{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "86f44ca3",
   "metadata": {},
   "source": [
    "# 67 Rotating GemPy Input Data\n",
    "\n",
    "This notebook illustrates how to rotate GemPy Input data. Input data may be rotated because subsurface structures do not trend West-East or North-South but rather oblique. As GemPy only takes extents that are oriented N-S and W-E, there would be a large area that would be discarded after the modeling process and does not need to be modeled in the first place. Rotating the data will therefore reduce the area that needs to be calculated and therefore computing times which may allow for a higher resolution or the integration of more input data. The rotation implemented in GemGIS is performed around the center point of the extent of the model. This does not only allow for rotating the data at its place (instead of rotating it around (0,0) which may cause a translation of the model to extreme coordinates, but also for rotating the resulting meshes back to their original orientation. \n",
    "\n",
    "<img src=\"../images/tutorial67_cover.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7147456",
   "metadata": {},
   "source": [
    "## Set File Paths and download Tutorial Data\n",
    "\n",
    "If you downloaded the latest `GemGIS` version from the Github repository, append the path so that the package can be imported successfully. Otherwise, it is recommended to install `GemGIS` via `pip install gemgis` and import `GemGIS` using `import gemgis as gg`. In addition, the file path to the folder where the data is being stored is set. The tutorial data is downloaded using Pooch (https://www.fatiando.org/pooch/latest/index.html) and stored in the specified folder. Use `pip install pooch` if Pooch is not installed on your system yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb8af06a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No module named 'osgeo'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (aesara.configdefaults): g++ not available, if using conda: `conda install m2w64-toolchain`\n",
      "WARNING (aesara.configdefaults): g++ not detected!  Aesara will be unable to compile C-implementations and will default to Python. Performance may be severely degraded. To remove this warning, set Aesara flags cxx to an empty string.\n",
      "WARNING (aesara.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import gempy as gp\n",
    "import gemgis as gg\n",
    "import pandas as pd\n",
    "from shapely.geometry import Polygon\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pyvista as pv\n",
    "pv.set_jupyter_backend('client')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4128de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path ='data/67_rotating_gempy_input_data/'\n",
    "gg.download_gemgis_data.download_tutorial_data(filename=\"67_rotation_gempy_input_data.zip\", dirpath=file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0219a63e",
   "metadata": {},
   "source": [
    "## Loading Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c8c178c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T08:52:05.255549Z",
     "start_time": "2022-07-07T08:52:05.241536Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "     X    Y    Z formation\n",
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     "metadata": {},
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   "source": [
    "interfaces = pd.read_csv(file_path + 'interfaces.csv', delimiter=';')\n",
    "interfaces.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bd8aa29b",
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     "start_time": "2022-07-07T08:52:05.289548Z"
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   },
   "outputs": [
    {
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       "      <th>dip</th>\n",
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       "      <th>polarity</th>\n",
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       "     X    Y    Z formation  dip  azimuth  polarity\n",
       "0  200  500 -100    Layer1    0        0         1\n",
       "1  800  500 -100    Layer1    0        0         1\n",
       "2  500  500 -300    Layer1    0        0         1\n",
       "3  250  500 -100    Fault1   60       90         1\n",
       "4  750  500 -100    Fault2   60      270         1"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orientations = pd.read_csv(file_path + 'orientations.csv', delimiter=';')\n",
