{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "86f44ca3",
   "metadata": {},
   "source": [
    "# 70 Reprojecting Seismic Data and extracting path and CDP points from Seismic Data\n",
    "\n",
    "This notebook illustrates how to reproject 2D seismic data from the original coordinate system to a new coordinate system. In this tutorial, we are making use of the `segysak` package (https://segysak.readthedocs.io/en/latest/) to load the seismic data in combination with `GeoPandas` (https://geopandas.org/en/stable/) to perform the coordinate reprojection. Further, it is shown how to export the path of the seismic data as LineString, as CDP points and as points every n-th meter along the line.\n",
    "\n",
    "<img src=\"../images/tutorial70_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": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# import gemgis as gg\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import Union\n",
    "import pyproj\n",
    "import numpy as np\n",
    "import shapely\n",
    "from shapely.geometry import LineString"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4128de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path ='data/70_reprojecting_seismic_data/'\n",
    "# gg.download_gemgis_data.download_tutorial_data(filename=\"70_reprojecting_seismic_data.zip\", dirpath=file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee09444-505a-4834-b669-2cd2a1223f60",
   "metadata": {},
   "source": [
    "## Defining functions\n",
    "\n",
    "As there may be conflicts between the dependencies of GemGIS and the `segysak` package, we define the functions that are needed to reproject the seismic data and for the further contents of the notebook. However, they are also implemented in the GemGIS `utils` module. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7bbd5398-7730-40c8-b29b-b5d072ed9642",
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   "source": [
    "def convert_crs_seismic_data(path_in: str,\n",
    "                             path_out: str,\n",
    "                             crs_in: Union[str, pyproj.crs.crs.CRS],\n",
    "                             crs_out: Union[str, pyproj.crs.crs.CRS],\n",
    "                             cdpx: int = 181,\n",
    "                             cdpy: int = 185,\n",
    "                             vert_domain: str = 'TWT',\n",
    "                             coord_scalar: int = None):\n",
    "    \"\"\"Convert CDP coordinates of seismic data to a new CRS.\n",
    "\n",
    "    Parameters\n",
    "    __________\n",
    "        path_in : str\n",
    "            Path to the original seismic data, e.g. ``path_in='seismic.sgy'``.\n",
    "        path_out : str\n",
    "            Path to the converted seismic data, e.g. ``path_out='seismic_converted.sgy'``.\n",
    "        crs_in : str, pyproj.crs.crs.CRS\n",
    "            Coordinate reference system of the original seismic data.\n",
    "        crs_out : str, pyproj.crs.crs.CRS\n",
    "            Coordinate reference system of the converted seismic data.\n",
    "        cdpx : int\n",
    "            Byte position for the X coordinates, default is ``cdpx=181``.\n",
    "        cdpy : int\n",
    "            Byte position for the Y coordinates, default is ``cdpx=185``.\n",
    "        vert_domain : str\n",
    "            Vertical sampling domain. Options include ``'TWT'`` and ``'DEPTH'``, default is ``vert_domain='TWT'``.\n",
    "        coord_scalar: int\n",
    "            Coordinate scalar value to set if `NaN` columns are returned, default is `coord_scalar=None`. \n",
    "\n",
    "    .. versionadded:: 1.1.1\n",
    "\n",
    "    \"\"\"\n",
    "    # Trying to import segysak but returning error if segysak is not installed\n",
    "    try:\n",
    "        from segysak.segy import segy_loader, segy_writer\n",
    "    except ModuleNotFoundError:\n",
    "        raise ModuleNotFoundError(\n",
    "            'segysak package is not installed. Use pip install segysak to install the latest version')\n",
    "\n",
    "    # Checking that path_in is of type string\n",
    "    if not isinstance(path_in, str):\n",
    "        raise TypeError('path_in must be provided as string')\n",
    "\n",
    "    # Checking that path_out is of type string\n",
    "    if not isinstance(path_out, str):\n",
    "        raise TypeError('path_out must be provided as string')\n",
    "\n",
    "    # Checking that crs_in is of type string or pyproj CRS\n",
    "    if not isinstance(crs_in, (str, pyproj.crs.crs.CRS)):\n",
    "        raise TypeError('crs_in must be provided as string or pyproj CRS')\n",
    "\n",
    "    # Checking that crs_out is of type string or pyproj CRS\n",
