{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "arranged-tenant",
   "metadata": {},
   "source": [
    "# Example 20 - Sill\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "japanese-fitting",
   "metadata": {},
   "source": [
    "This example will show how to convert the geological map below using ``GemGIS`` to a `GemPy` model. This example is based on digitized data. The area is 1381 m wide (W-E extent) and 1768 m high (N-S extent). The vertical model extent varies between -500 m and 250 m. The model represents a wedge shaped sill that was encountered in boreholes. \n",
    "\n",
    "The map has been georeferenced with QGIS. The stratigraphic boundaries were digitized in QGIS. Strikes lines were digitized in QGIS as well and will be used to calculate orientations for the `GemPy` model. The contour lines were also digitized and will be interpolated with `GemGIS` to create a topography for the model. \n",
    "\n",
    "Map Source: An Introduction to Geological Structures and Maps by G.M. Bennison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "documented-hurricane",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:36.577492Z",
     "start_time": "2022-04-05T09:17:35.830817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "img = mpimg.imread('../images/cover_example20.png')\n",
    "plt.figure(figsize=(10, 10))\n",
    "imgplot = plt.imshow(img)\n",
    "plt.axis('off')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alike-configuration",
   "metadata": {},
   "source": [
    "## Licensing\n",
    "\n",
    "Computational Geosciences and Reservoir Engineering, RWTH Aachen University, Authors: Alexander Juestel. For more information contact: alexander.juestel(at)rwth-aachen.de\n",
    "\n",
    "\n",
    "This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "allied-vessel",
   "metadata": {},
   "source": [
    "## Import GemGIS\n",
    "\n",
    "If you have installed ``GemGIS`` via pip and conda, you can import ``GemGIS`` like any other package. If you have downloaded the repository, append the path to the directory where the ``GemGIS`` repository is stored and then import ``GemGIS``. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "neither-funeral",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:37.678910Z",
     "start_time": "2022-04-05T09:17:36.580493Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import gemgis as gg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "latin-packaging",
   "metadata": {},
   "source": [
    "## Importing Libraries and loading Data\n",
    "\n",
    "All remaining packages can be loaded in order to prepare the data and to construct the model. The example data is downloaded from an external server using `pooch`. It will be stored in a data folder in the same directory where this notebook is stored.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "polar-appliance",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:37.694447Z",
     "start_time": "2022-04-05T09:17:37.681911Z"
    }
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import rasterio "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "healthy-queens",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:38.275901Z",
     "start_time": "2022-04-05T09:17:37.696447Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading file 'example20_sill.zip' from 'https://rwth-aachen.sciebo.de/s/AfXRsZywYDbUF34/download?path=%2Fexample20_sill.zip' to 'C:\\Users\\ale93371\\Documents\\gemgis\\docs\\getting_started\\example\\data\\example20'.\n"
     ]
    }
   ],
   "source": [
    "file_path = 'data/example20/'\n",
    "gg.download_gemgis_data.download_tutorial_data(filename=\"example20_sill.zip\", dirpath=file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impressed-database",
   "metadata": {},
   "source": [
    "## Creating Digital Elevation Model from Contour Lines\n",
    "\n",
    "The digital elevation model (DEM) will be created by creating a NumPy array containing the height values. For this example, the height is 0, meaning that a flat topography at sea level is present. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inclusive-florence",
   "metadata": {},
   "source": [
    "### Creating the raster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "revolutionary-calibration",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:38.291907Z",
     "start_time": "2022-04-05T09:17:38.278901Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "topo_raster = np.zeros((138, 176))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "experimental-press",
   "metadata": {},
   "source": [
    "### Plotting the raster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "frozen-gazette",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:38.735004Z",
     "start_time": "2022-04-05T09:17:38.293907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1768.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fix, ax = plt.subplots(1, figsize=(10, 10))\n",
    "im = plt.imshow(topo_raster, origin='lower', extent=[0, 1381, 0, 1768], cmap='gist_earth')\n",
    "cbar = plt.colorbar(im)\n",
    "cbar.set_label('Altitude [m]')\n",
    "ax.set_xlabel('X [m]')\n",
    "ax.set_ylabel('Y [m]')\n",
    "ax.set_xlim(0, 1381)\n",
    "ax.set_ylim(0, 1768)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "common-franklin",
   "metadata": {},
   "source": [
    "### Saving the raster to disc\n",
    "\n",
    "After the interpolation of the contour lines, the raster is saved to disc using `gg.raster.save_as_tiff()`. The function will not be executed as a raster is already provided with the example data. "
   ]
  },
  {
   "cell_type": "raw",
   "id": "strategic-constant",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T14:30:44.324227Z",
     "start_time": "2021-04-04T14:30:44.268772Z"
    }
   },
   "source": [
    "gg.raster.save_as_tiff(raster=topo_raster, path=file_path + 'raster20.tif', extent=[0, 1381, 0, 1768], crs='EPSG:4326', overwrite_file=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "under-pursuit",
   "metadata": {},
   "source": [
    "### Opening Raster\n",
    "\n",
    "The previously computed and saved raster can now be opened using rasterio. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "silver-defendant",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:38.799018Z",
     "start_time": "2022-04-05T09:17:38.739005Z"
    }
   },
   "outputs": [],
   "source": [
    "topo_raster = rasterio.open(file_path + 'raster20.tif')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "central-option",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-28T12:43:27.113805Z",
     "start_time": "2021-03-28T12:43:27.067793Z"
    }
   },
   "source": [
    "## Interface Points of stratigraphic boundaries\n",
    "\n",
    "The interface points will be extracted from LineStrings digitized from the georeferenced map using QGIS. It is important to provide a formation name for each layer boundary. The vertical position of the interface point will be extracted from the digital elevation model using the `GemGIS` function `gg.vector.extract_xyz()`. The resulting GeoDataFrame now contains single points including the information about the respective formation. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "inclusive-subscription",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:39.054076Z",
     "start_time": "2022-04-05T09:17:38.802019Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "img = mpimg.imread('../images/interfaces_example20.png')\n",
    "plt.figure(figsize=(10, 10))\n",
    "imgplot = plt.imshow(img)\n",
    "plt.axis('off')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "activated-namibia",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:39.134122Z",
     "start_time": "2022-04-05T09:17:39.056075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>formation</th>\n",
       "      <th>Z</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-80</td>\n",
       "      <td>POINT (311.718 603.282)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-120</td>\n",
       "      <td>POINT (993.233 130.266)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>Top</td>\n",
       "      <td>-50</td>\n",
       "      <td>POINT (1052.804 601.233)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-200</td>\n",
       "      <td>POINT (1052.804 601.233)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>Top</td>\n",
       "      <td>-100</td>\n",
       "      <td>POINT (399.981 974.235)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id formation    Z                  geometry\n",
       "0  None      Base  -80   POINT (311.718 603.282)\n",
       "1  None      Base -120   POINT (993.233 130.266)\n",
       "2  None       Top  -50  POINT (1052.804 601.233)\n",
       "3  None      Base -200  POINT (1052.804 601.233)\n",
       "4  None       Top -100   POINT (399.981 974.235)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interfaces = gpd.read_file(file_path + 'interfaces20.shp')\n",
    "interfaces.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prospective-drink",
   "metadata": {},
   "source": [
    "### Extracting Z coordinate from Digital Elevation Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "stopped-saint",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:39.181133Z",
     "start_time": "2022-04-05T09:17:39.137123Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>formation</th>\n",
       "      <th>Z</th>\n",
       "      <th>geometry</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Top</td>\n",
       "      <td>-50.00</td>\n",
       "      <td>POINT (1052.804 601.233)</td>\n",
       "      <td>1052.80</td>\n",