    "orientations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0412059",
   "metadata": {},
   "source": [
    "## Creating Polygon Extent\n",
    "\n",
    "The original extent of the model will be translated into a Shapely Polygon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc6acd98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"300\" height=\"300\" viewBox=\"-40.0 -40.0 1080.0 1080.0\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1000.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"7.2\" opacity=\"0.6\" d=\"M 0.0,0.0 L 0.0,1000.0 L 1000.0,1000.0 L 1000.0,0.0 L 0.0,0.0 z\" /></g></svg>"
      ],
      "text/plain": [
       "<POLYGON ((0 0, 0 1000, 1000 1000, 1000 0, 0 0))>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extent = Polygon([(0,0), (0,1000), (1000, 1000), (1000,0)])\n",
    "extent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a15f37a",
   "metadata": {},
   "source": [
    "## Performing initial rotation for Tutorial\n",
    "\n",
    "The input data will be rotated a first time to create a non-N-S-W-E oriented dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6df1289c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([-191.65480148022556,\n",
       "  1191.6548014802256,\n",
       "  -191.65480148022556,\n",
       "  1191.6548014802256,\n",
       "  -600,\n",
       "  0],\n",
       "        X      Y       Z formation                 geometry\n",
       " 0 112.24 453.72 -100.00    Layer1  POINT (112.239 453.724)\n",
       " 1 248.40 663.39 -100.00    Layer1  POINT (248.399 663.392)\n",
       " 2 384.56 873.06 -100.00    Layer1  POINT (384.559 873.059)\n",
       " 3 112.24 453.72 -200.00    Layer2  POINT (112.239 453.724)\n",
       " 4 248.40 663.39 -200.00    Layer2  POINT (248.399 663.392),\n",
       "        X      Y       Z formation   dip  azimuth  polarity   \n",
       " 0 248.40 663.39 -100.00    Layer1  0.00     0.00      1.00  \\\n",
       " 1 751.60 336.61 -100.00    Layer1  0.00     0.00      1.00   \n",
       " 2 500.00 500.00 -300.00    Layer1  0.00     0.00      1.00   \n",
       " 3 290.33 636.16 -100.00    Fault1 60.00    90.00      1.00   \n",
       " 4 709.67 363.84 -100.00    Fault2 60.00   270.00      1.00   \n",
       " \n",
       "                   geometry  \n",
       " 0  POINT (248.399 663.392)  \n",
       " 1  POINT (751.601 336.608)  \n",
       " 2  POINT (500.000 500.000)  \n",
       " 3  POINT (290.332 636.160)  \n",
       " 4  POINT (709.668 363.840)  ,\n",
       "                                             geometry\n",
       " 0  POLYGON ((-191.655 352.984, 352.984 1191.655, ...)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extent_rotated, interfaces_rotated, orientations_rotated, extent_gdf = gg.utils.rotate_gempy_input_data(extent=extent,\n",
    "                                                                                                        interfaces=interfaces,\n",
    "                                                                                                        orientations=orientations,\n",
    "                                                                                                        zmin=-600, \n",
    "                                                                                                        zmax=0, \n",
    "                                                                                                        return_extent_gdf=True,\n",
    "                                                                                                        manual_rotation_angle=33)\n",
    "extent_rotated, interfaces_rotated.head(), orientations_rotated.head(), extent_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9183e74",
   "metadata": {},
   "source": [
    "## Plotting the non-N-S-W-E Model Data\n",
    "\n",
    "Plotting the input data shows that the graben structure is striking NW-SE. This is not ideal for modeling in GemPy. In order to model the entire black extent that is desired, the red extent would have to be modeled which results in a much larger model than necessary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7794f5ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(7,7))\n",
    "\n",
    "extent_gdf.boundary.plot(ax=ax, color='black')\n",
    "interfaces_rotated.plot(ax=ax)\n",
    "orientations_rotated.plot(ax=ax)\n",
