SimPEG-FDEM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4c21ab4f",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "4uRXh0OBcPxv5N02ybNM",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 1
     }
    }
   },
   "source": [
    "# SimPEG: FDEM\n",
    "\n",
    "Some oddities with SimPEG. I mentioned them in Slack and we discussed it to some extent, but in Slack they will get lost over time..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90d41c40",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "jlm00rV35kXzA1ZETCxL",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "import SimPEG\n",
    "import discretize\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pymatsolver import Pardiso\n",
    "from matplotlib.colors import LogNorm\n",
    "import SimPEG.electromagnetics.frequency_domain as fdem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a3425a2a",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "ffpSnKh66mgK80FggWKj",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 3
     },
     "outputId": {
      "block": "VYz7eZxNpGgMpOffYz7K",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "  <tr>\n",
       "    <td style='font-weight: bold; font-size: 1.2em; text-align: center;' colspan='3'>TensorMesh</td>\n",
       "    <td style='font-size: 1.2em; text-align: center;'colspan='4'>32,768 cells</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th></th>\n",
       "    <th></th>\n",
       "    <th colspan='2' style='padding: 5px 20px 5px 20px;'>MESH EXTENT</th>\n",
       "    <th colspan='2' style='padding: 5px 20px 5px 20px;'>CELL WIDTH</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>FACTOR</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>dir</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>nC</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>min</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>max</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>min</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>max</th>\n",
       "    <th style='padding: 5px 20px 5px 20px;'>max</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>x</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>32</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>-7,832.70</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>7,832.70</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>100.00</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>1,792.16</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>1.30</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>y</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>32</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>-7,832.70</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>7,832.70</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>100.00</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>1,792.16</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>1.30</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>z</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>32</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>-7,832.70</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>7,832.70</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>100.00</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>1,792.16</td>\n",
       "    <td style='padding: 5px 20px 5px 20px;'>1.30</td>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "\n",
       "  TensorMesh: 32,768 cells\n",
       "\n",
       "                      MESH EXTENT             CELL WIDTH      FACTOR\n",
       "  dir    nC        min           max         min       max      max\n",
       "  ---   ---  ---------------------------  ------------------  ------\n",
       "   x     32     -7,832.70      7,832.70    100.00  1,792.16    1.30\n",
       "   y     32     -7,832.70      7,832.70    100.00  1,792.16    1.30\n",
       "   z     32     -7,832.70      7,832.70    100.00  1,792.16    1.30\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simple small mesh\n",
    "#hx = [(120, 5, -1.2), (120, 14), (120, 5, 1.2)]\n",
    "hx = [(100.0, 11, -1.3), (100.0, 10), (100.0, 11, 1.3)]\n",
    "mesh = discretize.TensorMesh([hx, hx, hx], x0=\"CCC\")\n",
    "mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0fe287b9",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "ffpSnKh66mgK80FggWKj",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 2
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Frequency and conductivity model\n",
    "frequency = 1\n",
    "cond_model = np.ones(mesh.n_cells)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68991083",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "XO5wOmfzQ84vyLs3yWw5",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 1
     }
    }
   },
   "source": [
    "## 1. Repeatability\n",
    "\n",
    "### Issue\n",
