4-inversion-1D.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b548e105-3957-422a-a40d-c01dff54a068",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#\n",
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd52ea0b-53d3-456e-8f95-c8b21103e91c",
   "metadata": {},
   "source": [
    "# 1D inversion of AEM data over a layered Earth\n",
    "\n",
    "[![License: CC BY 4.0](https://licensebuttons.net/l/by/4.0/80x15.png)](https://creativecommons.org/licenses/by/4.0/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6d3cc68-8dc8-4514-850c-844bf1e034ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm, LinearSegmentedColormap, Normalize, SymLogNorm\n",
    "from matplotlib import cm as colormap\n",
    "import time\n",
    "import ipywidgets\n",
    "\n",
    "# SimPEG, discretize/\n",
    "import discretize\n",
    "from discretize import utils\n",
    "from simpeg.electromagnetics import time_domain as tdem\n",
    "from simpeg.utils import mkvc, plot_1d_layer_model\n",
    "from simpeg import (\n",
    "    maps,\n",
    "    data,\n",
    "    data_misfit,\n",
    "    inverse_problem,\n",
    "    regularization,\n",
    "    optimization,\n",
    "    directives,\n",
    "    inversion,\n",
    "    utils,\n",
    ")\n",
    "\n",
    "# solver \n",
    "from simpeg.utils.solver_utils import get_default_solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f019875-3a8a-4222-8776-deea769a2f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Solver = get_default_solver()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dffc99fa-b301-4885-add6-7a9c07082dd8",
   "metadata": {},
   "source": [
    "## define survey and model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "71a90664-6dcf-4ea8-9b00-7d95448f4695",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target conductances: 4.0\n"
     ]
    }
   ],
   "source": [
    "# conductivity values\n",
    "rho_back = 100\n",
    "\n",
    "sigma_target = 0.1\n",
    "sigma_back = 1/rho_back\n",
    "\n",
    "# target geometry\n",
    "target_thickness = 40\n",
    "target_depth = 200 + target_thickness/2 * np.r_[-1, 1]\n",
    "\n",
    "print(f\"target conductances: {sigma_target * target_thickness}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c61ebb0-f28a-4210-adef-80c14dd7caca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transmitter\n",
    "tx_height = 30\n",
    "tx_radius = 10\n",
    "\n",
    "# receiver times\n",
    "rx_times = 1e-3 * np.logspace(-1, 1, 30)  # 30 time-channels between 0.1ms and 10ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0141b7b3-bb2d-4852-ac49-1215cb7648bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def diffusion_distance(sigma, t):\n",
    "    return 1260 * np.sqrt(t/sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "664bbcce-cf58-4297-8b16-d9bfe8c45ac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(39.844698518121575)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# minimum diffusion distance (max conductivity, earliest time) \n",
    "diffusion_distance(sigma_target, rx_times.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e152439-3e8a-491d-a155-cb5c1dbe66c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1260.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# maximum diffusion distance (background conductivity, latest time)\n",
    "diffusion_distance(sigma_back, rx_times.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa57ae4-7c1c-4281-859d-d812223c40cf",
   "metadata": {},
   "source": [
    "## create our survey "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1c93a89-77e6-4f40-b257-8493ce672e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rx_z = tdem.receivers.PointMagneticFluxTimeDerivative(\n",
