docs/Example usage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c183d92e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import libaarhusxyz\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2441fd48",
   "metadata": {},
   "source": [
    "# Resistivity modeles\n",
    "\n",
    "A resistivity model can be read from an XYZ file, plotted and examined, or modified and saved to a new file. Plotting is done per flightline as set of cross sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67a434ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = libaarhusxyz.XYZ(\"synthetic_model.xyz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "58326035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "synthetic_model.xyz\n",
       "--------------------------------\n",
       "                          0\n",
       "source  synthetic_model.xyz\n",
       "\n",
       "Soundings: 300\n",
       "Flightlines: 1\n",
       "Maximum layer depth: 316.227766016838\n",
       "Projection: None\n",
       "           x    y\n",
       "min      0.0  0.0\n",
       "max  14950.0  0.0\n",
       "       Resistivity\n",
       "count  7800.000000\n",
       "mean     86.604356\n",
       "std     107.801996\n",
       "min      30.000000\n",
       "25%      30.000000\n",
       "50%      30.000000\n",
       "75%      30.000000\n",
       "max     300.000000\n",
       "\n",
       "Layer data: resistivity\n",
       "Layer params: layer, dep_top, dep_bot"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e83ae67b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9021381f",
   "metadata": {},
   "source": [
    "## Data access\n",
    "\n",
    "The data making up the resistivity model is both stored in a set of Pandas DataFrames, and accessible that way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85315098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'source': 'synthetic_model.xyz'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f31a6538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xdist</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>interface_depth</th>\n",
       "      <th>line_no</th>\n",
       "      <th>elevation</th>\n",
       "      <th>tx_alt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>10.635452</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>11.270903</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>11.906355</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>12.541806</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>14750</td>\n",
       "      <td>14750</td>\n",
       "      <td>0</td>\n",
       "      <td>197.458194</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>14800</td>\n",
       "      <td>14800</td>\n",
       "      <td>0</td>\n",
       "      <td>198.093645</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>0</td>\n",
       "      <td>198.729097</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>14900</td>\n",
       "      <td>14900</td>\n",
       "      <td>0</td>\n",
       "      <td>199.364548</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>14950</td>\n",
       "      <td>14950</td>\n",
       "      <td>0</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     xdist      x  y  interface_depth  line_no  elevation  tx_alt\n",
       "0        0      0  0        10.000000        0        0.0      30\n",
       "1       50     50  0        10.635452        0        0.0      30\n",
       "2      100    100  0        11.270903        0        0.0      30\n",
       "3      150    150  0        11.906355        0        0.0      30\n",
       "4      200    200  0        12.541806        0        0.0      30\n",
       "..     ...    ... ..              ...      ...        ...     ...\n",
       "295  14750  14750  0       197.458194        0        0.0      30\n",
       "296  14800  14800  0       198.093645        0        0.0      30\n",
       "297  14850  14850  0       198.729097        0        0.0      30\n",
       "298  14900  14900  0       199.364548        0        0.0      30\n",
       "299  14950  14950  0       200.000000        0        0.0      30\n",
       "\n",
       "[300 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.flightlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "87400b06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['resistivity', 'dep_top', 'dep_bot'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layer_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa917358",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>298</th>\n",
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       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>281.525201</td>\n",
       "      <td>300.0</td>\n",
