1_Workflow_Intro_with_Altair_Interrogation_Log_data_Lasio-Costa-HW_wells.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reservoir Characterization Workflow Part 1: Well Log Data\n",
    "\n",
    "## Interrogation of Well Log data used for this Costa HW Carbonate Reservoir Characterization Workflow:\n",
    "\n",
    "### Interrogate Well Log data using Python's Altair to create interactive depth plots, cross plots and histograms. Altair is one of the best interactive software python libraries that allows the user to select data from an assortment of plots and observe the selected data on other depth plots, cross plots and histograms using your own user-defined layout."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "# Code\n",
    "\n",
    "## Python Requirements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from pandas import DataFrame, read_csv\n",
    "import altair as alt\n",
    "#alt.renderers.enable('altair_viewer')\n",
    "\n",
    "import altair_transform\n",
    "alt.data_transformers.disable_max_rows()\n",
    "\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import lasio\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uncomment the Costa HW Well that you want to evaluate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GR</th>\n",
       "      <th>RHOB</th>\n",
       "      <th>NPHI</th>\n",
       "      <th>DT</th>\n",
       "      <th>PHIE</th>\n",
       "      <th>RT</th>\n",
       "      <th>SW</th>\n",
       "      <th>SO</th>\n",
       "      <th>CORE_POR</th>\n",
       "      <th>CORE_PERM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEPT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7805.0</th>\n",
       "      <td>16.28</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.04</td>\n",
       "      <td>56.07</td>\n",
       "      <td>0.02</td>\n",
       "      <td>76.28</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7805.5</th>\n",
       "      <td>18.55</td>\n",
       "      <td>2.26</td>\n",
       "      <td>0.04</td>\n",
       "      <td>56.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7806.0</th>\n",
       "      <td>19.75</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.03</td>\n",
       "      <td>56.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7806.5</th>\n",
       "      <td>20.95</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.04</td>\n",
       "      <td>56.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7807.0</th>\n",
       "      <td>22.15</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.04</td>\n",
       "      <td>56.89</td>\n",
       "      <td>0.01</td>\n",
       "      <td>34.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           GR  RHOB  NPHI     DT  PHIE     RT    SW    SO  CORE_POR  CORE_PERM\n",
       "DEPT                                                                          \n",
       "7805.0  16.28  2.24  0.04  56.07  0.02  76.28  0.73  0.27       0.4        NaN\n",
       "7805.5  18.55  2.26  0.04  56.14   NaN  70.30   NaN   NaN       0.4        NaN\n",
       "7806.0  19.75  2.27  0.03  56.21   NaN  64.33   NaN   NaN       0.4        NaN\n",
       "7806.5  20.95  2.25  0.04  56.55   NaN  49.21   NaN   NaN       0.4        NaN\n",
       "7807.0  22.15  2.22  0.04  56.89  0.01  34.00   NaN   NaN       0.4        NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las = lasio.read(\"./data/HW-3.las\");elev_meas_ref = 160\n",
    "#las = lasio.read(\"./data/HW-4.las\");elev_meas_ref = 29\n",
    "#las = lasio.read(\"./data/HW-5.las\");elev_meas_ref = 36\n",
    "#las = lasio.read(\"./data/HW-6.las\");elev_meas_ref = 49\n",
    "#las = lasio.read(\"./data/HW-7.las\");elev_meas_ref = 367\n",
    "#las = lasio.read(\"./data/HW-8.las\");elev_meas_ref = 416\n",
    "#las = lasio.read(\"./data/HW-9.las\");elev_meas_ref = 492\n",
    "#las = lasio.read(\"./data/HW-10.las\");elev_meas_ref = 397\n",
    "#las = lasio.read(\"./data/HW-24.las\");elev_meas_ref = 426\n",
    "#las = lasio.read(\"./data/HW-25.las\");elev_meas_ref = 386\n",