    "    if not isinstance(crs_out, (str, pyproj.crs.crs.CRS)):\n",
    "        raise TypeError('crs_out must be provided as string or pyproj CRS')\n",
    "\n",
    "    # Checking that vert_domain is of type str\n",
    "    if not isinstance(vert_domain, str):\n",
    "        raise TypeError('vert_domain must be provided as string')\n",
    "\n",
    "    # Checking that the coord_scalar is of type int or None\n",
    "    if not isinstance(coord_scalar, (int, type(None))):\n",
    "        raise TypeError('coord_scalar must be provided as int')        \n",
    "\n",
    "    # Loading seismic data\n",
    "    seismic = segy_loader(path_in,\n",
    "                          vert_domain=vert_domain,\n",
    "                          cdpx=cdpx,\n",
    "                          cdpy=cdpy)\n",
    "\n",
    "    # Converting Seismic to DataFrame\n",
    "    df_seismic = seismic.to_dataframe()\n",
    "\n",
    "    # Checking that the CDP coordinates are in the DataFrame\n",
    "    if not {'cdp_x', 'cdp_y'}.issubset(df_seismic.columns):\n",
    "        raise ValueError(\n",
    "            'No coordinates found, please provide the byte positions where the X and Y data of the CDPs is stored')\n",
    "\n",
    "    # Extracting the length of the samples to reduce computing time\n",
    "    samples = len(df_seismic.index.get_level_values(1).unique())\n",
    "\n",
    "    # Resample DataFrame\n",
    "    df_seismic_resampled = df_seismic[::samples]\n",
    "\n",
    "    # Reprojecting Coordinates\n",
    "    df_seismic_reprojected = gpd.GeoDataFrame(geometry=gpd.points_from_xy(x=df_seismic_resampled['cdp_x'].values,\n",
    "                                                                          y=df_seismic_resampled['cdp_y'].values),\n",
    "                                              crs=crs_in).to_crs(crs_out)\n",
    "\n",
    "    # Extracting reprojected coordinates\n",
    "    x = df_seismic_reprojected.geometry.x.values\n",
    "    y = df_seismic_reprojected.geometry.y.values\n",
    "\n",
    "    # Assigning new coordinates\n",
    "    seismic['cdp_x'][:] = x\n",
    "    seismic['cdp_y'][:] = y\n",
    "\n",
    "    # Optionally setting a new coord_scalar\n",
    "    if coord_scalar:\n",
    "        seismic.attrs['coord_scalar'] = coord_scalar\n",
    "\n",
    "    # Saving reprojected seismic data to file\n",
    "    segy_writer(seismic,\n",
    "                path_out,\n",
    "                trace_header_map=dict(cdp_x=181,\n",
    "                                      cdp_y=185))\n",
    "\n",
    "    print('Seismic data was successfully reprojected and saved to file')\n",
    "\n",
    "\n",
    "def get_cdp_linestring_of_seismic_data(path_in: str,\n",
    "                                       crs_in: Union[str, pyproj.crs.crs.CRS],\n",
    "                                       cdpx: int = 181,\n",
    "                                       cdpy: int = 185,\n",
    "                                       vert_domain: str = 'TWT'):\n",
    "    \"\"\"Extracting the path of the seismic data as LineString.\n",
    "\n",
    "    Parameters\n",
    "    __________\n",
    "        path_in : str\n",
    "            Path to the original seismic data, e.g. ``path_in='seismic.sgy'``.\n",
    "        crs_in : str, pyproj.crs.crs.CRS\n",
    "            Coordinate reference system of the original seismic data.\n",
    "        cdpx : int\n",
    "            Byte position for the X coordinates, default is ``cdpx=181``.\n",
    "        cdpy : int\n",
    "            Byte position for the Y coordinates, default is ``cdpx=185``.\n",
    "        vert_domain : str\n",
    "            Vertical sampling domain. Options include ``'TWT'`` and ``'DEPTH'``, default is ``vert_domain='TWT'``.\n",
    "\n",
    "    Returns\n",
    "    _______\n",
    "        gpd.GeoDataFrame\n",
    "            GeoDataFrame containing the surface path of the seismic data as LineString.\n",
    "\n",
    "    .. versionadded:: 1.1.1\n",
    "\n",
    "    \"\"\"\n",
    "    # Trying to import segysak but returning error if segysak is not installed\n",
    "    try:\n",
    "        from segysak.segy import segy_loader, segy_writer\n",
    "    except ModuleNotFoundError:\n",
    "        raise ModuleNotFoundError(\n",
    "            'segysak package is not installed. Use pip install segysak to install the latest version')\n",
    "\n",
    "    # Checking that path_in is of type string\n",
    "    if not isinstance(path_in, str):\n",
    "        raise TypeError('path_in must be provided as string')\n",
    "\n",
    "    # Checking that crs_in is of type string or pyproj CRS\n",
    "    if not isinstance(crs_in, (str, pyproj.crs.crs.CRS)):\n",