       "      <td>601.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Top</td>\n",
       "      <td>-100.00</td>\n",
       "      <td>POINT (399.981 974.235)</td>\n",
       "      <td>399.98</td>\n",
       "      <td>974.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Top</td>\n",
       "      <td>-300.00</td>\n",
       "      <td>POINT (1324.290 1301.740)</td>\n",
       "      <td>1324.29</td>\n",
       "      <td>1301.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Top</td>\n",
       "      <td>-350.00</td>\n",
       "      <td>POINT (1213.755 1499.718)</td>\n",
       "      <td>1213.76</td>\n",
       "      <td>1499.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Base</td>\n",
       "      <td>-80.00</td>\n",
       "      <td>POINT (311.718 603.282)</td>\n",
       "      <td>311.72</td>\n",
       "      <td>603.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  formation       Z                   geometry       X       Y\n",
       "2       Top  -50.00   POINT (1052.804 601.233) 1052.80  601.23\n",
       "4       Top -100.00    POINT (399.981 974.235)  399.98  974.24\n",
       "6       Top -300.00  POINT (1324.290 1301.740) 1324.29 1301.74\n",
       "8       Top -350.00  POINT (1213.755 1499.718) 1213.76 1499.72\n",
       "0      Base  -80.00    POINT (311.718 603.282)  311.72  603.28"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interfaces_coords = gg.vector.extract_xyz(gdf=interfaces, dem=None)\n",
    "interfaces_coords = interfaces_coords.sort_values(by='formation', ascending=False)\n",
    "interfaces_coords = interfaces_coords[interfaces_coords['formation'].isin(['Base', 'Top'])]\n",
    "interfaces_coords.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "valued-coast",
   "metadata": {},
   "source": [
    "### Plotting the Interface Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "involved-association",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:39.515789Z",
     "start_time": "2022-04-05T09:17:39.183134Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1768.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(10, 10))\n",
    "\n",
    "interfaces.plot(ax=ax, column='formation', legend=True, aspect='equal')\n",
    "interfaces_coords.plot(ax=ax, column='formation', legend=True, aspect='equal')\n",
    "plt.grid()\n",
    "ax.set_xlabel('X [m]')\n",
    "ax.set_ylabel('Y [m]')\n",
    "ax.set_xlim(0, 1381)\n",
    "ax.set_ylim(0, 1768)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inner-mathematics",
   "metadata": {},
   "source": [
    "## Orientations from Borehole Observations\n",
    "\n",
    "Orientations of the sill will be calculated as it was a three point problem with `calculate_orientation_for_three_point_problem()`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "sufficient-intention",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:39.750862Z",
     "start_time": "2022-04-05T09:17:39.517789Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "img = mpimg.imread('../images/orientations_example20.png')\n",
    "plt.figure(figsize=(10, 10))\n",
    "imgplot = plt.imshow(img)\n",
    "plt.axis('off')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "configured-evaluation",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:39.766102Z",
     "start_time": "2022-04-05T09:17:39.752852Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>formation</th>\n",
       "      <th>Z</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-80</td>\n",
       "      <td>POINT (311.718 603.282)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-120</td>\n",
       "      <td>POINT (993.233 130.266)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>Top</td>\n",
       "      <td>-50</td>\n",
       "      <td>POINT (1052.804 601.233)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-200</td>\n",
       "      <td>POINT (1052.804 601.233)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>Top</td>\n",
       "      <td>-100</td>\n",
       "      <td>POINT (399.981 974.235)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-150</td>\n",
       "      <td>POINT (399.981 974.235)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>Top</td>\n",
       "      <td>-300</td>\n",
       "      <td>POINT (1324.290 1301.740)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-350</td>\n",
       "      <td>POINT (1324.290 1301.740)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>None</td>\n",
       "      <td>Top</td>\n",
       "      <td>-350</td>\n",
       "      <td>POINT (1213.755 1499.718)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-360</td>\n",
       "      <td>POINT (1213.755 1499.718)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id formation    Z                   geometry\n",
       "0  None      Base  -80    POINT (311.718 603.282)\n",
       "1  None      Base -120    POINT (993.233 130.266)\n",
       "2  None       Top  -50   POINT (1052.804 601.233)\n",
       "3  None      Base -200   POINT (1052.804 601.233)\n",
       "4  None       Top -100    POINT (399.981 974.235)\n",
       "5  None      Base -150    POINT (399.981 974.235)\n",
       "6  None       Top -300  POINT (1324.290 1301.740)\n",
       "7  None      Base -350  POINT (1324.290 1301.740)\n",
       "8  None       Top -350  POINT (1213.755 1499.718)\n",
       "9  None      Base -360  POINT (1213.755 1499.718)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interfaces"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imposed-elite",
   "metadata": {},
   "source": [
    "### Calculate Orientations for each formation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "active-france",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:39.797365Z",
     "start_time": "2022-04-05T09:17:39.768370Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id</th>\n",
       "      <th>formation</th>\n",
       "      <th>Z</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-80</td>\n",
       "      <td>POINT (311.718 603.282)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-120</td>\n",
       "      <td>POINT (993.233 130.266)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-200</td>\n",
       "      <td>POINT (1052.804 601.233)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-150</td>\n",
       "      <td>POINT (399.981 974.235)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-350</td>\n",
       "      <td>POINT (1324.290 1301.740)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>None</td>\n",
       "      <td>Base</td>\n",
       "      <td>-360</td>\n",
       "      <td>POINT (1213.755 1499.718)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index    id formation    Z                   geometry\n",
       "0      0  None      Base  -80    POINT (311.718 603.282)\n",
       "1      1  None      Base -120    POINT (993.233 130.266)\n",
       "2      3  None      Base -200   POINT (1052.804 601.233)\n",
       "3      5  None      Base -150    POINT (399.981 974.235)\n",
       "4      7  None      Base -350  POINT (1324.290 1301.740)\n",
       "5      9  None      Base -360  POINT (1213.755 1499.718)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interfaces_base = interfaces[interfaces['formation'] == 'Base'].reset_index()\n",
    "interfaces_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "working-manufacturer",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:39.843359Z",
     "start_time": "2022-04-05T09:17:39.799358Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>formation</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dip</th>\n",
       "      <th>polarity</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-133.33</td>\n",
       "      <td>Base</td>\n",
       "      <td>312.61</td>\n",
       "      <td>12.44</td>\n",
       "      <td>1.00</td>\n",
       "      <td>785.92</td>\n",
       "      <td>444.93</td>\n",
       "      <td>POINT (785.918 444.927)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Z formation  azimuth   dip  polarity      X      Y  \\\n",
       "0 -133.33      Base   312.61 12.44      1.00 785.92 444.93   \n",
       "\n",
       "                  geometry  \n",
       "0  POINT (785.918 444.927)  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orientations1 = gg.vector.calculate_orientation_for_three_point_problem(gdf=interfaces_base.loc[:2])\n",
    "orientations1['Z'] = orientations1['Z'].astype(float)\n",
    "orientations1['azimuth'] = orientations1['azimuth'].astype(float)\n",
    "orientations1['dip'] = orientations1['dip'].astype(float)\n",
    "orientations1['dip'] = 180 - orientations1['dip']\n",
    "orientations1['azimuth'] = 180 - orientations1['azimuth']\n",
    "orientations1['polarity'] = orientations1['polarity'].astype(float)\n",
    "orientations1['X'] = orientations1['X'].astype(float)\n",
    "orientations1['Y'] = orientations1['Y'].astype(float)\n",
    "orientations1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "prospective-access",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:39.891368Z",
     "start_time": "2022-04-05T09:17:39.845359Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>formation</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dip</th>\n",
       "      <th>polarity</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-156.67</td>\n",
       "      <td>Base</td>\n",
       "      <td>312.69</td>\n",
       "      <td>12.42</td>\n",
       "      <td>1</td>\n",
       "      <td>815.34</td>\n",
       "      <td>568.58</td>\n",
       "      <td>POINT (815.339 568.578)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Z formation azimuth   dip polarity      X      Y  \\\n",
