{
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   "source": [
    "<img src=\"../../data/images/gempy_logo.png\" />\n",
    "\n",
    "# <center> From Maps to Models - Tutorials for structural geological modeling using GemPy and GemGIS</center>\n",
    "\n",
    "# Model 1 - Horizontal Layers\n",
    "\n",
    "\n",
    "This first notebook illustrates how to create a simple sample model of horizontal layers in `GemPy`. The model consists of four parallel layers plus basement layers and has an extent of 1000 m by 1000 m with a vertical extent of 600 m. No folded, faulted or truncated layers are present in this model.\n",
    "\n",
    "If you have not gone through the introduction notebook for the course, please check it out: [Introduction Notebook](../00_introduction_to_structural_modeling.ipynb) ([notebook on Github](https://nbviewer.org/github/cgre-aachen/gemgis_data/blob/main/notebooks/00_introduction_to_structural_modeling.ipynb))\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>In this tutorial, you will learn the following:</b> <br>\n",
    "- How to import input data into <b>GemPy</b> via CSV-Files (comma-separated-values) and what the files have to look like<br>\n",
    "- How to build a simple model consisting of <b>horizontal layers</b> belonging to <b>one Series</b><br>\n",
    "- How to visualize the resulting model with cross sections in 2D and the entire model in 3D\n",
    "\n",
    "</div>\n",
    "\n",
    "## Contents\n",
    "\n",
    "1. [Installing GemPy](#installing-gempy)\n",
    "2. [Importing Libraries](#importing-libraries)\n",
    "3. [Data Preparation](#data-preparation)\n",
    "    1. [Importing Interface Points](#importing-interface-points)\n",
    "    2. [Importing Orientations](#importing-orientations)\n",
    "4. [GemPy Model Calculation](#gempy-model-calculation)\n",
    "    1. [Creating the GemPy Model](#creating-the-gempy-model)\n",
    "    2. [Inspecting the Surfaces](#inspecting-the-surfaces)\n",
    "    3. [Inspecting the Input Data](#inspecting-the-input-data)\n",
    "    4. [Map Stack to Surfaces](#map-stack-to-surfaces)\n",
    "    5. [Plotting Input Data in 2D](#plotting-the-input-data-in-2d)\n",
    "    6. [Plotting Input Data in 3D](#plotting-the-input-data-in-3d)\n",
    "    7. [Setting the Interpolator](#setting-the-interpolator)\n",
    "    8. [Computing the Model](#computing-the-model)\n",
    "5. [Model Visualization and Post-Processing](#model-visualization-and-post-processing)\n",
    "    1. [Visualizing Cross Sections of the Computed Model](#visualizing-cross-sections-of-the-computed-model)\n",
    "    2. [Visualizing the computed model in 3D](#visualizing-the-computed-model-in-3d)\n",
    "6. [Conclusions](#conclusions)\n",
    "7. [Outlook](#outlook)\n",
    "8. [Licensing](#licensing)\n",
    "\n",
    "\n",
    "\n",
    "<img src=\"../images/model1.png\" width=500/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4e7c892",
   "metadata": {},
   "source": [
    "The input data is provided as already prepared CSV-files (comma-separated-values) and will be loaded as Pandas `DataFrames` (see Figure below, [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)). The first file contains the interface points, the boundaries between the four stratigraphic units. It should consist of four columns with the headers `X`, `Y`, `Z` for the location of the interface points in meter and `formation` for name of the layer the interface belongs to (see Figure below). The second file contains the orientation of the layers with the same columns as before plus three more columns, `dip`, `azimuth`, and `polarity` indicating the dip the layer and the dip direction (azimuth, see Figure below) in degrees. The dip varies from 0° for horizontal layers to 90° for vertical layers. The azimuth varies from 0° (N) via 180° (S) to 360° (N). Here, we only provide the orientations for one layer. This will be explained later on in more detail. The `polarity` value is mostly set to 1.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Important:</b> Interface points always represent the <b>base</b> of the respective layer. However, orientations of layers must not be located at the location of the layer boundaries but may also be located within the layer itself as the orientations will \"only\" constrain the gradient of the scalar field interpolated by GemPy.   \n",