    "        raise TypeError('crs_in must be provided as string or pyproj CRS')\n",
    "\n",
    "    # Checking that vert_domain is of type str\n",
    "    if not isinstance(vert_domain, str):\n",
    "        raise TypeError('vert_domain must be provided as string')\n",
    "\n",
    "    # Loading seismic data\n",
    "    seismic = segy_loader(path_in,\n",
    "                          vert_domain=vert_domain,\n",
    "                          cdpx=cdpx,\n",
    "                          cdpy=cdpy)\n",
    "\n",
    "    # Converting Seismic to DataFrame\n",
    "    df_seismic = seismic.to_dataframe()\n",
    "\n",
    "    # Checking that the CDP coordinates are in the DataFrame\n",
    "    if not {'cdp_x', 'cdp_y'}.issubset(df_seismic.columns):\n",
    "        raise ValueError(\n",
    "            'No coordinates found, please provide the byte positions where the X and Y data of the CDPs is stored')\n",
    "\n",
    "    # Extracting the length of the samples to reduce computing time\n",
    "    samples = len(df_seismic.index.get_level_values(1).unique())\n",
    "\n",
    "    # Resample DataFrame\n",
    "    df_seismic_resampled = df_seismic[::samples]\n",
    "\n",
    "    # Creating LineString from coordinates\n",
    "    linestring = LineString(np.c_[(df_seismic_resampled['cdp_x'].values,\n",
    "                                   df_seismic_resampled['cdp_y'].values)])\n",
    "\n",
    "    # Reprojecting Coordinates\n",
    "    gdf_seismic = gpd.GeoDataFrame(geometry=[linestring],\n",
    "                                   crs=crs_in)\n",
    "\n",
    "    return gdf_seismic\n",
    "\n",
    "\n",
    "def get_cdp_points_of_seismic_data(path_in: str,\n",
    "                                   crs_in: Union[str, pyproj.crs.crs.CRS],\n",
    "                                   cdpx: int = 181,\n",
    "                                   cdpy: int = 185,\n",
    "                                   vert_domain: str = 'TWT',\n",
    "                                   filter: int = None,\n",
    "                                   n_meter: Union[int, float] = None):\n",
    "    \"\"\"Extracting the path of the seismic data as LineString.\n",
    "\n",
    "    Parameters\n",
    "    __________\n",
    "        path_in : str\n",
    "            Path to the original seismic data, e.g. ``path_in='seismic.sgy'``.\n",
    "        crs_in : str, pyproj.crs.crs.CRS\n",
    "            Coordinate reference system of the original seismic data.\n",
    "        cdpx : int\n",
    "            Byte position for the X coordinates, default is ``cdpx=181``.\n",
    "        cdpy : int\n",
    "            Byte position for the Y coordinates, default is ``cdpx=185``.\n",
    "        vert_domain : str\n",
    "            Vertical sampling domain. Options include ``'TWT'`` and ``'DEPTH'``, default is ``vert_domain='TWT'``.\n",
    "        filter : int\n",
    "            Filtering the points to only return every n-th point, e.g. ``filter=100`` to return only every 100-th point.\n",
    "        n_meter : int, float\n",
    "            Parameter to select a point along the line every n-th meter.\n",
    "\n",
    "    Returns\n",
    "    _______\n",
    "        gpd.GeoDataFrame\n",
    "            GeoDataFrame containing the CDPs as Points.\n",
    "\n",
    "    .. versionadded:: 1.1.1\n",
    "\n",
    "    \"\"\"\n",
    "    # Trying to import segysak but returning error if segysak is not installed\n",
    "    try:\n",
    "        from segysak.segy import segy_loader, segy_writer\n",
    "    except ModuleNotFoundError:\n",
    "        raise ModuleNotFoundError(\n",
    "            'segysak package is not installed. Use pip install segysak to install the latest version')\n",
    "\n",
    "    # Checking that path_in is of type string\n",
    "    if not isinstance(path_in, str):\n",
    "        raise TypeError('path_in must be provided as string')\n",
    "\n",
    "    # Checking that crs_in is of type string or pyproj CRS\n",
    "    if not isinstance(crs_in, (str, pyproj.crs.crs.CRS)):\n",
    "        raise TypeError('crs_in must be provided as string or pyproj CRS')\n",
    "\n",
    "    # Checking that vert_domain is of type str\n",
    "    if not isinstance(vert_domain, str):\n",
    "        raise TypeError('vert_domain must be provided as string')\n",
    "\n",
    "    # Loading seismic data\n",
    "    seismic = segy_loader(path_in,\n",
    "                          vert_domain=vert_domain,\n",
    "                          cdpx=cdpx,\n",
    "                          cdpy=cdpy)\n",
    "\n",
    "    # Converting Seismic to DataFrame\n",
    "    df_seismic = seismic.to_dataframe()\n",
    "\n",
    "    # Checking that the CDP coordinates are in the DataFrame\n",