       "0 -156.67      Base  312.69 12.42        1 815.34 568.58   \n",
       "\n",
       "                  geometry  \n",
       "0  POINT (815.339 568.578)  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orientations2 = gg.vector.calculate_orientation_for_three_point_problem(gdf=interfaces_base.loc[1:3])\n",
    "orientations2['azimuth'] = 360-orientations2['azimuth']\n",
    "orientations2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "lasting-thousand",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:39.955383Z",
     "start_time": "2022-04-05T09:17:39.894369Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>formation</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dip</th>\n",
       "      <th>polarity</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-233.33</td>\n",
       "      <td>Base</td>\n",
       "      <td>312.84</td>\n",
       "      <td>12.52</td>\n",
       "      <td>1.00</td>\n",
       "      <td>925.69</td>\n",
       "      <td>959.07</td>\n",
       "      <td>POINT (925.692 959.069)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Z formation  azimuth   dip  polarity      X      Y  \\\n",
       "0 -233.33      Base   312.84 12.52      1.00 925.69 959.07   \n",
       "\n",
       "                  geometry  \n",
       "0  POINT (925.692 959.069)  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orientations3 = gg.vector.calculate_orientation_for_three_point_problem(gdf=interfaces_base.loc[2:4])\n",
    "orientations3['Z'] = orientations3['Z'].astype(float)\n",
    "orientations3['azimuth'] = orientations3['azimuth'].astype(float)\n",
    "orientations3['dip'] = orientations3['dip'].astype(float)\n",
    "orientations3['dip'] = 180 - orientations3['dip']\n",
    "orientations3['azimuth'] = 180 - orientations3['azimuth']\n",
    "orientations3['polarity'] = orientations3['polarity'].astype(float)\n",
    "orientations3['X'] = orientations3['X'].astype(float)\n",
    "orientations3['Y'] = orientations3['Y'].astype(float)\n",
    "orientations3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "backed-integral",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:40.019399Z",
     "start_time": "2022-04-05T09:17:39.957383Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>formation</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dip</th>\n",
       "      <th>polarity</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-286.67</td>\n",
       "      <td>Base</td>\n",
       "      <td>310.80</td>\n",
       "      <td>12.35</td>\n",
       "      <td>1.00</td>\n",
       "      <td>979.34</td>\n",
       "      <td>1258.56</td>\n",
       "      <td>POINT (979.342 1258.564)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Z formation  azimuth   dip  polarity      X       Y  \\\n",
       "0 -286.67      Base   310.80 12.35      1.00 979.34 1258.56   \n",
       "\n",
       "                   geometry  \n",
       "0  POINT (979.342 1258.564)  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orientations4 = gg.vector.calculate_orientation_for_three_point_problem(gdf=interfaces_base.loc[3:5])\n",
    "orientations4['Z'] = orientations4['Z'].astype(float)\n",
    "orientations4['azimuth'] = orientations4['azimuth'].astype(float)\n",
    "orientations4['dip'] = orientations4['dip'].astype(float)\n",
    "orientations4['dip'] = 180 - orientations4['dip']\n",
    "orientations4['azimuth'] = 180 - orientations4['azimuth']\n",
    "orientations4['polarity'] = orientations4['polarity'].astype(float)\n",
    "orientations4['X'] = orientations4['X'].astype(float)\n",
    "orientations4['Y'] = orientations4['Y'].astype(float)\n",
    "orientations4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "moving-translation",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:40.050403Z",
     "start_time": "2022-04-05T09:17:40.022400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id</th>\n",
       "      <th>formation</th>\n",
       "      <th>Z</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>Top</td>\n",
       "      <td>-50</td>\n",
       "      <td>POINT (1052.804 601.233)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>Top</td>\n",
       "      <td>-100</td>\n",
       "      <td>POINT (399.981 974.235)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>Top</td>\n",
       "      <td>-300</td>\n",
       "      <td>POINT (1324.290 1301.740)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>None</td>\n",
       "      <td>Top</td>\n",
       "      <td>-350</td>\n",
       "      <td>POINT (1213.755 1499.718)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index    id formation    Z                   geometry\n",
       "0      2  None       Top  -50   POINT (1052.804 601.233)\n",
       "1      4  None       Top -100    POINT (399.981 974.235)\n",
       "2      6  None       Top -300  POINT (1324.290 1301.740)\n",
       "3      8  None       Top -350  POINT (1213.755 1499.718)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interfaces_top = interfaces[interfaces['formation'] == 'Top'].reset_index()\n",
    "interfaces_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "general-animal",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:40.098416Z",
     "start_time": "2022-04-05T09:17:40.052405Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>formation</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dip</th>\n",
       "      <th>polarity</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-150.0</td>\n",
       "      <td>Top</td>\n",
       "      <td>341.77</td>\n",
       "      <td>18.43</td>\n",
       "      <td>1</td>\n",
       "      <td>925.69</td>\n",
       "      <td>959.07</td>\n",
       "      <td>POINT (925.692 959.069)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Z formation azimuth   dip polarity      X      Y  \\\n",
       "0 -150.0       Top  341.77 18.43        1 925.69 959.07   \n",
       "\n",
       "                  geometry  \n",
       "0  POINT (925.692 959.069)  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orientations5 = gg.vector.calculate_orientation_for_three_point_problem(gdf=interfaces_top.loc[0:2])\n",
    "orientations5['azimuth'] = 360 - orientations5['azimuth']\n",
    "orientations5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "alternative-slovakia",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:40.146425Z",
     "start_time": "2022-04-05T09:17:40.101417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>formation</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dip</th>\n",
       "      <th>polarity</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-250.0</td>\n",
       "      <td>Top</td>\n",
       "      <td>341.23</td>\n",
       "      <td>18.22</td>\n",
       "      <td>1</td>\n",
       "      <td>979.34</td>\n",
       "      <td>1258.56</td>\n",
       "      <td>POINT (979.342 1258.564)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Z formation azimuth   dip polarity      X       Y  \\\n",
       "0 -250.0       Top  341.23 18.22        1 979.34 1258.56   \n",
       "\n",
       "                   geometry  \n",
       "0  POINT (979.342 1258.564)  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orientations6 = gg.vector.calculate_orientation_for_three_point_problem(gdf=interfaces_top.loc[1:3])\n",
    "orientations6['azimuth'] = 180 - orientations6['azimuth']\n",
    "orientations6['dip'] = 180 - orientations6['dip']\n",
    "orientations6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "great-daisy",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:40.192436Z",
     "start_time": "2022-04-05T09:17:40.151426Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>formation</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dip</th>\n",
       "      <th>polarity</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-233.33</td>\n",
       "      <td>Top</td>\n",
       "      <td>340.65</td>\n",
       "      <td>18.41</td>\n",
       "      <td>1</td>\n",
       "      <td>1196.95</td>\n",
       "      <td>1134.23</td>\n",
       "      <td>POINT (1196.950 1134.230)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Z formation azimuth   dip polarity       X       Y  \\\n",
       "0 -233.33       Top  340.65 18.41        1 1196.95 1134.23   \n",
       "\n",
       "                    geometry  \n",
       "0  POINT (1196.950 1134.230)  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orientations7 = gg.vector.calculate_orientation_for_three_point_problem(gdf=interfaces_top.loc[[0, 2, 3]])\n",
    "orientations7['azimuth'] = 180 - orientations7['azimuth']\n",
    "orientations7['dip'] = 180 - orientations7['dip']\n",
    "orientations7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "latter-speed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:40.240446Z",
     "start_time": "2022-04-05T09:17:40.196437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>formation</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dip</th>\n",
       "      <th>polarity</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-166.67</td>\n",
       "      <td>Top</td>\n",
       "      <td>341.82</td>\n",
       "      <td>18.36</td>\n",
       "      <td>1</td>\n",
       "      <td>888.85</td>\n",
       "      <td>1025.06</td>\n",
       "      <td>POINT (888.847 1025.062)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Z formation azimuth   dip polarity      X       Y  \\\n",
       "0 -166.67       Top  341.82 18.36        1 888.85 1025.06   \n",
       "\n",
       "                   geometry  \n",