    "    \n",
    "</div>\n",
    "\n",
    "<img src=\"../../data/images/model1_fig1.png\" width=700/>\n",
    "<img src=\"../../data/images/fig3.png\" width=500/>\n",
    "\n",
    "By CrunchyRocks, after Karla Panchuck - https://openpress.usask.ca/physicalgeology/chapter/13-5-measuring-geological-structures/, CC BY 4.0, https://commons.wikimedia.org/w/index.php?curid=113554289"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "solid-hormone",
   "metadata": {},
   "source": [
    "<a id='installing-gempy'></a>\n",
    "\n",
    "# Installing GemPy\n",
    "\n",
    "If you have not installed `GemPy` yet, please follow the [installation instructions](https://docs.gempy.org/installation.html). If you encounter any issues, feel free to open a new discussion at [GemPy Discussions](https://github.com/cgre-aachen/gempy/discussions). If you encounter an error in the installation process, feel free to also open an issue at [GemPy Issues](https://github.com/cgre-aachen/gempy/issues). There, the `GemPy` development team will help you out. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "artificial-customs",
   "metadata": {},
   "source": [
    "<a id='importing-libraries'></a>\n",
    "\n",
    "# Importing Libraries\n",
    "\n",
    "For this notebook, we need the `pandas` library for the data preparation, `matplotlib` for plotting and of course the `gempy` library. The `gempy_viewer` library is used to plot the modeling results. Any warnings that may appear can be ignored for now. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f498618",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-23T07:41:01.590117Z",
     "start_time": "2023-02-23T07:40:57.359047Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting Backend To: AvailableBackends.numpy\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import gempy as gp\n",
    "import gempy_viewer as gpv\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jewish-complex",
   "metadata": {},
   "source": [
    "<a id='data-preparation'></a>\n",
    "# Data Preparation\n",
    "\n",
    "For this model, the only thing that needs to be done is loading the already created interface points and orientations. In the next tutorials, you will create the data yourself and process it further to make it usable for GemPy. \n",
    "\n",
    "<a id='importing-interface-points'></a>\n",
    "## Importing Interface Points\n",
    "\n",
    "We are using the `pandas` library ([Pandas](https://pandas.pydata.org/docs/)) to load the interface points that were prepared beforehand and stored as CSV-file (comma-separated-file). The only information that is needed are the location of the interface point (`X`, `Y`, `Z`) and the `formation` it belongs to. You may have to adjust the `delimiter` (`'\\t'`, `','`, `';'`, `' '`) when loading the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c8c178c",
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       "     X    Y    Z formation\n",
       "0  200  250 -150    Layer1\n",
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   "source": [
    "interfaces = pd.read_csv('../../data/model1/model1_interfaces.csv', \n",
    "                         delimiter = '\\t')\n",
    "interfaces.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occupied-simulation",
   "metadata": {},
   "source": [
    "<a id='importing-orientations'></a>\n",
    "\n",
    "## Importing Orientations\n",
    "\n",
    "The orientations will also be loaded using `pandas`. In addition to the location and the formation the orientation belongs to, the dip value, azimuth value (dip direction) and a polarity value (mostly set to 1 by default) needs to be provided. As the model will feature horizontal layers, the dip is equal to 0. These three provided orientations belonging to `Layer1` are all the orientations that are needed to compute the model. There are no other orientations needed as the potential field approach implemented in `GemPy` allows to combine subparallel layers in on so-called `Series` where only at least one orientation is needed for the entire series."
