 Horizontal stratigraphic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8039c2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gempy as gp\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d12db6b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active grids: ['regular']\n"
     ]
    }
   ],
   "source": [
    "data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n",
    "geo_data = gp.create_data('horizontal', extent=[0, 1000, 0, 1000, 0, 1000], resolution=[50, 50, 50],\n",
    "                          path_o=data_path + \"/data/input_data/jan_models/model1_orientations.csv\",\n",
    "                          path_i=data_path + \"/data/input_data/jan_models/model1_surface_points.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d346cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a7176_row0_col3 {\n",
       "  background-color: #015482;\n",
       "}\n",
       "#T_a7176_row1_col3 {\n",
       "  background-color: #9f0052;\n",
       "}\n",
       "#T_a7176_row2_col3 {\n",
       "  background-color: #ffbe00;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a7176_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >surface</th>\n",
       "      <th class=\"col_heading level0 col1\" >series</th>\n",
       "      <th class=\"col_heading level0 col2\" >order_surfaces</th>\n",
       "      <th class=\"col_heading level0 col3\" >color</th>\n",
       "      <th class=\"col_heading level0 col4\" >id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a7176_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_a7176_row0_col0\" class=\"data row0 col0\" >rock2</td>\n",
       "      <td id=\"T_a7176_row0_col1\" class=\"data row0 col1\" >Strat_Series</td>\n",
       "      <td id=\"T_a7176_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_a7176_row0_col3\" class=\"data row0 col3\" >#015482</td>\n",
       "      <td id=\"T_a7176_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7176_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_a7176_row1_col0\" class=\"data row1 col0\" >rock1</td>\n",
       "      <td id=\"T_a7176_row1_col1\" class=\"data row1 col1\" >Strat_Series</td>\n",
       "      <td id=\"T_a7176_row1_col2\" class=\"data row1 col2\" >2</td>\n",
       "      <td id=\"T_a7176_row1_col3\" class=\"data row1 col3\" >#9f0052</td>\n",
       "      <td id=\"T_a7176_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7176_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_a7176_row2_col0\" class=\"data row2 col0\" >basement</td>\n",
       "      <td id=\"T_a7176_row2_col1\" class=\"data row2 col1\" >Basement_Series</td>\n",
       "      <td id=\"T_a7176_row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "      <td id=\"T_a7176_row2_col3\" class=\"data row2 col3\" >#ffbe00</td>\n",
       "      <td id=\"T_a7176_row2_col4\" class=\"data row2 col4\" >3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "    surface           series  order_surfaces    color  id\n",
       "0     rock2     Strat_Series               1  #015482   1\n",
       "1     rock1     Strat_Series               2  #9f0052   2\n",
       "2  basement  Basement_Series               1  #ffbe00   3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.map_stack_to_surfaces(geo_data, {\"Strat_Series\": ('rock2', 'rock1'), \"Basement_Series\": ('basement')})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6ebf22fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AHMED\\anaconda3\\lib\\site-packages\\gempy\\plot\\plot_api.py:261: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.fig.show()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy.plot.visualization_2d.Plot2D at 0x1d31a7144c0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.plot_2d(geo_data, direction=['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "030bb7d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting kriging parameters to their default values.\n",
      "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             1732.050808\n",
      "$C_o$            71428.571429\n",
      "drift equations        [3, 3]\n"
     ]
    }
   ],
   "source": [
    "interp_data = gp.set_interpolator(geo_data, compile_theano=True,\n",
    "                                  theano_optimizer='fast_compile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "413ef209",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol = gp.compute_model(geo_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4a053481",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AHMED\\anaconda3\\lib\\site-packages\\gempy\\plot\\plot_api.py:261: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.fig.show()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy.plot.visualization_2d.Plot2D at 0x1d3a33d3a00>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.plot_2d(geo_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1aede27",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AHMED\\anaconda3\\lib\\site-packages\\pyvista\\plotting\\tools.py:571: PyvistaDeprecationWarning: The usage of `parse_color` is deprecated in favor of the new `Color` class.\n",
      "  warnings.warn(\n",
      "C:\\Users\\AHMED\\anaconda3\\lib\\site-packages\\pyvista\\utilities\\helpers.py:507: UserWarning: Points is not a float type. This can cause issues when transforming or applying filters. Casting to ``np.float32``. Disable this by passing ``force_float=False``.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy.plot.vista.GemPyToVista at 0x1d3a3774b50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.plot_3d(geo_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ec0f6a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13b88d4c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}