    "    np.r_[0, 0, tx_height], times=rx_times, orientation=\"z\"\n",
    ")\n",
    "\n",
    "src = tdem.sources.CircularLoop(\n",
    "    location=np.r_[0, 0, tx_height], radius=tx_radius, receiver_list=[rx_z], #waveform=waveform\n",
    ")\n",
    "\n",
    "survey = tdem.Survey([src])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14fb7bb3-aa8e-48f9-81e1-f45dddf561eb",
   "metadata": {},
   "source": [
    "## create our true model and data \n",
    "- construct a 3 layer model\n",
    "- run simulation to generate data for background, 3 layer model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e966bbd2-602d-41d9-9626-22f5f761cd58",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_true = np.log(np.r_[sigma_back, sigma_target, sigma_back])\n",
    "thicknesses_true = np.r_[target_depth.min(), target_thickness]\n",
    "simulation_3layer = tdem.Simulation1DLayered(\n",
    "    survey=tdem.Survey([src]), thicknesses=thicknesses_true, sigmaMap=maps.ExpMap(nP=3)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50171105-cccb-41e6-8aaf-7492b362fe54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440.0, 0.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(2, 4))\n",
    "plot_1d_layer_model(thicknesses_true, np.exp(m_true), ax=ax)\n",
    "ax.grid()\n",
    "ax.set_xlim(0.5*sigma_back, 1.5*sigma_target)\n",
    "ax.set_ylim([target_depth.max()*2, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a2764a6-675b-4fd0-96bf-d93b23bb5bfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "relative_error = 0.05\n",
    "d_background = simulation_3layer.dpred(np.log(sigma_back)*np.ones(3))\n",
    "d_true = simulation_3layer.dpred(m_true)\n",
    "d_noisy = d_true + relative_error*np.abs(d_true)*np.random.randn(len(d_true))\n",
    "dobs = d_noisy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a99251a9-8ef6-466b-9a45-4279762cbf26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
    "ax.loglog(rx_times*1e3, -d_background, label=\"background\")\n",
    "ax.loglog(rx_times*1e3, -d_true, label=\"clean\")\n",
    "ax.loglog(rx_times*1e3, -d_noisy, \".\", label=\"dobs\")\n",
    "ax.set_xlabel(\"time (ms)\")\n",
    "ax.set_ylabel(\"db/dt\")\n",
    "ax.legend()\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f347b2-ca44-4fc4-9b96-ee6fd07ad73a",
   "metadata": {},
   "source": [
    "## define a 1D mesh for the inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0b8573ab-50be-4778-a7c4-cbf85ab74cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_size = 5\n",
    "layer_thicknesses = np.hstack([[cell_size]*int(300/cell_size), cell_size * np.logspace(0, 1.5, 20)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "589c7543-7c8c-4e14-bc3a-c6566ab3b5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a mesh with one extra layer so we can regularize the value that goes to \"infinite\" depth\n",
    "mesh = discretize.TensorMesh([(np.r_[layer_thicknesses, layer_thicknesses[-1]])], origin=\"0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d93e5521-76a2-4762-9adf-b156ca785080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'depth (m)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 2))\n",
    "ax.plot(mesh.nodes, np.zeros_like(mesh.nodes), \"-|\")\n",
    "ax.plot(mesh.cell_centers, np.zeros_like(mesh.cell_centers), \"o\", ms=4)\n",
    "ax.set_xlim([-1, 500])\n",
    "ax.set_xlabel(\"depth (m)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c845b471-4463-4af4-bde7-754f7f777c15",
   "metadata": {},
   "source": [
    "define the simulation for the inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3eb1fc77-6f03-488c-996c-0ae1591fb1bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "simulation = tdem.Simulation1DLayered(\n",