    "gpd.GeoDataFrame(geometry=[box(*extent_gdf.total_bounds)]).boundary.plot(ax=ax, color='red')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e3a96b",
   "metadata": {},
   "source": [
    "## Rotating the Model back to a N-S-W-E-oriented model\n",
    "\n",
    "The non-N-S-W-E-oriented model can be rotated to a N-S-W-E-oriented model using the GemGIS function ```gg.utils.rotate_gempy_input_data(...)```. The rotation angle is either calculated automatically or can be set manually. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2990b718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([-1.1368683772161603e-13, 1000.0, -2.842170943040401e-14, 1000.0, -600, 0],\n",
       "        X      Y       Z formation                 geometry\n",
       " 0 200.00 250.00 -100.00    Layer1  POINT (200.000 250.000)\n",
       " 1 200.00 500.00 -100.00    Layer1  POINT (200.000 500.000)\n",
       " 2 200.00 750.00 -100.00    Layer1  POINT (200.000 750.000)\n",
       " 3 200.00 250.00 -200.00    Layer2  POINT (200.000 250.000)\n",
       " 4 200.00 500.00 -200.00    Layer2  POINT (200.000 500.000),\n",
       "        X      Y       Z formation   dip  azimuth  polarity   \n",
       " 0 200.00 500.00 -100.00    Layer1  0.00     0.00      1.00  \\\n",
       " 1 800.00 500.00 -100.00    Layer1  0.00     0.00      1.00   \n",
       " 2 500.00 500.00 -300.00    Layer1  0.00     0.00      1.00   \n",
       " 3 250.00 500.00 -100.00    Fault1 60.00    90.00      1.00   \n",
       " 4 750.00 500.00 -100.00    Fault2 60.00   270.00      1.00   \n",
       " \n",
       "                   geometry  \n",
       " 0  POINT (200.000 500.000)  \n",
       " 1  POINT (800.000 500.000)  \n",
       " 2  POINT (500.000 500.000)  \n",
       " 3  POINT (250.000 500.000)  \n",
       " 4  POINT (750.000 500.000)  ,\n",
       "                                             geometry\n",
       " 0  POLYGON ((-0.000 -0.000, -0.000 1000.000, 1000...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extent_straight, interfaces_straight, orientations_straight, extent_gdf_straight = gg.utils.rotate_gempy_input_data(extent=extent_gdf,\n",
    "                                                                                                                    interfaces=interfaces_rotated,\n",
    "                                                                                                                    orientations=orientations_rotated,\n",
    "                                                                                                                    zmin=-600, \n",
    "                                                                                                                    zmax=0, \n",
    "                                                                                                                    rotate_reverse_direction=True,                            \n",
    "                                                                                                                    return_extent_gdf=True)\n",
    "extent_straight, interfaces_straight.head(), orientations_straight.head(), extent_gdf_straight"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e6b3adc",
   "metadata": {},
   "source": [
    "## Plotting the N-S-W-E Model Data\n",
    "\n",
    "The rotated data can be plotted as before. We can see that the data was rotated around the center of the extent and that the size of the red frame is now equal to the size of the black frame as desired. Now, the modeling can be performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "954e284c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-300.0, 1300.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax) = plt.subplots(1,2, figsize=(10,7))\n",
    "\n",
    "extent_gdf_straight.boundary.plot(ax=ax, color='black')\n",
    "interfaces_straight.plot(ax=ax)\n",
    "orientations_straight.plot(ax=ax)\n",
    "gpd.GeoDataFrame(geometry=[box(*extent_gdf_straight.total_bounds)]).boundary.plot(ax=ax, color='red')\n",
    "ax.grid()\n",
    "ax.set_ylim(-300,1300)\n",
    "ax.set_xlim(-300,1300)\n",
    "\n",
    "\n",
    "extent_gdf.boundary.plot(ax=ax1, color='black')\n",
    "interfaces_rotated.plot(ax=ax1)\n",
    "orientations_rotated.plot(ax=ax1)\n",
    "gpd.GeoDataFrame(geometry=[box(*extent_gdf.total_bounds)]).boundary.plot(ax=ax1, color='red')\n",
    "ax1.grid()\n",