       "0  POINT (888.847 1025.062)  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orientations8 = gg.vector.calculate_orientation_for_three_point_problem(gdf=interfaces_top.loc[[0, 1, 3]])\n",
    "orientations8['azimuth'] = 360 - orientations8['azimuth']\n",
    "orientations8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "friendly-token",
   "metadata": {},
   "source": [
    "### Merging Orientations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "sitting-reproduction",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:40.271454Z",
     "start_time": "2022-04-05T09:17:40.242447Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Z</th>\n",
       "      <th>formation</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dip</th>\n",
       "      <th>polarity</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-133.33</td>\n",
       "      <td>Base</td>\n",
       "      <td>312.61</td>\n",
       "      <td>12.44</td>\n",
       "      <td>1.00</td>\n",
       "      <td>785.92</td>\n",
       "      <td>444.93</td>\n",
       "      <td>POINT (785.918 444.927)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-156.67</td>\n",
       "      <td>Base</td>\n",
       "      <td>312.69</td>\n",
       "      <td>12.42</td>\n",
       "      <td>1.00</td>\n",
       "      <td>815.34</td>\n",
       "      <td>568.58</td>\n",
       "      <td>POINT (815.339 568.578)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-233.33</td>\n",
       "      <td>Base</td>\n",
       "      <td>312.84</td>\n",
       "      <td>12.52</td>\n",
       "      <td>1.00</td>\n",
       "      <td>925.69</td>\n",
       "      <td>959.07</td>\n",
       "      <td>POINT (925.692 959.069)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>-286.67</td>\n",
       "      <td>Base</td>\n",
       "      <td>310.80</td>\n",
       "      <td>12.35</td>\n",
       "      <td>1.00</td>\n",
       "      <td>979.34</td>\n",
       "      <td>1258.56</td>\n",
       "      <td>POINT (979.342 1258.564)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-150.00</td>\n",
       "      <td>Top</td>\n",
       "      <td>341.77</td>\n",
       "      <td>18.43</td>\n",
       "      <td>1.00</td>\n",
       "      <td>925.69</td>\n",
       "      <td>959.07</td>\n",
       "      <td>POINT (925.692 959.069)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>-250.00</td>\n",
       "      <td>Top</td>\n",
       "      <td>341.23</td>\n",
       "      <td>18.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>979.34</td>\n",
       "      <td>1258.56</td>\n",
       "      <td>POINT (979.342 1258.564)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>-233.33</td>\n",
       "      <td>Top</td>\n",
       "      <td>340.65</td>\n",
       "      <td>18.41</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1196.95</td>\n",
       "      <td>1134.23</td>\n",
       "      <td>POINT (1196.950 1134.230)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>-166.67</td>\n",
       "      <td>Top</td>\n",
       "      <td>341.82</td>\n",
       "      <td>18.36</td>\n",
       "      <td>1.00</td>\n",
       "      <td>888.85</td>\n",
       "      <td>1025.06</td>\n",
       "      <td>POINT (888.847 1025.062)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index       Z formation  azimuth   dip  polarity       X       Y  \\\n",
       "0      0 -133.33      Base   312.61 12.44      1.00  785.92  444.93   \n",
       "1      0 -156.67      Base   312.69 12.42      1.00  815.34  568.58   \n",
       "2      0 -233.33      Base   312.84 12.52      1.00  925.69  959.07   \n",
       "3      0 -286.67      Base   310.80 12.35      1.00  979.34 1258.56   \n",
       "4      0 -150.00       Top   341.77 18.43      1.00  925.69  959.07   \n",
       "5      0 -250.00       Top   341.23 18.22      1.00  979.34 1258.56   \n",
       "6      0 -233.33       Top   340.65 18.41      1.00 1196.95 1134.23   \n",
       "7      0 -166.67       Top   341.82 18.36      1.00  888.85 1025.06   \n",
       "\n",
       "                    geometry  \n",
       "0    POINT (785.918 444.927)  \n",
       "1    POINT (815.339 568.578)  \n",
       "2    POINT (925.692 959.069)  \n",
       "3   POINT (979.342 1258.564)  \n",
       "4    POINT (925.692 959.069)  \n",
       "5   POINT (979.342 1258.564)  \n",
       "6  POINT (1196.950 1134.230)  \n",
       "7   POINT (888.847 1025.062)  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "orientations = pd.concat([orientations1, orientations2, orientations3, orientations4, orientations5, orientations6, orientations7, orientations8]).reset_index()\n",
    "orientations = orientations[orientations['formation'].isin(['Base', 'Top'])]\n",
    "orientations['Z'] = orientations['Z'].astype(float)\n",
    "orientations['azimuth'] = orientations['azimuth'].astype(float)\n",
    "orientations['dip'] = orientations['dip'].astype(float)\n",
    "orientations['polarity'] = orientations['polarity'].astype(float)\n",
    "orientations['X'] = orientations['X'].astype(float)\n",
    "orientations['Y'] = orientations['Y'].astype(float)\n",
    "orientations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "textile-christianity",
   "metadata": {},
   "source": [
    "### Plotting the Orientations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "detected-certificate",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:40.665563Z",
     "start_time": "2022-04-05T09:17:40.273455Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1768.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(10, 10))\n",
    "\n",
    "interfaces.plot(ax=ax, column='formation', legend=True, aspect='equal')\n",
    "interfaces_coords.plot(ax=ax, column='formation', legend=True, aspect='equal')\n",
    "orientations.plot(ax=ax, color='red', aspect='equal')\n",
    "plt.grid()\n",
    "ax.set_xlabel('X [m]')\n",
    "ax.set_ylabel('Y [m]')\n",
    "ax.set_xlim(0, 1381)\n",
    "ax.set_ylim(0, 1768)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wired-vietnamese",
   "metadata": {},
   "source": [
    "## GemPy Model Construction\n",
    "\n",
    "The structural geological model will be constructed using the `GemPy` package. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "governing-brazilian",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:42.017383Z",
     "start_time": "2022-04-05T09:17:40.667554Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.configdefaults): g++ not available, if using conda: `conda install m2w64-toolchain`\n",
      "WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.\n",
      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    }
   ],
   "source": [
    "import gempy as gp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optical-realtor",
   "metadata": {},
   "source": [
    "### Creating new Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "binding-fishing",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:42.812069Z",
     "start_time": "2022-04-05T09:17:42.019363Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model20  2022-04-05 11:17"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model = gp.create_model('Model20')\n",
    "geo_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stopped-poster",
   "metadata": {},
   "source": [
    "### Initiate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "assisted-nothing",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:43.066746Z",
     "start_time": "2022-04-05T09:17:42.814058Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active grids: ['regular']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Model20  2022-04-05 11:17"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.init_data(geo_model, [0, 1381, 0, 1768, -500, 250], [100, 100, 100],\n",
    "             surface_points_df=interfaces_coords[interfaces_coords['Z'] != 0],\n",
    "             orientations_df=orientations,\n",
    "             default_values=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "integrated-pension",
   "metadata": {},
   "source": [
    "### Model Surfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cutting-energy",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:43.224309Z",
     "start_time": "2022-04-05T09:17:43.067747Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_9b8d6_row0_col3{\n",
       "            background-color:  #015482;\n",
       "        }#T_9b8d6_row1_col3{\n",
       "            background-color:  #9f0052;\n",
       "        }</style><table id=\"T_9b8d6_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >surface</th>        <th class=\"col_heading level0 col1\" >series</th>        <th class=\"col_heading level0 col2\" >order_surfaces</th>        <th class=\"col_heading level0 col3\" >color</th>        <th class=\"col_heading level0 col4\" >id</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_9b8d6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_9b8d6_row0_col0\" class=\"data row0 col0\" >Top</td>\n",
       "                        <td id=\"T_9b8d6_row0_col1\" class=\"data row0 col1\" >Default series</td>\n",
       "                        <td id=\"T_9b8d6_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "                        <td id=\"T_9b8d6_row0_col3\" class=\"data row0 col3\" >#015482</td>\n",
       "                        <td id=\"T_9b8d6_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9b8d6_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_9b8d6_row1_col0\" class=\"data row1 col0\" >Base</td>\n",
       "                        <td id=\"T_9b8d6_row1_col1\" class=\"data row1 col1\" >Default series</td>\n",
       "                        <td id=\"T_9b8d6_row1_col2\" class=\"data row1 col2\" >2</td>\n",