    "    survey=survey, thicknesses=layer_thicknesses, sigmaMap=maps.ExpMap(mesh)  # invert for log conductivity\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ac0a37-2b04-43f5-9db6-798e2be77316",
   "metadata": {},
   "source": [
    "# set up the inversion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c00d72-8465-4e01-bc96-e8df695a70c8",
   "metadata": {},
   "source": [
    "standard_deviation = relative_error * np.abs(dobs) + noise_floor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "945b15c3-44af-4ced-b6b9-c4a13fb71394",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_invert = data.Data(survey, dobs=dobs, relative_error=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "772afb0e-0345-423c-850c-7c96208bd37b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SaveInversionProgress(directives.InversionDirective):\n",
    "    \"\"\"\n",
    "    A custom directive to save items of interest during the course of an inversion\n",
    "    \"\"\"\n",
    "    \n",
    "    def initialize(self):\n",
    "        \"\"\"\n",
    "        This is called when we first start running an inversion\n",
    "        \"\"\"\n",
    "        # initialize an empty dictionary for storing results \n",
    "        self.inversion_results = {\n",
    "            \"iteration\":[],\n",
    "            \"beta\":[],\n",
    "            \"phi_d\":[],\n",
    "            \"phi_m\":[],\n",
    "            \"phi_m_small\":[],\n",
    "            \"phi_m_smooth\":[],\n",
    "            \"dpred\":[],\n",
    "            \"model\":[]\n",
    "        }\n",
    "\n",
    "    def endIter(self):\n",
    "        \"\"\"\n",
    "        This is run at the end of every iteration. So here, we just append \n",
    "        the new values to our dictionary\n",
    "        \"\"\"\n",
    "        \n",
    "        # Save the data\n",
    "        self.inversion_results[\"iteration\"].append(self.opt.iter)\n",
    "        self.inversion_results[\"beta\"].append(self.invProb.beta)\n",
    "        self.inversion_results[\"phi_d\"].append(self.invProb.phi_d)\n",
    "        self.inversion_results[\"phi_m\"].append(self.invProb.phi_m)\n",
    "        self.inversion_results[\"dpred\"].append(self.invProb.dpred)\n",
    "        self.inversion_results[\"model\"].append(self.invProb.model)\n",
    "        \n",
    "        # grab the components of the regularization and evaluate them here\n",
    "        # the regularization has a list of objective functions  \n",
    "        # objfcts = [smallness, smoothness_x, smoothness_z]\n",
    "        # and the multipliers contain the alpha values\n",
    "        # multipliers = [alpha_s, alpha_x, alpha_z]\n",
    "        reg = self.reg.objfcts[0] \n",
    "        phi_s = reg.objfcts[0](self.invProb.model) * reg.multipliers[0]\n",
    "        phi_x = reg.objfcts[1](self.invProb.model) * reg.multipliers[1]\n",
    "        \n",
    "        self.inversion_results[\"phi_m_small\"].append(phi_s)\n",
    "        self.inversion_results[\"phi_m_smooth\"].append(phi_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c745b785-2979-47e7-a7d2-e3e6b1a3491c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def setup_inversion(alpha_s=0.1, alpha_x=1): \n",
    "    \"\"\"\n",
    "    Simple, resable function for setting up inversions with different \n",
    "    choices of regularization parameters\n",
    "    \"\"\"\n",
    "    # define misfit and regularization \n",
    "    dmis = data_misfit.L2DataMisfit(simulation=simulation, data=data_invert)\n",
    "    reg = regularization.WeightedLeastSquares(\n",
    "        mesh, \n",
    "        alpha_s=alpha_s, alpha_x=alpha_x, \n",
    "        # reference_model=np.log(sigma_back),\n",
    "    )\n",
    "\n",