    "    if not {'cdp_x', 'cdp_y'}.issubset(df_seismic.columns):\n",
    "        raise ValueError(\n",
    "            'No coordinates found, please provide the byte positions where the X and Y data of the CDPs is stored')\n",
    "\n",
    "    # Extracting the length of the samples to reduce computing time\n",
    "    samples = len(df_seismic.index.get_level_values(1).unique())\n",
    "\n",
    "    # Resample DataFrame\n",
    "    df_seismic_resampled = df_seismic[::samples]\n",
    "\n",
    "    if n_meter:\n",
    "\n",
    "        # Creating LineString from coordinates\n",
    "        linestring = LineString(np.c_[(df_seismic_resampled['cdp_x'].values,\n",
    "                                       df_seismic_resampled['cdp_y'].values)])\n",
    "\n",
    "        # Defining number of samples\n",
    "        samples = np.arange(0, round(linestring.length / n_meter) + 1, 1)\n",
    "\n",
    "        # Getting points every n_meter\n",
    "        points = [shapely.line_interpolate_point(linestring, n_meter * sample) for sample in samples]\n",
    "\n",
    "        # Creating GeoDataFrame from points\n",
    "        gdf_seismic = gpd.GeoDataFrame(geometry=points,\n",
    "                                       crs=crs_in)\n",
    "\n",
    "        # Appending distance\n",
    "        gdf_seismic['distance'] = samples * n_meter\n",
    "\n",
    "    else:\n",
    "        # Creating Points from coordinates\n",
    "        gdf_seismic = gpd.GeoDataFrame(geometry=gpd.points_from_xy(x=df_seismic_resampled['cdp_x'].values,\n",
    "                                                                   y=df_seismic_resampled['cdp_y'].values),\n",
    "                                       data=df_seismic_resampled,\n",
    "                                       crs=crs_in).reset_index().drop(['twt', 'data'], axis=1)\n",
    "\n",
    "        # Returning only every nth point\n",
    "        if filter:\n",
    "            gdf_seismic = gdf_seismic[::filter]\n",
    "\n",
    "    return gdf_seismic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0219a63e",
   "metadata": {},
   "source": [
    "## Reprojecting Seismic Data\n",
    "\n",
    "The seismic data used here can be freely downloaded from NLOG.nl and is provided in the Coordinate References System (CRS) with `EPSG:28992`. To reproject the data, we are using the function `convert_crs_seismic_data(...)`. The output path and the output CRS are defined, optionally, the byte positions for the X and Y coordinates of each CDP and the vertical domain can be defined. "
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "796e20c6-f719-4608-9603-2332154dc8c7",
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   "outputs": [
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      "text/plain": [
       "  0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
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     "text": [
      "Loading as 2D\n"
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      "text/plain": [
       "Converting SEGY:   0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
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    {
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     "output_type": "stream",
     "text": [
      "Seismic data was successfully reprojected and saved to file\n"
     ]
    }
   ],
   "source": [
    "convert_crs_seismic_data(path_in=file_path+'Seismic_EPSG_28992.sgy',\n",
    "                         path_out=file_path+'Seismic_EPSG_25832.sgy',\n",
    "                         crs_in='EPSG:28992',\n",
    "                         crs_out='EPSG:25832',\n",
    "                         cdpx = 181,\n",
    "                         cdpy = 185,\n",
    "                         vert_domain = 'TWT')"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Accounting for coordinate scalar errors\n",
    "\n",
    "It may happen that columns are filled with `NaN` values. The problem is not so much the Python side but the SEGY side. For a more detailed description, see https://github.com/trhallam/segysak/issues/109#issuecomment-1717610828. The error can be mitigated by providing a `coord_scalar` value. "
   ]
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   "id": "becbad18-36c9-44ee-b2f0-b7a6854effc9",
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      "text/plain": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading as 2D\n"
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      "text/plain": [
       "Converting SEGY:   0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>287958.90625</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2702</th>\n",