   ]
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       "     X    Y    Z formation  dip  azimuth  polarity\n",
       "0  200  500 -150    Layer1    0        0         1\n",
       "1  800  500 -150    Layer1    0        0         1\n",
       "2  500  500 -150    Layer1    0        0         1"
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   "source": [
    "orientations = pd.read_csv('../../data/model1/model1_orientations.csv', \n",
    "                           delimiter='\\t')\n",
    "orientations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "veterinary-acceptance",
   "metadata": {},
   "source": [
    "<a id='gempy-model-calculation'></a>\n",
    "\n",
    "# GemPy Model Calculation\n",
    "\n",
    "The following part introduces the main steps of creating a model in `GemPy`. \n",
    "\n",
    "The creation of a `GemPy` Model follows particular steps which will be performed in the following:\n",
    "\n",
    "1. Create new model and data initiation: `gp.create_geomodel()`\n",
    "2. Map Stack to Surfaces: `gp.map_stack_to_surfaces()`\n",
    "3. [...]\n",
    "4. Computing the Model: `gp.compute_model()`\n",
    "\n",
    "<a id='creating-the-gempy-model'></a>\n",
    "\n",
    "## Creating the GemPy Model and Data Initiation\n",
    "\n",
    "The first step is to create a new empty `GemPy` model by providing a name for it. \n",
    "\n",
    "In addition, the `extent` of the model (`xmin`, `xmax`, `ymin`, `ymax`, `zmin`, `zmax`) and the `resolution` in `X`, `Y`and `Z` direction (`res_x`, `res_y`, `res_z`, equal to the number of cells in each direction) will be set using lists of values. If you want to provide cells with a certain size, you would have to calculate the following. It is important to convert the resulting number of cells into an `int` as only integer values for the number of cells are valid. \n",
    "\n",
    "```python\n",
    "res_x = int((xmax-xmin)/cell_size_x)\n",
    "res_y = int((ymax-ymin)/cell_size_y)\n",
    "res_z = int((zmax-zmin)/cell_size_z)\n",
    "```\n",
    "\n",
    "The interface points (`surface_points_df`) and orientations (`orientations_df`) will be passed as strings with the location of the CSV files. Please mind that you may have to edit the `pandas_reader_kwargs` by adding the `'sep'` key and the delimiter `'\\t'` as item of a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eb3ec438",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-27T11:06:11.168832Z",
     "start_time": "2022-03-27T11:06:09.084241Z"
    }
   },
   "outputs": [],
   "source": [
    "geo_model: gp.data.GeoModel = gp.create_geomodel(\n",
    "    project_name='Model1_Horizontal_Layers',\n",
    "    extent=[0, 1000, 0, 1000, -600, 0], \n",
    "    resolution=[100, 100, 100],  # * Here we define the number of octree levels. If octree levels are defined, the resolution is ignored.\n",
    "    importer_helper=gp.data.ImporterHelper(\n",
    "        path_to_orientations='../../data/model1/model1_orientations.csv',\n",
    "        path_to_surface_points='../../data/model1/model1_interfaces.csv',\n",
    "        pandas_reader_kwargs = {'sep': '\\t'}\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7b16e48",
   "metadata": {},
   "source": [
    "It is possible to check out the different attributes of the `GemPy` model object using `vars()`. It is mostly empty for now but will be filled in the following steps. The most important attributes/objects of the `GemPy` model are:\n",
    "\n",
    "1. `meta`\n",
    "2. `structural_frame`\n",
    "3. `grid`\n",
    "4. `geophysics_input`\n",
    "5. `input_transform`\n",
    "6. `interpolation_grid`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c85daba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'meta': GeoModelMeta(name='Model1_Horizontal_Layers', creation_date='2025-09-09T12:40:32.133342', last_modification_date=None, owner=None),\n",
       " 'structural_frame': StructuralFrame(\n",
       " \tstructural_groups=[\n",
       " StructuralGroup(\n",
       " \tname=default_formation,\n",
       " \tstructural_relation=StackRelationType.ERODE,\n",
       " \telements=[\n",
       " Element(\n",
       " \tname=Layer1,\n",
       " \tcolor=\u001b[38;2;1;84;130m#015482\u001b[0m,\n",
       " \tis_active=True\n",
       " ),\n",
       " Element(\n",
       " \tname=Layer2,\n",
       " \tcolor=\u001b[38;2;159;0;82m#9f0052\u001b[0m,\n",
       " \tis_active=True\n",
       " ),\n",
       " Element(\n",
       " \tname=Layer3,\n",
       " \tcolor=\u001b[38;2;255;190;0m#ffbe00\u001b[0m,\n",
       " \tis_active=True\n",
       " ),\n",
       " Element(\n",
       " \tname=Layer4,\n",
       " \tcolor=\u001b[38;2;114;143;2m#728f02\u001b[0m,\n",
       " \tis_active=True\n",
       " )\n",
       " ]\n",
       " )\n",
       " ],\n",
       " \tfault_relations=\n",
       " [[False]],,\n",
       " 'grid': Grid(values=array([[   5.,    5., -597.],\n",
       "        [   5.,    5., -591.],\n",
       "        [   5.,    5., -585.],\n",
       "        ...,\n",
       "        [ 995.,  995.,  -15.],\n",