    "ax1.set_ylim(-300,1300)\n",
    "ax1.set_xlim(-300,1300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aad5d629",
   "metadata": {},
   "source": [
    "## Creating the GemPy Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eb3ec438",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T16:23:35.919153Z",
     "start_time": "2022-07-10T16:23:35.665279Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Graben_Model  2023-08-01 14:29"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model = gp.create_model('Graben_Model')\n",
    "geo_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae4bb825",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T16:23:36.455831Z",
     "start_time": "2022-07-10T16:23:36.064742Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active grids: ['regular']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Graben_Model  2023-08-01 14:29"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.init_data(geo_model, [0, 1000, 0, 1000, -600, 0], [50,50,50],\n",
    "             surface_points_df=interfaces_straight,\n",
    "             orientations_df=orientations_straight,\n",
    "             default_values=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e8519ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T16:23:36.582473Z",
     "start_time": "2022-07-10T16:23:36.554848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_d7848_row0_col3 {\n",
       "  background-color: #015482;\n",
       "}\n",
       "#T_d7848_row1_col3 {\n",
       "  background-color: #9f0052;\n",
       "}\n",
       "#T_d7848_row2_col3 {\n",
       "  background-color: #ffbe00;\n",
       "}\n",
       "#T_d7848_row3_col3 {\n",
       "  background-color: #728f02;\n",
       "}\n",
       "#T_d7848_row4_col3 {\n",
       "  background-color: #443988;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d7848\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d7848_level0_col0\" class=\"col_heading level0 col0\" >surface</th>\n",
       "      <th id=\"T_d7848_level0_col1\" class=\"col_heading level0 col1\" >series</th>\n",
       "      <th id=\"T_d7848_level0_col2\" class=\"col_heading level0 col2\" >order_surfaces</th>\n",
       "      <th id=\"T_d7848_level0_col3\" class=\"col_heading level0 col3\" >color</th>\n",
       "      <th id=\"T_d7848_level0_col4\" class=\"col_heading level0 col4\" >id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d7848_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_d7848_row0_col0\" class=\"data row0 col0\" >Layer1</td>\n",
       "      <td id=\"T_d7848_row0_col1\" class=\"data row0 col1\" >Default series</td>\n",
       "      <td id=\"T_d7848_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_d7848_row0_col3\" class=\"data row0 col3\" >#015482</td>\n",
       "      <td id=\"T_d7848_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7848_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_d7848_row1_col0\" class=\"data row1 col0\" >Layer2</td>\n",
       "      <td id=\"T_d7848_row1_col1\" class=\"data row1 col1\" >Default series</td>\n",
       "      <td id=\"T_d7848_row1_col2\" class=\"data row1 col2\" >2</td>\n",
       "      <td id=\"T_d7848_row1_col3\" class=\"data row1 col3\" >#9f0052</td>\n",
       "      <td id=\"T_d7848_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7848_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_d7848_row2_col0\" class=\"data row2 col0\" >Layer3</td>\n",
       "      <td id=\"T_d7848_row2_col1\" class=\"data row2 col1\" >Default series</td>\n",
       "      <td id=\"T_d7848_row2_col2\" class=\"data row2 col2\" >3</td>\n",
       "      <td id=\"T_d7848_row2_col3\" class=\"data row2 col3\" >#ffbe00</td>\n",
       "      <td id=\"T_d7848_row2_col4\" class=\"data row2 col4\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7848_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_d7848_row3_col0\" class=\"data row3 col0\" >Fault1</td>\n",
       "      <td id=\"T_d7848_row3_col1\" class=\"data row3 col1\" >Default series</td>\n",
       "      <td id=\"T_d7848_row3_col2\" class=\"data row3 col2\" >4</td>\n",
       "      <td id=\"T_d7848_row3_col3\" class=\"data row3 col3\" >#728f02</td>\n",
       "      <td id=\"T_d7848_row3_col4\" class=\"data row3 col4\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d7848_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_d7848_row4_col0\" class=\"data row4 col0\" >Fault2</td>\n",