       "      <td>30.0</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
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       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>278.291416</td>\n",
       "      <td>300.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0     1     2     3     4     5     6     7     8     9   ...     16  \\\n",
       "0    30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  ...  300.0   \n",
       "1    30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  ...  300.0   \n",
       "2    30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  ...  300.0   \n",
       "3    30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  ...  300.0   \n",
       "4    30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  ...  300.0   \n",
       "..    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...    ...   \n",
       "295  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  ...   30.0   \n",
       "296  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  ...   30.0   \n",
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       "298  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  ...   30.0   \n",
       "299  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  30.0  ...   30.0   \n",
       "\n",
       "        17     18     19     20     21     22          23     24    25  \n",
       "0    300.0  300.0  300.0  300.0  300.0  300.0  300.000000  300.0  30.0  \n",
       "1    300.0  300.0  300.0  300.0  300.0  300.0  300.000000  300.0  30.0  \n",
       "2    300.0  300.0  300.0  300.0  300.0  300.0  300.000000  300.0  30.0  \n",
       "3    300.0  300.0  300.0  300.0  300.0  300.0  300.000000  300.0  30.0  \n",
       "4    300.0  300.0  300.0  300.0  300.0  300.0  300.000000  300.0  30.0  \n",
       "..     ...    ...    ...    ...    ...    ...         ...    ...   ...  \n",
       "295   30.0   30.0   30.0   30.0   30.0   30.0  291.226558  300.0  30.0  \n",
       "296   30.0   30.0   30.0   30.0   30.0   30.0  287.992772  300.0  30.0  \n",
       "297   30.0   30.0   30.0   30.0   30.0   30.0  284.758987  300.0  30.0  \n",
       "298   30.0   30.0   30.0   30.0   30.0   30.0  281.525201  300.0  30.0  \n",
       "299   30.0   30.0   30.0   30.0   30.0   30.0  278.291416  300.0  30.0  \n",
       "\n",
       "[300 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layer_data[\"resistivity\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62efa2e6",
   "metadata": {},
   "source": [
    "# Data modification\n",
    "\n",
    "It is permitted to both modify the dataframes above, and to assign new dataframes, as long as the indices match between all the dataframes.\n",
    "\n",
    "Here, as an example we change the representation of resistivity to log 10 resistivity instead\n",
    "of absolute values, add a new header and store the result as a new xyz file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e057822c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.info[\"Modified by\"] = \"Joe Random\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8944725e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.layer_data[\"resistivity\"] = np.log10(model.layer_data[\"resistivity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c22d551c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.dump(\"test.xyz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff2506c",
   "metadata": {},
   "source": [
    "# Export 3D model for ParaView"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d0f30b6d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resistivity_variance_factor  is not in the given dataframe. this attribute will not be written to the VTK.\n",
      "line_id  is not in the given dataframe. this attribute will not be written to the VTK.\n",
      "invalt  is not in the given dataframe. this attribute will not be written to the VTK.\n",
      "invaltstd  is not in the given dataframe. this attribute will not be written to the VTK.\n",
      "deltaalt  is not in the given dataframe. this attribute will not be written to the VTK.\n",
      "numdata  is not in the given dataframe. this attribute will not be written to the VTK.\n",
      "resdata  is not in the given dataframe. this attribute will not be written to the VTK.\n",
      "restotal  is not in the given dataframe. this attribute will not be written to the VTK.\n",
      "doi_conservative  is not in the given dataframe. this attribute will not be written to the VTK.\n",
      "doi_standard  is not in the given dataframe. this attribute will not be written to the VTK.\n"
     ]
    }
   ],
   "source": [
    "model.to_vtk(\"test.vtk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4890411",