       "        [ 995.,  995.,   -9.],\n",
       "        [ 995.,  995.,   -3.]], shape=(1000000, 3)), length=array([], dtype=float64), _octree_grid=None, _dense_grid=RegularGrid(resolution=array([100, 100, 100]), extent=array([   0., 1000.,    0., 1000., -600.,    0.]), values=array([[   5.,    5., -597.],\n",
       "        [   5.,    5., -591.],\n",
       "        [   5.,    5., -585.],\n",
       "        ...,\n",
       "        [ 995.,  995.,  -15.],\n",
       "        [ 995.,  995.,   -9.],\n",
       "        [ 995.,  995.,   -3.]], shape=(1000000, 3)), mask_topo=array([], shape=(0, 3), dtype=bool), _transform=None), _custom_grid=None, _topography=None, _sections=None, _centered_grid=None, _transform=None, _octree_levels=-1),\n",
       " 'geophysics_input': None,\n",
       " 'input_transform': {'_cached_pivot': None,\n",
       "  '_is_default_transform': False,\n",
       "  'position': array([-500., -500.,  300.]),\n",
       "  'rotation': array([0., 0., 0.]),\n",
       "  'scale': array([0.00083333, 0.00083333, 0.00083333])},\n",
       " 'interpolation_grid': None}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(geo_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d0e2dd",
   "metadata": {},
   "source": [
    "The attributes can easily be accessed via the `geo_model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8284ddcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GeoModelMeta(name='Model1_Horizontal_Layers', creation_date='2025-09-09T12:40:32.133342', last_modification_date=None, owner=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5c6a0b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "        <table>\n",
       "          <tr><td>Structural Groups:</td><td>\n",
       "    <table style=\"border-left:1.2px solid black;>\n",
       "      <tr><th colspan=\"2\"><b>StructuralGroup:</b></th></tr>\n",
       "      <tr><td>Name:</td><td>default_formation</td></tr>\n",
       "      <tr><td>Structural Relation:</td><td>StackRelationType.ERODE</td></tr>\n",
       "      <tr><td>Elements:</td><td>\n",
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       "      <tr><th colspan=\"2\"><b>StructuralElement:</b></th></tr>\n",
       "      <tr><td>Name:</td><td>Layer1</td></tr>\n",
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       "      <tr><td>Name:</td><td>Layer2</td></tr>\n",
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       "StructuralFrame(\n",
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       "StructuralGroup(\n",
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       "[[False]],"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "geo_model.structural_frame"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1253e5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_model.geophysics_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3bc1e8f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_cached_pivot': None,\n",
       " '_is_default_transform': False,\n",
       " 'position': array([-500., -500.,  300.]),\n",
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     "execution_count": 9,
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d93cd05e",
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_model.interpolation_grid"
   ]
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  {
   "cell_type": "markdown",
   "id": "armed-story",
   "metadata": {},
   "source": [
    "<a id='inspecting-the-surfaces'></a>\n",
    "\n",
    "## Inspecting the Surfaces\n",
    "\n",
    "The model consists of four different layers or surfaces now which all belong to the `Default series`. During the next step, the proper `Series` will be assigned to the surface. Using the `structural_frame`-attribute again, we can check which layers were loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e8519ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-27T11:06:11.756189Z",
     "start_time": "2022-03-27T11:06:11.542858Z"
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   "outputs": [
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     "execution_count": 11,
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  {
   "cell_type": "markdown",
   "id": "a2dbc68b",
   "metadata": {},
   "source": [
    "<a id='inspecting-the-input-data'></a>\n",
    "\n",
    "## Inspecting the Input Data\n",
    "\n",
    "The loaded interface points and orientations can again be inspected using the `surface_points`- and `orientations`-attributes. Using the `df`-attribute of this object will convert the displayed table in a `pandas` `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2fd4f8d8",
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  {
   "cell_type": "markdown",
   "id": "occupational-coaching",
   "metadata": {},
   "source": [
    "<a id='map-stack-to-surfaces'></a>\n",
    "\n",
    "## Map Stack to Surfaces\n",
    "\n",
    "We want our geological units to appear in the correct order relative to age. Such order might for example be given by a depositional sequence of stratigraphy, unconformities due to erosion or other lithological genesis events such as igneous intrusions. Defining the correct order of series is vital to the construction of the model!\n",
