AEM-SG-Grid-Load.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import rioxarray\n",
    "import pyvista as pv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data\n",
    "- Exploring for the Future AusAEM Eastern Resources Corridor: 2021 Airborne Electromagnetic Survey TEMPEST® airborne electromagnetic data and GALEI inversion conductivity estimates\n",
    "- https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/145744"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Example\n",
    "\\*  \n",
    "\\*   X   Y   Z  Conductivity  I   J   K  \n",
    "\\*  \n",
    "339282.7 7338231.4   131.8   0.218729    0    0    0  \n",
    "339291.9 7338231.8   131.7   0.207454    1    0    0   \n",
    "339301.0 7338232.2   131.6   0.206051    2    0    0   \n",
    "339310.2 7338232.7   131.5   0.201894    3    0    0   \n",
    "339319.4 7338233.1   131.4   0.194041    4    0    0  \n",
    "339328.6 7338233.5   131.6   0.180258    5    0    0   \n",
    "339337.8 7338234.0   131.4   0.175926    6    0    0   \n",
    "339347.0 7338234.4   131.2   0.182470    7    0    0   \n",
    "339356.2 7338234.8   131.0   0.190379    8    0    0   \n",
    "339365.4 7338235.2   130.5   0.216839    9    0    0   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data\n",
    "This format is simply handled by numpy directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Points = np.loadtxt(r\"J:\\AusAEM\\AusAEM_East_Resources_Corridor_ GA_layer_earth_inversion\\GA_gocad_sgrids\\1001001.sg.data\", skiprows = 3) #Loading the data only\n",
    "mesh_points = Points[:,0:3]   ##x, y, z and conductivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Point Cloud\n",
    "View as a point cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "point_cloud = pv.PolyData(mesh_points)\n",
    "point_cloud['conductivity'] = Points[:,3]\n",
    "point_cloud['logCon'] = np.log(Points[:,3])  #make a log of the conductivity for display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>PolyData</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>1106550</td></tr>\n",
       "<tr><td>N Points</td><td>1106550</td></tr>\n",
       "<tr><td>X Bounds</td><td>3.393e+05, 7.200e+05</td></tr>\n",
       "<tr><td>Y Bounds</td><td>7.338e+06, 7.339e+06</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.376e+02, 3.103e+02</td></tr>\n",
       "<tr><td>N Arrays</td><td>2</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td><b>conductivity</b></td><td>Points</td><td>float64</td><td>1</td><td>1.406e-03</td><td>2.381e+00</td></tr>\n",
       "<tr><td>logCon</td><td>Points</td><td>float64</td><td>1</td><td>-6.567e+00</td><td>8.673e-01</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "PolyData (0x1adb584fca0)\n",
       "  N Cells:\t1106550\n",
       "  N Points:\t1106550\n",
       "  X Bounds:\t3.393e+05, 7.200e+05\n",
       "  Y Bounds:\t7.338e+06, 7.339e+06\n",
       "  Z Bounds:\t-5.376e+02, 3.103e+02\n",
       "  N Arrays:\t2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x1ADB5A7E520>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[(954294.4495370872, 7763015.499537087, 424535.4995370872),\n",
       " (529645.3, 7338366.35, -113.65),\n",
       " (0.0, 0.0, 1.0)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_cloud.plot(cmap=\"rainbow\", notebook=True, scalars='logCon')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a 3D version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(528840.1574913937, 7336277.1474497225, 3844.0229498677654),\n",
       " (529645.3, 7338366.35, 197.05),\n",
       " (-0.17857221021845454, -0.8362409423881803, -0.5184718430266239)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Triangles = Points[:,4:7]\n",
    "Triangles = Triangles.astype(np.int32) # Removing the index column and making sure the ndarray is an integer:\n",
    "\n",
    "meshvista = pv.make_tri_mesh(mesh_points, Triangles)\n",
    "meshvista['conductivity'] = Points[:,3]\n",
    "meshvista['logCon'] = np.log(Points[:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>PolyData</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>1106550</td></tr>\n",
       "<tr><td>N Points</td><td>1106550</td></tr>\n",
       "<tr><td>X Bounds</td><td>3.393e+05, 7.200e+05</td></tr>\n",
       "<tr><td>Y Bounds</td><td>7.338e+06, 7.339e+06</td></tr>\n",
       "<tr><td>Z Bounds</td><td>8.380e+01, 3.103e+02</td></tr>\n",
       "<tr><td>N Arrays</td><td>2</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td><b>conductivity</b></td><td>Points</td><td>float64</td><td>1</td><td>1.406e-03</td><td>2.381e+00</td></tr>\n",
       "<tr><td>logCon</td><td>Points</td><td>float64</td><td>1</td><td>-6.567e+00</td><td>8.673e-01</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "PolyData (0x1adada82160)\n",
       "  N Cells:\t1106550\n",
       "  N Points:\t1106550\n",
       "  X Bounds:\t3.393e+05, 7.200e+05\n",
       "  Y Bounds:\t7.338e+06, 7.339e+06\n",
       "  Z Bounds:\t8.380e+01, 3.103e+02\n",
       "  N Arrays:\t2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meshvista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x1ADAE6A7280>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[(954293.471616054, 7763014.521616054, 424845.22161605384),\n",
       " (529645.3, 7338366.35, 197.05),\n",
       " (0.0, 0.0, 1.0)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meshvista.plot(cmap=\"jet\",scalars=\"logCon\", notebook=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:gempy2021] *",
   "language": "python",
   "name": "conda-env-gempy2021-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}