    "    opt = optimization.InexactGaussNewton(maxIter=30, tolCG=1e-3)\n",
    "    inv_prob = inverse_problem.BaseInvProblem(dmis, reg, opt)\n",
    "    \n",
    "    # Defining a starting value for the trade-off parameter (beta) between the data\n",
    "    # misfit and the regularization.\n",
    "    starting_beta = directives.BetaEstimate_ByEig(beta0_ratio=10)\n",
    "    beta_cooling = directives.BetaSchedule(coolingRate=1, coolingFactor=1.5)\n",
    "\n",
    "    # Options for outputting recovered models and predicted data for each beta.\n",
    "    save_iteration = SaveInversionProgress()\n",
    "\n",
    "    # stop the inversion when we reach the target misfit -- here we overfit to explore models\n",
    "    target_misfit = directives.TargetMisfit(chifact=0.1)\n",
    "\n",
    "    # The directives are defined as a list.\n",
    "    directives_list = [\n",
    "        starting_beta,\n",
    "        beta_cooling,\n",
    "        target_misfit,\n",
    "        save_iteration,\n",
    "    ]\n",
    "    \n",
    "    # Here we combine the inverse problem and the set of directives\n",
    "    inv = inversion.BaseInversion(inv_prob, directives_list)\n",
    "    \n",
    "    return inv, save_iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1fd0097c-48ec-4f9e-88ea-bf5d518f0bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the inversion object\n",
    "inv, save_iteration = setup_inversion(alpha_s=1/cell_size**2, alpha_x=1)\n",
    "# inv, save_iteration = setup_inversion(alpha_s=1e-3, alpha_x=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1d2ffcb2-b64a-4569-b0ed-22deaf2abc25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running inversion with SimPEG v0.22.2.dev41+ge21040475\n",
      "simpeg.InvProblem will set Regularization.reference_model to m0.\n",
      "simpeg.InvProblem will set Regularization.reference_model to m0.\n",
      "simpeg.InvProblem will set Regularization.reference_model to m0.\n",
      "\n",
      "                        simpeg.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "                        ***Done using same Solver, and solver_opts as the Simulation1DLayered problem***\n",
      "                        \n",
      "model has any nan: 0\n",
      "============================ Inexact Gauss Newton ============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "x0 has any nan: 0\n",
      "   0  6.92e+02  2.34e+03  4.95e-32  2.34e+03    4.74e+02      0              \n",
      "   1  4.61e+02  1.86e+03  2.81e-01  1.99e+03    2.02e+02      0              \n",
      "   2  3.08e+02  1.57e+03  7.31e-01  1.79e+03    1.84e+02      0   Skip BFGS  \n",
      "   3  2.05e+02  1.22e+03  1.53e+00  1.54e+03    1.85e+02      0   Skip BFGS  \n",
      "   4  1.37e+02  8.24e+02  2.86e+00  1.22e+03    1.80e+02      0              \n",
      "   5  9.11e+01  4.49e+02  4.64e+00  8.72e+02    1.52e+02      0              \n",
      "   6  6.08e+01  2.07e+02  6.30e+00  5.89e+02    1.04e+02      0   Skip BFGS  \n",
      "   7  4.05e+01  9.73e+01  7.36e+00  3.95e+02    5.87e+01      0   Skip BFGS  \n",
      "   8  2.70e+01  5.58e+01  7.85e+00  2.68e+02    2.80e+01      0              \n",
      "   9  1.80e+01  4.00e+01  8.00e+00  1.84e+02    1.74e+01      0              \n",
      "  10  1.20e+01  3.40e+01  7.99e+00  1.30e+02    2.20e+01      0              \n",
      "  11  8.00e+00  3.24e+01  7.91e+00  9.57e+01    1.40e+01      0   Skip BFGS  \n",
      "  12  5.33e+00  3.14e+01  7.92e+00  7.36e+01    1.05e+01      0   Skip BFGS  \n",
      "  13  3.56e+00  3.07e+01  7.95e+00  5.89e+01    4.92e+00      0   Skip BFGS  \n",
      "  14  2.37e+00  3.01e+01  8.02e+00  4.91e+01    7.69e+00      0              \n",
      "  15  1.58e+00  2.93e+01  8.17e+00  4.22e+01    4.33e+00      0              \n",
