sample_rangitoto_plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6b92e030-dc4b-48a7-b8b7-1a9722e10a00",
   "metadata": {},
   "source": [
    "# Import Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1ca004e8-0b11-4ac7-ba2a-b26072dd0207",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Polygon\n",
    "from pyproj import Proj, Transformer\n",
    "from gmsys_reader import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dc8b92d-2d1b-4dc8-8722-c27d4a3e8070",
   "metadata": {},
   "source": [
    "# Read the Model Files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76f48159-86a5-42e4-a167-5f0a235a4f79",
   "metadata": {},
   "source": [
    "First we load the model's block file (.blk), coordinate system (.ecs), body geometry (.sur), gravity data (.grv), and magnetic data (.mag)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "24ff0bb9-e72f-4756-a10c-e0ea12313592",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_main_name = r'rangi_residual_grav_2x5D_2x5KM_WE_B3'\n",
    "blk_filename = model_main_name+'.blk'\n",
    "model_properties = parse_blk_file(blk_filename)\n",
    "model_coordinate = parse_ecs_file(model_main_name+'.ecs')\n",
    "model_geom = parse_sur_file(model_main_name+'.sur')\n",
    "model_gravData = grav_file_reader(model_main_name+'.grv')\n",
    "model_magData = mag_file_reader(r'rangi_totalMag_2x5D_WE_C2G.mag')\n",
    "model_wells = well_file_reader(model_main_name+'.wel')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da7fe276-0c97-45e5-b9d3-41dbd2f1f195",
   "metadata": {},
   "source": [
    "Change the coordinate system of the model from NZTM to NZMG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59f6e1d0-97c9-42cb-b700-cb7a37818b2e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_geom['Geometry_xyz (m)'] = \"\"\n",
    "model_projection = model_coordinate[-1]\n",
    "change_coord = Proj('epsg:27200') \n",
    "transform_coord = Transformer.from_proj(model_projection, change_coord, always_xy=True)\n",
    "for units in model_geom.index:\n",
    "    xz_loc = np.array(model_geom.loc[units,'Geometry (Xm,Ym)'])\n",
    "    # Extract x coordinates\n",
    "    distance_coords = xz_loc[:,0]\n",
    "    # Calculate corresponding y coordinates using trigonometry\n",
    "    model_origin_X, model_origin_Y = transform_coord.transform(model_coordinate[0], model_coordinate[1]) #this works\n",
    "    #model_origin_X, model_origin_Y = model_coordinate[0], model_coordinate[1]\n",
    "    x_coords = model_origin_X + (distance_coords*np.cos(np.deg2rad(90-model_coordinate[2])))\n",
    "    y_coords = model_origin_Y + (distance_coords*np.sin(np.deg2rad(90-model_coordinate[2])))\n",
    "    # Combine x, y, and z coordinates into a single array\n",
    "    xyz_coord = np.column_stack((x_coords, y_coords, xz_loc[:,1]))\n",
    "    model_geom.at[units,'Geometry_xyz (m)'] = [vertex.tolist() for vertex in xyz_coord]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c99fc681-679e-4bbe-9c06-72772edab929",
   "metadata": {},
   "source": [
    "View the combined model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbacb93f-cd30-4748-8f86-d08c75caf38d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model units</th>\n",
       "      <th>Density (kg/m^3)</th>\n",
       "      <th>Inclination (deg)</th>\n",
       "      <th>Declination (deg)</th>\n",
       "      <th>Magsus (SI)</th>\n",
       "      <th>RemMag (A/m)</th>\n",
       "      <th>Colour (RGB)</th>\n",
       "      <th>Geometry (Xm,Ym)</th>\n",
       "      <th>Geometry_xyz (m)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Body index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Background</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(1.0, 1.0, 1.0)</td>\n",
       "      <td>[[7501.76, 1.20107], [7631.1, 14.3107], [7760....</td>\n",
       "      <td>[[2679777.2829241976, 6490208.69691182, 1.2010...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basalt</td>\n",