   "metadata": {},
   "source": [
    "# Measurement data\n",
    "\n",
    "Data can be read from an XYZ file, plotted and examined, or modified and saved to a new file.\n",
    "Plotting is done per flightline as set of cross sections of gate values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b47367b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = libaarhusxyz.XYZ(\"synthetic_fwd.xyz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b603cc43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "synthetic_fwd.xyz\n",
       "--------------------------------\n",
       "                                                                          0\n",
       "gate times for channel 1  [3.162277660168379e-06, 5.411695265464638e-06,...\n",
       "source                                                    synthetic_fwd.xyz\n",
       "\n",
       "Soundings: 300\n",
       "Flightlines: 1\n",
       "Maximum layer depth: None\n",
       "Projection: None\n",
       "           x    y\n",
       "min      0.0  0.0\n",
       "max  14950.0  0.0\n",
       "\n",
       "\n",
       "Layer data: dbdt_ch1gt\n",
       "Layer params: layer"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a529107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b873b30",
   "metadata": {},
   "source": [
    "# Data access\n",
    "\n",
    "The representation in memory of data is very similar to models:\n",
    "data.info and data.flightlines have the same structure. The main difference is that the DataFrames in layer_data\n",
    "do not have layers as columns, but gates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "160f243b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gate times for channel 1': [3.162277660168379e-06,\n",
       "  5.411695265464638e-06,\n",
       "  9.261187281287937e-06,\n",
       "  1.584893192461114e-05,\n",
       "  2.7122725793320296e-05,\n",
       "  4.641588833612782e-05,\n",
       "  7.943282347242822e-05,\n",
       "  0.00013593563908785255,\n",
       "  0.00023263050671536262,\n",
       "  0.00039810717055349735,\n",
       "  0.0006812920690579615,\n",
       "  0.0011659144011798323,\n",
       "  0.001995262314968879,\n",
       "  0.003414548873833601,\n",
       "  0.005843414133735175,\n",
       "  0.01],\n",
       " 'source': 'synthetic_fwd.xyz'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "df324902",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xdist</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>interface_depth</th>\n",
       "      <th>line_no</th>\n",
       "      <th>elevation</th>\n",
       "      <th>tx_alt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>10.635452</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>11.270903</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>11.906355</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>12.541806</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>14750</td>\n",
       "      <td>14750</td>\n",
       "      <td>0</td>\n",
       "      <td>197.458194</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>14800</td>\n",
       "      <td>14800</td>\n",
       "      <td>0</td>\n",
       "      <td>198.093645</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>0</td>\n",
       "      <td>198.729097</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>14900</td>\n",
       "      <td>14900</td>\n",
       "      <td>0</td>\n",
       "      <td>199.364548</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>14950</td>\n",
       "      <td>14950</td>\n",
       "      <td>0</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     xdist      x  y  interface_depth  line_no  elevation  tx_alt\n",
       "0        0      0  0        10.000000        0        0.0      30\n",
       "1       50     50  0        10.635452        0        0.0      30\n",
       "2      100    100  0        11.270903        0        0.0      30\n",
       "3      150    150  0        11.906355        0        0.0      30\n",
       "4      200    200  0        12.541806        0        0.0      30\n",
       "..     ...    ... ..              ...      ...        ...     ...\n",
       "295  14750  14750  0       197.458194        0        0.0      30\n",
       "296  14800  14800  0       198.093645        0        0.0      30\n",
       "297  14850  14850  0       198.729097        0        0.0      30\n",
       "298  14900  14900  0       199.364548        0        0.0      30\n",
       "299  14950  14950  0       200.000000        0        0.0      30\n",
       "\n",