      "  16  1.05e+00  2.87e+01  8.43e+00  3.76e+01    2.56e+01      0              \n",
      "  17  7.02e-01  2.82e+01  8.86e+00  3.44e+01    3.66e+01      0              \n",
      "  18  4.68e-01  2.76e+01  8.80e+00  3.17e+01    3.31e+00      0              \n",
      "  19  3.12e-01  2.72e+01  9.37e+00  3.02e+01    2.15e+01      0              \n",
      "  20  2.08e-01  2.72e+01  9.19e+00  2.91e+01    8.65e+00      0              \n",
      "  21  1.39e-01  2.71e+01  9.26e+00  2.84e+01    1.23e+01      0              \n",
      "  22  9.25e-02  2.68e+01  1.01e+01  2.78e+01    2.71e+01      1              \n",
      "  23  6.17e-02  2.67e+01  1.11e+01  2.74e+01    3.49e+01      1              \n",
      "  24  4.11e-02  2.65e+01  1.15e+01  2.70e+01    3.15e+01      2              \n",
      "  25  2.74e-02  2.65e+01  1.22e+01  2.68e+01    3.35e+01      1              \n",
      "  26  1.83e-02  2.64e+01  1.15e+01  2.66e+01    2.72e+01      1              \n",
      "  27  1.22e-02  2.63e+01  1.27e+01  2.65e+01    3.04e+01      0              \n",
      "  28  8.12e-03  2.61e+01  1.23e+01  2.62e+01    1.94e+01      0              \n",
      "  29  5.41e-03  2.60e+01  1.36e+01  2.61e+01    1.36e+01      0   Skip BFGS  \n",
      "  30  3.61e-03  2.60e+01  1.29e+01  2.60e+01    1.61e+01      1              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 6.8704e-02 <= tolF*(1+|f0|) = 2.3408e+02\n",
      "1 : |xc-x_last| = 3.7733e-01 <= tolX*(1+|x0|) = 4.2447e+00\n",
      "0 : |proj(x-g)-x|    = 1.6090e+01 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 1.6090e+01 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      30    <= iter          =     30\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "# Run the inversion\n",
    "starting_model = np.log(sigma_back)*np.ones(mesh.n_cells)\n",
    "recovered_model = inv.run(starting_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8bf26a33-2ce4-43a6-ac1a-090f5985358f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_recovered_model(model, ax=None): \n",
    "    if ax is None: \n",
    "        fig, ax = plt.subplots(1, 1, figsize=(3, 5))\n",
    "\n",
    "    plot_1d_layer_model(simulation_3layer.thicknesses, np.exp(m_true), ax=ax, color=\"k\", label=\"True\")\n",
    "    plot_1d_layer_model(mesh.h[0], np.exp(model), ax=ax, label=\"Recovered\")\n",
    "    \n",
    "    ax.set_ylim([450, 0])\n",
    "    ax.set_xlim(sigma_back*0.5, sigma_target*2)\n",
    "    ax.grid(\"both\", alpha=0.7)\n",
    "    ax.legend()\n",
    "    \n",
    "    ax.set_xlabel(\"conductivity (S/m)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6860e317-5cc9-45f8-9344-72598f4930d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_obs_pred_data(model, dpred, ax=None): \n",
    "    if ax is None: \n",
    "        fig, ax = plt.subplots(1, 1, figsize=(3.5, 3))\n",
    "\n",
    "    ax.loglog(rx_times*1e3, -dobs, \"ko\", label=\"dobs\", ms=4)\n",
    "    ax.loglog(rx_times*1e3, -dpred, label=\"Recovered\")\n",
    "    \n",
    "    ax.legend()\n",
    "    ax.grid(\"both\", alpha=0.7)\n",
    "    ax.set_xlabel(\"time (ms)\")\n",
    "    ax.set_ylabel(\"db/dt\")\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cda26214-0b50-44af-8a8f-ec2492ccf0fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(14)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model that reaches target misfit\n",
    "diff_from_target = save_iteration.inversion_results[\"phi_d\"] - survey.nD\n",
    "iter_target = np.where(diff_from_target == np.max(diff_from_target[diff_from_target < 0]))[0][0]\n",
    "iter_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e8adc516-0e8c-4557-bda8-f39d166df0a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(8, 4), gridspec_kw={\"width_ratios\":[1, 2]})\n",