       "      <th>5092.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>314511.46875</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>314511.46875</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5096.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>314511.46875</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5098.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>314511.46875</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5100.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>314511.46875</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6892802 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             data         cdp_x  cdp_y\n",
       "cdp  twt                              \n",
       "1    0.0      0.0  287958.90625    NaN\n",
       "     2.0      0.0  287958.90625    NaN\n",
       "     4.0      0.0  287958.90625    NaN\n",
       "     6.0      0.0  287958.90625    NaN\n",
       "     8.0      0.0  287958.90625    NaN\n",
       "...           ...           ...    ...\n",
       "2702 5092.0   0.0  314511.46875    NaN\n",
       "     5094.0   0.0  314511.46875    NaN\n",
       "     5096.0   0.0  314511.46875    NaN\n",
       "     5098.0   0.0  314511.46875    NaN\n",
       "     5100.0   0.0  314511.46875    NaN\n",
       "\n",
       "[6892802 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from segysak.segy import segy_loader\n",
    "seismic = segy_loader(file_path+'Seismic_EPSG_25832.sgy',\n",
    "                         cdpx = 181,\n",
    "                         cdpy = 185,\n",
    "                         vert_domain = 'TWT')\n",
    "\n",
    "# Converting Seismic to DataFrame\n",
    "df_seismic = seismic.to_dataframe()\n",
    "df_seismic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40823604-8693-454d-a8e1-b10be1bf13a1",
   "metadata": {},
   "source": [
    "## Reprojecting Seismic Data - again\n",
    "\n",
    "As you can see, reprojecting the seismic data again using a `coord_scalar` will add the correct coordinates to the segy file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5e429db-ac79-4cb6-b1ef-a4ab897639c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38aa7ddcb5144222923d798ab5c75337",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading as 2D\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1221f8fd0534c04b7ed26ee65ec086d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Converting SEGY:   0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seismic data was successfully reprojected and saved to file\n"
     ]
    }
   ],
   "source": [
    "convert_crs_seismic_data(path_in=file_path+'Seismic_EPSG_28992.sgy',\n",
    "                         path_out=file_path+'Seismic_EPSG_25832.sgy',\n",
    "                         crs_in='EPSG:28992',\n",
    "                         crs_out='EPSG:25832',\n",
    "                         cdpx = 181,\n",
    "                         cdpy = 185,\n",
    "                         vert_domain = 'TWT',\n",
    "                         coord_scalar=-100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7595b751-657c-4527-8aef-a4dff230c287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9316f31dce14fcbbac2ca387de8c143",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading as 2D\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "953fdbda7a1543839179c97615942973",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Converting SEGY:   0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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></th>\n",
       "      <th>data</th>\n",
       "      <th>cdp_x</th>\n",
       "      <th>cdp_y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cdp</th>\n",
       "      <th>twt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>0.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>287958.8750</td>\n",
       "      <td>5702121.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>287958.8750</td>\n",
       "      <td>5702121.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>287958.8750</td>\n",
       "      <td>5702121.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>287958.8750</td>\n",
       "      <td>5702121.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>287958.8750</td>\n",
       "      <td>5702121.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2702</th>\n",
       "      <th>5092.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>314511.4375</td>\n",
       "      <td>5700494.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>314511.4375</td>\n",
       "      <td>5700494.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5096.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>314511.4375</td>\n",