       "      <td>410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.5019607843137255, 0.5019607843137255, 0.501...</td>\n",
       "      <td>[[4204.98667126113, -28.8461538461539], [4137....</td>\n",
       "      <td>[[2676595.691469413, 6489344.873899898, -28.84...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>scoria</td>\n",
       "      <td>-300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(1.0, 0.5019607843137255, 1.0)</td>\n",
       "      <td>[[4204.98667126113, -28.8461538461539], [4420....</td>\n",
       "      <td>[[2676595.691469413, 6489344.873899898, -28.84...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ymt</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.5019607843137255, 0.0, 1.0)</td>\n",
       "      <td>[[-678.783121231021, -515.0115773863721], [-37...</td>\n",
       "      <td>[[2671882.5496517834, 6488065.224888267, -515....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Qva</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(1.0, 0.0, 0.0)</td>\n",
       "      <td>[[3492.2, 153.833], [3362.86, 137.491], [3233....</td>\n",
       "      <td>[[2675907.807967506, 6489158.109003488, 153.83...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Qva</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(1.0, 0.0, 0.0)</td>\n",
       "      <td>[[4963.39938478518, -13.451231755348001], [514...</td>\n",
       "      <td>[[2677327.6069418886, 6489543.593766006, -13.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mwe_C#1</td>\n",
       "      <td>-135.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(1.0, 0.5019607843137255, 0.0)</td>\n",
       "      <td>[[3569.375443577, -44.755579581434894], [3225....</td>\n",
       "      <td>[[2675982.2870739894, 6489178.330570902, -44.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TJw_C#1</td>\n",
       "      <td>280.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.5019607843137255, 1.0, 1.0)</td>\n",
       "      <td>[[-3710.92269882514, -515.0115773863721], [-14...</td>\n",
       "      <td>[[2668956.3462377694, 6487270.741409631, -515....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Model units  Density (kg/m^3)  Inclination (deg)  \\\n",
       "Body index                                                    \n",
       "1           Background               0.0                0.0   \n",
       "2               Basalt             410.0                0.0   \n",
       "3               scoria            -300.0                0.0   \n",
       "4                  Ymt             500.0                0.0   \n",
       "5                  Qva             145.0                0.0   \n",
       "6                  Qva             145.0                0.0   \n",
       "7              Mwe_C#1            -135.0                0.0   \n",
       "8              TJw_C#1             280.0                0.0   \n",
       "\n",
       "            Declination (deg)  Magsus (SI)  RemMag (A/m)  \\\n",
       "Body index                                                 \n",
       "1                         0.0          0.0           0.0   \n",
       "2                         0.0          0.0           0.0   \n",
       "3                         0.0          0.0           0.0   \n",
       "4                         0.0          0.0           0.0   \n",
       "5                         0.0          0.0           0.0   \n",
       "6                         0.0          0.0           0.0   \n",
       "7                         0.0          0.0           0.0   \n",
       "8                         0.0          0.0           0.0   \n",
       "\n",
       "                                                 Colour (RGB)  \\\n",
       "Body index                                                      \n",
       "1                                             (1.0, 1.0, 1.0)   \n",
       "2           (0.5019607843137255, 0.5019607843137255, 0.501...   \n",
       "3                              (1.0, 0.5019607843137255, 1.0)   \n",
       "4                              (0.5019607843137255, 0.0, 1.0)   \n",