    "#las = lasio.read(\"./data/HW-26.las\");elev_meas_ref = 375\n",
    "#las = lasio.read(\"./data/HW-27.las\");elev_meas_ref = 453\n",
    "#las = lasio.read(\"./data/HW-28.las\");elev_meas_ref = 430\n",
    "#las = lasio.read(\"./data/HW-29.las\");elev_meas_ref = 466\n",
    "#las = lasio.read(\"./data/HW-30.las\");elev_meas_ref = 590\n",
    "#las = lasio.read(\"./data/HW-31.las\");elev_meas_ref = 417\n",
    "#las = lasio.read(\"./data/HW-32.las\");elev_meas_ref = 441\n",
    "\n",
    "\n",
    "df_log = las.df()\n",
    "\n",
    "df_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>GR</th>\n",
       "      <th>RHOB</th>\n",
       "      <th>NPHI</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>431.000000</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>355.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.321949</td>\n",
       "      <td>2.377146</td>\n",
       "      <td>0.098608</td>\n",
       "      <td>78.352219</td>\n",
       "      <td>0.244330</td>\n",
       "      <td>37.938445</td>\n",
       "      <td>0.120156</td>\n",
       "      <td>0.879844</td>\n",
       "      <td>21.533411</td>\n",
       "      <td>33.505972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.870233</td>\n",
       "      <td>0.124291</td>\n",
       "      <td>0.050236</td>\n",
       "      <td>11.763271</td>\n",
       "      <td>0.095283</td>\n",
       "      <td>26.912727</td>\n",
       "      <td>0.145055</td>\n",
       "      <td>0.145055</td>\n",
       "      <td>13.169713</td>\n",
       "      <td>97.437032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.130000</td>\n",
       "      <td>2.180000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>52.790000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.730000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.785000</td>\n",
       "      <td>2.290000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>68.655000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>20.975000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.820000</td>\n",
       "      <td>2.340000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>84.295000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>29.370000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>29.250000</td>\n",
       "      <td>7.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.875000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>88.287500</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>48.035000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>31.725000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27.320000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>90.620000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>186.310000</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>35.800000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               GR        RHOB        NPHI          DT        PHIE          RT  \\\n",
       "count  431.000000  431.000000  431.000000  356.000000  351.000000  431.000000   \n",
       "mean    12.321949    2.377146    0.098608   78.352219    0.244330   37.938445   \n",
       "std      3.870233    0.124291    0.050236   11.763271    0.095283   26.912727   \n",
       "min      8.130000    2.180000    0.010000   52.790000    0.000000    6.730000   \n",
       "25%      9.785000    2.290000    0.040000   68.655000    0.230000   20.975000   \n",
       "50%     10.820000    2.340000    0.110000   84.295000    0.290000   29.370000   \n",
       "75%     13.875000    2.400000    0.140000   88.287500    0.300000   48.035000   \n",
       "max     27.320000    2.760000    0.170000   90.620000    0.330000  186.310000   \n",
       "\n",
       "               SW          SO    CORE_POR   CORE_PERM  \n",
       "count  321.000000  321.000000  431.000000  355.000000  \n",
       "mean     0.120156    0.879844   21.533411   33.505972  \n",
       "std      0.145055    0.145055   13.169713   97.437032  \n",
       "min      0.040000    0.060000    0.300000    0.010000  \n",