       "      <td id=\"T_d7848_row4_col1\" class=\"data row4 col1\" >Default series</td>\n",
       "      <td id=\"T_d7848_row4_col2\" class=\"data row4 col2\" >5</td>\n",
       "      <td id=\"T_d7848_row4_col3\" class=\"data row4 col3\" >#443988</td>\n",
       "      <td id=\"T_d7848_row4_col4\" class=\"data row4 col4\" >5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "  surface          series  order_surfaces    color  id\n",
       "0  Layer1  Default series               1  #015482   1\n",
       "1  Layer2  Default series               2  #9f0052   2\n",
       "2  Layer3  Default series               3  #ffbe00   3\n",
       "3  Fault1  Default series               4  #728f02   4\n",
       "4  Fault2  Default series               5  #443988   5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.surfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ca6dfa3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T16:23:37.073588Z",
     "start_time": "2022-07-10T16:23:36.681498Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fault colors changed. If you do not like this behavior, set change_color to False.\n",
      "Fault colors changed. If you do not like this behavior, set change_color to False.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_bf980_row0_col3, #T_bf980_row1_col3 {\n",
       "  background-color: #527682;\n",
       "}\n",
       "#T_bf980_row2_col3 {\n",
       "  background-color: #ffbe00;\n",
       "}\n",
       "#T_bf980_row3_col3 {\n",
       "  background-color: #728f02;\n",
       "}\n",
       "#T_bf980_row4_col3 {\n",
       "  background-color: #443988;\n",
       "}\n",
       "#T_bf980_row5_col3 {\n",
       "  background-color: #ff3f20;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_bf980\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_bf980_level0_col0\" class=\"col_heading level0 col0\" >surface</th>\n",
       "      <th id=\"T_bf980_level0_col1\" class=\"col_heading level0 col1\" >series</th>\n",
       "      <th id=\"T_bf980_level0_col2\" class=\"col_heading level0 col2\" >order_surfaces</th>\n",
       "      <th id=\"T_bf980_level0_col3\" class=\"col_heading level0 col3\" >color</th>\n",
       "      <th id=\"T_bf980_level0_col4\" class=\"col_heading level0 col4\" >id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_bf980_level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
       "      <td id=\"T_bf980_row0_col0\" class=\"data row0 col0\" >Fault1</td>\n",
       "      <td id=\"T_bf980_row0_col1\" class=\"data row0 col1\" >Fault1</td>\n",
       "      <td id=\"T_bf980_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_bf980_row0_col3\" class=\"data row0 col3\" >#527682</td>\n",
       "      <td id=\"T_bf980_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf980_level0_row1\" class=\"row_heading level0 row1\" >4</th>\n",
       "      <td id=\"T_bf980_row1_col0\" class=\"data row1 col0\" >Fault2</td>\n",
       "      <td id=\"T_bf980_row1_col1\" class=\"data row1 col1\" >Fault2</td>\n",
       "      <td id=\"T_bf980_row1_col2\" class=\"data row1 col2\" >1</td>\n",
       "      <td id=\"T_bf980_row1_col3\" class=\"data row1 col3\" >#527682</td>\n",
       "      <td id=\"T_bf980_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf980_level0_row2\" class=\"row_heading level0 row2\" >0</th>\n",
       "      <td id=\"T_bf980_row2_col0\" class=\"data row2 col0\" >Layer1</td>\n",
       "      <td id=\"T_bf980_row2_col1\" class=\"data row2 col1\" >Strata1</td>\n",
       "      <td id=\"T_bf980_row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "      <td id=\"T_bf980_row2_col3\" class=\"data row2 col3\" >#ffbe00</td>\n",
       "      <td id=\"T_bf980_row2_col4\" class=\"data row2 col4\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf980_level0_row3\" class=\"row_heading level0 row3\" >1</th>\n",
       "      <td id=\"T_bf980_row3_col0\" class=\"data row3 col0\" >Layer2</td>\n",
       "      <td id=\"T_bf980_row3_col1\" class=\"data row3 col1\" >Strata1</td>\n",
       "      <td id=\"T_bf980_row3_col2\" class=\"data row3 col2\" >2</td>\n",
       "      <td id=\"T_bf980_row3_col3\" class=\"data row3 col3\" >#728f02</td>\n",
       "      <td id=\"T_bf980_row3_col4\" class=\"data row3 col4\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf980_level0_row4\" class=\"row_heading level0 row4\" >2</th>\n",
       "      <td id=\"T_bf980_row4_col0\" class=\"data row4 col0\" >Layer3</td>\n",