       "5                                             (1.0, 0.0, 0.0)   \n",
       "6                                             (1.0, 0.0, 0.0)   \n",
       "7                              (1.0, 0.5019607843137255, 0.0)   \n",
       "8                              (0.5019607843137255, 1.0, 1.0)   \n",
       "\n",
       "                                             Geometry (Xm,Ym)  \\\n",
       "Body index                                                      \n",
       "1           [[7501.76, 1.20107], [7631.1, 14.3107], [7760....   \n",
       "2           [[4204.98667126113, -28.8461538461539], [4137....   \n",
       "3           [[4204.98667126113, -28.8461538461539], [4420....   \n",
       "4           [[-678.783121231021, -515.0115773863721], [-37...   \n",
       "5           [[3492.2, 153.833], [3362.86, 137.491], [3233....   \n",
       "6           [[4963.39938478518, -13.451231755348001], [514...   \n",
       "7           [[3569.375443577, -44.755579581434894], [3225....   \n",
       "8           [[-3710.92269882514, -515.0115773863721], [-14...   \n",
       "\n",
       "                                             Geometry_xyz (m)  \n",
       "Body index                                                     \n",
       "1           [[2679777.2829241976, 6490208.69691182, 1.2010...  \n",
       "2           [[2676595.691469413, 6489344.873899898, -28.84...  \n",
       "3           [[2676595.691469413, 6489344.873899898, -28.84...  \n",
       "4           [[2671882.5496517834, 6488065.224888267, -515....  \n",
       "5           [[2675907.807967506, 6489158.109003488, 153.83...  \n",
       "6           [[2677327.6069418886, 6489543.593766006, -13.4...  \n",
       "7           [[2675982.2870739894, 6489178.330570902, -44.7...  \n",
       "8           [[2668956.3462377694, 6487270.741409631, -515....  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_model = model_properties.join(model_geom)\n",
    "combined_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "762028c2-bd01-40d8-bb07-33ca475b2ee7",
   "metadata": {},
   "source": [
    "Change the coordinate system of the gravity data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e3e36c7-645e-4c60-8852-3d32c68014e8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Profile Dist (m)</th>\n",
       "      <th>Offset from Profile (m)</th>\n",
       "      <th>Elevation (m)</th>\n",
       "      <th>Observed (mGal)</th>\n",
       "      <th>Modelled (mGal)</th>\n",
       "      <th>Real world X (m)</th>\n",
       "      <th>Real world Y (m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1482.785958</td>\n",
       "      <td>49.013194</td>\n",
       "      <td>4.068000</td>\n",
       "      <td>-3.702695</td>\n",
       "      <td>-3.58834</td>\n",
       "      <td>2673968.59835</td>\n",
       "      <td>6488631.600834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2085.227070</td>\n",
       "      <td>-913.128822</td>\n",
       "      <td>35.424779</td>\n",
       "      <td>-5.009340</td>\n",
       "      <td>-5.16735</td>\n",
       "      <td>2674549.99152</td>\n",
       "      <td>6488789.452905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2612.984470</td>\n",
       "      <td>-663.231583</td>\n",
       "      <td>60.824904</td>\n",
       "      <td>-5.466544</td>\n",
       "      <td>-5.44034</td>\n",
       "      <td>2675059.310259</td>\n",
       "      <td>6488927.736293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2948.936612</td>\n",
       "      <td>-389.027061</td>\n",
       "      <td>85.471271</td>\n",
       "      <td>-5.732613</td>\n",
       "      <td>-5.65994</td>\n",
       "      <td>2675383.524985</td>\n",
       "      <td>6489015.762724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3210.752802</td>\n",
       "      <td>-248.892810</td>\n",
       "      <td>113.505000</td>\n",
       "      <td>-6.687281</td>\n",
       "      <td>-6.55297</td>\n",
       "      <td>2675636.193904</td>\n",