    "Repeated runs of exactly the same **do not produce exactly the same** results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4906cf51",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "DASGiZg9NLuz2aUoPT4g",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 2
     },
     "outputId": {
      "block": "GNuo6C1xsOcU295spAHV",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 2
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max diff between two SimPEG runs: 1.32e-19\n"
     ]
    }
   ],
   "source": [
    "# Source and Survey\n",
    "src = fdem.sources.LineCurrent([], location=[[-100, 0, 0], [100, 0, 0]], frequency=frequency)\n",
    "survey = fdem.Survey([src, ])\n",
    "\n",
    "# Simulation\n",
    "sim = fdem.simulation.Simulation3DElectricField(\n",
    "    mesh, survey=survey, solver=Pardiso,\n",
    "    sigmaMap=SimPEG.maps.IdentityMap(mesh),\n",
    ")\n",
    " \n",
    "# Run two runs\n",
    "se1 = sim.fields(cond_model)[survey.source_list[0], 'e'][:, 0]\n",
    "se2 = sim.fields(cond_model)[survey.source_list[0], 'e'][:, 0]\n",
    "\n",
    "# Compare\n",
    "diff = abs(se1 - se2)\n",
    "print(f\"Max diff between two SimPEG runs: {max(diff):.2e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aacf491f",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "bWy4epDnXBDNurwdIMoX",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 2
     },
     "outputId": {
      "block": "gvYOWLDxLAQF73lHmXRZ",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 2
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fd8f724c070>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 648x432 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inp = {'view': 'abs', 'range_x': [-3000, 3000], 'range_y': [-3000, 3000]}\n",
    "inp_f = {**inp, 'pcolor_opts': {'norm': LogNorm(vmin=1e-14, vmax=1e-6)}}\n",
    "inp_d = {**inp, 'pcolor_opts': {'norm': LogNorm(vmin=1e-26, vmax=1e-18), 'cmap': 'magma'}}\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(9, 6), sharex=True, sharey=True, constrained_layout=True)\n",
    "\n",
    "f1 = mesh.plot_slice(se1[:mesh.n_edges_x], v_type='Ex', ax=axs[0, 0], **inp_f)\n",
    "mesh.plot_slice(se1[mesh.n_edges_x:-mesh.n_edges_z], v_type='Ey', ax=axs[0, 1], **inp_f)\n",
    "mesh.plot_slice(se1[-mesh.n_edges_z:], v_type='Ez', ax=axs[0, 2], **inp_f)\n",
    "axs[0, 0].set_title('$|E_x^1$|')\n",
    "axs[0, 1].set_title('$|E_y^1$|')\n",
    "axs[0, 2].set_title('$|E_z^1$|')\n",
    "fig.colorbar(f1[0], ax=axs[0, :], fraction=0.07)\n",
    "\n",
    "f1 = mesh.plot_slice(diff[:mesh.n_edges_x], v_type='Ex', ax=axs[1, 0], **inp_d)\n",
    "mesh.plot_slice(diff[mesh.n_edges_x:-mesh.n_edges_z], v_type='Ey', ax=axs[1, 1], **inp_d)\n",
    "mesh.plot_slice(diff[-mesh.n_edges_z:], v_type='Ez', ax=axs[1, 2], **inp_d)\n",
    "axs[1, 0].set_title('$\\Delta|E_x^1-E_x^2$|')\n",
    "axs[1, 1].set_title('$\\Delta|E_y^1-E_y^2$|')\n",
    "axs[1, 2].set_title('$\\Delta|E_z^1-E_z^2$|')\n",
    "\n",
    "fig.colorbar(f1[0], ax=axs[1, :], fraction=0.07)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f7094b",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "DhTVsVeI3nN4GEflJtSf",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 2
     }
    }
   },
   "source": [
    "**NOTE** to above figure: the difference (lower row) should be zero.\n",
    "\n",
    "\n",
    "## 2. Vertical magnetic field\n",
    "\n",
    "### Issue\n",
    "The vertical electric field due to a vertical magnetic source should be **all zero**, but it isn't, and it has a strange pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dad92fdd",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "UyLHJCzBELNt9dC40QTO",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Source and Survey\n",
    "sources = [\n",
    "    fdem.sources.MagDipole(frequency=frequency, location=np.r_[0, 0, 0], orientation=\"z\"),\n",
    "]\n",
    "survey = fdem.Survey(sources)\n",
    "\n",
    "# Simulation\n",
    "simulation_simpeg = fdem.Simulation3DMagneticFluxDensity(\n",
    "    mesh, survey=survey, solver=Pardiso,\n",
    "    sigmaMap=SimPEG.maps.IdentityMap(mesh),\n",
    ")\n",
    "\n",
    "# Get Ez field\n",
    "fields = simulation_simpeg.fields(cond_model)\n",
    "sefield = fields[survey.source_list[0], 'e']\n",
    "sez = sefield[-mesh.n_edges_z:].reshape(mesh.shape_edges_z, order='F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2ac93c7",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "UCkovBhKckuaTVuY72gJ",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 1
     },
     "outputId": {
      "block": "0kDs51f0BYGqhNX4elyT",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "opts = {'view': 'abs', 'xlim': [-3000, 3000], 'ylim': [-3000, 3000], 'zlim': [-3000, 3000]}\n",
    "props = {'cmap': 'magma', 'norm': LogNorm(vmin=1e-20, vmax=1e-12, clip=True)}\n",
    "fig = plt.figure(figsize=(10, 9))\n",
