nlpCQwLx58/j444+5cuUKr776Ku+//z7+ObzR0Cx1M8t5SJlTVXUuGTS3qqoOzs2+7XKoA8DCwwsJ\n9A6kfKHyoqNINjJkCGzcqD3ba42mV7I9RVHw9fWlTp06XLt2jVu3bomOZAzt2mnTlVWvDi++qI3t\nvXdPdCrpgevXrxMUFMSgQYNo2LAhx44dY+7cuTlueiXJSBRF8VEU5XNFUTzT+Vp+RVEmK4qSvZc8\n0rDLxnf50eUsObKEEXVHiI4i2dCOHTBgAMhXyswnPDwcZ2dn3NzcREcxDj8/7aWPBQtg1SqoXRuO\nHxedSgJ++uknLl26xIEDB1i0aBHlypUTHUmSbGkE4Kmq6hOrjamqGgV4AO/mdOd21/ieuHGCvqv6\n8n9B/0f/6v2tfrzt27db/RiWYqSsOXH/vmXH86bH7DW0pazWMiEhgT179lCrVi2rNb6mva6Koj3b\n+88/2mptNWtmuJ63XmqglxzW9PPPP9O6dWsqV65ssX2apW5mOQ8pU63RFqnIyI9Ak5zu3K4a3xux\nN2i7qC1+nn7Mbj/bJitATZo0yerHsBQjZc2J5s3hhx+0eXutxew1tKWs1tLR0REvLy/OnDlDcnKy\n0CyGVb68thxxvnwwZ066m+ilBnrJYU2lS5fmwIEDJCYmWmyfZqmbWc5DylRp4GImXw8H/HO6c7tp\nfFVVpfPyztxNuMuabmvwdHli6IhVLFmyxCbHsQQjZc2JTz7RZmx6803tyS1rMHsNbSmrtXR0dKRD\nhw5cvXqVXbt2Cc1iaB4e0KkTLFkC6ayOp5ca6CWHNY0ZM4YLFy7w008/WWyfZqmbrc4j6sgWbu5e\n+chbbLh9DAWKDT/+xLlHHdliywhxZN7Y+j/YJkfsZlaH30//zqZzm1jbbS3+Xv42O27evHltdqzc\nMlLWnChUCL7+WluoqmVLbdliSzN7DW0pO7UMDw8HoGjRosKzGNrQofD99zB6NEyf/siX9FIDveSw\npuDgYDp06MAnn3xCz549yWOBMVpmqZutziN/cGOcvazz/4ne5fUrT16/R2/8T7gTwfWtC20VYTfw\nCo8uWpFWT2BPTnduN8/4jt8ynoYlG9K6bGvRUSSBunfXFqx4/XXtpnbJ+K5fv87GjRupWbMmZcuW\nFR3H2AICYNIkmDEjwyEPkm289957nDlzhp9//ll0FEmytc+B3g9mdkj960NRlKKKonwB9HqwTY7Y\nReN7KOIQ/1z5h7fqvmWTcb2Svn39Nfj7Q+fO2gJWknElJSXxyy+/4OXlRYsWLUTHMYfBg2HgQOjX\nD95+G6w0blrKXNWqValXrx5Lly4VHUWSbEpV1c3A68AQ4IqiKLcVRbkFXHnw+FBVVTfldP920fgu\nPryYQnkL0Sagjc2PPXLkSJsfM6eMlDU33N1h2TI4c0ab3sySq93aSw1t4Wm1TElJYfXq1dy4cYOO\nHTvi5OQkLIupODhoi1p89RV8+aU2ATb6qYFecthCs2bN2LBhA6oFbkowS93Mch5S5lRV/Q4oA7wN\nLAKWAG8BZVVV/TY3+7aLMb6nbp2iWrFqODs62/zYJUuWtPkxc8pIWXMrKAjmzYOuXaF4cZg82TL7\ntacaWltmtUxOTmbFihUcPXqUF198kWLFignLYkqKAm+8od0FOmIEDBummxroJYe13b59m++//56m\nTZta5JVKs9TNVueRP6gRbr5y/uSH4i6ftOUYXwBUVb0MfGnp/dpF43s99jrF8xUXcuyhQ4cKOW5O\nGCmrJXTurE1t9sYb2pNcn36qvc8Ne6uhNWVUy/DwcNatW8e1a9fo1KkTFSpUEJbF9F57Db74AiZP\nZqhOxvzay7WYPHky169fZ+bMmRbZn1nqZpbzkMSxi6EOFQtVZO/VvaJjSDo0dChMmaI949upU7qz\nOEk6ERUVxa+//sqcOXNISkqiZ8+eNml67ZqLC7Rvr83xK9lUo0aNSEpKYsuWLaKj2CdFQZFvqW+Y\n6P4ou2h82wS04fSt05y6eUp0FEmHhg+HlSth