       "      <td>6489084.363998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3362.275242</td>\n",
       "      <td>-187.117496</td>\n",
       "      <td>127.787043</td>\n",
       "      <td>-6.436686</td>\n",
       "      <td>-6.59229</td>\n",
       "      <td>2675782.42249</td>\n",
       "      <td>6489124.066021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3484.445427</td>\n",
       "      <td>-217.019067</td>\n",
       "      <td>138.118000</td>\n",
       "      <td>-6.678870</td>\n",
       "      <td>-6.35865</td>\n",
       "      <td>2675900.324322</td>\n",
       "      <td>6489156.077144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3643.180732</td>\n",
       "      <td>-102.285593</td>\n",
       "      <td>161.705000</td>\n",
       "      <td>-5.267084</td>\n",
       "      <td>-5.31515</td>\n",
       "      <td>2676053.513771</td>\n",
       "      <td>6489197.669088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3769.318542</td>\n",
       "      <td>-101.021712</td>\n",
       "      <td>163.320721</td>\n",
       "      <td>-4.460467</td>\n",
       "      <td>-4.31618</td>\n",
       "      <td>2676175.244608</td>\n",
       "      <td>6489230.719811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3841.628726</td>\n",
       "      <td>-47.433699</td>\n",
       "      <td>173.974000</td>\n",
       "      <td>-4.417550</td>\n",
       "      <td>-4.50724</td>\n",
       "      <td>2676245.028437</td>\n",
       "      <td>6489249.666579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Profile Dist (m)  Offset from Profile (m)  Elevation (m)  Observed (mGal)  \\\n",
       "0       1482.785958                49.013194       4.068000        -3.702695   \n",
       "1       2085.227070              -913.128822      35.424779        -5.009340   \n",
       "2       2612.984470              -663.231583      60.824904        -5.466544   \n",
       "3       2948.936612              -389.027061      85.471271        -5.732613   \n",
       "4       3210.752802              -248.892810     113.505000        -6.687281   \n",
       "5       3362.275242              -187.117496     127.787043        -6.436686   \n",
       "6       3484.445427              -217.019067     138.118000        -6.678870   \n",
       "7       3643.180732              -102.285593     161.705000        -5.267084   \n",
       "8       3769.318542              -101.021712     163.320721        -4.460467   \n",
       "9       3841.628726               -47.433699     173.974000        -4.417550   \n",
       "\n",
       "   Modelled (mGal) Real world X (m) Real world Y (m)  \n",
       "0         -3.58834    2673968.59835   6488631.600834  \n",
       "1         -5.16735    2674549.99152   6488789.452905  \n",
       "2         -5.44034   2675059.310259   6488927.736293  \n",
       "3         -5.65994   2675383.524985   6489015.762724  \n",
       "4         -6.55297   2675636.193904   6489084.363998  \n",
       "5         -6.59229    2675782.42249   6489124.066021  \n",
       "6         -6.35865   2675900.324322   6489156.077144  \n",
       "7         -5.31515   2676053.513771   6489197.669088  \n",
       "8         -4.31618   2676175.244608   6489230.719811  \n",
       "9         -4.50724   2676245.028437   6489249.666579  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gravData[0]['Real world X (m)']=\"\"\n",
    "model_gravData[0]['Real world Y (m)']=\"\"\n",
    "for idx in model_gravData[0].index:\n",
    "    #model_origin_X, model_origin_Y = model_coordinate[0], model_coordinate[1]\n",
    "    x_coords = model_origin_X + (model_gravData[0].loc[idx,'Profile Dist (m)']*np.cos(np.deg2rad(90-model_coordinate[2])))\n",
    "    y_coords = model_origin_Y + (model_gravData[0].loc[idx,'Profile Dist (m)']*np.sin(np.deg2rad(90-model_coordinate[2])))\n",
    "    model_gravData[0].loc[idx,'Real world X (m)'] = x_coords\n",
    "    model_gravData[0].loc[idx,'Real world Y (m)'] = y_coords\n",
    "\n",