       "[300 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.flightlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e5a026dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['dbdt_ch1gt'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.layer_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eae5f10e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
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       "      <td>-0.000005</td>\n",
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       "      <td>-1.727774e-11</td>\n",
       "      <td>-6.952547e-12</td>\n",
       "      <td>-2.653889e-12</td>\n",
       "      <td>-9.546026e-13</td>\n",
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       "      <th>1</th>\n",
       "      <td>-0.000020</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-6.500865e-07</td>\n",
       "      <td>-1.721135e-07</td>\n",
       "      <td>-4.117183e-08</td>\n",
       "      <td>-9.293841e-09</td>\n",
       "      <td>-1.992713e-09</td>\n",
       "      <td>-4.319155e-10</td>\n",
       "      <td>-1.169626e-10</td>\n",
       "      <td>-4.261761e-11</td>\n",
       "      <td>-1.737605e-11</td>\n",
       "      <td>-6.978748e-12</td>\n",
       "      <td>-2.661046e-12</td>\n",
       "      <td>-9.565077e-13</td>\n",
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       "      <th>2</th>\n",
       "      <td>-0.000019</td>\n",
       "      <td>-0.000011</td>\n",
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       "      <td>-2.027159e-07</td>\n",
       "      <td>-4.875569e-08</td>\n",
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       "      <td>-2.299771e-09</td>\n",
       "      <td>-4.849767e-10</td>\n",
       "      <td>-1.251384e-10</td>\n",
       "      <td>-4.395726e-11</td>\n",
       "      <td>-1.766855e-11</td>\n",
       "      <td>-7.055733e-12</td>\n",
       "      <td>-2.681966e-12</td>\n",
       "      <td>-9.620612e-13</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.000019</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-7.499683e-07</td>\n",
       "      <td>-2.044552e-07</td>\n",
       "      <td>-4.921264e-08</td>\n",
       "      <td>-1.099247e-08</td>\n",
       "      <td>-2.319185e-09</td>\n",
       "      <td>-4.883621e-10</td>\n",
       "      <td>-1.256616e-10</td>\n",
       "      <td>-4.404186e-11</td>\n",
       "      <td>-1.768666e-11</td>\n",
       "      <td>-7.060454e-12</td>\n",
       "      <td>-2.683244e-12</td>\n",
       "      <td>-9.624001e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000019</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-7.583760e-07</td>\n",
       "      <td>-2.074451e-07</td>\n",
       "      <td>-5.000101e-08</td>\n",
       "      <td>-1.116453e-08</td>\n",
       "      <td>-2.352752e-09</td>\n",
       "      <td>-4.942174e-10</td>\n",
       "      <td>-1.265666e-10</td>\n",
       "      <td>-4.418782e-11</td>\n",
       "      <td>-1.771780e-11</td>\n",
       "      <td>-7.068559e-12</td>\n",
       "      <td>-2.685437e-12</td>\n",
       "      <td>-9.629813e-13</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>295</th>\n",
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       "      <td>-0.000005</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-9.898843e-07</td>\n",
       "      <td>-4.002619e-07</td>\n",
       "      <td>-1.499122e-07</td>\n",
       "      <td>-5.245952e-08</td>\n",
       "      <td>-1.734299e-08</td>\n",
       "      <td>-5.534583e-09</td>\n",
       "      <td>-1.710490e-09</td>\n",
       "      <td>-4.839389e-10</td>\n",
       "      <td>-1.231594e-10</td>\n",
       "      <td>-2.999163e-11</td>\n",
       "      <td>-7.573920e-12</td>\n",
       "      <td>-2.037114e-12</td>\n",
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       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>-0.000016</td>\n",
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       "      <td>-0.000002</td>\n",
       "      <td>-9.898843e-07</td>\n",
       "      <td>-4.002619e-07</td>\n",
       "      <td>-1.499122e-07</td>\n",
       "      <td>-5.245952e-08</td>\n",
       "      <td>-1.734294e-08</td>\n",
       "      <td>-5.534455e-09</td>\n",
       "      <td>-1.710445e-09</td>\n",
       "      <td>-4.839563e-10</td>\n",
       "      <td>-1.231761e-10</td>\n",
       "      <td>-2.999829e-11</td>\n",
       "      <td>-7.575833e-12</td>\n",
       "      <td>-2.037584e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>-0.000016</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-9.898843e-07</td>\n",
       "      <td>-4.002619e-07</td>\n",
       "      <td>-1.499122e-07</td>\n",
       "      <td>-5.245952e-08</td>\n",
       "      <td>-1.734288e-08</td>\n",
       "      <td>-5.534324e-09</td>\n",
       "      <td>-1.710398e-09</td>\n",