       "25%      0.070000    0.900000    4.500000    5.150000  \n",
       "50%      0.090000    0.910000   29.250000    7.300000  \n",
       "75%      0.100000    0.930000   31.725000   19.000000  \n",
       "max      0.940000    0.960000   35.800000  990.000000  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_log.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising Data Extent (from Andy McDonald):\n",
    "\n",
    "Instead of the summary provided by the pandas describe() function, we can create a visualisation using matplotlib. Firstly, we need to work out where we have nulls (nan values). We can do this by creating a second dataframe and calling .notnull() on our well dataframe.  \n",
    "  \n",
    "As this returns a boolean (True or False) for each depth, we need to multiply by 1 to convert the values from True and False to 1 and 0 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "well_nan = df_log.notnull() * 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7806.5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7807.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "        GR  RHOB  NPHI  DT  PHIE  RT  SW  SO  CORE_POR  CORE_PERM\n",
       "DEPT                                                             \n",
       "7805.0   1     1     1   1     1   1   1   1         1          0\n",
       "7805.5   1     1     1   1     0   1   0   0         1          0\n",
       "7806.0   1     1     1   1     0   1   0   0         1          0\n",
       "7806.5   1     1     1   1     0   1   0   0         1          0\n",
       "7807.0   1     1     1   1     1   1   0   0         1          0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_nan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x864 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.subplots(figsize=(10,12))\n",
    "\n",
    "#Set up the plot axes\n",
    "ax1 = plt.subplot2grid((1,10) , (0,0), rowspan=1, colspan = 1) \n",
    "ax2 = plt.subplot2grid((1,10) , (0,1), rowspan=1, colspan = 1)\n",
    "ax3 = plt.subplot2grid((1,10) , (0,2), rowspan=1, colspan = 1)\n",
    "ax4 = plt.subplot2grid((1,10) , (0,3), rowspan=1, colspan = 1)\n",
    "ax5 = plt.subplot2grid((1,10) , (0,4), rowspan=1, colspan = 1)\n",
    "ax6 = plt.subplot2grid((1,10) , (0,5), rowspan=1, colspan = 1)\n",
    "ax7 = plt.subplot2grid((1,10) , (0,6), rowspan=1, colspan = 1)\n",
    "ax8 = plt.subplot2grid((1,10) , (0,7), rowspan=1, colspan = 1)\n",
    "ax9 = plt.subplot2grid((1,10) , (0,8), rowspan=1, colspan = 1)\n",
    "ax10 = plt.subplot2grid((1,10), (0,9), rowspan=1, colspan = 1)\n",
    "\n",
    "\n",
    "\n",
    "columns = well_nan.columns\n",
    "axes = [ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9, ax10]\n",
    "\n",
    "for i, ax in enumerate(axes):\n",
    "    ax.plot(well_nan.iloc[:,i], well_nan.index, lw=0)\n",
    "    #ax.set_ylim(3000, 0)\n",
    "    ax.set_ylim(max(las[\"DEPT\"]),min(las[\"DEPT\"]))\n",
    "    ax.set_xlim(0, 1)\n",
    "    ax.set_title(columns[i])\n",
    "    ax.set_facecolor('whitesmoke')\n",
    "    ax.fill_betweenx(well_nan.index, 0, well_nan.iloc[:,i], facecolor='red')\n",
    "    # Remove tick labels from each subplot\n",
    "    if i > 0:\n",
    "        plt.setp(ax.get_yticklabels(), visible = False)\n",
    "    plt.setp(ax.get_xticklabels(), visible = False)\n",
    "\n",
    "ax1.set_ylabel('Depth', fontsize=14)\n",
    "\n",
    "plt.subplots_adjust(wspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Interrogation of Well Log Data Using Python’s Altair \n",
    "\n",
    "### Geolog is quite capable of displaying well log data in the form of cross plots, frequency histograms and layouts, where all the data is dynamically linked. However, python’s Altair package allows the user to plot all data in a user-defined layout, all on one page, where all data is dynamically linked to all other plot types. The user selects data from one plot and the selected samples then show up on other depth plots, cross plots and histograms. The unselected data is grayed out and only the selected data actually appears in color. \n",