       "      <td>5700494.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5098.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>314511.4375</td>\n",
       "      <td>5700494.0</td>\n",
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       "    <tr>\n",
       "      <th>5100.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>314511.4375</td>\n",
       "      <td>5700494.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6892802 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             data        cdp_x      cdp_y\n",
       "cdp  twt                                 \n",
       "1    0.0      0.0  287958.8750  5702121.5\n",
       "     2.0      0.0  287958.8750  5702121.5\n",
       "     4.0      0.0  287958.8750  5702121.5\n",
       "     6.0      0.0  287958.8750  5702121.5\n",
       "     8.0      0.0  287958.8750  5702121.5\n",
       "...           ...          ...        ...\n",
       "2702 5092.0   0.0  314511.4375  5700494.0\n",
       "     5094.0   0.0  314511.4375  5700494.0\n",
       "     5096.0   0.0  314511.4375  5700494.0\n",
       "     5098.0   0.0  314511.4375  5700494.0\n",
       "     5100.0   0.0  314511.4375  5700494.0\n",
       "\n",
       "[6892802 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from segysak.segy import segy_loader\n",
    "seismic = segy_loader(file_path+'Seismic_EPSG_25832.sgy',\n",
    "                         cdpx = 181,\n",
    "                         cdpy = 185,\n",
    "                         vert_domain = 'TWT')\n",
    "\n",
    "# Converting Seismic to DataFrame\n",
    "df_seismic = seismic.to_dataframe()\n",
    "df_seismic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "797a8a28-c05e-409b-9c4e-983e779651f3",
   "metadata": {},
   "source": [
    "## Plotting the results\n",
    "\n",
    "We are now plotting the surface trace of both seismic lines to illustrate the change in coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a3cbf3e-b22d-4399-9361-d1d83a3c4299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fae25262a7194c2c9126db832e8eb176",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading as 2D\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2b64f7990c9467e84dcaa849b70c2c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Converting SEGY:   0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>cdp_x</th>\n",
       "      <th>cdp_y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cdp</th>\n",
       "      <th>twt</th>\n",
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       "      <td>194105.421875</td>\n",
       "      <td>382534.0625</td>\n",
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       "      <th>2.0</th>\n",
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       "      <th>8.0</th>\n",
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       "      <td>194105.421875</td>\n",
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       "      <th>5094.0</th>\n",
       "      <td>0.0</td>\n",
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       "      <th>5096.0</th>\n",
       "      <td>0.0</td>\n",
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       "      <th>5098.0</th>\n",
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       "      <th>5100.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>220702.828125</td>\n",
       "      <td>382218.6250</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>6892802 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             data          cdp_x        cdp_y\n",
       "cdp  twt                                     \n",
       "1    0.0      0.0  194105.421875  382534.0625\n",
       "     2.0      0.0  194105.421875  382534.0625\n",
       "     4.0      0.0  194105.421875  382534.0625\n",
       "     6.0      0.0  194105.421875  382534.0625\n",
       "     8.0      0.0  194105.421875  382534.0625\n",
       "...           ...            ...          ...\n",
       "2702 5092.0   0.0  220702.828125  382218.6250\n",
       "     5094.0   0.0  220702.828125  382218.6250\n",
       "     5096.0   0.0  220702.828125  382218.6250\n",
       "     5098.0   0.0  220702.828125  382218.6250\n",
       "     5100.0   0.0  220702.828125  382218.6250\n",
       "\n",
       "[6892802 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seismic_old = segy_loader(file_path+'Seismic_EPSG_28992.sgy',\n",
    "                          vert_domain='TWT',\n",
    "                          cdpx=181,\n",
    "                          cdpy=185)\n",
    "# Converting Seismic to DataFrame\n",
    "df_seismic_old = seismic_old.to_dataframe()\n",
    "df_seismic_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8c0415c6-d123-4686-a48e-eb5bb087a3d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, figsize = (10,4))\n",