       "      <td id=\"T_bf980_row4_col1\" class=\"data row4 col1\" >Strata1</td>\n",
       "      <td id=\"T_bf980_row4_col2\" class=\"data row4 col2\" >3</td>\n",
       "      <td id=\"T_bf980_row4_col3\" class=\"data row4 col3\" >#443988</td>\n",
       "      <td id=\"T_bf980_row4_col4\" class=\"data row4 col4\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf980_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_bf980_row5_col0\" class=\"data row5 col0\" >Basement</td>\n",
       "      <td id=\"T_bf980_row5_col1\" class=\"data row5 col1\" >Strata1</td>\n",
       "      <td id=\"T_bf980_row5_col2\" class=\"data row5 col2\" >4</td>\n",
       "      <td id=\"T_bf980_row5_col3\" class=\"data row5 col3\" >#ff3f20</td>\n",
       "      <td id=\"T_bf980_row5_col4\" class=\"data row5 col4\" >6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "    surface   series  order_surfaces    color  id\n",
       "3    Fault1   Fault1               1  #527682   1\n",
       "4    Fault2   Fault2               1  #527682   2\n",
       "0    Layer1  Strata1               1  #ffbe00   3\n",
       "1    Layer2  Strata1               2  #728f02   4\n",
       "2    Layer3  Strata1               3  #443988   5\n",
       "5  Basement  Strata1               4  #ff3f20   6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.map_stack_to_surfaces(geo_model,\n",
    "                         {\n",
    "                          'Fault1': ('Fault1'),\n",
    "                          'Fault2': ('Fault2'),\n",
    "                          'Strata1': ('Layer1', 'Layer2', 'Layer3'),                          \n",
    "                         },\n",
    "                         remove_unused_series=True)\n",
    "geo_model.add_surfaces('Basement')\n",
    "geo_model.set_is_fault(['Fault1', 'Fault2'])\n",
    "geo_model.surfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe77ff3f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T16:23:40.554833Z",
     "start_time": "2022-07-10T16:23:37.171618Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-120.    0.]\n",
      "Active grids: ['regular' 'topography']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Grid Object. Values: \n",
       "array([[  10.        ,   10.        , -594.        ],\n",
       "       [  10.        ,   10.        , -582.        ],\n",
       "       [  10.        ,   10.        , -570.        ],\n",
       "       ...,\n",
       "       [1000.        ,  959.18367347,  -48.98763452],\n",
       "       [1000.        ,  979.59183673,  -56.49305639],\n",
       "       [1000.        , 1000.        ,  -61.62003486]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.set_topography(source='random')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "lyric-sequence",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T16:23:41.231568Z",
     "start_time": "2022-07-10T16:23:41.202546Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_36d5f_row0_col3, #T_36d5f_row1_col3 {\n",
       "  background-color: #527682;\n",
       "}\n",
       "#T_36d5f_row2_col3 {\n",
       "  background-color: #ffbe00;\n",
       "}\n",
       "#T_36d5f_row3_col3 {\n",
       "  background-color: #728f02;\n",
       "}\n",
       "#T_36d5f_row4_col3 {\n",
       "  background-color: #443988;\n",
       "}\n",
       "#T_36d5f_row5_col3 {\n",
       "  background-color: #ff3f20;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_36d5f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_36d5f_level0_col0\" class=\"col_heading level0 col0\" >surface</th>\n",
       "      <th id=\"T_36d5f_level0_col1\" class=\"col_heading level0 col1\" >series</th>\n",
       "      <th id=\"T_36d5f_level0_col2\" class=\"col_heading level0 col2\" >order_surfaces</th>\n",
       "      <th id=\"T_36d5f_level0_col3\" class=\"col_heading level0 col3\" >color</th>\n",
       "      <th id=\"T_36d5f_level0_col4\" class=\"col_heading level0 col4\" >id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_36d5f_level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
       "      <td id=\"T_36d5f_row0_col0\" class=\"data row0 col0\" >Fault1</td>\n",
       "      <td id=\"T_36d5f_row0_col1\" class=\"data row0 col1\" >Fault1</td>\n",
       "      <td id=\"T_36d5f_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_36d5f_row0_col3\" class=\"data row0 col3\" >#527682</td>\n",