/Xpo0ADOnhWdSHrcwYMHmT59OufOnSMkJIQBAwaY\n5qVb3fP3h1OnrDf5tZSuVq1a0aVLF4YPH87NmzdFx5Ek07CLxrdp6aY4Ozrz++nfbX7s48eNM+G1\nkbJaWkgI7NgB0dFQowasXZuz/dhzDS3t+PHjpKSksH79elauXElwcDBDhgyhWrVqOOR2TEoOstit\nFSugQQOOnzghOglgX9fiyy+/JDExkdGjR+d6X2apm1nOQxLHLhrffM75eK7Uc6w9lcNuJhdGjRpl\n82PmlJGyWkOVKvDvv1CvHrRrBx98kP2ZnOy9hpb09ttvs2TJEnbv3k2bNm0ICQnBxcVFSBa7va4H\nDsDff8PQobqpgV5y2EKxYsUIDQ1lzpw5bNuW0Vz+WWOWupnlPCRx7KLxBWhTtg1bzm8hNtG2E7dO\nf2z1Iz0zUlZrKVAAVq2CDz+Ejz7SGuBbt7L+/bKGlhEfH0+TJk04f/483bt3p1atWkLn4Lbb67p4\nsbbkYdu2uqmBXnLYSv/+/albty4DBw4kPj4+x/sxS93Mch6SOHbT+D4f8DzxyfH8efZPmx7XSOMQ\njZTVmhwcYNw4+P132LMHGjbUhkBkhayhZfzxxx/ExsbyyiuvUKZMGdFx7PO6JiXBokXw8svg5KSb\nGuglh604ODjw3XffcfLkSRYsWJDj/ZilbmY5D0kcu5jODCDQO5Dyhcrzy7FfaB/YXnQcyQBatdLG\n/dauDT16aDfA2Xhoqd0qU6YM//77L4mJiaKj2K9VqyA8HPr3F53E7lWqVIlSpUpx6pS8QdtWoo5s\nIS78mOgYupFwJ0J0BIuxm1/jiqLwYuCLQm5wk4yrfHntSa/Vq0G+wmY7gYGB+Pn5sWrVKq5duyY6\njn2aPVsb8F69uugkEuDn5ycbX0myALtpfAGK5itKTIJtJ2oNDQ216fFyw0hZbaltW+jXDyZMgNu3\nM99W1tAyFEUhPDwcNzc35s6dS1hYmNA8dndd4+Nh61Z46aXUh/RSA73ksLXWrVuzbt06YnI42bhZ\n6maW85DEsavG19nRmfjkeOKTcn6DQHbFxtr2ZrrcMFJWW/vwQ60XmDgx8+1kDS0nJSWFPn36EBgY\nyPLly4VOY2R313XHDoiLg6ZNUx/SSw30ksPWOnfuTGxsLL//nrNXLc1SN7OchySOXTW+DUo2ICkl\nia0XttrsmBMmTLDZsXLLSFltrVgxGD0apk3LfIELWUPLmTBhAk5OTnTo0IHAwEBWrVrF3bt3hWWx\nK8uWQalSUK1a6kN6qYFecthamTJlCAoKYvXq1Tn6frPUzSznIYljV41vpSKV8PfyZ+mRpaKjSAY0\nYgQULgx9+kBCgug09kNRFEJCQlAUhb/++kt0HPNLSYFff4X/+z9TLVNqBm3btuX3338nJSVFdBRJ\nMiy7anwVRaF/9f4sOrKIG7E3RMeRDMbdHZYs0ebzHzRIruBqS3nz5qVMmTLyRjdbOHIErl+H1q1F\nJ5Ee07ZtWyIjI/n3339FR5Ekw7Krxhegf/X+xCfFs+rEKpsc78YN4zTYRsoqSoMG2s3uc+fC558/\n+XVZQ8t5vJbFixfnypUrhIeHC89iWvfuwdtvQ758ULfuI1/SSw30kkOEh4u4HD16NNvfa5a6meU8\nJHHsrvEt7F6Y/K75uRl70ybH69Onj02OYwlGyipSz54wdqw25nflyke/JmtoOY/XskaNGvj4+LBo\n0SJu3rTNz29GWUwpMhKaNIFdu7R/2G5uj3xZLzXQSw5bCwsL44UXXqBhw4Z07tw5299vlrqZ5Twk\nceyu8QXIjVpeJwAAIABJREFU75KfW3HZWIc2F8aPH2+T41iCkbKK9uGH2kxPvXtrT5I9JGtoOY/X\n0snJia5du5I3b15+/fVXVBuONbGL6zp4MFy8CNu2QbNmT3xZLzXQSw5b+/zzz7l9+zahoaG4PfZH\nSVaYpW5mOQ9JHLtsfEvmL8mFqAs2OVZ1A03+bqSsojk4wJdfwt278OOP/z0ua2g56dXSzc2Ndu3a\nceXKFY4ds92qSqa/rhcuwIoV2mTVVaumu4leaqCXHLY2ceJEKlWqRLt27di1a1e2v98sdTPLeUji\n2GXjW7ZgWY7dkEsRSrlTsiS0aQM//yw6iX0pVaoUHh4enDx5UnQU81i3Trtbs0cP0UmkDPj4+LBt\n2zbKlCnDoEGDRMeRJMOyy8a3QckGHIo4RNT9KNFRJIPz8Xl0qINkfUePHuXu3btUSzPHrJRLHh5y\nmhID8PLy4sUXX+TSpUuio0iSYdll4xtUOIgUNYVTt6y/7vmcOXOsfgxLMVJWvXB0hMTE/z6XNbSc\n9GoZFxfHhg0bCAgIoFSpUkKzmMrDf8TxGa9qqZca6CWHKKVLl+bmzZvZfsXDLHUzy3lI4thl4xt2\nPQwHxYHyhcpb/Vj79u2z+jEsxUhZ9SIqCry8/vtc1tBy0qvlmjVrSEhIoG3btsKzmMovv0CdOuDt\nneEmeqmBXnKI8tJLL+Hj48PHH3+cre8zS93Mch6SOHbZ+P5z+R8qFKpAPud8Vj/WjBkzrH4MSzFS\nVr2IidGmPH1I1tByHq9lYmIix48fx9fXF09PT6FZTOXqVW2Mb9eumW6mlxroJYcorq6ujB07lgUL\nFrBjx44sf59Z6maW85DEscvG99+r/1LDp4boGJIJuLhk+uqwZEFOTk689NJLnDlzhj///FN0HPP4\n7jtwctImqJYMYeDAgdSpU4devXoRExMjOo4kGYpdNr6nbp6iYuGKomNIJuDoCElJolPYj6CgIEqV\nKsX+/fttOo+vqf3zD9So8eiYHUnXHB0d+eGHH7hy5Qo9evQgOTlZdCRJMgy7bHxVVBwVR9ExJBOI\njQV3d9Ep7MehQ4e4cOECrVu3Tl2+Vcql1q1h5064ZZtFfSTLKFeuHEuXLmXVqlW8+eab8g9BScoi\nu2x8XfO4EpsYa5NjhYSE2OQ4lmCkrHpx796jja+soeU8Xsvbt2+zdu1aKleuTKVKlYRmMZWXXtJe\nttiyJdPN9FIDveTQg3bt2vHNN98wffp0vvjii0y3NUvdzHIekjh22fiW8CzBxaiLNjnWkCFDbHIc\nSzBSVr14/OY2WUPLSVtLVVVZsWIFbm5utGnTRmgW0/H1hUKF4MiRTDfTSw30kkMvBg4cyJgxYxg5\nciQ/Z7KajlnqZpbzkMSxy8a3StEqrD+znvgk69+V1LJlS6sfw1KMlFUv7t17tPGVNbSctLWMiYnh\n0qVLNGrUCFdXV6FZTCc+HlJSIC4u0830UgO95NCTjz/+mIYNGzJr1qwMtzFL3cxyHpI4dtn4jqw/\nkst3LzN732zRUSSDi4mRY3xtwd3dHRcXF+7JZfIsb8kSbXxv796ik0g5pCgKbm5u5M+fX3QUSdI9\nu2x8yxcqT0hgCEuOLBEdRTK45GRwsMufIttSFIUiRYoQGRkpOor5bNyoLV5RrpzoJFIuyJkdJClr\n7PZXdk2fmoRdD7P6nbArV6606v4tyUhZ9cLLC+7c+e9zWUPLebyWRYoUISIiQhdZTCUyEkqUeOpm\neqmBXnLozTPPPJPpMsZmqZtZzkMSx24b39Jepblz/w4xidad/Hvx4sVW3b8lGSmrXhQs+OgsULKG\nlvN4LV1dXbl//74usphKZCQUKfLUzfRSA73k0AtVVfnmm2/48ccfKVmyZIbbmaVuZjkPSRy7bXzz\nOOQBIDnFui8PLV261Kr7tyQjZdWLAgXg9u3/Ppc1tJzHaxkZGUmRLDRotshiKllsfPVSA73k0IOk\npCQ6derE66+/Tv/+/Vm+fHmG25qlbmY5D0kcu218fTx8ADh6/ajgJJKROTlBYqLoFPbB1dWVq1ev\nyiVaLSk2FiIiwM9PdBIpB2JiYli7di3du3dn2rRpQmY8kSSjsdvGt45fHYq6F2X50Yz/Qpakp7l9\nW3vWV7K+li1boqoq//vf/0RHMY9jx0BVIThYdBIpB/Lnz8+IESP45ZdfuHr1qug4kmQIeUQHEMXR\nwZEi7kW4dV8u0ynlXHg4BAaKTmEf8uXLR9OmTVm9ejVxcXG4ubmJjmR8u3dDnjwQFCQ6iZRDgYGB\n3L9/nzt37lC8eHHRcSR75JOC6p+S9e3jsrGtFdjtM77h0eEcjjxM6zKtrXqc3gaaG9NIWfUgKQlO\nn3608ZU1tJz0aun34CV5W09rZtrrumkT1K6dpcmo9VIDveTQi08//ZS2bdtSoUKFTLczS93Mch6S\nOHbb+CYmawMzHR0crXocI60yY6SsenD+vDa+N23jK2toOenVslChQnh4eLB3717hWUwhLg7y5s3S\npnqpgV5y6MnVq1efOuOJWepmlvOQxLHbxrd0gdI8W/xZFh+x7tQoXbt2ter+LclIWfXgxAntfdrG\nV9bQctKrpaOjI40aNeLw4cOEhVl/Hu7MsphC48awfTskJDx1U73UQC859GLhwoWEhYXxzjvvZLqd\nWepmlvOQxLHbxhega3BX1pxcQ9T9KNFRJAOKjtbee3uLzWFvqlatSkBAAMuXL2f27NmcOnXKZg2w\n6Tg7a8sPpogdcyflXLVq1ejUqRM7d+4UHUWSDMGuG9/OwZ2JT45n5XG5EoyUfQ/vrYqLE5vD3jg6\nOtKtWzdeffVVnJycWLRoEfPmzePevXuioxnP5s1Qty7IabAMLSIiAi8vL9ExJMkQ7Lrx9fP049ni\nz7L5/GarHWP79u1W27elGSmrHjx8pjftfVayhpbztFr6+/vTq1cvunfvzq1bt/jtt9+s9syvKa/r\nvXuwYQO0ztoNvnqpgV5y6EmTJk3YuHEjw4cPJzk5/UWZzFI3s5yHJI5dN74AtX1rs+fyHqvtf9Kk\nSVbbt6UZKasePPOM9v7Mmf8ekzW0nKzUUlEUypYtS+PGjTl9+jQHDhwQlsVw1q7VXq5o1y5Lm+ul\nBnrJoSfvvvsu06ZN4+uvv6Zt27ZcvHjxiW3MUjeznIckjt03voXyFiI6Ptpq+1+yZInV9m1pRsqq\nB56e2vu0C4nJGlpOVmp57do1fvnlF9auXYu7uzseHh7CshiOvz94eECPHpBOo/Q4vdRALzn0ZsiQ\nIaxdu5ZDhw5RoUIFQkNDSUhz06JZ6maW85DEsfvG91bcLQq6FbTa/vNmcaogPTBSVj2IjdXep11H\nQdbQcjKrpaqq/PLLL3z33XeEh4fTpk0bhg0bRtmyZW2exbBq1YKdOyEqCmrWhOvXM91cLzXQSw49\natWqFcePH2fgwIGMHTuW6tWrc+uWtkiTWepmlvOQxLH7xrdQ3kJcvnuZFFXe1Sxlz8OhZhUris1h\nj65cucKRI0do1aoVQ4cOpWbNmjg5OYmOZTzBwTB3rjZQPTxcdBrJAjw9PZkyZQr//vsvp06dYt68\neaIjSZKu2H3jW79kfW7F3eLo9aOio0gGs2wZVK0KAQGik9ifsLAw3N3dqVWrFg4Odv/fWO4cOwYO\nDlC+vOgkkgVVrVqVDh06MHPmTGIfvjwlSZJsfOuXqI+HswfLjy63yv5Hjhxplf1ag5GyihYTA6tX\nQ+fOjz4ua2g5mdUyKSkJNzc3mzW9pr2usbEQGgrPP//omJ106KUGeslhBGPGjOHKlSv06NGDt956\nS3Qci5DXX8otu2983Zzc6FSxE/MPzic5Jf1pYHKjZMmSFt+ntRgpq2jr1mk9Q6dOjz4ua2g5mdWy\nSJEi3Lx5k6SkJOFZDO2zzyAiAqZOfeqmeqmBXnIYQaVKlVi0aBErVqzg1KlTouNYhLz+Um7ZfeML\nMLDGQM7fOW+VZ32HDh1q8X1ai5Gyinblijbnf5kyjz4ua2g5mdUyMjKSfPny4ejoKDyLYV2/DlOm\nwLBhT/5DTodeaqCXHEaQmJjIokWLcHR0pH///qLjWIS8/lJuycYXqOVbixbPtOD9Le9z9e5V0XEk\ng0hJAblSru3FxMRw5MgRgoODURRFdBzjmj4dFAVGjRKdRLKSESNGsGLFCn7++Wfat28vOo4k6YJs\nfB+Y2noq9xLuUev7Why4Zp1J8CXzCAyEhAQ4Ku+JtKmEhAQWLVqEg4MDtWvXFh3H2BwcIE8ekEvd\nmpaXlxdOTk40aNBAdBRJ0g3Z+D5QsXBF9vTbQ1H3otSfW5/pe6ZbZMzv8ePHLZDONoyUVbSGDcHF\nRVv8Ki1ZQ8tJr5Z//vkn169fp3v37uTPn19oFsNr3Bju3IHDh7O0uV5qoJccRtChQwdiY2OZOXOm\naepmlvOQxJGNbxq+nr5s672NVyq/wtDfh1JnTh32Xd2Xq32OMtDLiEbKKpqbG7z8svZqcWLif4/L\nGlpOerV0cHDA2dmZokWLCs9ieA9vDMziggB6qYFecuiZqqrMnTuXRo0aUaJECdq0aWOaupnlPCRx\nZOP7mLxOeZnZbiY7++wkPimemrNr0nFZR/534n8kJic+fQePmT59uhVSWoeRsurB6NHaSq8//PDf\nY7KGlpNeLYODg4mJieFiFpbYtXYWw9u1S3vZIgs3toF+aqCXHHqUnJzMb7/9RqNGjejbty8vv/wy\nhw8fpkaNGqapm1nOQxInj+gAelW3RF32DtjL7H2zmb1vNiFLQijiXoTulbrTq2ovKhetnKX9GGnq\nFSNl1YNKleCVV7QGOCQEihaVNbSk9Gr58Ga2PHls+1+X6a5rZCRMngw9e0IWZ8bQSw30kkNP7t69\ny7x58/j66685c+YM9erVY82aNTz//POp25ilbrY6j6bhCyly0yaHMoTIOFgqOoSFyMY3E06OTgyu\nOZjBNQdz4NoB5h+Yz4JDC/hy15eUyl+KSkUrEVw4WHtfJJhA70Bc8riIji3Z0JQp8PvvMHgwLF+u\n3SQvWV5sbCzHjh3jwIEDKIpCkSJFREcyti++gHv34OOPRSeRckFVVebMmcPIkSO5e/cunTp1YtGi\nRdSqVUt0NEnSLdn4ZlHVYlWp2roqk1pMYt3pdfx18S+ORB5h4eGFXNpxCYA8Dnko512O4CLBBBcO\nJriI1hSX9iqNo4Nt5huVbKtQIfj2W20hix9+gN69RScyj5iYGI4fP87Ro0c5d+4cAP7+/nTq1Aln\nZ2fB6QyuVSvtGd8ZM2DCBNFppBw4d+4cAwYM4I8//qBXr158+OGHlChRQnQs0wiu+i2lC8v16B86\nd/0US88MEh3DImTjm01Ojk60D2xP+8D/5kS8c/8OYZFhHIk8wuHIwxyJPMIfZ//gVtwt2A4ujVwo\nmb8kJfOXpFT+UpTyKvXIx36efjg7iv9FHhoayujRo0XHMJyXX4ZeveCNN+DixVA++EDWMLd27drF\ne++9R8OGDfH39+f555+nQoUKuLu7C8ljup+Npk3ho4/gvfegY0eo/PShW3qpgV5yiHT8+HFq1qxJ\nwYIFWb9+PS1btnzq95ilbmY5D0kc2fhagJerF/VL1qd+yfqpj6mqyrV71xh1YxQ1W9TkYtRFLkRd\n4HDkYdacWkNETETqtgoKxT2K/9cM53/QGHv993F+V+tP3RQbG2v1Y5jVyJHaM75nz8oa5oaqqmzd\nupWtW7dSpEgR3nrrLWHNblqm/Nl4+2344APtJrcsNL56qYFecoiiqiqDBw+maNGi7Nu3D09Pzyx9\nn1nqZqvzUBTkAjlpmKkUsvG1EkXRmtmfpv2U7tfjEuMIjw7nQtQFLty5kNoYX4i6wD9X/uFS1CUS\nU/6bRcLD2QM/T78n3nw9fFM/LuhWMFc/qBPkS545Vrasth5AjRqyhrlx6tQptm7dStOmTfnggw9E\nx0llyp8NFxcoVw62boUBA566uV5qoJcconz++eds3ryZdevWZbnpBfPUzSznIYkjG19B3JzcCPAO\nIMA7/TFEySnJRMREcOGO1gyHR4cTHh3O5buXOXbjGBvPbuTK3SukqCmp3+OaxzXdhjjtY0XzFcVB\nkbPYWdr69doSxvXqiU5iDpWz8AykZAGDB2tjdIYNA3lDlO7NmDGDUaNGMWbMGFq1aiU6jqkd2T+I\nSDfRKfQjMk50AsuRja9OOTo44uPhg4+HD3VL1E13m6SUJCLuRaQ2xA+b4/DocC5GXeTv8L8Jjw4n\nITkh9Xvc8rhRzrsc5bzLEegdSGChwNSPbTGcwqx++gmqV4dnnxWdxNiKFSsGQFhYGPXkXxHW99pr\nMGcO9O+vDXlwk7/p9Wr37t0MGTKEESNG8LGcjUOSckw2vlZ248YNChUqZJV953HIg6+nL76evhlu\no6oqN2JvEB4dzqXoS5y9fZYTN05w4uYJdlzawZW7V1K3LUQhKpSsQKD3g2a4UCCB3oEEeAfIZ4mf\nIiwMmjWz7vW2B56entSpU4eNGzcC6Kb5Ne11zZNHG5xepw4MGaI1wRnQSw30ksPWlixZQvHixZk8\neXKOhrSZpW5mOQ9JHNnNWFmfPn2EHl9RFAq7F6Za8WqEBIbwZp03+bbdt2x6dROXR1zm7rt32Ttg\nL4s6LKLghoL4efqx79o+Ptz2IS8seYHyM8pTa3YtTt08JfQ89O76dbh1S/z1NoOWLVtSrlw5+vXr\nh6qqouMAJr+uVapoc/LNnQsP/uBIj15qoJcctrZ161bc3d1Tp/bLLrPUzSznIYkjG18rGz9+vOgI\nmcrnnI/qxavTtVJXFk9bzKKOi9g7YC/R70RzecRlVnddTVR8FNW+q8aqE6tEx9WtDz+EhQuhZMnx\noqMY3sNnszp27Kibu6r1/nOca6++CuXLw4IFGW6ilxroJYetTZs2jeTkZCpXrsyMGTNISUl5+jel\nYZa6meU8JHFk42tl1atXFx0hy9JmTVFTiLgXwbk75yhfqDwxiTFM2CLvps3Ia6/B+PEwY0Z1lppl\nXUdBkpOTOX369CPLrYpmpJ/jHFEUbULqNWsy3EQvNdBLDlurX78+hw4d4tVXX2XIkCHUqVOHrVu3\nZvn7zVI3s5yHJI5sfCVUVSXiXgQbz2xk/JbxtPipBV6hXlSfVZ0R60dwI/YGb9V9iy9afiE6qq69\n/z507Qp9+8KxY6LTGJeqqqSkpOhi/l67UqQImGSuV7PKly8f33zzDVu3bkVVVRo3bkz79u05evSo\n6GiSZBjy5jY7kJSSxOXoy6lzBj/yPkqbQ/h+0n0AvN28qVeiHmMbjqV+ifrU8KmBm5N2p/e+fftE\nnobuKQpMmgTLlsHHH2tDH6Tsu39f+7fo6CiX+bYpNzeIj4d9+7QpSiTdeu6559i9ezc///wz7777\nLtWrV2f16tU0b95cdDRJ0j3Z+FrZnDlz6Nu3r1WPEZsYy6WoS/8tgpGmqb1wR5sDOFlNTt3e2807\ndVW458s+n/px2Powxg4bq5txlUZz7x40ajSH/Pn7MmaM6DTGdfToURwcHNi2bRtBQUGi4wC2+TkW\nrmtX+O47aN8edu8GP79HvqyXGuglh2gODg507tyZF154gQ4dOvDCCy+wbt06GjZsmO72ZqmbWc5D\nEkc2vla2b9++XP2QxifFc/nuZS5FXeJS9KX/3qf5+FbcrdTtFRR8PHxSm9l6fvVSP3743t05/ZeQ\n/5j+h2x6c2jtWhg6FC5d2sfOnX3RSb9mSOHh4Xh4eHD48GHRUVLl9ufYENzdYdUqqF0bnnsOfvsN\nKlVK/bJeaqCXHHrh6urKL7/8Qtu2bWnatCkDBgzgvffeS50T+yGz1M0s5yGJIxtfK5sxY0amX78Z\ne5MTN09wMepiuk1tZEzkI9sXdCtICc8SlMhfgnol6uHn6Zf6ecn8JfHz9MPZ0dkqWaUnXbyoLXq1\ncqU2j+/atTMIDBSdythq165NWFgYHTt2FB0lld38bBQvDtu2wYsvQt262sosL70E6KcGesmhJ25u\nbqxdu5avv/6aTz/9lPnz5zNixAhGjRpFvnz5APPUzSznIYkjG18bUFWVa/eucezGMY5eP8rR60dT\nP07b2Ho4e1AifwlKeJagWjFt3t2HTW0JzxL4efpl+GytZHvbt8MLL4CrKyxdCp06aeN8pdzx9fWl\ncePGbNq0iatXr9KkSRN8fTNepEWyMH9/2LEDeveGDh1gwgQYNw4c5L3Qeubq6sqoUaPo378/n332\nGZMnT+batWvMmjVLdDRJ0hXZ+FrYvYR77Li4g7DrYRy7foyjN7RG9879OwA4OzpTzrscFQtXpHGp\nxlQsXJHyhcpTyqsUni6egtNLWbVsGfTsqT0p9uuvUKCA6ETm0qBBA7y9vdmyZQvff/895cqVo2nT\nphQtWlR0NPvg7q79NVe5Mrz3Hhw8CPPnw4NnDyX9KlCgAKGhoeTLl4/Q0FC++OILPDw8RMeSJN2Q\nja8FXL17lVUnVrHq5Cr+PPsn8cnxuOVxo0LhClQsXJG2AW2pWLgiFQtX5JkCz5DHQZbdyLZs0e4D\n6tJFW+zKxUV0IvNRFIWKFStSvnx5jhw5wtatW5k9ezadOnUiUI4lsQ1F0Z7prVQJevSAevW0cb+l\nS4tOJmVBr169+OCDD1i6dCn9+vUTHUeSdEO+dpVDZ26d4ZO/PqH297XxmeLD4LWDiUmI4dNmn3L8\n9ePcG3OPvQP2EjUvijENx/Bi+Rcp511O101vSEiI6Ai6FxkJ3bpBo0bw449PNr2yhpYTEhKCg4MD\nlStXZtCgQZQrV46lS5dy8OBBIVns1gsvwK5dhJw5AzVran/5CWTX1yIbSpQoQevWrZkzZw5gnrqZ\n5TwkcfTbhenU35f+5vO/P2fFsRW4O7vTumxrhtQcwvMBz+Od1/uJ7YcMGSIgZc4YKaso774LiYna\nHL3pTTMra2g5aWuZJ08eXn75Zf73v/+xcuVKwsLCaNGiBYULF7Z5FrsUFMSQH3+EGTO0cb/XroFz\nzm6izS27vxbZ8HC4UEpKimnqZpbzkMSRjW8WrT21lk/++oQdl3ZQzrsc37X7jh6Ve6Qu7pCRli1b\n2ihh7hkpqwinT2vDHD//XLv5PT2yhpbzeC0dHBwICQkhICCAP/74g2+//ZYaNWrQrFkzXKw83kRe\nV2jZsSMEBmpDH9av1+b7FZFDXossmTlzJgsWLGD27Nk4ODiYpm5mOQ8p+xRFyQO4qqp6Lzf7kUMd\nnkJVVcZtGkfbRW1RUVnZeSXHXj9G/2f7P7Xplczlhx/AywsGDhSdxH49HPs7ePBgqlevzj///MPx\n48dFx7IfwcFQrZo2h9/586LTSBmYOnUqgwcPZsiQIXJ8r2Q4iqK0VxSl12OPjQXuAXcURdmgKEqO\nbymXjW8mklKS6PVbLyb+NZHQ5qFs772dF8q/gIMiy2aP/vlHu7/HTf69I5yqqpw/f55ixYoRHBws\nOo59WbFCm9rsuefg7FnRaaQ0VFXlzTffZPjw4bz99tt89dVXoiNJUk6MAFLnblUUpR7wIfAR8H9A\nCeC9nO5cdnCZmLxjMgsOLWBhh4WMqj8qR6uarVy50grJrMNIWUU4cYKnrsgma2g5mdVyw4YNREVF\n0aFDBxzTG2xtwyz2IrUGpUrB5s1w+bL2MoioHNITNm/ezFdffcVXX33F/7d351FSVNcDx7+XbQYU\nHGBYZRFQGAKIQsRdUYQxxDWKEzCOkRgRFXH5uR4NCMYoZkEUVNyCG4gbURAVkbhHAsi+OGxBZB3W\nYRkYZt7vj1eDTTtLL9VdU9X3c04d6K6qN7dudXW/fv3qvVGjRlEtZOzloOQtKMehKtQJ+Drk8ZXA\nDGPMn40x7wB3AjH3tdKKbzlWbl/JiM9HcMdpdzCgy4CYy5k4caKLUSWWn2L1wsGDlbf2ag7dU14u\nv//+e+bMmUOfPn2SdnObntewHOTlQUkJ9O3rbRzqCJMnT+a4445jyJAhP1sXlLwF5ThUheoC20Ie\nnwXMDHm8BGgea+F6c1sZtu3bxiUTL6F53eYM7zk8rrLeeOMNd4JKAj/F6oVDh6BGJVeM5tA95eUy\nLy8PgMzMTM9jSSWHc/Cvf8HAgfbnj1NP9S4O9TPz58/nrLPOKvPXyaDkLVnHMfPYAdTK0AlzSh3c\nuRlWvZ6sP/cj0BFYJyJHA12B20PWNwT2xVq4tviG2XtwL31f70v+vnymXz1dpwhWhxUVQc2aXkeh\nevfuTbt27XjttdcOV4JVEpSUwK23wmWX2f69n3+uc3RXMa1bt+bHH3/0Ogyl4vUmMFpErgGeAzYB\n/wlZ/0tgRayFa8U3hDGGG6