    "\n",
    "ax[0].plot(df_seismic_old[::2551]['cdp_x'], df_seismic_old[::2551]['cdp_y'])\n",
    "ax[0].grid()\n",
    "\n",
    "ax[0].set_xlim(min(df_seismic_old[::2551]['cdp_x'])-100, max(df_seismic_old[::2551]['cdp_x'])+100) \n",
    "ax[0].set_ylim(min(df_seismic_old[::2551]['cdp_y'])-100, max(df_seismic_old[::2551]['cdp_y'])+100) \n",
    "ax[0].set_title('EPSG:28992')\n",
    "\n",
    "ax[1].plot(df_seismic[::2551]['cdp_x'], df_seismic[::2551]['cdp_y'])\n",
    "ax[1].grid()\n",
    "\n",
    "ax[1].set_xlim(min(df_seismic[::2551]['cdp_x'])-100, max(df_seismic[::2551]['cdp_x'])+100) \n",
    "ax[1].set_ylim(min(df_seismic[::2551]['cdp_y'])-100, max(df_seismic[::2551]['cdp_y'])+100) \n",
    "ax[1].set_title('EPSG:25832')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4fd8504-2276-49c9-a2ab-44b1aa80307b",
   "metadata": {},
   "source": [
    "## Extracting the path of the seismic data\n",
    "\n",
    "The path of the seismic data can be extracted using the function `get_cdp_linestring_of_seismic_data`. A GeoDataFrame containing a LineString will be returned. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d5858f1-12f9-4fc6-a6e9-aad58cd139dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a88cb38217fe419283910ef2c3d03818",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading as 2D\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "107abc28f9cd43269c2383c33c4f7a23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Converting SEGY:   0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LINESTRING (194105.422 382534.062, 194115.391 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry\n",
       "0  LINESTRING (194105.422 382534.062, 194115.391 ..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_linestring = get_cdp_linestring_of_seismic_data(path_in=file_path+'Seismic_EPSG_28992.sgy',\n",
    "                                                    crs_in='EPSG:28992')\n",
    "gdf_linestring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9682d223-8247-461e-baec-d1f973d9a5d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(10,10))\n",
    "\n",
    "gdf_linestring.plot(ax=ax)\n",
    "\n",
    "ax.set_ylim(380000,384000)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f38397c5-933e-4f0f-9408-876b2421b30f",
   "metadata": {},
   "source": [
    "## Extracting the CDP points of the seismic data\n",
    "\n",
    "The CDP points of the seismic data can be extracted using the function `get_cdp_points_of_seismic_data`. By setting a `filter`, only the n-th point can be selected. A GeoDataFrame containing points will be returned. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8e184c39-fe78-494d-99f5-8265263abc9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "acf0478ec18643d499eebf8994177080",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading as 2D\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6effa719b09f4069ad0a7d6f7005e4df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Converting SEGY:   0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>cdp</th>\n",
       "      <th>cdp_x</th>\n",
       "      <th>cdp_y</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>194105.421875</td>\n",
       "      <td>382534.06250</td>\n",
       "      <td>POINT (194105.422 382534.062)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>251</td>\n",
       "      <td>196602.859375</td>\n",
       "      <td>382433.34375</td>\n",
       "      <td>POINT (196602.859 382433.344)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>501</td>\n",
       "      <td>199099.406250</td>\n",
       "      <td>382333.03125</td>\n",
       "      <td>POINT (199099.406 382333.031)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>751</td>\n",
       "      <td>201554.859375</td>\n",
       "      <td>381880.46875</td>\n",
       "      <td>POINT (201554.859 381880.469)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>1001</td>\n",
       "      <td>204022.796875</td>\n",
       "      <td>381520.40625</td>\n",
       "      <td>POINT (204022.797 381520.406)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>1251</td>\n",
       "      <td>206520.484375</td>\n",
       "      <td>381599.18750</td>\n",
       "      <td>POINT (206520.484 381599.188)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>1501</td>\n",
       "      <td>209018.203125</td>\n",
       "      <td>381503.37500</td>\n",
       "      <td>POINT (209018.203 381503.375)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>1751</td>\n",
       "      <td>211512.828125</td>\n",
       "      <td>381340.65625</td>\n",
       "      <td>POINT (211512.828 381340.656)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>2001</td>\n",