    "model_gravData[0][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9422a2d4-65e6-489b-b714-b9a28596d476",
   "metadata": {},
   "source": [
    "Change the coordinate system of the magnetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "00626df8-3fc6-42db-bfc2-2d2f42661008",
   "metadata": {
    "tags": []
   },
   "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>Profile Dist (m)</th>\n",
       "      <th>Offset from Profile (m)</th>\n",
       "      <th>Elevation (m)</th>\n",
       "      <th>Observed (nT)</th>\n",
       "      <th>Modelled (nT)</th>\n",
       "      <th>Real world X (m)</th>\n",
       "      <th>Real world Y (m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>131.136</td>\n",
       "      <td>-0.005775</td>\n",
       "      <td>449.474</td>\n",
       "      <td>76.41370</td>\n",
       "      <td>71.15623</td>\n",
       "      <td>2672664.172013</td>\n",
       "      <td>6488277.440502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>262.279</td>\n",
       "      <td>0.000950</td>\n",
       "      <td>445.686</td>\n",
       "      <td>72.97410</td>\n",
       "      <td>73.67114</td>\n",
       "      <td>2672790.733171</td>\n",
       "      <td>6488311.802689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>393.415</td>\n",
       "      <td>-0.004605</td>\n",
       "      <td>444.431</td>\n",
       "      <td>74.58200</td>\n",
       "      <td>73.12102</td>\n",
       "      <td>2672917.287573</td>\n",
       "      <td>6488346.163041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>524.561</td>\n",
       "      <td>-0.007526</td>\n",
       "      <td>443.623</td>\n",
       "      <td>73.37600</td>\n",
       "      <td>69.92820</td>\n",
       "      <td>2673043.851625</td>\n",
       "      <td>6488380.526014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>655.694</td>\n",
       "      <td>-0.003436</td>\n",
       "      <td>442.574</td>\n",
       "      <td>69.55690</td>\n",
       "      <td>64.34135</td>\n",
       "      <td>2673170.403132</td>\n",
       "      <td>6488414.885581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>786.840</td>\n",
       "      <td>-0.006357</td>\n",
       "      <td>436.543</td>\n",
       "      <td>63.68480</td>\n",
       "      <td>57.05482</td>\n",
       "      <td>2673296.967185</td>\n",
       "      <td>6488449.248554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>917.973</td>\n",
       "      <td>-0.002266</td>\n",
       "      <td>432.307</td>\n",
       "      <td>55.29120</td>\n",
       "      <td>47.20668</td>\n",
       "      <td>2673423.518692</td>\n",
       "      <td>6488483.608121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1049.110</td>\n",
       "      <td>-0.007822</td>\n",
       "      <td>432.708</td>\n",
       "      <td>41.57130</td>\n",
       "      <td>34.43934</td>\n",
       "      <td>2673550.074059</td>\n",
       "      <td>6488517.968735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1180.250</td>\n",
       "      <td>-0.001096</td>\n",
       "      <td>435.288</td>\n",
       "      <td>24.99840</td>\n",
       "      <td>18.92550</td>\n",
       "      <td>2673676.632321</td>\n",
       "      <td>6488552.330136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1311.390</td>\n",
       "      <td>-0.006652</td>\n",
       "      <td>434.292</td>\n",
       "      <td>8.69726</td>\n",
       "      <td>0.94681</td>\n",
       "      <td>2673803.190583</td>\n",
       "      <td>6488586.691537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Profile Dist (m)  Offset from Profile (m)  Elevation (m)  Observed (nT)  \\\n",
       "0           131.136                -0.005775        449.474       76.41370   \n",
       "1           262.279                 0.000950        445.686       72.97410   \n",
       "2           393.415                -0.004605        444.431       74.58200   \n",
       "3           524.561                -0.007526        443.623       73.37600   \n",
       "4           655.694                -0.003436        442.574       69.55690   \n",