       "                        <td id=\"T_9b8d6_row1_col3\" class=\"data row1 col3\" >#9f0052</td>\n",
       "                        <td id=\"T_9b8d6_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "  surface          series  order_surfaces    color  id\n",
       "0     Top  Default series               1  #015482   1\n",
       "1    Base  Default series               2  #9f0052   2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.surfaces"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "younger-young",
   "metadata": {},
   "source": [
    "### Mapping the Stack to Surfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "formed-thought",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:43.400350Z",
     "start_time": "2022-04-05T09:17:43.227309Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_c02f9_row0_col3{\n",
       "            background-color:  #015482;\n",
       "        }#T_c02f9_row1_col3{\n",
       "            background-color:  #9f0052;\n",
       "        }#T_c02f9_row2_col3{\n",
       "            background-color:  #ffbe00;\n",
       "        }</style><table id=\"T_c02f9_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >surface</th>        <th class=\"col_heading level0 col1\" >series</th>        <th class=\"col_heading level0 col2\" >order_surfaces</th>        <th class=\"col_heading level0 col3\" >color</th>        <th class=\"col_heading level0 col4\" >id</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c02f9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_c02f9_row0_col0\" class=\"data row0 col0\" >Top</td>\n",
       "                        <td id=\"T_c02f9_row0_col1\" class=\"data row0 col1\" >Strata1</td>\n",
       "                        <td id=\"T_c02f9_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "                        <td id=\"T_c02f9_row0_col3\" class=\"data row0 col3\" >#015482</td>\n",
       "                        <td id=\"T_c02f9_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c02f9_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_c02f9_row1_col0\" class=\"data row1 col0\" >Base</td>\n",
       "                        <td id=\"T_c02f9_row1_col1\" class=\"data row1 col1\" >Strata2</td>\n",
       "                        <td id=\"T_c02f9_row1_col2\" class=\"data row1 col2\" >1</td>\n",
       "                        <td id=\"T_c02f9_row1_col3\" class=\"data row1 col3\" >#9f0052</td>\n",
       "                        <td id=\"T_c02f9_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c02f9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_c02f9_row2_col0\" class=\"data row2 col0\" >Basement</td>\n",
       "                        <td id=\"T_c02f9_row2_col1\" class=\"data row2 col1\" >Strata2</td>\n",
       "                        <td id=\"T_c02f9_row2_col2\" class=\"data row2 col2\" >2</td>\n",
       "                        <td id=\"T_c02f9_row2_col3\" class=\"data row2 col3\" >#ffbe00</td>\n",
       "                        <td id=\"T_c02f9_row2_col4\" class=\"data row2 col4\" >3</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "    surface   series  order_surfaces    color  id\n",
       "0       Top  Strata1               1  #015482   1\n",
       "1      Base  Strata2               1  #9f0052   2\n",
       "2  Basement  Strata2               2  #ffbe00   3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.map_stack_to_surfaces(geo_model,\n",
    "                         {\n",
    "                          'Strata1': ('Top'),   \n",
    "                          'Strata2': ('Base'),\n",
    "                         },\n",
    "                         remove_unused_series=True)\n",
    "geo_model.add_surfaces('Basement')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subtle-techno",
   "metadata": {},
   "source": [
    "### Showing the Number of Data Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "forty-treaty",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:43.496371Z",
     "start_time": "2022-04-05T09:17:43.402352Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_077b3_row0_col3{\n",
       "            background-color:  #015482;\n",
       "        }#T_077b3_row1_col3{\n",
       "            background-color:  #9f0052;\n",
       "        }#T_077b3_row2_col3{\n",
       "            background-color:  #ffbe00;\n",
       "        }</style><table id=\"T_077b3_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >surface</th>        <th class=\"col_heading level0 col1\" >series</th>        <th class=\"col_heading level0 col2\" >order_surfaces</th>        <th class=\"col_heading level0 col3\" >color</th>        <th class=\"col_heading level0 col4\" >id</th>        <th class=\"col_heading level0 col5\" >No. of Interfaces</th>        <th class=\"col_heading level0 col6\" >No. of Orientations</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_077b3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_077b3_row0_col0\" class=\"data row0 col0\" >Top</td>\n",
       "                        <td id=\"T_077b3_row0_col1\" class=\"data row0 col1\" >Strata1</td>\n",
       "                        <td id=\"T_077b3_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "                        <td id=\"T_077b3_row0_col3\" class=\"data row0 col3\" >#015482</td>\n",
       "                        <td id=\"T_077b3_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "                        <td id=\"T_077b3_row0_col5\" class=\"data row0 col5\" >4</td>\n",
       "                        <td id=\"T_077b3_row0_col6\" class=\"data row0 col6\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_077b3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_077b3_row1_col0\" class=\"data row1 col0\" >Base</td>\n",
       "                        <td id=\"T_077b3_row1_col1\" class=\"data row1 col1\" >Strata2</td>\n",
       "                        <td id=\"T_077b3_row1_col2\" class=\"data row1 col2\" >1</td>\n",
       "                        <td id=\"T_077b3_row1_col3\" class=\"data row1 col3\" >#9f0052</td>\n",
       "                        <td id=\"T_077b3_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "                        <td id=\"T_077b3_row1_col5\" class=\"data row1 col5\" >6</td>\n",
       "                        <td id=\"T_077b3_row1_col6\" class=\"data row1 col6\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_077b3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_077b3_row2_col0\" class=\"data row2 col0\" >Basement</td>\n",
       "                        <td id=\"T_077b3_row2_col1\" class=\"data row2 col1\" >Strata2</td>\n",
       "                        <td id=\"T_077b3_row2_col2\" class=\"data row2 col2\" >2</td>\n",
       "                        <td id=\"T_077b3_row2_col3\" class=\"data row2 col3\" >#ffbe00</td>\n",
       "                        <td id=\"T_077b3_row2_col4\" class=\"data row2 col4\" >3</td>\n",
       "                        <td id=\"T_077b3_row2_col5\" class=\"data row2 col5\" >0</td>\n",
       "                        <td id=\"T_077b3_row2_col6\" class=\"data row2 col6\" >0</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "    surface   series  order_surfaces    color  id  No. of Interfaces  No. of Orientations\n",
       "0       Top  Strata1               1  #015482   1                  4                    4\n",
       "1      Base  Strata2               1  #9f0052   2                  6                    4\n",
       "2  Basement  Strata2               2  #ffbe00   3                  0                    0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg.utils.show_number_of_data_points(geo_model=geo_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medieval-deployment",
   "metadata": {},
   "source": [
    "### Loading Digital Elevation Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "subsequent-technique",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:43.735443Z",
     "start_time": "2022-04-05T09:17:43.499373Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cropped raster to geo_model.grid.extent.\n",
      "depending on the size of the raster, this can take a while...\n",
      "storing converted file...\n",
      "Active grids: ['regular' 'topography']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Grid Object. Values: \n",
       "array([[   6.905     ,    8.84      , -496.25      ],\n",
       "       [   6.905     ,    8.84      , -488.75      ],\n",
       "       [   6.905     ,    8.84      , -481.25      ],\n",
       "       ...,\n",
       "       [1377.07670455, 1735.97101449,    0.        ],\n",
       "       [1377.07670455, 1748.7826087 ,    0.        ],\n",
       "       [1377.07670455, 1761.5942029 ,    0.        ]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.set_topography(\n",
    "    source='gdal', filepath=file_path + 'raster20.tif')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extreme-drink",
   "metadata": {},
   "source": [
    "### Plotting Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "conservative-philip",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:44.035513Z",