    "\n",
    "### We have observed similar capabilities in TIBCO Spotfire, but now this is all available with python's Altair. \n",
    "\n",
    "## The following is an interrogation of well log data using Altair, which employs depth plots, cross plots and histograms in this visualization:"
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null, \"m\": null, \"n\": null}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "# python loglan\n",
    "\n",
    "# Imports\n",
    "\n",
    "\n",
    "'''\n",
    "  select the proper Neutron-Density Chartbook file\n",
    "'''\n",
    "\n",
    "#file = r'./data/cnl_chart_1pt0.xlsx'\n",
    "file = r'./data/cnl_chart_1pt1.xlsx'\n",
    "#file = r'./data/tnph_chart_1pt0.xlsx'\n",
    "#file = r'./data/tnph_chart_1pt19.xlsx'\n",
    "df_chart = pd.read_excel(file,index_col=False)\n",
    "#df_chart.head()\n",
    "\n",
    "\n",
    "'''\n",
    "  PEF vs. RHOB overlay\n",
    "'''\n",
    "\n",
    "#Load PEF vs. RHOB overlay chart\n",
    "file = r'./data/PEF_Rhob_chart.xlsx'\n",
    "df_pef = pd.read_excel(file,index_col=False)\n",
    "df_pef.head()\n",
    "\n",
    "\n",
    "'''    \n",
    "  Select the proper Pickett data\n",
    "  Adjust the Rw and m in the file designated below.\n",
    "'''\n",
    "\n",
    "file = r'./data/Pickett_Ro_chart.xlsx'\n",
    "df_pickett = pd.read_excel(file,index_col=False)\n",
    "#df_pickett.head()\n",
    "\n",
    "\n",
    "'''    \n",
    "  Select log curves for DataFrame called df\n",
    "'''\n",
    "#df = pd.DataFrame({'DEPTH':depth, 'RHOB':rhob, 'NPHI':nphi, 'ILD':ild, 'GR':gr, 'PHIT':phit,'PEF':pef, 'DT':dt })\n",
    "\n",
    "#read the file\n",
    "file = r'./data/main_well.xlsx'\n",
    "df_log = pd.read_excel(file,index_col=False)\n",
    " \n",
    " \n",
    "interval = alt.selection_interval()\n",
    "\n",
    "bottom = max(df_log['DEPTH'])\n",
    "top = min(df_log['DEPTH'])\n",
    "\n",
    "#------------------------------------------------\n",
    "#\n",
    "#       Depth of Depth Track\n",
    "#\n",
    "#------------------------------------------------\n",
    "\n",
    "base=alt.Chart(df_log).mark_point(clip=True).encode(\n",
    "    alt.Y('DEPTH:Q',\n",
    "        scale=alt.Scale(domain=(bottom, top))\n",
    "    ),\n",
    ").properties(\n",
    "    width=150,\n",
    "    height=600,\n",
    "    #title='GR',\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "#no depth labels for base2      \n",
    "base2=alt.Chart(df_log).mark_point(clip=True).encode(\n",
    "    alt.Y('DEPTH:Q',\n",
    "        scale=alt.Scale(domain=(bottom, top)), axis=alt.Axis(labels=False),title='',\n",
    "    ),\n",
    ").properties(\n",
    "    width=150,\n",
    "    height=600,\n",
    "    title='',\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "#------------------------------------------------\n",
    "#\n",
    "#       Log Curves of Depth Track\n",
    "#\n",
    "#------------------------------------------------\n",
    "gr = base.mark_circle(clip=True, size=50).encode(\n",
    "    x='GR:Q',  \n",
    "    #size=('PHIX:Q'),\n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(domain=(0, 75),scheme='rainbow'),legend=None),\n",
    "    #color=alt.condition(selector, 'Well_Name:O', alt.value('lightgray'), legend=None),\n",
    "    tooltip='GR:Q', \n",
    ").properties(\n",
    "    title='GR',\n",
    "    selection=interval\n",
    ")\n",
    "   \n",
    "rhob = base2.mark_circle(clip=True , size=50).encode(\n",
    "    alt.X('RHOB:Q',\n",
    "        scale=alt.Scale(domain=(2, 3))\n",
    "    ),     \n",
    "    #color=alt.value('red'),\n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    "    #color=alt.condition(selector, 'Well_Name:O', alt.value('lightgray'), legend=None),\n",
    "    tooltip='RHOB:Q', \n",
    ").properties(\n",
    "    title='RHOB',\n",
    "    selection=interval\n",
    ")\n",
    " \n",
    "nphi = base2.mark_circle(clip=True, size=50).encode(\n",