    "mesh.plot_3d_slicer(sez.ravel('F'), v_type='Ez', fig=fig, **opts, pcolor_opts=props)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7047dc47",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "igCAGUBXo43Dhwe0evuQ",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 1
     }
    }
   },
   "source": [
    "**Note** to above figure: the field should be zero everywhere."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "046d31af",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "OJswHpwfA90VGBpCjBb3",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 2
     },
     "outputId": {
      "block": "FtEZ5yilYukHD4ZCKdMQ",
      "project": "5nikFkqMA7ZGysNRRIxy",
      "version": 2
     }
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style='border: 3px solid #ddd;'>\n",
       "  <tr>\n",
       "     <td style='text-align: center; font-weight: bold; font-size: 1.2em; border: 2px solid #fff;' colspan='6'>Mon Feb 07 15:07:23 2022 CET</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>OS</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Linux</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>CPU(s)</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>4</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Machine</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>x86_64</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Architecture</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>64bit</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>RAM</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>15.5 GiB</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Environment</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Jupyter</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; border: 2px solid #fff;' colspan='6'>Python 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \n",
       "[GCC 9.4.0]</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>SimPEG</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>0.16.0</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>discretize</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>0.7.0</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>pymatsolver</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>0.1.3</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>vectormath</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>0.2.2</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>properties</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>0.6.1</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>numpy</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>1.21.2</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>scipy</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>1.7.1</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>cython</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>0.29.23</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>IPython</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>7.23.1</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>matplotlib</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>3.5.1</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>ipywidgets</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>7.6.3</td>\n",
       "    <td style= border: 2px solid #fff;'></td>\n",
       "    <td style= border: 2px solid #fff;'></td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; background-color: #ddd;border: 2px solid #fff;' colspan='6'>Intel(R) oneAPI Math Kernel Library Version 2021.3-Product Build 20210617 for Intel(R) 64 architecture applications</td>\n",
       "  </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "\n",
       "--------------------------------------------------------------------------------\n",
       "  Date: Mon Feb 07 15:07:23 2022 CET\n",
       "\n",
       "                OS : Linux\n",
       "            CPU(s) : 4\n",
       "           Machine : x86_64\n",
       "      Architecture : 64bit\n",
       "               RAM : 15.5 GiB\n",
       "       Environment : Jupyter\n",
       "\n",
       "  Python 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51)\n",
       "  [GCC 9.4.0]\n",
       "\n",
       "            SimPEG : 0.16.0\n",
       "        discretize : 0.7.0\n",
       "       pymatsolver : 0.1.3\n",
       "        vectormath : 0.2.2\n",
       "        properties : 0.6.1\n",
       "             numpy : 1.21.2\n",
       "             scipy : 1.7.1\n",
       "            cython : 0.29.23\n",
       "           IPython : 7.23.1\n",
       "        matplotlib : 3.5.1\n",
       "        ipywidgets : 7.6.3\n",
       "\n",
       "  Intel(R) oneAPI Math Kernel Library Version 2021.3-Product Build 20210617\n",
       "  for Intel(R) 64 architecture applications\n",
       "--------------------------------------------------------------------------------"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SimPEG.Report()"
   ]
  }
 ],
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