     "start_time": "2022-04-05T09:17:43.737425Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAHdCAYAAAAdPA7sAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABMWUlEQVR4nO3dd3wUdeLG8U/6JrQgJArSUuhiCBASilQVDykKnD9EEEE0iIgG4UA5BUkUBINSTgEPURBUiIVyWBEsB6IUQVFKSKQEkIQSSja7SXZ+f5DssYYSINkCz/v14gWZ7+zOMwvss1N2xsswDAMREbnuebs6gIiIuAcVgoiIACoEEREppEIQERFAhSAiIoVUCCIiAqgQrisFBQUsWrSI++67jxYtWhATE8P999/PqlWrLvu56tevz3vvvQfAzJkzadOmzQXnnTlzJvXr12fFihXFxg4cOED9+vX59ttvLzvD5RgwYAAJCQlluozSdKnXFKBTp0688sorFxwfMGAA9evXt/9q1KgRLVu2ZMiQIWzZssVh3rFjx3LfffeVSvaL2bJlCxs2bLD/fKl1EOfydXUAcQ6r1crDDz/Mvn37GD58OM2aNcMwDD7//HNGjRrFrl27eOqpp8o0Q2JiIrGxsYSGhpbpcq4FgwcP5oEHHrjq5+ncuTMvvPACAHl5efz555+88847DBgwgLlz59K6dWsAxo0bR0FBwVUv71L69u3LhAkTiI2NBSAlJYWAgIAyX66UjArhOjF9+nS2b9/OihUruPnmm+3TIyMj8fb2ZsaMGXTv3p2IiIgyWX65cuXw8fHhn//8J3Pnzi2TZVxLypUrR7ly5a76eQICAggJCbH/XL16dZo2bcqQIUN4/vnn+eyzz/D19aVChQpXvawrccMNN7hkuXJ+2mV0HcjLyyMlJYU+ffo4lEGRBx98kHfeeYcaNWrYp3388cfcfffdNGnShC5dujBr1iysVusVZwgMDGTixIl88803LF269ILzffTRR9SvXx+LxWKftmHDBurXr8+ePXuAs7tCpkyZwgsvvEDz5s2JjY3ltddeY9++fQwePJioqCg6derE8uXLHZ7bbDbzzDPPEB0dTVxcHK+88gp5eXn28dOnT/P888/TunVroqOj6du3L+vXr3fI1q5dO6ZOnUpMTAx9+/bFMAwGDBjAgAEDLrhOM2fO5P7772fx4sXcdtttREVFMWLECLKysnj++edp3rw5cXFxTJ482eEx5+4yOnLkCCNGjKB58+a0bt2aBQsWlOBVPz8vLy8GDRrE/v37+fnnnwHHXUZFu/Fmz55N27Ztue2228jIyMAwDObNm8ftt9/OrbfeSrdu3Vi0aBHnXuzg+PHjjBs3jlatWhEdHc2AAQP49ddfgbO7GQEmTJhgf73+ustoy5YtDBo0iBYtWtCiRQtGjBhBRkaGfXzAgAFMnjyZ559/ntjYWGJjYxk+fDhZWVn2ecaOHUunTp0uuP6dOnVy2JVW9Otif4fXCxXCdWD//v2cOHGC6Ojo846XK1eOli1b2jfdP/jgAxITE3nkkUdYtWoVzz77LJ988gljx469qhx33HEH3bt3Z/LkyRw8ePCqnmvBggXccMMNfPLJJzz44IO88cYbDBw4kN69e/PRRx8RHR3NuHHjOHHihP0xa9aswc/Pj5SUFJKSkvjwww+ZNGkSAIZhMGTIEHbv3s3MmTP56KOP6NSpE0OGDOGbb76xP8eff/7Jnj17+PDDD5kwYQJeXl7MnDmTmTNnXjTvr7/+yvfff8/bb7/NjBkzWL16NXfffTchISF8+OGHPPLII8yfP99hWUXy8/N5+OGHOXDgAG+99RZvvvkmX375pcMb5eVq2LAhADt27LjgPEuWLGHevHnMmjWLm2++mVdffZV33nmHMWPG8J///Idhw4Yxa9Yspk2bBpw9RvXwww+zbds2Xn31VT7++GOqV6/OoEGDOHbsGN9//z0Ao0aNOu/rtW3bNgYMGEC1atVYvHgxb775JkeOHOGBBx7g5MmT9vneffddgoKCeO+993jppZf44YcfSE5Oto+PGzeOlJSUC65XSkoK33//vf3X008/jZ+fH8OGDbu8F/EapF1G14Hs7GwAKlWqVKL5X3/9dYYMGcI999wDQM2aNQF49NFHGTlypMOWxOV6/vnn+fHHH3n22WeZP3/+FT9PnTp1eOKJJwAYNGgQM2bM4I477uDuu+8Gzu6DX7lyJX/88QdNmzYFoG7dukyYMAFvb28iIiI4evQoiYmJjBw5kl9++YUtW7bwzTffcNNNN9nX97fffuPf//437du3ty972LBh1KpVy/5zcHDwJfPm5eWRlJTEDTfcQEREBA0aNKCgoMC+Dg8//DCvv/46O3fudFgWwPr169m1axcrVqygXr16ACQnJ1/0U/ClVKxYEYBTp05dcJ7777/f/qk+JyeHt99+m8TERO644w7g7L+LEydOMHnyZIYPH87GjRvZvn07y5Yto0GDBgC88MILVKpUiePHj9t3R5YvX/68r9lbb71FnTp1SEpKwtv77GfVmTNn0rlzZz766CMeeugh+3KLPpyEh4dz991388MPP9if51K7v87dTbV+/XpmzJjBhAkTaNWq1UUfdz1QIVwHiv4DnPtp+UKOHTvG4cOHeeONN3jzzTft04t2C+zZs+eqCqFixYokJSXxyCOPsGjRIjp06HBFzxMWFmb/c1BQEAC1a9e2TzOZTAAOu56io6PtbzQAUVFR5OXlsWfPHrZv3w7A3/72N4fl5OXl2d88i5y7nJKqVKmSwxtRUFBQsf3nJpPJIW+RXbt2YTKZ7GUAEBoaai/qK1FUBH9dt3PVqVPH/ufU1FQsFgvPP/88EyZMsE8vKCjAYrFw4MABduzYgclkspdB0To9++yzJcq0c+dOYmJiHP6OQkJCCAsLc9iSCQ8Pd3hchQoVHHb9ldTu3bt54oknGDx4MH369Lnsx1+LVAjXgZo1a1K1alW2bNlC165di42fPn2aYcOGMWTIEBo1agSc3aw/35v1uQcor1S7du247777eOWVV4r95/by8io2f35+frFpvr7F/+me+0ZyPn8dt9ls9ue32Wz4+/vzySefXPJxRWVzOa4k718ZhuHw+vj5+V12jiJFBVj0930+5579U/SBYMqUKQ5v+EWqVat2VXkupujvpsi5f75SR44c4dFHH6VNmzYedTpyWdMxhOuAt7c3ffr04cMPP+TQoUPFxt999102bNjAzTffTJUqVahSpQr79u2jdu3a9l8HDx5kypQp5OTklEqmsWPHUqVKFcaPH+8wvehN5fTp0/Zpe/fuLZVlFr0JFvnpp5/w8/Ojbt261K9fH6vVyqlTpxzWe+nSpXz88celsvwr1ahRI3Jzc+0HZ+Hs1t6+ffuu+DkXLlxInTp17LvTLiU8PBw/Pz8yMjIcXp+tW7fy2muvAWfPWMvNzWXnzp32x+Xl5dGxY8diB/jPp379+mzevNle1HD2jfuPP/4gMjLystbvYnJychg6dCihoaFMmTLlvB9CrlcqhOvEY489RmRkJH379uWjjz5i7969/P7770ydOpXp06fz1FNPERERgZeXF48++ijvv/8+b731Fnv37uW7775j7NixmM1mqlatWip5ypUrx6RJk9i/f7/D9KioKLy9vZk+fTr79+/nm2++4a233iqVZW7fvp2kpCT27NnDqlWrmDVrFv369aNixYq0bduWxo0b8/TTT/P999+zf/9+Xn/9df7973877Do5nxMnTpRod9yVio2NJTo6mrFjx/LTTz+xY8cORo0aVaKzviwWC5mZmWRmZnLo0CE2bdrEE088wbp160hMTCzxm2GFChXo27cvs2bNIiUlhf379/Ppp5/ywgsvUL58efz9/WnVqhVRUVGMHTuWTZs2kZaWxrhx48jNzbV/36FcuXLs2bOHo0ePFlvG4MGD+eOPP/jnP//J7t27+fnnnxkxYgTBwcH06NGjxK/XqVOnOHbs2HnHCgoKSEhI4MSJE7z88sucOnXK/vpkZmaWeBnXKu0yuk6YTCYWLFjA22+/zfz580lKSsLX15e6desyffp07rzzTvu8Dz30EIGBgSxYsIBp06YRHBzMnXfeyciRI0s1U8uWLe2nvBapWbMmEydOZPbs2Xz00Uc0btyY5557jkcfffSql3fvvfdy9OhRevXqRYUKFejfvz/Dhw8HwMfHh3nz5vHKK68wevRozpw5Q+3atZkyZcol34yKDgwvXLjwqjOej7e3N3PmzOGll15i2LBheHl50bdvX4czby5k9erVrF69Gji79RUaGkrz5s1ZunSp/UyjknrmmWeoWrUqs2fPZsKECYSGhjJgwAAef/xx4Ozuvn/9619MnjyZoUOHUlBQQNOmTZk/f779g8Sjjz7KnDlz+PHHH4ttNdx666289dZbvPbaa/Tu3ZvAwEBat25NcnJyiQ7cF3nxxRf58ccf+frrr4uNHTp0iLVr1wLQpUuXYuPnbt1cj7x0xzQREQHtMhIRkUIqBBERAVQIIiJSSIUgIiLAdXyWUdF53SEhIfj4+Lg6johImSsoKCAzM5NbbrnlvF+wvG4L4ddffy2V682LiHiaRYsW0aJFi2LTr9tCKLoEw/z58x0uVObuLBYLu3fvpm7duh51YxHldi7ldi5PyX348GEeeOCBC16C5rothKLdRDfeeONVXazN2XJzczlx4gQ333zzFV1Tx1WU27mU27k8LfeFdpProLKIiAAqBBERKXTd7jIqifz8/PNeetmViq6Xf77r5ruz0szt6+t73stJi8jV0f+qCzh16hSGYeDn5+dWl8f18/MjLCyszK49X1ZKK7dhGOTk5ODl5eWyG8OLXKtUCOdhs9nIz8+ncuXKro5SjM1mwzAMTCbTZd9gxZVKM3dgYCDHjx/HZrN51Gsg4u70v+k88vLy3PrUMTl7N68ruW2iiFyYCuE8DMPQJ0835+Xlha7cLlK69K4nHsmdjuuIXCtUCGXMbNFuDRHxDDqoXAaOnzYz+ZP/8vaarWSezCGkYhAPdYxi7D1tqFw+sFSXNWTIEDZt2gScPaXT29vbfiZP9+7dmThxYqkuT0SuXSqEUnb8tJl2z7/D9v3/u2F35skcpi5bz6rNqXw7cWCplsK///1v+59HjBhB3bp17ff4FRG5HNplVMomf/JfhzI41/b9mbz8yTqn5MjKyuLpp58mNjaW9u3bM2XKFKxWKwBjx45l/Pjx9OrVi+joaAYOHEhGRoZTcomI+1IhlLL5X2+9+Pian52SY/jw4QCsXr2aJUuW8OOPPzJjxgz7+CeffMKYMWP44YcfqFWrFgkJCU7JJSLuS4VQisyWPLJO5Vx0nsyTOeRay/ZyGPv27WPLli2MGzeO8uXLc+ONN/Lkk0/y8ccf2+fp3r07sbGxBAQEMGrUKLZu3cr+/fvLNJeIuDcVQikKDPCjaoWgi84TUjEIk3/ZHro5evQoQUFB3HDDDfZp1atXJysry/5lrnPvAVGpUiWCgoLIysoq01wi4t5UCKVsUKeoi493bFrmGapXr05OTg7Hjh2zTztw4ADBwcH2M5COHDliHzt+/Dg5OTncdNNNZZ5NRNyXCqGUjb2nDY1rnv9uRI1rhjDmntZlnuHGG2+kVatWvPjii5w5c4Y///yTGTNm0L17d/s8y5cv57fffsNisTBlyhTi4uKoVq1amWcTEfelQihllcsH8u3EgfyjZ2tCKp7dfRRSMYh/9Gxd6qecXswrr7yCzWajc+fO9OzZk+bNmzN69Gj7eLNmzRg/fjytWrUiOzub5ORkp+QSEfel7yGUgcrlA5ncvzOT+3cm15pf5scMipx7FlHVqlV59dVXLzhvWFgYs2fPdkYsEfEQ2kIoY84qAxGRq6VCEBERQLuMrkuTJ092dQQRcUPaQhAREUCFICIihVQIIiICqBBERKSQCkFERAAVQpnLN1tcHUFEpER02mkZsBw/zS+TPyZ1/tfkZp7EFFKRyEGdaDL2XgIqly/VZdWvXx+TyYS3tzeGYRAcHEzfvn0ZOnRoqS5HRK59KoRSZjl+mk9v+ycntv/v3gK5mSf5dconHPjPJv72XVKpl8LSpUupV68eADt37mTgwIFERERwxx13lOpyROTapl1GpeyXyR87lMG5Tmzfzy8vf3zesdJSv359oqOj+e233wCw2Wy89tpr3HXXXURHR9O+fXvef/99+/xTp06lbdu2tGrViocffth+k5yCggJmzZpFp06daNWqFc888wynT58u0+wi4louKYRt27bRtm1b+89Wq5XExERiY2OJjY1l3Lhx9vv/GoZBcnIycXFxxMTEkJSUREFBgf2xK1eupHPnzkRHRxMfH+/ym7zsfuvri46nXmL8av3+++/88ssvtGvXDjh7mesvvviChQsXsnnzZp5++mleeuklzpw5w/r16/n0009ZsWIF3333HTfddBMzZ84EYP78+Xz55ZcsWrSIL7/8ktzcXBITE8s0u4i4llMLwTAMUlJSGDx4sP3OXQDTpk1j9+7dfP7553z++eekpqby1ltvAbBo0SLWrl3L8uXLWbVqFZs3b2bx4sUA7Nixg/HjxzNt2jTWr19P1apVeeGFF5y5Sg7yzRYsWScvOk9u5knyc62luty+ffvSokULoqKiuOeee2jQoAH169cH4Pbbb+edd96hatWq/PnnnwQEBGCxWMjOzqZ8+fIcPXqUpUuXsm/fPhITE5kyZQoAKSkpDB8+nGrVqlG+fHlGjRrF8uXLsVh0kFzkWuXUQpg9ezYLFixwOOCZl5fHBx98wPPPP09wcDDBwcEON3NZtmwZAwcOJDQ0lJCQEOLj41myZAkAK1asoHPnzkRFRWEymRg1ahSrV6/m6NGjzlwtO9/AAAKqVrzoPKaQivia/Et1ue+//z4bN25k69atfP/993h5eTFy5Ejg7OublJREbGws8fHxrFmzBji7K6lJkyZMmjSJb7/9lh49evC3v/2NtWvXAnDo0CH+8Y9/0KJFC1q0aEHPnj3x9fXl4MGDpZpdRNyHUw8q9+7dm6FDh/Ljjz/ap+3du5eCggK2bt3KsGHDMJvNdOvWzf6GlpaWRmRkpH3+sLAwUlNTMQyDtLQ0oqOj7WOVK1emQoUKpKWlUaVKlRJlslqt5ObmOkyzWCz4+flhs9kuex0jB3Vk+9RlFxyPGNTxip63SNFjz30Om81m/7lKlSr07duXkSNHYrPZSE5Oxmaz8c033xAQEMDBgwf5+OOPMQyDjIwM6tSpw4IFCzhz5gyLFy/mqaee4qeffiIkJISJEycSFxcHnC2WAwcOUKNGjSvKf77cV8NmszlsZZaVoi0iT9syUm7n8pTcl8rn1EIIDQ0tNu3EiRPk5eWxZs0aUlJSOHPmDPHx8VSoUMFeECaTyT5/YGAgNpsNq9VabKxo3Gw2lzjT3r17ycnJKTY9LCwMwzAuY+3OihxxF/tXbuTk7xnFxio2vJnIJ+66rHwXcu5frMVisT/nqVOnWLp0Kbfeeitms5ns7Gx8fHywWq0cO3bMfqXT06dPs2vXLpKTk3nzzTepUaMGJpOJChUqYLVaufvuu5k5cybVq1cnODiY119/nS+//JJPPvkEX98r/2dTWv9hLBYL6enppfJcJZGamuq0ZZUm5XYud8+dmZl50XGXn3bq7++PzWbjqaeeomLFilSsWJFBgwaxcOFChg0bhslkcngTMZvN+Pr6EhAQgMlkKvbp3mw2ExQUVOLl165dm7CwMIdpRVsIfy2bkggMDORv3yXx65RP2DN/jf17CBGDOnLLP+656lNObTYbFouFgIAAvL3P7vF78MEH7X/28/OjVatWTJgwgcDAQJ566inGjh1Lhw4dqFixIj169KBWrVpkZGTQvXt30tPTefjhhzlz5gxhYWFMnz6dwMBAhg0bhmEYPPTQQ5w6dYpGjRoxZ84cKlSoUGq5r4aXlxeRkZEEBARc9XNdjMViITU11SnLKk3K7Vyekjsjo/gH1XO5vBDq1KmDt7c3J0/+72DsuWcRRUREkJ6eTlRUFADp6emEh4c7jBU5duwY2dnZRERElHj5/v7+F3zjv9I3rsAqFYl5+UFiXn6Q/FxrqR8zgLPZvL292blz50Xni4iIYOnSpQ7Tnn76afufn3rqKZ566qlijwsICGDkyJH2XXelpSh3aTxP0YcCZ3DmskqTcjuXu+e+VFm5/HsIFStW5Pbbb+fVV1/l5MmT/Pnnn7zzzjvcddddAPTo0YN58+Zx+PBhsrKymDNnDj179gSgW7dufPHFF2zcuBGLxcK0adNo164dlStXduUqOSiLMhARKQsu30IAmDRpEi+//DJdu3YlLy+Pe+65h8GDBwPQr18/srKy6NOnD3l5eXTv3p1BgwYB0LBhQxITExk3bhyZmZm0aNGCSZMmuXJVREQ8lksKITY2lg0bNth/Ll++/AW/9OTj40NCQgIJCQnnHe/atStdu3Ytk5wiItcTl+8yEhER96BCEBERQIUgIiKFVAgiIgKoEMpewdV/K/l6lZ+fz+HDh10dQ+S6oUIoC3nHYc8Y+D4Uvg06+/ueMWenl7L69esTFRVFdHQ0TZs2pUOHDsyePbvUl+MKI0eO5KuvvnJ1DJHrhlt8D+GaknccttwGZ7afMy0T9k2Bo/+B6O/Ar3S/OHet3jHt+PHSL1ARuTBtIZS2fZMdy+BcZ7bDvpfLdPHOuGPaRx99xNChQxk7dizR0dHceeed/PTTTzz99NNER0dz9913s2PHDvsyFi9eTJcuXejYsSPDhw+3X2Brw4YNdO/enUmTJtGyZUvatWvHm2++CcCLL77Ixo0bmTx5sv2CfCJStlQIpe3QW1c3fpWcdce0NWvW0K5dOzZt2kRUVBQPPfQQd955Jxs2bKBhw4a8/vrrAHz66afMnTuXWbNm8dlnn1GzZk2HLxnu2rWLSpUqsW7dOp577jmmTZvG4cOHGTduHC1atGDs2LGMHTu2TF8zETlLu4xKU4EZ8i5xC8+8TCjIBZ/SuwBW37598fb2Ji8vj9zcXG677TaHO6a1adPmkndMu/3220lMTLRfeC4lJYWnn36aatWqATBq1Chuv/12Jk6cCMDNN99s/4Z4y5Yt+fnnn+nSpQsAcXFx9q2QlJQUHnroIerWrYvZbCYhIYGYmBj7RQl9fHx45JFH8PX15Y477iAoKIj9+/dz0003ldrrIyIlo0IoTT6B4Ff14qXgF1KqZQBn75hWdAwhMzOTZ599lpEjRzJ79mz7HdPWr19PtWrVaNiwIeB4x7TFixczY8YMbr75Zp555hk6dOhgv2Oaj4+PfTnn3jEtODj4f6vt4+NwWWxvb2/7jXAOHTrEa6+9xqxZszAMAy8vL7y8vDh48CC+vr5UqFABPz8/h2WU1k10ROTyqBBKW7XBZw8gX2y8DIWEhNCvXz/7Ja2nTZuGYRh89913DndMg7Nv1uHh4bz77rucOXOGRYsW8dRTT7Fp0yZCQkJITEykVatWwNk7pu3fv59atWqxZcuWy8ozePBgevXqhdlsJjAwkPT0dGrWrHlZzyMiZU/HEEpbrbFQrvH5x8o1hlpjynTxJ0+e5MMPP7TfWvT06dP4+/vj4+PD8ePHefnlswe18/Pz2bp1K/Hx8ezfv59y5crZb1Dk4+PDPffcw7/+9S+OHDlCXl4er732GkOGDLnsu8jde++9zJ8/n71792Kz2Xj33Xe57777SnTXOH9/f/uBbBEpe9pCKG1+lc+eWrrv5bMHkPMyz+4mqjb4bBmU8imnAH//+9/t+/79/f2Ji4tjypSzWykjRoxgzJgxxMTEULFiRXr27EmtWrXYs2cPd911Fzt37uT++++33zFtxowZAMTHx5OXl8f//d//cfLkSRo1asTcuXMv+/aZPXv25MSJEzz66KMcPXqU8PBw5syZQ6VKlS752G7dujFx4kQyMjIueDVcEXdyMusUO37YzOm9R/ALLsfNrepTo1EtV8cqMS/jSm4cfA04cOAAnTt3ZtWqVcXusFZ0W85SufNRKR9Attls9l0vpXHnMWcp7dyl+nd0ieVs376dxo0bu/WdsP5KuZ0rNzeXXT/sIHXs+5zcsNs+3T+4HE3+/RhNerdyYbr/KXrfW716NTVq1Cg27jnvKJ6qlA8gi4j7yT1jYe/kFQ5lAGA9cYafB8wgdf2OCzzSvagQRESuUsbGPRz9/OfzjhWYrRz5XoUgInJdOH3w4pdZsRw85qQkV0eFIB7pOj30JW6q3E3BFx0PqFb6J5OUBRXCeXh5eenLUW6u6EtuIu6gRkwElTud/