       "      <td>-4.839740e-10</td>\n",
       "      <td>-1.231932e-10</td>\n",
       "      <td>-3.000511e-11</td>\n",
       "      <td>-7.577789e-12</td>\n",
       "      <td>-2.038065e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>-0.000016</td>\n",
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       "      <td>-0.000002</td>\n",
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       "      <td>-4.002619e-07</td>\n",
       "      <td>-1.499122e-07</td>\n",
       "      <td>-5.245952e-08</td>\n",
       "      <td>-1.734282e-08</td>\n",
       "      <td>-5.534190e-09</td>\n",
       "      <td>-1.710351e-09</td>\n",
       "      <td>-4.839921e-10</td>\n",
       "      <td>-1.232107e-10</td>\n",
       "      <td>-3.001208e-11</td>\n",
       "      <td>-7.579791e-12</td>\n",
       "      <td>-2.038557e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>-0.000016</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-9.898843e-07</td>\n",
       "      <td>-4.002619e-07</td>\n",
       "      <td>-1.499122e-07</td>\n",
       "      <td>-5.245951e-08</td>\n",
       "      <td>-1.734277e-08</td>\n",
       "      <td>-5.534053e-09</td>\n",
       "      <td>-1.710302e-09</td>\n",
       "      <td>-4.840107e-10</td>\n",
       "      <td>-1.232286e-10</td>\n",
       "      <td>-3.001922e-11</td>\n",
       "      <td>-7.581840e-12</td>\n",
       "      <td>-2.039060e-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3             4             5   \\\n",
       "0   -0.000021 -0.000012 -0.000005 -0.000002 -6.170199e-07 -1.619608e-07   \n",
       "1   -0.000020 -0.000012 -0.000006 -0.000002 -6.500865e-07 -1.721135e-07   \n",
       "2   -0.000019 -0.000011 -0.000006 -0.000002 -7.450490e-07 -2.027159e-07   \n",
       "3   -0.000019 -0.000011 -0.000006 -0.000002 -7.499683e-07 -2.044552e-07   \n",
       "4   -0.000019 -0.000011 -0.000006 -0.000002 -7.583760e-07 -2.074451e-07   \n",
       "..        ...       ...       ...       ...           ...           ...   \n",
       "295 -0.000016 -0.000009 -0.000005 -0.000002 -9.898843e-07 -4.002619e-07   \n",
       "296 -0.000016 -0.000009 -0.000005 -0.000002 -9.898843e-07 -4.002619e-07   \n",
       "297 -0.000016 -0.000009 -0.000005 -0.000002 -9.898843e-07 -4.002619e-07   \n",
       "298 -0.000016 -0.000009 -0.000005 -0.000002 -9.898843e-07 -4.002619e-07   \n",
       "299 -0.000016 -0.000009 -0.000005 -0.000002 -9.898843e-07 -4.002619e-07   \n",
       "\n",
       "               6             7             8             9             10  \\\n",
       "0   -3.872673e-08 -8.783804e-09 -1.894789e-09 -4.150042e-10 -1.143491e-10   \n",
       "1   -4.117183e-08 -9.293841e-09 -1.992713e-09 -4.319155e-10 -1.169626e-10   \n",
       "2   -4.875569e-08 -1.089292e-08 -2.299771e-09 -4.849767e-10 -1.251384e-10   \n",
       "3   -4.921264e-08 -1.099247e-08 -2.319185e-09 -4.883621e-10 -1.256616e-10   \n",
       "4   -5.000101e-08 -1.116453e-08 -2.352752e-09 -4.942174e-10 -1.265666e-10   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "295 -1.499122e-07 -5.245952e-08 -1.734299e-08 -5.534583e-09 -1.710490e-09   \n",
       "296 -1.499122e-07 -5.245952e-08 -1.734294e-08 -5.534455e-09 -1.710445e-09   \n",
       "297 -1.499122e-07 -5.245952e-08 -1.734288e-08 -5.534324e-09 -1.710398e-09   \n",
       "298 -1.499122e-07 -5.245952e-08 -1.734282e-08 -5.534190e-09 -1.710351e-09   \n",
       "299 -1.499122e-07 -5.245951e-08 -1.734277e-08 -5.534053e-09 -1.710302e-09   \n",
       "\n",
       "               11            12            13            14            15  \n",
       "0   -4.217950e-11 -1.727774e-11 -6.952547e-12 -2.653889e-12 -9.546026e-13  \n",
       "1   -4.261761e-11 -1.737605e-11 -6.978748e-12 -2.661046e-12 -9.565077e-13  \n",
       "2   -4.395726e-11 -1.766855e-11 -7.055733e-12 -2.681966e-12 -9.620612e-13  \n",
       "3   -4.404186e-11 -1.768666e-11 -7.060454e-12 -2.683244e-12 -9.624001e-13  \n",
       "4   -4.418782e-11 -1.771780e-11 -7.068559e-12 -2.685437e-12 -9.629813e-13  \n",
       "..            ...           ...           ...           ...           ...  \n",
       "295 -4.839389e-10 -1.231594e-10 -2.999163e-11 -7.573920e-12 -2.037114e-12  \n",
       "296 -4.839563e-10 -1.231761e-10 -2.999829e-11 -7.575833e-12 -2.037584e-12  \n",
       "297 -4.839740e-10 -1.231932e-10 -3.000511e-11 -7.577789e-12 -2.038065e-12  \n",
       "298 -4.839921e-10 -1.232107e-10 -3.001208e-11 -7.579791e-12 -2.038557e-12  \n",
       "299 -4.840107e-10 -1.232286e-10 -3.001922e-11 -7.581840e-12 -2.039060e-12  \n",
       "\n",
       "[300 rows x 16 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.layer_data['dbdt_ch1gt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a9f634f6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class XYZ in module libaarhusxyz.xyz:\n",