    "    alt.X('NPHI:Q',\n",
    "        scale=alt.Scale(domain=(.45, -0.15))\n",
    "    ),     \n",
    "    #y=('DEPTH'),\n",
    "    #color=alt.value('green'),\n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    "    tooltip='NPHI:Q', \n",
    ").properties(\n",
    "    title='NPHI',\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "dt = base2.mark_circle(clip=True, size=50).encode(\n",
    "    alt.X('DT:Q',\n",
    "        scale=alt.Scale(domain=(112, 28))\n",
    "    ),     \n",
    "    #y=('DEPTH'),\n",
    "    #color=alt.value('green'),\n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    "    tooltip='DT:Q', \n",
    ").properties(\n",
    "    title='DT',\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "rt = base2.mark_circle(clip=True, size=50).encode(\n",
    "    alt.X('RT:Q', \n",
    "          scale=alt.Scale(type='log', domain=(0.2, 2000.0))\n",
    "    ),\n",
    "    #x='LRT:Q',  \n",
    "    #y=('DEPTH'),\n",
    "    #color=alt.value('black'),\n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    "    tooltip='RT:Q', \n",
    ").properties(\n",
    "    title='RT',\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "phit = base2.mark_circle(clip=True, size=50).encode(\n",
    "    alt.X('PHIE:Q',\n",
    "        scale=alt.Scale(domain=(.45, -0.15))\n",
    "    ),    \n",
    "    #color=alt.value('blue'),\n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    "    tooltip='PHIE:Q', \n",
    ").properties(\n",
    "    title='PHIE',\n",
    "    selection=interval\n",
    ")\n",
    "  \n",
    "\n",
    "#------------------------------------------------\n",
    "#\n",
    "#       Neutron-Density Cross Plot\n",
    "#\n",
    "#------------------------------------------------\n",
    "nd_chart = alt.Chart(df_chart).mark_line().encode(\n",
    "    alt.X('CNL_chart:Q',\n",
    "        scale=alt.Scale(domain=(-0.05, 0.6))\n",
    "    ),    \n",
    "    alt.Y('RHOB_chart:Q',\n",
    "        scale=alt.Scale(domain=(3, 1.9))\n",
    "    ),    \n",
    "    color=alt.condition(interval, 'Lith:O', alt.value('black'),scale=alt.Scale(scheme='sinebow'),legend=None),\n",
    "    #color=alt.value('black'),\n",
    ").properties(\n",
    "    #title='Neut-Den Xplot with GR on Color Axis',\n",
    "    width=250,\n",
    "    height=250\n",
    "    #selection=interval\n",
    ")\n",
    "\n",
    "nd_chart2 = alt.Chart(df_chart).mark_line().encode(\n",
    "    alt.X('CNL_chart:Q',\n",
    "        scale=alt.Scale(domain=(-0.05, 0.6))\n",
    "    ),    \n",
    "    alt.Y('RHOB_chart:Q',\n",
    "        scale=alt.Scale(domain=(3, 1.9))\n",
    "    ),    \n",
    "    color=alt.condition(interval, 'Por:O', alt.value('black'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    "    #color=alt.value('black'),\n",
    ").properties(\n",
    "    #title='Neut-Den Xplot with GR on Color Axis',\n",
    "    width=250,\n",
    "    height=250\n",
    "    #selection=interval\n",
    ")\n",
    "\n",
    "ndxplot = base.mark_circle(clip=True,size=50).encode(\n",
    "    alt.X('NPHI:Q',\n",
    "        scale=alt.Scale(domain=(-0.05, 0.6))\n",
    "    ),    \n",
    "    alt.Y('RHOB:Q',\n",
    "        scale=alt.Scale(domain=(3, 1.9))\n",
    "    ),    \n",
    "    #x='NPHI:Q',  \n",
    "    #y=('RHOB'),\n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    "    tooltip='RHOB:Q', \n",
    ").properties(\n",
    "    title='Neut-Den Xplot (GR on Color Axis)',\n",
    "    width=250,\n",
    "    height=250,\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "\n",
    " \n",
    "#------------------------------------------------\n",
    "#\n",
    "#       PEF-Density Cross Plot\n",
    "#\n",
    "#------------------------------------------------\n",
    "pef_chart = alt.Chart(df_pef).mark_line().encode(\n",
    "    alt.X('PEF_chart:Q',\n",
    "        scale=alt.Scale(domain=(0, 10))\n",
    "    ),    \n",
    "    alt.Y('RHOB_chart:Q',\n",
    "        scale=alt.Scale(domain=(3, 2))\n",
    "    ),    \n",