3RznwA/Qlo3cHKiK6NCOA8/Pz8sFourY8hFWCwWh284i7iSqbyJ8HG9KN+0jsN033Imot55nLptG7om2GXS9xDOw9vbG19fX7Kzs/H393erT6I2mw2LxYKXl5fHnXZaGrkNw8BqteLr6+tR6y/XPmulAtp+/A8Ort/Jmb1Z+FUK4qa4etSJDnd1tBJTIVxAhQoVyM/PJz8/39VRHOTl5ZGenk5kZCQBAQGujlNipZXby8uLcuXKXfYX5EScIfimStx0fztXx7hi+l91Eb6+vm77xhMQEOBRX9wp4qm5Ra4H2uYWERFAhSAiIoVUCCIiAqgQRESkkApBREQAFYKIiBRSIYiICKBCEBGRQioEEREBVAgiIlJIhSAiIoAKQURECrmkELZt20bbtm2LTbfZbAwYMICXX37ZPs0wDJKTk4mLiyMmJoakpCQKCgrs4ytXrqRz585ER0cTHx9PVlaWU9ZBRORa49RCMAyDlJQUBg8eTF5eXrHxt956i40bNzpMW7RoEWvXrmX58uWsWrWKzZs3s3jxYgB27NjB+PHjmTZtGuvXr6dq1aq88MILTlkXEZFrjVMLYfbs2SxYsIChQ4cWG9uxYwcfffQRd9xxh8P0ZcuWMXDgQEJDQwkJCSE+Pp4lS5YAsGLFCjp37kxUVBQmk4lRo0axevVqjh496pT1ERG5ljj1Yv+9e/dm6NCh/Pjjjw7TrVYrY8aMYeLEiaSkpDiMpaWlERkZaf85LCyM1NRUDMMgLS2N6Oho+1jlypWpUKECaWlpVKlSpUSZrFYrubm5V7FWzlV0a09Pu8WncjuXcjuXp+S+VD6nFkJoaOh5pycnJ9O2bVtatGhRrBDMZrPDDVUCAwOx2WxYrdZiY0XjZrO5xJn27t1LTk7OZayFe0hNTXV1hCui3M6l3M7l7rkzMzMvOu7y24GtX7+eH374gaVLl5533GQyObSa2WzG19fXfuetv366N5vNBAUFlXj5tWvXJiws7MrCu4DFYiE1NdXjbqGp3M6l3M7lKbkzMjIuOu7yQli1ahX79u2jdevWAOTm5uLl5UVaWhpz5swhIiKC9PR0oqKiAEhPTyc8/OxNq4vGihw7dozs7GwiIiJKvHx/f3+PvKWjp96KUrmdS7mdy91zX6qsXP49hMTERLZs2cLGjRvZuHEj3bp1o3///syZMweAHj16MG/ePA4fPkxWVhZz5syhZ8+eAHTr1o0vvviCjRs3YrFYmDZtGu3ataNy5cquXCUREY/k8i2ES+nXrx9ZWVn06dOHvLw8unfvzqBBgwBo2LAhiYmJjBs3jszMTFq0aMGkSZNcnFhExDO5pBBiY2PZsGHDeccmT57s8LOPjw8JCQkkJCScd/6uXbvStWvXUs8oInK9cfkuIxERcQ8qBBERAVQIIiJSSIUgIiKACkFERAqpEEREBFAhiIhIIRWCiIgAKgQRESmkQhAREUCFICIihVQIIiICqBBERDzS6ewcTh49VarP6faXvxYRkf/54+c0DvxnM0fe/RabJZ8b7omhxr0tqXdb46t+bhWCiIiH+GNrOj8NnMmZbfvs006/upLD89dgfDiK+p1uvarn1y4jEREPcWDVZocyKGI9cYa02V9gteRd1fOrEEREPEDOKTOZi7674PiRDzdw6LcDV7UMFYKIiAew2QowrPkXHDdsNgoKCq5qGSoEEREPUL5SeSrf2/KC48HtG3Fj3WpXtQwVgoiIh6jRIwa/KhWKTffy9SHyybspV6ncVT2/CkFExEPUbdOQmJRR3PTAbXj5nH37rtzpFpovHUmjnhfeeigpnXYqIuJB6nW4hbDW9Tk05h5s+TZCIm+iXIWgUnlubSGIiNOZzWbeeecdrFarq6N4JD9/P2o1qUOd6PBSKwNQIYiIC3zwwQc8/OhQGjRuwg8//ODqOFJIhSAiTjdpylQKakWT7h1C+8530P/Bhzh1qnSvyyOXT4UgIk6VkZFB2u87qJueTs09u7DmnOa995cQXrceO3fudHW865oKQUSc6t13F+HvX447C2qzvyATb7yIa92KwQ89RMWKFV0d77qms4xExKnmvjUfi/UM77CFKpUqs/TjD+nYsSMAubm5HDt2zMUJr18qBBFxqiEPDcRmK2DGtNfYkbqLypUruzqSFFIhiIhTPfPMWPLz8xk4cKDKwM3oGIKIOJ2vry81atRwdQz5CxWCiIgAKgQRESmkQhAREUCFICIihVQIIiICuKgQtm3bRtu2be0/Hz58mGHDhhEbG0ubNm1ITEy0XwXRMAySk5OJi4sjJiaGpKQkh9vErVy5ks6dOxMdHU18fDxZWVlOXx8RkWuBUwvBMAxSUlIYPHgweXl59umjR4/mpptu4ttvv+WTTz7hl19+4V//+hcAixYtYu3atSxfvpxVq1axefNmFi9eDMCOHTsYP34806ZNY/369VStWpUXXnjBmaskInLNcGohzJ49mwULFjB06FD7NKvVSmBgII899hgBAQGEhITQvXt3tmzZAsCyZcsYOHAgoaGhhISEEB8fz5IlSwBYsWIFnTt3JioqCpPJxKhRo1i9ejVHjx515mqJiFwTnFoIvXv3ZtmyZTRp0sQ+zd/fn7lz5xISEmKftmbNGho0aABAWloakZGR9rGwsDBSU1MxDKPYWOXKlalQoQJpaWlOWBsRkWuLUy9dERoaetFxwzB48cUXSUtLY+rUqcDZOyuZTCb7PIGBgdhsNqxWa7GxonGz2VziTFarldzc3MtYC9eyWCwOv3sK5XYu5XYuT8l9qXxucy2j3Nxc/vGPf7Bz504WLlxIlSpVADCZTA4rYTab8fX1JSAgAJPJVOzN3Gw2ExRU8lvK7d27l5ycnNJZCSdKTU11dYQrotzOpdzO5e65MzMzLzruFoVw4sQJhgwZQlBQEB988AHBwcH2sYiICNLT04mKigIgPT2d8PBwh7Eix44dIzs7m4iIiBIvu3bt2oSFhZXOijiBxWIhNTWVyMhIAgICXB2nxJTbuZTbuTwld0ZGxkXHXV4IhmHwxBNPULVqVWbOnImfn5/DeI8ePZg3bx5xcXH4+voyZ84cevbsCUC3bt3o378/vXv3pkmTJkybNo127dpd1hUU/f39i+128gRFW0ieRrmdS7mdy91zX6qsXF4IW7Zs4ccffyQgIICWLVvapzdq1IhFixbRr18/srKy6NOnD3l5eXTv3p1BgwYB0LBhQxITExk3bhyZmZm0aNGCSZMmuWpVREQ8mksKITY2lg0bNgDQrFmzi95H1cfHh4SEBBISEs473rVrV7p27VomOUVErie6dIWIiAAqBBERKaRCEBERQIUgIiKFVAgiIgKoEERErti+ffuIim7Op59+6uoopUKFICJyhRYsWMhvf56k/5DHePLp0Rw8eNDVka6KCkFE5Ar9++0F5NdrR077R9mQZXBrdDNmzvqXw028PIkKQUTkCvzyyy9kHj0GVWtBTjYFBhT4lWPEE8PtN/HyNC6/dIWIiCd6Z8FCcv3KEbh2Nuajh6lQqRJz58whLi6OWrVquTreFVEhiIhcgY+XLePmcj688vJ0YmJiOHLkiP3ujZ5KhSAicgU++89KwsLC8PX1JTc3l2PHjrk60lVTIYiIXIG6deu6OkKp00FlEREBVAgiIlJIhSAiIoAKQURECqkQREQEUCGIiEghFYKIiAAqBBERKaRCEBERQIUgIiKFVAgiIgKoEEREpJAKQUREABWCiIgUUiGIiAigQhARkUIqBBERAVQIInIJ+fn53Nu7D19++aWro0gZUyGIyEV9+eWXfL72e+65rx/9BjzIiRMnXB1JyogKQUQu6l+z52KuHUNO+3g+2phKeN36fPLJJ66OJWVAhSAiF3T8+HG++vJLqHkLeHtjqVSDE97luPfee5k5c6ar40kp83V1ABFxX++99z5GucoE/rIKc/o2AMY99xzRTZvSsWNHF6eT0qZCEJELWrh4MX7WU4x9+DH+dtdb1KhRgxtvvNHVsaSMuGSX0bZt22jbtq395+zsbB5//HGaN29Ohw4dWLp0qX3MMAySk5OJi4sjJiaGpKQkCgoK7OMrV66kc+fOREdHEx8fT1ZWllPXReRaNuf1f3E44wD/HPcszZs3Vxlc45xaCIZhkJKSwuDBg8nLy7NPf+655wgKCmLdunXMmDGDV155hR07dgCwaNEi1q5dy/Lly1m1ahWbN29m8eLFAOzYsYPx48czbdo01q9fT9WqVXnhhRecuUoi17Rbb72V8uXLuzqGOIlTC2H27NksWLCAoUOH2qedOXOGr776ihEjRhAQEMCtt95Kt27d7FsJy5YtY+DAgYSGhhISEkJ8fDxLliwBYMWKFXTu3JmoqChMJhOjRo1i9erVHD161JmrJSJyTXBqIfTu3Ztly5bRpEkT+7S9e/fi6+tLzZo17dPCwsLYvXs3AGlpaURGRjqMpaamYhhGsbHKlStToUIF0tLSnLA2IiLXFqceVA4NDS02LScnB5PJ5DDNZDKRm5sLgNlsdhgPDAzEZrNhtVqLjRWNm83mEmeyWq32ZXkCi8Xi8LunUG7nUm7n8pTcl8rn8rOMAgMDi70h5+bmEhQUBJwth3NXwmw24+vrS0BAgENxnDte9NiS2Lt3Lzk5OVexBq6Rmprq6ghXRLmdS7mdy91zZ2ZmXnTc5YVQu3Zt8vPzOXjwINWrVwcgPT3dvisoIiKC9PR0oqKi7GPh4eEOY0WOHTtGdnY2ERERl7X8sLCw0lqdMmexWEhNTSUyMpKAgABXxymRg9sP8OcPu7CeOENQzSrc3LoBN9S4wdWxSsQTX29QbmfzlNwZGRkXHXd5IZQvX57OnTuTnJxMUlISu3fvZuXKlcydOxeAHj16MG/ePOLi4vD19WXOnDn07NkTgG7dutG/f3969+5NkyZNmDZtGu3ataNy5colXr6/v3+x3U6eoGgLyd1t++B7tj38Bvln/rcl90fDGkT/+zEiWzdwYbLL4ymv918pt3O5e+5LlZXLCwEgMTGR8ePH0759e4KCghg9erR9i6Bfv35kZWXRp08f8vLy6N69O4MGDQKgYcOGJCYmMm7cODIzM2nRogWTJk1y5arIOVLX72DrwFkUWPIcpp/5/QC/jn6H0OXPULFKRRelE5G/ckkhxMbGsmHDBvvPwcHBTJ8+/bzz+vj4kJCQQEJCwnnHu3btSteuXcskp1ydI//dWawMipxYt4u963fSpFuMk1NJWfjuu+/YtWsXgwYNwttbl0jzVPqbkzKTl5V90fGczJNOSiJl7anRYxiW8A/i2tzGnj17XB1HrpAKQcqM343BFx0vX63kx3rEff34449s27QZa61mbMw4SaMmTUhMetHhEjPiGVQIUmZuatsQ3/LnP8B2w+1NqNOqvpMTSVl4Y/Yc/PINOv+2l9AD+8gz5/L8+OeJiWvlcIkacX8qBCkz4TF1iV70JAFVHQ8cV4qrS+MX+1GuUjkXJZPSYhgG//n0M/K9bNQ1KvOncZKg8uV5cOBDDH9sKL6+bnHeipSQ/rakTDXu0ZLg76uT8d8d5B47RcWwG6ndphGVbwp2dTQpBevWrSPz8EH88OFNr438/d4+LFi00K1PvZQLUyFImbu5fg2q1K7K9u3badC4sd4sriE1atRgwYIFvDF9Frd3uZOJLya6OpJcBRWCiFyx2rVrM2DAAO655x5dJvsaoEIQkatWoUIFV0eQUqCDyiIiAqgQRESkkApBREQAFYKIiBRSIYiICKBCEBGRQioEEREBVAgiIlJIhSAiIsBFCuHIkSPOzCEiIi52wUK4++67WbJkiTOziIiIC12wEPr3709SUhIDBgxg7969zswkIiIucMFCePLJJ1m+fDn+/v706NGDuXPn6pZ4IiLXsIte7bROnTrMmzePzz//nKlTp/Lpp5/y2GOPERAQ4DBf+/btyzSkiIiUvRJd/rpLly7k5OTw3HPPMWLECIcxLy8vfv/99zIJJyIiznPJQti2bRuJiYns2LGDhx9+mGHDhhXbQhAREc93wULIzs4mOTmZlJQUoqOj+eSTT4iIiHBmNhERcaILFsKdd94JwMSJE+nTp4/TAomIiGtcsBA6duzImDFjqFy5sjPziIiIi1ywECZPnuzMHCIi4mK6lpGIiAAqBBERKaRCEBERQIUgIiKFVAgiIgKoEEREpJAKQUREABWCiIgUcptC2Lx5M7169aJZs2Z06dKFFStWAGevqfT444/TvHlzOnTowNKlS+2PMQyD5ORk4uLiiImJISkpSfdsEBG5QiW6/HVZKygo4PHHH2f8+PHcddddbNy4kYEDBxIdHc2UKVMICgpi3bp17Ny5k0ceeYQmTZrQoEEDFi1axNq1a1m+fDleXl7Ex8ezePFiBgwY4OpVEhHxOG6xhXDy5EmOHTtGQUEBhmHg5eWFn58fPj4+fPXVV4wYMYKAgABuvfVWunXrZt9KWLZsGQMHDiQ0NJSQkBDi4+N1H2gRkSvkFoVQuXJl+vXrx8iRI2ncuDEPPPAAzz33HMePH8fX15eaNWva5w0LC2P37t0ApKWlERkZ6TCWmpqKYRhOXwcREU/nFruMbDYbJpOJ6dOn06lTJ9atW8fTTz/NG2+8gclkcpjXZDKRm5sLgNlsdhgPDAzEZrNhtVpLfBMfq9Vqfz5PYLFYHH73FMrtXMrtXJ6S+1L53KIQvvjiC7Zt28aYMWMA6NChAx06dGDmzJnF3qxzc3MJCgoCzpbDuStoNpvx9fW9rDu67d27l5ycnFJYC+dKTU11dYQrotzOpdzO5e65MzMzLzruFoVw6NAhrFarwzRfX18aN27Mpk2bOHjwINWrVwcgPT3dvpsoIiKC9PR0oqKi7GPh4eGXtezatWsTFhZWCmvhHBaLhdTUVCIjIz3qVqbK7VzK7VyekjsjI+Oi425RCK1btyY5OZkPP/yQXr168dNPP/Hll1/yzjvvkJGRQXJyMklJSezevZuVK1cyd+5cAHr06MG8efOIi4vD19eXOXPm0LNnz8tatr+/f7HdUp4gICBAuZ1IuZ1LucvGpcrKLQqhfv36zJgxg+nTp/Piiy9SvXp1Xn75ZZo0aUJiYiLjx4+nffv2BAUFMXr0aPsWQb9+/cjKyqJPnz7k5eXRvXt3Bg0a5OK1ERHxTG5RCACdOnWiU6dOxaYHBwczffr08z7Gx8eHhIQEEhISyjqeiMg1zy1OOxUREddTIYiICKBCEBGRQioEEREBVAgiIlJIhSAiIoAKQURECqkQREQEUCGIiEghFYKIiAAqBBERKaRCEBERQIUgIiKFVAgiIgKoEEREpJAKQUREABWCiIgUUiGIiAigQhARkUIqBBERAVQIIiJSSIUgIiKACkFERAqpEEREBFAhiIhIIRWCiIgAKgQRESmkQhAREUCFICIihVQIIiICqBBERKSQCkFERAAVgoiIFFIhiIgIoEIQEZFCvq4OIO4nz2zhdOphDJtB+fAb8a8Q6OpIIuIEKgRxcPjb39ialMKhL7cCENKqHk0n/B/V74jCy8vLxelE/mfPT7v489vfMaf9iX9oRaq2bkC9jk3w8fVxdTSP5Ta7jA4fPkx8fDzNmjWjXbt2LFiwAIDs7Gwef/xxmjdvTocOHVi6dKn9MYZhkJycTFxcHDExMSQlJVFQUOCqVfB4R9bv5Mu7Eu1lAJC5fhdfdX2RQ1//4sJkIo5+/2wzG+5IZNeoBex//XP2TFjKj3e9yOY3v6QgX+8BV8otCsEwDIYNG0Z4eDgbNmxg3rx5zJo1i82bN/Pcc88RFBTEunXrmDFjBq+88go7duwAYNGiRaxdu5bly5ezatUqNm/ezOLFi128Np7JMAz2vPstBWZr8bECGztmfUq+Jc8FyUQcZe7L5JfH5mLNznGYbths/DZ8Hqnf/+6iZJ7PLQph69atHDlyhFGjRuHn50fdunV5//33ufHGG/nqq68YMWIEAQEB3HrrrXTr1s2+lbBs2TIGDhxIaGgoISEhxMfHs2TJEhevjWeyHD3F3qXrLji+b9lPmA8dc2IikfPbv34nOX9knnfMsNnI+mGXkxNdO9ziGML27dupW7cuU6dOZcWKFZQvX56hQ4dSv359fH19qVmzpn3esLAwvvjiCwDS0tKIjIx0GEtNTcUwjBLv77ZareTm5pbuCpUhi8Xi8HtpKTBs+AYFXHDcx+SHDa74tSqr3GVNuZ2rJLmtJ89c9DnyT5md/n/aU17vS+Vzi0LIzs5mw4YNxMXFsWbNGn799VeGDBnC3LlzMZlMDvOaTCb7X7bZbHYYDwwMxGazYbVaCQi48Jvbufbu3UtOTs6lZ3Qzqamppfp8Xl5e1HmkE7/+84Pzjkc82pn0E4coOHrgqpZT2rmdRbmd62K5TdUrX/Sx/jdXZvv27aUdqUTc/fXOzDz/llURtygEf39/KlWqRHx8PADNmjWjS5cuzJgxo1jT5+bmEhQUBJwth3Mbz2w24+vrW+IyAKhduzZhYWGlsBbOYbFYSE1NJTIy8rLWsyRygkI4+PFPHNuU5jC9QsSN1BvUmfL1q13xc5dl7rKk3M5VktzWCCsH/96KI0vXFxsz1Q7h5vaNCY24sayjOvCU1zsjI+Oi425RCGFhYZjNZvLz8/H1PRupoKCARo0asXHjRg4ePEj16tUBSE9Pt+8mioiIID09naioKPtYeHj4ZS3b39+/2FaIJwgICCj13KYGNem4dBSHvtrG7nfWYljziRjQnupdmlKpXvVSWUZZ5HYG5Xaui+U2mUzc8vx97Aj059C732HYbAAEt67HLZMHUKtxbWdGdeDur/elysotCqFNmzZUrFiR5ORknn76abZt28aXX37J/PnzycjIIDk5maSkJHbv3s3KlSuZO3cuAD169GDevHnExcXh6+vLnDlz6Nmzp4vXxrNVCLuRCo/cQd0ht4Nh4OXtFucdiDiodUstbpr7GH883JmT+7MIrFKeWrH1qVC5vKujeTS3KASTycTChQuZOHEirVu3pnz58vzzn/+kadOmJCYmMn78eNq3b09QUBCjR4+2bxH069ePrKws+vTpQ15eHt27d2fQoEEuXptrg5eXF+iLaB5v0YKF/Pfb76laLZSQ0FDCw8Pp1avXNfElQ/8AP+q1a+zqGNcUtygEOLsvf968ecWmBwcHM3369PM+xsfHh4SEBBISEso6nohH+m7td/zy3mqqU4FXvXYT1zKWXr16uTqWuCntDxC5hjWMakywfzlWspMbqlbhq2/WXBNbB1I2VAgi17B69erxTd4eWreIZeOmTW59Boy4ntvsMhKR0nfLLbdQ4+abSVn+MdWqXflpw3J9UCGIXMNq1qxJ+r692k0kJaJdRiLXOJWBlJQKQUREABWCiIgUUiGIiAigQhARkUIqBBERAVQIIiJSSIUgIiKACkFERAqpEEREBFAhiIhIIRWCiIgAKgQRESmkQhAREUCFICIihVQIIiICqBBERKSQCkFERAAVgoiIFFIhiIgIoEIQEZFCKgQREQFUCCIeLzc3lx07dnDkyBHy8vJcHUc8mApBxMO9/fbbRDeJon7tCAIDTFSqUJF9+/a5OpZ4IBWCiIerVasWDYKqMyy3OQWGjeeff56aNWu6OpZ4IF9XBxCRq1O/fn32WY/yEn9wV+c7eHr0KHJzc10dSzyQthBEPFydOnU4lZ/L448+xqdffeHqOOLBtIUg4uF8fHx47/33+Nvf/ubqKOLhVAgi14DevXu7