       "      <td id=\"T_36d5f_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_36d5f_level0_row1\" class=\"row_heading level0 row1\" >4</th>\n",
       "      <td id=\"T_36d5f_row1_col0\" class=\"data row1 col0\" >Fault2</td>\n",
       "      <td id=\"T_36d5f_row1_col1\" class=\"data row1 col1\" >Fault2</td>\n",
       "      <td id=\"T_36d5f_row1_col2\" class=\"data row1 col2\" >1</td>\n",
       "      <td id=\"T_36d5f_row1_col3\" class=\"data row1 col3\" >#527682</td>\n",
       "      <td id=\"T_36d5f_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_36d5f_level0_row2\" class=\"row_heading level0 row2\" >0</th>\n",
       "      <td id=\"T_36d5f_row2_col0\" class=\"data row2 col0\" >Layer1</td>\n",
       "      <td id=\"T_36d5f_row2_col1\" class=\"data row2 col1\" >Strata1</td>\n",
       "      <td id=\"T_36d5f_row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "      <td id=\"T_36d5f_row2_col3\" class=\"data row2 col3\" >#ffbe00</td>\n",
       "      <td id=\"T_36d5f_row2_col4\" class=\"data row2 col4\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_36d5f_level0_row3\" class=\"row_heading level0 row3\" >1</th>\n",
       "      <td id=\"T_36d5f_row3_col0\" class=\"data row3 col0\" >Layer2</td>\n",
       "      <td id=\"T_36d5f_row3_col1\" class=\"data row3 col1\" >Strata1</td>\n",
       "      <td id=\"T_36d5f_row3_col2\" class=\"data row3 col2\" >2</td>\n",
       "      <td id=\"T_36d5f_row3_col3\" class=\"data row3 col3\" >#728f02</td>\n",
       "      <td id=\"T_36d5f_row3_col4\" class=\"data row3 col4\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_36d5f_level0_row4\" class=\"row_heading level0 row4\" >2</th>\n",
       "      <td id=\"T_36d5f_row4_col0\" class=\"data row4 col0\" >Layer3</td>\n",
       "      <td id=\"T_36d5f_row4_col1\" class=\"data row4 col1\" >Strata1</td>\n",
       "      <td id=\"T_36d5f_row4_col2\" class=\"data row4 col2\" >3</td>\n",
       "      <td id=\"T_36d5f_row4_col3\" class=\"data row4 col3\" >#443988</td>\n",
       "      <td id=\"T_36d5f_row4_col4\" class=\"data row4 col4\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_36d5f_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_36d5f_row5_col0\" class=\"data row5 col0\" >Basement</td>\n",
       "      <td id=\"T_36d5f_row5_col1\" class=\"data row5 col1\" >Strata1</td>\n",
       "      <td id=\"T_36d5f_row5_col2\" class=\"data row5 col2\" >4</td>\n",
       "      <td id=\"T_36d5f_row5_col3\" class=\"data row5 col3\" >#ff3f20</td>\n",
       "      <td id=\"T_36d5f_row5_col4\" class=\"data row5 col4\" >6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "    surface   series  order_surfaces    color  id\n",
       "3    Fault1   Fault1               1  #527682   1\n",
       "4    Fault2   Fault2               1  #527682   2\n",
       "0    Layer1  Strata1               1  #ffbe00   3\n",
       "1    Layer2  Strata1               2  #728f02   4\n",
       "2    Layer3  Strata1               3  #443988   5\n",
       "5  Basement  Strata1               4  #ff3f20   6"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.surfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6751531a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T16:23:42.152463Z",
     "start_time": "2022-07-10T16:23:41.328128Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "048714d990f347bfbacb7751365c6b11",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Widget(value=\"<iframe src='http://localhost:57216/index.html?ui=P_0x1b00e903100_0&reconnect=auto' style='width…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<gempy.plot.vista.GemPyToVista at 0x1b0112cb5e0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.plot_3d(geo_model, image=False, plotter_type='basic', notebook=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8e27b70d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T16:23:46.644247Z",
     "start_time": "2022-07-10T16:23:42.285341Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling aesara function...\n",
      "Level of Optimization:  fast_compile\n",
      "Device:  cpu\n",
      "Precision:  float64\n",
      "Number of faults:  2\n",
      "Compilation Done!\n",
      "Kriging values: \n",
      "                     values\n",
      "range              1536.23\n",
      "$C_o$             56190.48\n",
      "drift equations  [3, 3, 3]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy.core.interpolator.InterpolatorModel at 0x1b0112cbe50>"