    "    color=alt.condition(interval, 'Lith:O', alt.value('black'),scale=alt.Scale(scheme='sinebow'),legend=None),\n",
    "    #color=alt.value('black'),\n",
    ").properties(\n",
    "    #title='Neut-Den Xplot with GR on Color Axis',\n",
    "    width=250,\n",
    "    height=250\n",
    "    #selection=interval\n",
    ")\n",
    "\n",
    "pef_chart2 = alt.Chart(df_pef).mark_line().encode(\n",
    "    alt.X('PEF_chart:Q',\n",
    "        scale=alt.Scale(domain=(0, 10))\n",
    "    ),    \n",
    "    alt.Y('RHOB_chart:Q',\n",
    "        scale=alt.Scale(domain=(3, 2))\n",
    "    ),    \n",
    "    color=alt.condition(interval, 'Por:O', alt.value('black'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    "    #color=alt.value('black'),\n",
    ").properties(\n",
    "    #title='Neut-Den Xplot with GR on Color Axis',\n",
    "    width=250,\n",
    "    height=250\n",
    "    #selection=interval\n",
    ")\n",
    "\n",
    "pefxplot = base.mark_circle(clip=True, size=50).encode(\n",
    "    alt.X('PEF:Q',\n",
    "        scale=alt.Scale(domain=(0, 10))\n",
    "    ),    \n",
    "    alt.Y('RHOB:Q',\n",
    "        scale=alt.Scale(domain=(3, 2))\n",
    "    ),    \n",
    "    #x='NPHI:Q',  \n",
    "    #y=('RHOB'),\n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    "    tooltip='PEF:Q', \n",
    ").properties(\n",
    "    title='PEF-RHOB Xplot (GR on Color Axis)',\n",
    "    width=250,\n",
    "    height=250,\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "#------------------------------------------------\n",
    "#\n",
    "#       Pickett Plot\n",
    "#\n",
    "#------------------------------------------------\n",
    "pickett_chart = alt.Chart(df_pickett).mark_line(clip=True, size=2 ,strokeDash=[5,5]  ).encode(\n",
    "    alt.X('Rt_Pickett:Q',\n",
    "        scale=alt.Scale(type='log',domain=(.01, 1000))\n",
    "    ),    \n",
    "    alt.Y('Por_at_Ro:Q',\n",
    "        scale=alt.Scale(type='log',domain=(0.01, 1.0))\n",
    "    ),    \n",
    "    #color=alt.condition(interval, 'Lith:O', alt.value('black'),scale=alt.Scale(scheme='sinebow')),\n",
    "    color=alt.value('blue'),\n",
    ").properties(\n",
    "    #title='Neut-Den Xplot with GR on Color Axis',\n",
    "    width=250,\n",
    "    height=250\n",
    "    #selection=interval\n",
    ")\n",
    "\n",
    "pickett_chart8 = alt.Chart(df_pickett).mark_line(clip=True, size=2 ,strokeDash=[5,5]).encode(\n",
    "    alt.X('Rt_Pickett:Q',\n",
    "        scale=alt.Scale(type='log',domain=(.01, 1000))\n",
    "    ),    \n",
    "    alt.Y('Por_at_0pt75:Q',\n",
    "        scale=alt.Scale(type='log',domain=(0.01, 1.0))\n",
    "    ),    \n",
    "    #color=alt.condition(interval, 'Lith:O', alt.value('black'),scale=alt.Scale(scheme='sinebow')),\n",
    "    color=alt.value('cyan'),\n",
    ").properties(\n",
    "    #title='Neut-Den Xplot with GR on Color Axis',\n",
    "    width=250,\n",
    "    height=250\n",
    "    #selection=interval\n",
    ")\n",
    "pickett_chart6 = alt.Chart(df_pickett).mark_line(clip=True, size=2 ,strokeDash=[5,5]  ).encode(\n",
    "    alt.X('Rt_Pickett:Q',\n",
    "        scale=alt.Scale(type='log',domain=(.01, 1000))\n",
    "    ),    \n",
    "    alt.Y('Por_at_0pt5:Q',\n",
    "        scale=alt.Scale(type='log',domain=(0.01, 1.0))\n",
    "    ),    \n",
    "    #color=alt.condition(interval, 'Lith:O', alt.value('black'),scale=alt.Scale(scheme='sinebow')),\n",
    "    color=alt.value('yellow'),\n",
    ").properties(\n",
    "    #title='Neut-Den Xplot with GR on Color Axis',\n",
    "    width=250,\n",
    "    height=250\n",
    "    #selection=interval\n",
    ")\n",
    "pickett_chart4 = alt.Chart(df_pickett).mark_line(clip=True, size=2 ,strokeDash=[5,5]  ).encode(\n",
    "    alt.X('Rt_Pickett:Q',\n",
    "        scale=alt.Scale(type='log',domain=(.01, 1000))\n",
    "    ),    \n",
    "    alt.Y('Por_at_0pt25:Q',\n",
    "        scale=alt.Scale(type='log',domain=(0.01, 1.0))\n",
    "    ),    \n",