OoJcA7TLSEREABWCiIgUUiGIiAjghoWQlZVFq1atWLNmDQDZ2dk8/vjjNG/enA4dOrB06VL7vIZhkJycTFxcHDExMSQlJVFQUOCq6CIiHs3tCmHcuHGcOHHC/vNzzz1HUFAQ69atY8aMGbzyyivs2LEDgEWLFrF27VqWL1/OqlWr2Lx5M4sXL3ZRchERz+ZWhfDee+8RGBhItWrVADhz5gxfffUVI0aMICAggFtvvZVu3brZtxKWLVvGwIEDCQ0NJSQkhPj4eJYsWeLKVRAR8Vhuc9rpH3/8wfz581myZAm9evUCYO/evfj6+jp8DT8sLIwvvjj75Zu0tDQiIyMdxlJTUzEMAy8vrxIt12q1etS3Oi0Wi8PvnkK5nUu5nctTcl8qn1sUQn5+PqNHj2bcuHEEBwfbp+fk5GAymRzmNZlM9jdws9nsMB4YGIjNZsNqtRIQEFCiZe/du5ecnJyrXwknS01NdXWEK6LczqXczuXuuTMzMy867haF8Prrr9OwYUPat2/vMD0wMLDYp/fc3FyCgoKAs+VwbuOZzWZ8fX1LXAYAtWvXJiws7CrSO5fFYiE1NZXIyMjLWk9XU27nUm7n8pTcGRkZFx13i0JYtWoVmZmZrFq1CoDTp08zcuRIhgwZQn5+PgcPHqR69eoApKen23cTRUREkJ6eTlRUlH0sPDz8spbt7+9fbCvEEwQEBCi3Eym3cyl32bhUWblFIXz22WcOP3fq1InnnnuOjh07smPHDpKTk0lKSmL37t2sXLmSuXPnAtCjRw/mzZtHXFwcvr6+zJkzh549e7piFUREPJ5bFMLFJCYmMn78eNq3b09QUBCjR4+2bxH069ePrKws+vTpQ15eHt27d2fQoEEuTiwi4pncshC+/vpr+5+Dg4OZPn36eefz8fEhISGBhIQEZ0UTEblmudX3EERExHVUCCIiAqgQRESkkApBREQAFYKIiBRSIYiICKBCEBGRQioEEREBVAgiIlJIhSAiIoAKQURECqkQREQEUCGIiEghFYKIiAAqBBERKaRCEBERQIUgIiKFVAgiIgKoEEREpJAKQUREABWCiIgUUiGIiAigQhARkUIqBBERAVQIIiJSSIUgIiKACkFERAqpEEREBFAhiIhIIRWCiIgAKgQRESmkQhAREUCFICIihVQIIiICqBBERKSQCkFERAA3KoSNGzfy97//nebNm3P77bfz/vvvA5Cdnc3jjz9O8+bN6dChA0uXLrU/xjAMkpOTiYuLIyYmhqSkJAoKCly1CiIiHs3X1QHg7Jv+sGHD+Oc//0m3bt34/fffGTRoELVq1eL9998nKCiIdevWsXPnTh555BGaNGlCgwYNWLRoEWvXrmX58uV4eXkRHx/P4sWLGTBggKtXSUTE47jFFsLBgwdp3749PXr0wNvbm8aNGxMbG8vmzZv56quvGDFiBAEBAdx6661069bNvpWwbNkyBg4cSGhoKCEhIcTHx7NkyRIXr42IiGdyiy2Ehg0bMnXqVPvP2dnZbNy4kfr16+Pr60vNmjXtY2FhYXzxxRcApKWlERkZ6TCWmpqKYRh4eXmVaNlWq5Xc3NxSWpOyZ7FYHH73FMrtXMrtXJ6S+1L53KIQznXq1CmGDh1q30pYsGCBw7jJZLK/gZvNZkwmk30sMDAQm82G1WolICCgRMvbu3cvOTk5pbcCTpKamurqCFdEuZ1LuZ3L3XNnZmZedNytCmH//v0MHTqUmjVr8tprr7Fnz55in95zc3MJCgoCzpbDuY1nNpvx9fUtcRkA1K5dm7CwsNJZASewWCykpqYSGRl5WevpasrtXMrtXJ6SOyMj46LjblMI27dvZ8iQIfTo0YMxY8bg7e1N7dq1yc/P5+DBg1SvXh2A9PR0+26iiIgI0tPTiYqKso+Fh4df1nL9/f0dtjI8RUBAgHI7kXI7l3KXjUuVlVscVM7KymLIkCEMGjSIZ555Bm/vs7HKly9P586dSU5Oxmw2s23bNlauXEn37t0B6NGjB/PmzePw4cNkZWUxZ84cevbs6cpVERHxWG6xhZCSksKxY8d44403eOONN+zTH3zwQRITExk/fjzt27cnKCiI0aNH27cI+vXrR1ZWFn369CEvL4/u3bszaNAgV62GiIhHc4tCGDp0KEOHDr3g+PTp08873cfHh4SEBBISEsoqmojIdcMtdhmJiIjrqRBERARQIYiISCEVgoiIACoEEREp5BZnGYnI9e30/ixO7jyI4Q3lKvq7Os51S4UgIi6Tb7aS/v73bBz1DpZjpwEoH3ETQdMHUevuFi5Od/3RLiMRcZkDKzby38H/spcBwOk9h1l771T+/O43Fya7PqkQRMQlrCdO83PS0vOO2fLySXvvewzDcHKq65sKQURcIufwCU78su+C4xmfbiHvtOfcq+RaoEIQEZfwCQzAJ/DCB5ADq1fGJ8DPiYlEhSAiLlGhdggNn+h6wfF6j96Bj7/Oe3EmFYKIuEzdhztTpUVEselh/W6jxl3RLkh0fVP9iojLVKpXnU4f/YM/v/+dP7/7HR+TP0HNalL79mgCbwx2dbzrjgpBRFyqXM2qhN9/G+H330Zubi7bt2/HLzjI1bGuS9plJCIigApBREQKqRBERARQIYiISCEVgoiIACoEEREppEIQERFAhSAiIoVUCCIiAqgQRESkkApBREQAFYKIiBRSIYiICKBCEBGRQioEEREBVAgiIlJIhSAiIoAKQURECqkQREQEUCGIiEghFYKIiADXQCH89ttv9OnTh6ZNm9KzZ09+/vlnV0cSEfFIHl0IFouFoUOH0qtXL3766ScGDBjA8OHDsVqtro4mIuJxPLoQfvjhB7y9venXrx9+fn706dOHypUrs2bNGldHExHxOL6uDnA10tPTiYiIcJgWFhbG7t276dKly0UfW1BQAMD+/fvLLF9ZsFqtZGZmkp6ejr+/v6vjlJhyO5dyO5en5P7zzz+B/73//ZVHF0JOTg6BgYEO00wmE7m5uZd8bGZmJgDx8fFlkk1ExF1lZmZSu3btYtM9uhACAwOLvfnn5uYSFBR0ycfecsstLFq0iJCQEHx8fMoqooiI2ygoKCAzM5NbbrnlvOMeXQjh4eG8++67DtPS09Pp1q3bJR9rMplo0aJFWUUTEXFL59syKOLRB5VbtWqF1Wpl4cKF5OXlkZKSQlZWFm3btnV1NBERj+NlGIbh6hBXY8eOHUyYMIGdO3dSu3ZtJkyYQNOmTV0dS0TE43h8IYiISOnw6F1GIiJSelQIIiICqBBERKTQdVkI7nxBvI0bN/L3v/+d5s2bc/vtt/P+++8DkJ2dzeOPP07z5s3p0KEDS5cutT/GMAySk5OJi4sjJiaGpKSkC34TsaxlZWXRqlUr++VD3D334cOHiY+Pp1mzZrRr144FCxZ4RO7NmzfTq1cvmjVrRpcuXVixYoVb5962bZvD2X9Xk3PlypV07tyZ6Oho4uPjycrKclruw4cPM2zYMGJjY2nTpg2JiYn2a6e5U+4rZlxncnNzjdtuu81YtGiRYbVajaVLlxpt2rQxLBaLq6MZJ06cMGJiYoxly5YZBQUFxq+//mrExMQY//3vf40nnnjCGDVqlJGbm2ts3brVaNmypfH7778bhmEYCxcuNLp162b8+eefxpEjR4x7773XWLBggUvW4dFHHzUaNGhgfP3114ZhGG6d22azGffee68xefJkw2q1Grt27TJiYmKMTZs2uXXu/Px8Iy4uzvj0008NwzCMn376yWjUqJGxf/9+t8tts9mMpUuXGs2bNzdatmxpn36lOX///XejWbNmxs8//2yYzWbj2WefNYYPH+603P379zdeeOEFIzc31zhy5Ijx97//3Zg2bZrb5L5a110hrF271mjfvr3DtG7duhmfffaZawKd47fffjNGjRrlMG348OHGzJkzjYYNGxr79u2zT584caIxceJEwzAMo0+fPsbSpUvtY5999pnRrVs354Q+x+LFi40nn3zS6Nixo/H1118bp0+fduvcW7ZsMdq0aWPk5+fbp+3Zs8c4cOCAW+c+duyYUa9ePWPlypWGzWYzNm7caERFRRkHDx50u9yvv/660b17d+PNN9+0v7Fezb+LKVOmGKNHj7aPHTt2zGjYsKGRlZVV5rktFovxyCOPGEeOHLHPt2DBAmPAgAFuk/tqXXe7jC52QTxXa9iwIVOnTrX/nJ2dzcaNGwHw9fWlZs2a9rFzM6elpREZGekwlpqaiuHEM4r/+OMP5s+fz4QJE+zT9u7d69a5t2/fTt26dZk6dSpt2rShS5cubN26lezsbLfOXblyZfr168fIkSNp3LgxDzzwAM899xzHjx93u9y9e/dm2bJlNGnSxD7tav5d/HWscuXKVKhQgbS0tDLP7e/vz9y5cwkJCbFPW7NmDQ0aNHCb3FfruiuEq7kgnjOdOnWKoUOH0rhxY2JjYzGZTA7j52Y2m80O44GBgdhsNqfdFyI/P5/Ro0czbtw4goOD7dNzcnLcOnd2djYbNmywXzJ90qRJJCYmun1um82GyWRi+vTp/Pzzz8yePZuXXnqJ06dPu13u0NBQvLy8HKZdzev717GicbPZXOa5z2UYBklJSaSlpdkvkOkOua/WdVcIV3NBPGfZv38/ffv2pVKlSsyaNYugoKCLZjaZTFgsFvuY2WzG19eXgIAAp+R9/fXXadiwIe3bt3eYfqnX2tW5/f39qVSpEvHx8fj7+9sP0M6YMcOtc3/xxRds27aNu+66C39/fzp06ECHDh2YOXOmW+cucjX/Ls734c1sNjv1/29ubi5PPvkk3333HQsXLqRKlSoekbskrrtCCA8PJz093WFaenq6w+acK23fvp377ruPtm3b8vrrr2Mymahduzb5+fkcPHjQPt+5mSMiIhzWKT09nfDwcKdlXrVqFf/5z39o0aIFLVq04ODBg4wcOZK1a9e6de6wsDDMZjP5+fn2aQUFBTRq1Mitcx86dKjYp3pfX18aN27s1rmLXM2/57+OHTt2jOzs7GK7gcvKiRMn6N+/PydOnOCDDz5w2O3lzrlLzIXHL1zCYrEYbdu2NRYsWGA/yyguLs44c+aMq6MZmZmZRlxcnDFnzpxiY8OHDzdGjhxp5OTk2M/K+Pnnnw3DOHtgq1u3bsahQ4eMzMxM49577zXefPNNZ8e3Kzqo7O65zWazcdtttxmTJ0828vLyjE2bNhlNmzY1tmzZ4ta5d+zYYTRu3NhISUkxbDabsWHDBiM6OtrYtm2b2+b+4YcfHM7WudKcv/32m9GsWTPjp59+MnJzc41x48YZjzzyiFNy22w2o3///kZ8fLxhtVqLzetOua/UdVcIhnH2FLD/+7//M5o2bWr07NnT2LJli6sjGYZhGG+88YZRr149o2nTpg6/pk2bZhw/ftwYMWKEERMTY7Rv397hbIb8/Hxj2rRpRps2bYyWLVsaiYmJDmfOONu5heDuuf/44w9j8ODBRkxMjNGxY0cjJSXFI3KvXr3a6NGjhxEdHW3cfffdxhdffOHWuf9aCFeT8z//+Y9x5513GtHR0cYjjzxSpmfqnJt706ZNRr169YwmTZo4/P/s16+f2+W+Urq4nYiIANfhMQQRETk/FYKIiAAqBBERKaRCEBERQIUgIiKFVAgiIgKoEEScYvfu3TRp0oS5c+cWG3vyySfp1asXeXl5Lkgm8j8qBBEnqFu3Lo8//jgzZ85kz5499ukff/wxa9asYcqUKfj5+bkwoQjoi2kiTpKfn899992Hn58f7733HgcPHqRnz5488cQTPPTQQ66OJ6JCEHGmnTt30rt3b0aPHs2aNWswDIO33377opdaFnEWFYKIk82aNYs33niDwMBAVqxYQbVq1VwdSQTQMQQRp+vUqRP5+fnUqlWLG2+80dVxROxUCCJOZLVaefbZZ2nevDk7d+7k7bffdnUkETsVgogTzZw5k4yMDF599VUeeughXnvttWI3bBJxFR1DEHGSzZs3079/f1566SXuueceLBYLPXv2JDg4mMWLF+Ptrc9n4lr6FyjiBDk5OYwZM4Z27dpxzz33ABAQEMBLL73E1q1btetI3IIKQcQJJk+ezMmTJ0lMTHSY3qxZM/r3789rr71GWlqai9KJnKVdRiIiAmgLQURECqkQREQEUCGIiEghFYKIiAAqBBERKaRCEBERQIUgIiKFVAgiIgKoEEREpND/A8pfHDEj6pEeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.plot_2d(geo_model, direction='z', show_lith=False, show_boundaries=False)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "vocational-intranet",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:44.579636Z",
     "start_time": "2022-04-05T09:17:44.037503Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x25117F98A60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<gempy.plot.vista.GemPyToVista at 0x25117b97730>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.plot_3d(geo_model, image=False, plotter_type='basic', notebook=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "geological-triple",
   "metadata": {},
   "source": [
    "### Setting the Interpolator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "external-clerk",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:17:47.886311Z",
     "start_time": "2022-04-05T09:17:44.581626Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling theano function...\n",
      "Level of Optimization:  fast_compile\n",
      "Device:  cpu\n",
      "Precision:  float64\n",
      "Number of faults:  0\n",
      "Compilation Done!\n",
      "Kriging values: \n",
      "                    values\n",
      "range             2365.48\n",
      "$C_o$           133225.83\n",
      "drift equations    [3, 3]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy.core.interpolator.InterpolatorModel at 0x2511175ab20>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.set_interpolator(geo_model,\n",
    "                    compile_theano=True,\n",
    "                    theano_optimizer='fast_compile',\n",
    "                    verbose=[],\n",
    "                    update_kriging=False\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "above-lending",
   "metadata": {},
   "source": [
    "### Computing Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "impressed-adams",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:18:02.626305Z",
     "start_time": "2022-04-05T09:17:47.888298Z"
    }
   },
   "outputs": [],
   "source": [
    "sol = gp.compute_model(geo_model, compute_mesh=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "developing-wallet",
   "metadata": {},
   "source": [
    "### Plotting Cross Sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "champion-collectible",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:18:03.980612Z",
     "start_time": "2022-04-05T09:18:02.636308Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gempy.plot.visualization_2d.Plot2D at 0x25119b53f10>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1797.12x1235.52 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.plot_2d(geo_model, direction=['x', 'x', 'y', 'y'], cell_number=[25, 75, 25, 75], show_topography=True, show_data=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intimate-connectivity",
   "metadata": {},
   "source": [
    "### Plotting 3D Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "developing-comparison",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:18:04.922843Z",
     "start_time": "2022-04-05T09:18:03.982613Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x251191D2610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gpv = gp.plot_3d(geo_model, image=False, show_topography=False,\n",
    "                 plotter_type='basic', notebook=True, show_lith=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "digital-maryland",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}