      ]
     },
     "execution_count": 17,
     "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": "code",
   "execution_count": 18,
   "id": "5b7b2377",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T16:24:27.468449Z",
     "start_time": "2022-07-10T16:23:46.725299Z"
    }
   },
   "outputs": [],
   "source": [
    "sol = gp.compute_model(geo_model, compute_mesh=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43e38934",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af5fbea2f1314af39e2b2c1219740c88",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Widget(value=\"<iframe src='http://localhost:57216/index.html?ui=P_0x1b024d4c880_1&reconnect=auto' style='width…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<gempy.plot.vista.GemPyToVista at 0x1b024d4c130>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.plot_3d(geo_model, notebook=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8757c7c",
   "metadata": {},
   "source": [
    "## Extracting Depth Maps from GemPy Model\n",
    "\n",
    "We now extract the depth maps from the GemPy Model which will then be rotated using Py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f1ee002c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Layer2': [PolyData (0x1b024dab9a0)\n",
       "    N Cells:    7938\n",
       "    N Points:   4100\n",
       "    N Strips:   0\n",
       "    X Bounds:   1.000e+01, 9.900e+02\n",
       "    Y Bounds:   1.000e+01, 9.900e+02\n",
       "    Z Bounds:   -4.000e+02, -2.000e+02\n",
       "    N Arrays:   1,\n",
       "  '#728f02'],\n",
       " 'Fault1': [PolyData (0x1b024da8160)\n",
       "    N Cells:    5820\n",
       "    N Points:   3038\n",
       "    N Strips:   0\n",
       "    X Bounds:   2.096e+02, 5.352e+02\n",
       "    Y Bounds:   1.000e+01, 9.900e+02\n",
       "    Z Bounds:   -5.940e+02, -3.000e+01\n",
       "    N Arrays:   1,\n",
       "  '#527682'],\n",
       " 'Fault2': [PolyData (0x1b024da9240)\n",
       "    N Cells:    5720\n",
       "    N Points:   2990\n",
       "    N Strips:   0\n",
       "    X Bounds:   4.648e+02, 7.904e+02\n",
       "    Y Bounds:   1.000e+01, 9.900e+02\n",
       "    Z Bounds:   -5.940e+02, -3.000e+01\n",
       "    N Arrays:   1,\n",
       "  '#527682']}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meshes = gg.visualization.create_depth_maps_from_gempy(geo_model, surfaces=['Layer2', 'Fault1', 'Fault2'])\n",
    "meshes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e81b5b6",
   "metadata": {},
   "source": [
    "## Rotating Meshes\n",
    "\n",
    "The meshes will be rotated around the center of the extent to reach their original position. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1e002524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500.00000000000006, 500.00000000000006)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y = list(extent_gdf.iloc[0]['geometry'].centroid.coords)[0][0], list(extent_gdf.iloc[0]['geometry'].centroid.coords)[0][1]\n",
    "x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4c09b4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_layer2 = meshes['Layer2'][0].rotate_z(angle=33, point=(x,y,0))\n",
    "mesh_fault1 = meshes['Fault1'][0].rotate_z(angle=33, point=(x,y,0))\n",
    "mesh_fault2 = meshes['Fault2'][0].rotate_z(angle=33, point=(x,y,0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2f05bb",
   "metadata": {},
   "source": [
    "## Plotting the rotates Meshes\n",
    "\n",
    "Rotating the meshes will result in the original position of the layers. The meshes/surfaces can now be used for further calculations and computations or exported to a GIS System."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5248e39e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4589c21dddd1486db9ed58c7af96d22e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Widget(value=\"<iframe src='http://localhost:57216/index.html?ui=P_0x1b02de3ac20_2&reconnect=auto' style='width…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = pv.Plotter(notebook=True)\n",
    "\n",
    "p.add_mesh(mesh_layer2, color= meshes['Layer2'][1])\n",
    "\n",
    "p.add_mesh(mesh_fault1, color= meshes['Fault1'][1])\n",
    "\n",
    "p.add_mesh(mesh_fault2, color= meshes['Fault2'][1])\n",
    "\n",
    "p.set_background('white')\n",
    "p.show_grid(color='black')\n",
    "p.show()"
   ]
  }
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