       "5           786.840                -0.006357        436.543       63.68480   \n",
       "6           917.973                -0.002266        432.307       55.29120   \n",
       "7          1049.110                -0.007822        432.708       41.57130   \n",
       "8          1180.250                -0.001096        435.288       24.99840   \n",
       "9          1311.390                -0.006652        434.292        8.69726   \n",
       "\n",
       "   Modelled (nT) Real world X (m) Real world Y (m)  \n",
       "0       71.15623   2672664.172013   6488277.440502  \n",
       "1       73.67114   2672790.733171   6488311.802689  \n",
       "2       73.12102   2672917.287573   6488346.163041  \n",
       "3       69.92820   2673043.851625   6488380.526014  \n",
       "4       64.34135   2673170.403132   6488414.885581  \n",
       "5       57.05482   2673296.967185   6488449.248554  \n",
       "6       47.20668   2673423.518692   6488483.608121  \n",
       "7       34.43934   2673550.074059   6488517.968735  \n",
       "8       18.92550   2673676.632321   6488552.330136  \n",
       "9        0.94681   2673803.190583   6488586.691537  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_magData[0]['Real world X (m)']=\"\"\n",
    "model_magData[0]['Real world Y (m)']=\"\"\n",
    "for idx in model_magData[0].index:\n",
    "    #model_origin_X, model_origin_Y = model_coordinate[0], model_coordinate[1]\n",
    "    x_coords = model_origin_X + (model_magData[0].loc[idx,'Profile Dist (m)']*np.cos(np.deg2rad(90-model_coordinate[2])))\n",
    "    y_coords = model_origin_Y + (model_magData[0].loc[idx,'Profile Dist (m)']*np.sin(np.deg2rad(90-model_coordinate[2])))\n",
    "    model_magData[0].loc[idx,'Real world X (m)'] = x_coords\n",
    "    model_magData[0].loc[idx,'Real world Y (m)'] = y_coords\n",
    "    \n",
    "model_magData[0][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "343b5658-1235-4597-9dcb-acf687d58d95",
   "metadata": {},
   "source": [
    "# Plot the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "70136368-b7b2-4c4e-b588-6bf49717f55b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and subplots\n",
    "#### PLOT MAGNETIC DATA ####\n",
    "fig, axs = plt.subplots(3, 1, figsize=(10,10), height_ratios=[1.25, 1, 2.2], gridspec_kw={'hspace':0.1})\n",
    "\n",
    "mag_colour = 'purple'\n",
    "axs[0].plot(model_magData[0]['Real world X (m)'], model_magData[0]['Observed (nT)'], linewidth=0, marker='o', markerfacecolor='xkcd:dark '+mag_colour,\n",
    "            markeredgecolor='none', zorder=1, label='Observed data')\n",
    "axs[0].plot(model_magData[0]['Real world X (m)'], model_magData[0]['Modelled (nT)'], linewidth=2, linestyle='-', color='xkcd:light '+mag_colour,\n",
    "            zorder=2, label='Modelled data')\n",
    "axs[0].grid(zorder=0, color='xkcd:light gray', alpha=0.6)\n",
    "axs[0].set_ylim(model_magData[0]['Observed (nT)'].min()-100,model_magData[0]['Observed (nT)'].max()+100)\n",
    "axs[0].set_ylabel('Magnetic Anomaly (nT)')\n",
    "\n",
    "#### PLOT GRAVITY DATA ####\n",
    "grav_colour = 'blue'\n",
    "axs[1].plot(model_gravData[0]['Real world X (m)'], model_gravData[0]['Observed (mGal)'], linewidth=0, marker='o', markerfacecolor='xkcd:ocean '+grav_colour,\n",
    "            markeredgecolor='none', zorder=1, label='Observed data')\n",
    "axs[1].plot(model_gravData[0]['Real world X (m)'], model_gravData[0]['Modelled (mGal)'], linewidth=2, linestyle='-', color='xkcd:light '+grav_colour,\n",
    "            zorder=2, label='Modelled data')\n",
    "axs[1].grid(zorder=0, color='xkcd:light gray', alpha=0.6)\n",
    "axs[1].set_ylim(model_gravData[0]['Observed (mGal)'].min()-1.0,model_gravData[0]['Observed (mGal)'].max()+1.0)\n",
    "axs[1].set_ylabel('Gravity Anomaly (mGal)')\n",
    "\n",
    "#### PLOT DATA LOCATIONS ####\n",
    "axs[2].plot(model_gravData[0]['Real world X (m)'], model_gravData[0]['Elevation (m)'], linewidth=0, marker='v', markerfacecolor='xkcd:ocean '+grav_colour,\n",