    "\n",
    "During this step, all four layers of the model are assigned to the `Strata1` series. Per definition, only orientations for one layer are necessary to compute the model. However, it is of course possible to provide orientation measurements for the other layers as well. If the layers were not parallel as shown in the next models, multiple series would be defined. The order within one series also defines the age relations within this series and has to be according to the depositional events of the layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca6dfa3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-27T11:06:11.992240Z",
     "start_time": "2022-03-27T11:06:11.760192Z"
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   },
   "outputs": [
    {
     "data": {
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       "\n",
       "        <table>\n",
       "          <tr><td>Structural Groups:</td><td>\n",
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      "text/plain": [
       "StructuralFrame(\n",
       "\tstructural_groups=[\n",
       "StructuralGroup(\n",
       "\tname=Strata1,\n",
       "\tstructural_relation=StackRelationType.ERODE,\n",
       "\telements=[\n",
       "Element(\n",
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       "\tcolor=\u001b[38;2;1;84;130m#015482\u001b[0m,\n",
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       "\tis_active=True\n",
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       "Element(\n",
       "\tname=Layer3,\n",
       "\tcolor=\u001b[38;2;255;190;0m#ffbe00\u001b[0m,\n",
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       "\tfault_relations=\n",
       "[[False]],"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.map_stack_to_surfaces(gempy_model=geo_model,\n",
    "                         mapping_object=\n",
    "                         {\n",
    "                          'Strata1': ('Layer1', 'Layer2', 'Layer3', 'Layer4'),                          \n",
    "                         },\n",
    "                         remove_unused_series=True)\n",
    "geo_model.structural_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "directed-immigration",
   "metadata": {},
   "source": [
    "<a id='plotting-the-input-data-in-2d'></a>\n",
    "\n",
    "## Plotting the input data in 2D using Matplotlib\n",
    "\n",
    "The input data can now be visualized in 2D using `matplotlib`. This might for example be useful to check if all points and measurements are defined the way we want them to. Using the function `plot_2d()`, we attain a 2D projection of our data points onto a plane of chosen direction (we can choose this attribute to be either `'x'`, `'y'`, or `'z'`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee36f42b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-27T11:06:12.543419Z",
     "start_time": "2022-03-27T11:06:12.000245Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ale93371\\Anaconda3\\envs\\gemgis_12\\Lib\\site-packages\\gempy_viewer\\API\\_plot_2d_API.py:176: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  p.fig.show()\n"
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",
      "text/plain": [
       "<Figure size 1040x715 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = gpv.plot_2d(geo_model, \n",
    "                   direction='z', \n",
    "                   show_lith=False, \n",
    "                   show_boundaries=False)\n",
    "\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "objective-standard",
   "metadata": {},
   "source": [
    "<a id='plotting-the-input-data-in-3d'></a>\n",
    "\n",
    "## Plotting the input data in 3D using PyVista\n",
    "\n",
    "The input data can also be viszualized using the `pyvista` package. In this view, the interface points are visible as well as the orientations (marked as arrows) which indicate the normals of each orientation value. \n",
    "\n",
    "The `pyvista` package requires the Visualization Toolkit (VTK) to be installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "03cfc42d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-27T11:06:13.610826Z",
     "start_time": "2022-03-27T11:06:12.551420Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ale93371\\Anaconda3\\envs\\gemgis_12\\Lib\\site-packages\\pyvista\\jupyter\\notebook.py:56: UserWarning: Failed to use notebook backend: \n",
      "\n",
      "cannot import name 'vtk' from 'trame.widgets' (C:\\Users\\ale93371\\Anaconda3\\envs\\gemgis_12\\Lib\\site-packages\\trame\\widgets\\__init__.py)\n",
      "\n",
      "Falling back to a static output.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<gempy_viewer.modules.plot_3d.vista.GemPyToVista at 0x1fd5dcb0bd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpv.plot_3d(geo_model, \n",
    "            image=False, \n",
    "            show_lith=False,\n",
    "            plotter_type='basic',\n",