       "      <td>213993.937500</td>\n",
       "      <td>381043.43750</td>\n",
       "      <td>POINT (213993.938 381043.438)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>2251</td>\n",
       "      <td>216479.656250</td>\n",
       "      <td>380814.06250</td>\n",
       "      <td>POINT (216479.656 380814.062)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>2501</td>\n",
       "      <td>218918.343750</td>\n",
       "      <td>381326.12500</td>\n",
       "      <td>POINT (218918.344 381326.125)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cdp          cdp_x         cdp_y                       geometry\n",
       "0        1  194105.421875  382534.06250  POINT (194105.422 382534.062)\n",
       "250    251  196602.859375  382433.34375  POINT (196602.859 382433.344)\n",
       "500    501  199099.406250  382333.03125  POINT (199099.406 382333.031)\n",
       "750    751  201554.859375  381880.46875  POINT (201554.859 381880.469)\n",
       "1000  1001  204022.796875  381520.40625  POINT (204022.797 381520.406)\n",
       "1250  1251  206520.484375  381599.18750  POINT (206520.484 381599.188)\n",
       "1500  1501  209018.203125  381503.37500  POINT (209018.203 381503.375)\n",
       "1750  1751  211512.828125  381340.65625  POINT (211512.828 381340.656)\n",
       "2000  2001  213993.937500  381043.43750  POINT (213993.938 381043.438)\n",
       "2250  2251  216479.656250  380814.06250  POINT (216479.656 380814.062)\n",
       "2500  2501  218918.343750  381326.12500  POINT (218918.344 381326.125)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_points = get_cdp_points_of_seismic_data(path_in=file_path+'Seismic_EPSG_28992.sgy',\n",
    "                                            crs_in='EPSG:28992',\n",
    "                                            filter=250)\n",
    "gdf_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a061760-4109-4639-8295-450aa32c18cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(10,10))\n",
    "\n",
    "for label, x, y in zip(gdf_points['cdp'], gdf_points['cdp_x'], gdf_points['cdp_y']):\n",
    "    plt.text(x+200,y+200,label)\n",
    "    \n",
    "gdf_points.plot(ax=ax)\n",
    "\n",
    "ax.set_ylim(380000,384000)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dac0da3-8a30-4bf6-98fe-8a25d8fce938",
   "metadata": {},
   "source": [
    "## Extracting the CDP Points every n-th meter\n",
    "A point every n-th meter along the seismic data can be extracted using the function `get_cdp_points_of_seismic_data`. By setting a value for the parameter `n_meter`, points only every n-th will be returned. A GeoDataFrame containing points will be returned. Be aware that these may not be the CDP points!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f1def53-97e4-4cea-9b11-333f1b1c7bc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2413eae6ff9b4f2796798a6be8e90d3e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading as 2D\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94507823b9ca4597a15f970ee1746a90",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Converting SEGY:   0%|          | 0.00/2.70k [00:00<?, ? traces/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>geometry</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (194105.422 382534.062)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (198102.572 382412.468)</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (202044.081 381777.218)</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (206020.565 381595.751)</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POINT (210016.378 381442.716)</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        geometry  distance\n",
       "0  POINT (194105.422 382534.062)         0\n",
       "1  POINT (198102.572 382412.468)      4000\n",
       "2  POINT (202044.081 381777.218)      8000\n",
       "3  POINT (206020.565 381595.751)     12000\n",
       "4  POINT (210016.378 381442.716)     16000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_points = get_cdp_points_of_seismic_data(path_in=file_path+'Seismic_EPSG_28992.sgy',\n",
    "                                            crs_in='EPSG:28992',\n",
    "                                            n_meter=4000)\n",
    "gdf_points.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a2b2492a-4922-48d7-8c6a-2cd47c0e6a58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(10,10))\n",
    "\n",
    "for label, x, y in zip(gdf_points['distance'], gdf_points.geometry.x, gdf_points.geometry.y):\n",
    "    plt.text(x+200,y+200,str(label) + ' m')\n",
    "    \n",
    "gdf_points.plot(ax=ax)\n",
    "\n",
    "ax.set_ylim(380000,384000)\n",
    "ax.grid()"
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}