fdyJItS5h+9XTaN2zvdUiqiigshD17QGf+9F6tWrW4/PLLAVi4cKHH\n0aSQb7+FJ5+Exx+383Q3aOB1RCpMy5YtWb9+vddhBIOILuFL8owA/guMAU4CfmeMKQ5Z3x94P9bC\ntatDiJfmv3T4Zrbuzbt7HY6qQhYtsl0dunXzOhIF8NFHH1GjRg169erldSipY9EiO2vLLbdoS28V\ndfTRR7NvX8y/ACtVJRhj9gO5Faw/L57ytcU3xOQlkzm39blx3cwW7rrrrnOtrETzU6zJ9vXXdqKq\nE0+seDvNoXvKy2V+fj6LFi0iOzubjIwMT2NJJddNmGC7O8yf720cei7KVVRUdMRIDqGCkregHIeK\nnoicKyJ94xnDF7Tie4Q2GW3Yvn+7q2X6aZYZP8WabLNmwemnQ3p6xdtpDt1TXi4XLVpEWloaXbp0\n8TyWVNJn8GA45RTIzQUPWxX1XJRv1apVtG3btsx1QclbUI5DlU9E7hGRkSGPRUQ+BGYBU4FlIlLJ\n4KLl04pviA6ZHcjbnkeJKXGtzP79+7tWVqL5KdZkKi6Gzz6D8yL4cUVz6J7ycnnw4EEA9u7d63ks\nqaT/735n5+xeswbGjvUuDj0XZVqzZg1Tp07l9NNPL3N9UPIWlONQFcoBFoc8vhI4BzgbyATmAMNi\nLVwP533cAAAgAElEQVQrviHaN2xP4aFCftj1g9ehqCpk4ULYuTOyiq9KvHPOOYe0tDSmTJmCMcbr\ncFJLVhZcfz089hgUFHgdjXIYY7j++uvJzMzk/vvv9zocpeLVBgi9c7kv8JYx5itjzHbgYaDsb3gR\n0IpviK17twLENEObCq5Zs2wXBw+GLFVlqF27NhdddBFr167lhx/0S2rS3X8/bN8Ob77pdSTKMXfu\nXD799FPGjBlDXR16RvlfDeBAyOPTOXImtw3Ylt+YaMU3xPh547mg7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b4ce2e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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O0K2b/vVEBhMdYf2ioqIIDw9/LNG9du0aACVKlCA4OJjRo0cTHBxMUFBQtj8c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\nY4wxKeH/Ab0w9ZMtxf+PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b65ab1978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_facies_log_plot(\n",
    "    well_data[well_data['Well Name'] == 'STUART'],\n",
    "    facies_colors=facies_colors)\n",
    "\n",
    "make_facies_log_plot(\n",
    "    well_data[well_data['Well Name'] == 'CRAWFORD'],\n",
    "    facies_colors=facies_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we can write out a csv file with the well data along with the facies classification results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "well_data.to_csv('well_data_with_facies.csv')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py3]",
   "language": "python",
   "name": "conda-env-py3-py"
  },
  "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": 0
}