    "            kwargs_plotter= {'notebook':True},)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elementary-camping",
   "metadata": {},
   "source": [
    "<a id='setting-the-interpolator'></a>\n",
    "## Setting the interpolator\n",
    "\n",
    "Unlike previous versions, GemPy 3 doesn’t rely on theano or asera. Instead, it utilizes numpy or tensorflow. Consequently, we no longer need to recompile all theano functions (TensorFlow uses eager execution; we found no notable speed difference after profiling the XLA compiler).\n",
    "\n",
    "The parameters used for the interpolation are stored in gempy.core.data.GeoModel.interpolation_options. These parameters have sensible default values that you can modify if necessary. However, we advise caution when changing these parameters unless you fully understand their implications.\n",
    "\n",
    "Display the current interpolation options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8e27b70d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-27T11:06:18.212432Z",
     "start_time": "2022-03-27T11:06:13.614839Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InterpolationOptions(kernel_options=KernelOptions(range=1.7, c_o=10.0, uni_degree=1, i_res=4.0, gi_res=2.0, number_dimensions=3, kernel_function=AvailableKernelFunctions.cubic, kernel_solver=Solvers.DEFAULT, compute_condition_number=False, optimizing_condition_number=False, condition_number=None), evaluation_options=EvaluationOptions(_number_octree_levels=1, _number_octree_levels_surface=4, octree_curvature_threshold=-1.0, octree_error_threshold=1.0, octree_min_level=2, mesh_extraction=True, mesh_extraction_masking_options=<MeshExtractionMaskingOptions.INTERSECT: 3>, mesh_extraction_fancy=True, evaluation_chunk_size=500000, compute_scalar_gradient=False, verbose=False), debug=True, cache_mode=<CacheMode.IN_MEMORY_CACHE: 3>, cache_model_name='Model1_Horizontal_Layers', block_solutions_type=<BlockSolutionType.DENSE_GRID: 2>, sigmoid_slope=5000000, debug_water_tight=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.interpolation_options"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "innovative-cradle",
   "metadata": {},
   "source": [
    "<a id='computing-the-model'></a>\n",
    "\n",
    "## Computing the model\n",
    "\n",
    "At this point, we have all we need to compute our full model via `gp.compute_model()`. By default, this will return two separate solutions in the form of arrays. The first provides information on the lithological formations, the second on the fault network in the model, which is not present in this example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5b7b2377",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-27T11:06:35.778810Z",
     "start_time": "2022-03-27T11:06:18.217433Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting Backend To: AvailableBackends.numpy\n",
      "Chunking done: 89 chunks\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ale93371\\Anaconda3\\envs\\gemgis_12\\Lib\\site-packages\\gempy_engine\\modules\\activator\\_soft_segment.py:95: RuntimeWarning: overflow encountered in exp\n",
      "  return 1.0 / (1.0 + bt.t.exp(x))\n"
     ]
    }
   ],
   "source": [
    "sol = gp.compute_model(geo_model, \n",
    "                       compute_mesh=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "779580c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b>Solutions:</b> 4 Octree Levels, 4 DualContouringMeshes"
      ],
      "text/plain": [
       "Solutions(4 Octree Levels, 4 DualContouringMeshes)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea2d8eee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b>Solutions:</b> 4 Octree Levels, 4 DualContouringMeshes"
      ],
      "text/plain": [
       "Solutions(4 Octree Levels, 4 DualContouringMeshes)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "protected-parcel",
   "metadata": {},
   "source": [
    "<a id='model-visualization-and-post-processing'></a>\n",
    "\n",
    "# Model Visualization and Post-Processing\n",
    "\n",
    "<a id='visualizing-cross-sections-of-the-computed-model'></a>\n",
    "\n",
    "## Visulazing Cross Sections of the computed model\n",
    "\n",
    "Cross sections in different `direction`s and at different `cell_number`s can be displayed. Here, we only see the horizontal layers of the model. Not very much exciting yet. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eb54b87a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-27T11:06:36.846561Z",
     "start_time": "2022-03-27T11:06:35.780806Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ale93371\\Anaconda3\\envs\\gemgis_12\\Lib\\site-packages\\gempy_viewer\\API\\_plot_2d_API.py:176: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  p.fig.show()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy_viewer.modules.plot_2d.visualization_2d.Plot2D at 0x1fd5d85b610>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2496x1716 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gpv.plot_2d(geo_model, \n",
    "           direction=['x', 'x', 'y', 'y'], \n",