    "    #color=alt.condition(interval, 'Lith:O', alt.value('black'),scale=alt.Scale(scheme='sinebow')),\n",
    "    color=alt.value('orange'),\n",
    ").properties(\n",
    "    #title='Neut-Den Xplot with GR on Color Axis',\n",
    "    width=250,\n",
    "    height=250\n",
    "    #selection=interval\n",
    ")\n",
    "pickett_chart2 = alt.Chart(df_pickett).mark_line(clip=True , size=2 ,strokeDash=[5,5] ).encode(\n",
    "    alt.X('Rt_Pickett:Q',\n",
    "        scale=alt.Scale(type='log',domain=(.01, 1000))\n",
    "    ),    \n",
    "    alt.Y('Por_at_0pt1:Q',\n",
    "        scale=alt.Scale(type='log',domain=(0.01, 1.0))\n",
    "    ),    \n",
    "    #color=alt.condition(interval, 'Lith:O', alt.value('black'),scale=alt.Scale(scheme='sinebow')),\n",
    "    color=alt.value('red'),\n",
    ").properties(\n",
    "    #title='Neut-Den Xplot with GR on Color Axis',\n",
    "    width=250,\n",
    "    height=250\n",
    "    #selection=interval\n",
    ")\n",
    "pickett = base.mark_circle(clip=True, size=50).encode(\n",
    "    alt.X('RT:Q',\n",
    "        scale=alt.Scale(type='log',domain=(.01, 100))\n",
    "    ),    \n",
    "    alt.Y('PHIE:Q',\n",
    "        scale=alt.Scale(type='log',domain=(.01, 1))\n",
    "    ),    \n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    "    tooltip='RT:Q', \n",
    ").properties(\n",
    "    title='Pickett Plot (GR on Color Axis)',\n",
    "    width=250,\n",
    "    height=250,\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "#------------------------------------------------\n",
    "#\n",
    "#       Histograms\n",
    "#\n",
    "#------------------------------------------------\n",
    "grhist = alt.Chart(df_log).mark_bar(clip=True,size=5).encode(\n",
    "    #alt.X(\"GR:Q\", bin=alt.Bin(maxbins=75)),\n",
    "    alt.X('GR:Q',\n",
    "        bin=alt.Bin(maxbins=75),\n",
    "        scale=alt.Scale(domain=(0,50)),\n",
    "    ),\n",
    "    y='count():Q',\n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(scheme='rainbow'),legend=None),    \n",
    ").properties(\n",
    "    title='GR Hist',\n",
    "    width=250,\n",
    "    height=250,\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "rhobhist = alt.Chart(df_log).mark_bar(clip=True,size=5).encode(\n",
    "    #alt.X(\"RHOB:Q\", bin=alt.Bin(maxbins=75)),\n",
    "    alt.X('RHOB:Q',\n",
    "        #bin=True,\n",
    "        bin=alt.Bin(maxbins=75),\n",
    "        scale=alt.Scale(domain=(2.0,3.0)),\n",
    "    ),       \n",
    "    y='count():Q',\n",
    "    #color=alt.value('red'),\n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    ").properties(\n",
    "    title='RHOB Hist',\n",
    "    width=250,\n",
    "    height=250,\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "nphihist = alt.Chart(df_log).mark_bar(clip=True,size=5).encode(\n",
    "    #alt.X(\"NPHI:Q\",  bin=alt.Bin(maxbins=75)),\n",
    "    alt.X('NPHI:Q',\n",
    "        #bin=True,\n",
    "        bin=alt.Bin(maxbins=75),\n",
    "        scale=alt.Scale(domain=(0.45, -0.05)),\n",
    "    ),\n",
    "    y='count():Q',\n",
    "    #color=alt.value('green'),\n",
    "    color=alt.condition(interval, 'GR:Q', alt.value('lightgray'),scale=alt.Scale(scheme='rainbow'),legend=None),\n",
    ").properties(\n",
    "    title='NPHI Hist',\n",
    "    width=250,\n",
    "    height=250,\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "\n",
    "#------------------------------------------------\n",
    "#\n",
    "#       Define Plot Regions for Altair\n",
    "#\n",
    "#------------------------------------------------\n",
    "\n",
    "depth = gr | rhob | nphi |  phit | rt \n",
    "\n",
    "xplot = ndxplot+nd_chart+nd_chart2| pefxplot+pef_chart+pef_chart2 |pickett+pickett_chart+pickett_chart8+pickett_chart6+pickett_chart4  \n",
    "   \n",
    "hist =  grhist | rhobhist | nphihist\n",
    "\n",
    "plot = depth & xplot & hist\n",
    "\n",
    "plot\n",
    "\n",
    "#plot.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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