    "            markeredgecolor='none', zorder=3, label='Grav. Data')\n",
    "axs[2].plot(model_magData[0]['Real world X (m)'], model_magData[0]['Elevation (m)'], linewidth=0, marker='v', markerfacecolor='xkcd:dark '+mag_colour,\n",
    "            markeredgecolor='none', zorder=3, label='Mag. Data')\n",
    "\n",
    "#### PLOT THE MODELS ####\n",
    "for units in model_geom.index:\n",
    "    vertices = np.array(combined_model.loc[units,'Geometry_xyz (m)'])\n",
    "    facecolor = combined_model.loc[units,'Colour (RGB)']\n",
    "    vertices = np.vstack([vertices[:,0],vertices[:,2]]).T\n",
    "    polygon = Polygon(vertices, closed=True, edgecolor='black', facecolor=facecolor, label=combined_model.loc[units,'Model units'])\n",
    "    axs[2].add_patch(polygon)\n",
    "    #axs[2].plot(xy_test[:,0], xy_test[:,2])\n",
    "    axs[2].set_xlim(model_origin_X+np.array([-100,10200]))\n",
    "    axs[1].set_xlim(model_origin_X+np.array([-100,10200]))\n",
    "    axs[0].set_xlim(model_origin_X+np.array([-100,10200]))\n",
    "    axs[2].set_ylim(-1000,650)\n",
    "    \n",
    "#### PLOT THE WELLS ####\n",
    "for index in model_wells.index:\n",
    "    well_srf_dist = model_wells.loc[index,'Profile X (m)']\n",
    "    x_coords = model_origin_X + (well_srf_dist*np.cos(np.deg2rad(90-model_coordinate[2])))\n",
    "    #y_coords = model_origin_Y + (1e+3*well_srf_dist*np.sin(np.deg2rad(90-model_coordinate[2])))\n",
    "    well_srf_z0 = model_wells.loc[index,'Elevation (m)']\n",
    "    units_list = model_wells.at[index,'Units']\n",
    "    depth_list = model_wells.at[index,'Depths (m)']\n",
    "    axs[2].text(x_coords, well_srf_z0+25, model_wells.at[index,'Well name'], ha='center', va='bottom', zorder=5)\n",
    "    axs[2].plot(x_coords, well_srf_z0, marker='o', markersize=8, markerfacecolor='xkcd:black', markeredgecolor='None', zorder=5)\n",
    "    for idx in range(len(units_list)):\n",
    "        if idx < len(units_list)-1:\n",
    "            axs[2].annotate('', xy=(x_coords, depth_list[idx]), xytext=(x_coords, well_srf_z0),\n",
    "                         arrowprops=dict(arrowstyle='|-|,widthA=0,widthB=0.3', linewidth=0.8, color='xkcd:black', capstyle='butt'),\n",
    "                         zorder=4)\n",
    "        elif idx == len(units_list)-1:\n",
    "            axs[2].annotate('', xy=(x_coords, depth_list[idx]), xytext=(x_coords, well_srf_z0),\n",
    "                     arrowprops=dict(arrowstyle='|-|,widthA=0,widthB=0.4', linewidth=1.0, color='xkcd:black', capstyle='butt'),\n",
    "                     zorder=4)\n",
    "\n",
    "axs[2].set_axisbelow(True)\n",
    "axs[2].grid(zorder=0, color='xkcd:light gray', alpha=0.6)\n",
    "axs[2].set_xlabel('NZMG Easting (m)')\n",
    "axs[2].set_ylabel('Depth (m)')\n",
    "\n",
    "for direction in ['top', 'bottom', 'left', 'right']:\n",
    "    # Change the linewidth of the axes spines\n",
    "    axs[0].spines[direction].set_linewidth(2)\n",
    "    axs[1].spines[direction].set_linewidth(2)\n",
    "    axs[2].spines[direction].set_linewidth(2)\n",
    "    \n",
    "# Prevent labelling using the scientific notation\n",
    "axs[0].set_xticklabels([])\n",
    "axs[1].set_xticklabels([])\n",
    "axs[2].ticklabel_format(useOffset=False, style='plain')\n",
    "\n",
    "# legend?\n",
    "axs[0].legend(loc='upper right')\n",
    "axs[1].legend(loc='lower right')\n",
    "axs[2].legend(loc='lower left',bbox_to_anchor=(0,-0.45),ncols=2)\n",
    "\n",
    "# Giving the ABC letter\n",
    "for idx in range(3):\n",
    "    if idx==2:\n",
    "        y_text = 0.98\n",
    "    else:\n",
    "        y_text = 0.95\n",
    "    axs[idx].text(0.05, y_text, f\"({chr(65 + idx)})\", transform=axs[idx].transAxes, ha='right', va='top', fontsize=14, fontweight='bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78a16a09-7d02-469f-9c25-66f0bc7891dd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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