    "           cell_number=[25, 50, 25, 75], \n",
    "           show_topography=False, \n",
    "           show_data=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74135615",
   "metadata": {},
   "source": [
    "Next to the lithology data, we can also plot the calculated scalar field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e7181346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gempy_viewer.modules.plot_2d.visualization_2d.Plot2D at 0x1fd03217210>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x715 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gpv.plot_2d(geo_model, show_data=False, show_scalar=True, show_lith=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "several-artwork",
   "metadata": {},
   "source": [
    "<a id='visualizing-the-computed-model-in-3d'></a>\n",
    "\n",
    "## Visualizing the computed model in 3D\n",
    "\n",
    "The computed model can be visualized in 3D using the `pyvista` library. Setting `kwargs_plotter= {'notebook':True}` will open an interactive windows and the model can be rotated and zooming is possible. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "838f0e34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-27T11:06:37.661135Z",
     "start_time": "2022-03-27T11:06:36.848563Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ale93371\\Anaconda3\\envs\\gemgis_12\\Lib\\site-packages\\gempy_viewer\\modules\\plot_3d\\drawer_surfaces_3d.py:38: PyVistaDeprecationWarning: \n",
      "../../../../Anaconda3/envs/gemgis_12/Lib/site-packages/gempy_viewer/modules/plot_3d/drawer_surfaces_3d.py:38: Argument 'color' must be passed as a keyword argument to function 'BasePlotter.add_mesh'.\n",
      "From version 0.50, passing this as a positional argument will result in a TypeError.\n",
      "  gempy_vista.surface_actors[element.name] = gempy_vista.p.add_mesh(\n",
      "C:\\Users\\ale93371\\Anaconda3\\envs\\gemgis_12\\Lib\\site-packages\\pyvista\\jupyter\\notebook.py:56: UserWarning: Failed to use notebook backend: \n",
      "\n",
      "cannot import name 'vtk' from 'trame.widgets' (C:\\Users\\ale93371\\Anaconda3\\envs\\gemgis_12\\Lib\\site-packages\\trame\\widgets\\__init__.py)\n",
      "\n",
      "Falling back to a static output.\n",
      "  warnings.warn(\n"
     ]
    },
    {
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<gempy_viewer.modules.plot_3d.vista.GemPyToVista at 0x1fd02e13b90>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpv.plot_3d(geo_model, \n",
    "                 image=False, \n",
    "                 show_topography=True,\n",
    "                 plotter_type='basic', \n",
    "                 kwargs_plotter= {'notebook':True},\n",
    "                 show_lith=True)"
   ]
  },
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   "id": "smooth-characterization",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "# Conclusions\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>In this tutorial, you learnt the following:</b> <br>\n",
    "- How to import input data into <b>GemPy</b> via CSV-Files (comma-separated-values) and what the files have to look like<br>\n",
    "- How to build a simple model consisting of <b>horizontal layers</b> belonging to <b>one Series</b><br>\n",
    "- How to visualize the resulting model with cross sections in 2D and the entire model in 3D\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "<a id='outlook'></a>\n",
    "# Outlook\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>In the next tutorial, you will learn the following:</b> <br>\n",
    "- Get a better undestanding of what orientations mean in GemPy and how to plot them using <b>mplstereonet</b><br>\n",
    "- How to build a simple model consisting of <b>folded layers</b> belonging to <b>one Series</b><br>\n",
    "\n",
    "\n",
    "</div>\n",
    "\n",
    "[Take me to the next notebook on Github](https://nbviewer.org/github/cgre-aachen/gemgis_data/blob/main/notebooks/01_basic_modeling/model2_Folded_Layers.ipynb)\n",
    "\n",
    "[Take me to the next notebook locally](model2_Folded_Layers.ipynb)\n",
    "\n",
    "<img src=\"../../jose/images/fig1.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4190b239",
   "metadata": {},
   "source": [
    "<a id='licensing'></a>\n",
    "\n",
    "## Licensing\n",
    "\n",
    "Institute for Computational Geoscience, Geothermics and Reservoir Geophysics, RWTH Aachen University & Fraunhofer IEG, Fraunhofer Research Institution for Energy Infrastructures and Geotechnologies IEG, Authors: Alexander Juestel. For more information contact: alexander.juestel(at)ieg.fraunhofer.de\n",
    "\n",
    "All notebooks are licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0, http://creativecommons.org/licenses/by/4.0/). References for each displayed map are provided. Most of the maps originate from the books of [Powell (1992)](https://link.springer.com/book/9783540586074) and [Bennison (1990)](https://link.springer.com/book/10.1007/978-1-4615-9630-1). References for maps with unknown origin will gladly be added."
   ]
  }
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