      "\n",
      "class XYZ(builtins.object)\n",
      " |  XYZ(*arg, **kw)\n",
      " |  \n",
      " |  Usage:\n",
      " |  \n",
      " |  xyz = XYZ(source, **kw)\n",
      " |  \n",
      " |  Where source can be a filename to parse, or an already parsed\n",
      " |  model dictionary (in the same format returned by xyz.to_dict()).\n",
      " |  \n",
      " |  Where kw can be:\n",
      " |  \n",
      " |  alcfile=filename\n",
      " |    Read column mappings from filename (a .ALC file)\n",
      " |  normalize=bool (default False)\n",
      " |    Normalize data after reading.\n",
      " |  \n",
      " |  Any additional arguments are sent to\n",
      " |  libaarhusxyz.normalizer.normalize()\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __getattr__(self, name)\n",
      " |  \n",
      " |  __getitem__(self, line_id)\n",
      " |  \n",
      " |  __iter__(self)\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setattr__(self, name, value)\n",
      " |      Implement setattr(self, name, value).\n",
      " |  \n",
      " |  add_defaults(self, required_columns=None)\n",
      " |  \n",
      " |  calculate_doi_layer(self)\n",
      " |  \n",
      " |  calculate_height(self)\n",
      " |  \n",
      " |  calculate_xdist(self)\n",
      " |  \n",
      " |  calculate_z(self)\n",
      " |  \n",
      " |  dump(self, nameorfile, alcfile=None)\n",
      " |      Write to XYZ file.\n",
      " |      \n",
      " |      nameorfile: either a file path as a string, or an open file object\n",
      " |      to write this object to.\n",
      " |      \n",
      " |      alcfile: optional file path or open file object to write a\n",
      " |      Aarhus Workbench style ALC file with column mappings to.\n",
      " |  \n",
      " |  get_column(self, name)\n",
      " |  \n",
      " |  normalize(self, **kw)\n",
      " |      This function\n",
      " |      * Normalizes naming and format to our internal format\n",
      " |        (or one specified by naming_standard)\n",
      " |      * Replaces * with NaN:s\n",
      " |      * Reprojects coordinates\n",
      " |        To lat/lon, web mercator and optionally to a project_crs\n",
      " |      * Calculates xdist\n",
      " |      * Calculate z coordinates\n",
      " |      * Add missing default columns (filled with NaNs)\n",
      " |  \n",
      " |  normalize_coordinates(self, project_crs=None)\n",
      " |  \n",
      " |  normalize_dates(self)\n",
      " |  \n",
      " |  normalize_depths(self)\n",
      " |  \n",
      " |  normalize_naming(self, naming_standard='libaarhusxyz')\n",
      " |  \n",
      " |  normalize_nans(self, nan_value=None)\n",
      " |  \n",
      " |  normalize_projection(self)\n",
      " |  \n",
      " |  plot(self, fig=None)\n",
      " |      Plot this object using matplotlib as a figure. To plot individual\n",
      " |      flightlines / cross sections, use `plot_line(line_no)`.\n",
      " |  \n",
      " |  plot_line(self, line_no, ax=None, **kw)\n",
      " |      Plots a single flightline/cross section using matplotlib. Any extra\n",
      " |      arguments are sent to `ax.plot()`.\n",
      " |  \n",
      " |  to_dict(self)\n",
      " |  \n",
      " |  to_vtk(self, nameorfile, *arg, **kw)\n",
      " |      Write a 3d model to VTK file (only works for resistivity models,\n",
      " |      not data!).\n",
      " |      \n",
      " |      nameorfile: either a file path as a string, or an open file object\n",
      " |      to write the VTK file to.\n",
      " |      \n",
      " |      attr_out: attributes (column names in flightlines, keys in\n",
      " |      layer_data) to include in output.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  __new__(cls, *arg, **kw)\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties defined here:\n",
      " |  \n",
      " |  alt_column\n",
      " |  \n",
      " |  layer_params\n",
      " |  \n",
      " |  line_id_column\n",
      " |  \n",
      " |  title\n",
      " |  \n",
      " |  x_column\n",
      " |  \n",
      " |  y_column\n",
      " |  \n",
      " |  z_column\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  data\n",
      " |  \n",
      " |  file_meta\n",
      " |  \n",
      " |  flightlines\n",
      " |  \n",
      " |  info\n",
      " |  \n",
      " |  layer_data\n",
      " |  \n",
      " |  model_info\n",
      " |  \n",
      " |  projection\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(libaarhusxyz.XYZ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ad842e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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