    "plot_recovered_model(save_iteration.inversion_results[\"model\"][iter_target], ax=ax[0])\n",
    "plot_obs_pred_data(recovered_model, save_iteration.inversion_results[\"dpred\"][iter_target], ax=ax[1])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "05387d91-67fd-4eda-8367-b8f08c991d4b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def plot_tikhonov_curves(inversion_results, iteration=None, ax=None, log=False):\n",
    "    if ax is None: \n",
    "        fig, ax = plt.subplots(1, 4, figsize=(10, 2.5), layout=\"constrained\")\n",
    "\n",
    "    ax[0].plot(inversion_results[\"iteration\"], inversion_results[\"beta\"], \"-o\", ms=4, color=\"C4\")\n",
    "    ax[0].set_title(\"$\\\\beta$\")\n",
    "    ax[0].set_ylabel(\"$\\\\beta$\")\n",
    "    if iteration is not None: \n",
    "        ax[0].plot(\n",
    "            inversion_results[\"iteration\"][iteration], inversion_results[\"beta\"][iteration], \"s\", ms=6, color=\"k\"\n",
    "        )   \n",
    "    \n",
    "    ax[1].plot(inversion_results[\"iteration\"], inversion_results[\"phi_d\"], \"-o\", ms=4, color=\"C3\")\n",
    "    ax[1].plot(inversion_results[\"iteration\"], survey.nD*np.ones_like(inversion_results[\"iteration\"]), \"--\", color=\"k\" )\n",
    "    ax[1].text(1, survey.nD*2, \"$\\phi_d^*$\")\n",
    "    ax[1].set_title(\"$\\phi_d$\")\n",
    "    ax[1].set_ylabel(\"$\\phi_d$\")\n",
    "    if iteration is not None: \n",
    "        ax[1].plot(\n",
    "            inversion_results[\"iteration\"][iteration], inversion_results[\"phi_d\"][iteration], \"s\", ms=6, color=\"k\",\n",
    "            label=f\"$\\chi={inversion_results['phi_d'][iteration] / len(rx_times):1.1f}$\"\n",
    "        )\n",
    "        ax[1].legend()\n",
    "    \n",
    "    ax[2].plot(inversion_results[\"iteration\"], inversion_results[\"phi_m\"], \"-o\", ms=4, label=\"$\\phi_m$\")\n",
    "    ax[2].plot(inversion_results[\"iteration\"], inversion_results[\"phi_m_small\"], \"-o\", ms=4, label=\"smallness\")\n",
    "    ax[2].plot(inversion_results[\"iteration\"], inversion_results[\"phi_m_smooth\"], \"-o\", ms=4, label=\"smoothness\")\n",
    "    ax[2].set_title(\"$\\phi_m$\")\n",
    "    ax[2].set_ylabel(\"$\\phi_m$\")\n",
    "    ax[2].legend()\n",
    "    if iteration is not None: \n",
    "        ax[2].plot(\n",
    "            inversion_results[\"iteration\"][iteration], inversion_results[\"phi_m\"][iteration], \"s\", ms=6, color=\"k\"\n",
    "        )\n",
    "        ax[2].plot(\n",
    "            inversion_results[\"iteration\"][iteration], inversion_results[\"phi_m_small\"][iteration], \"s\", ms=6, color=\"k\"\n",
    "        )\n",
    "        ax[2].plot(\n",
    "            inversion_results[\"iteration\"][iteration], inversion_results[\"phi_m_smooth\"][iteration], \"s\", ms=6, color=\"k\"\n",
    "        )\n",
    "    \n",
    "    for a in ax[:3]:\n",
    "        a.set_xlabel(\"iteration\")\n",
    "        xticks = inversion_results[\"iteration\"]\n",
    "        while len(xticks) > 10: \n",
    "            xticks = xticks[::2]\n",
    "        a.set_xticks(xticks)\n",
    "    \n",
    "    ax[3].plot(inversion_results[\"phi_m\"], inversion_results[\"phi_d\"],  \"-o\", ms=4, color=\"k\")\n",
    "    ax[3].plot(inversion_results[\"phi_m\"], survey.nD*np.ones_like(inversion_results[\"iteration\"]), \"--\", color=\"k\" )\n",
    "    ax[3].text(inversion_results[\"phi_m\"][0], survey.nD*2, \"$\\phi_d^*$\")\n",
    "    ax[3].set_xlabel(\"$\\phi_m$\")\n",
    "    ax[3].set_ylabel(\"$\\phi_d$\")\n",
    "    ax[3].set_title(\"$\\phi_d$ vs. $\\phi_m$\")\n",
    "    if iteration is not None: \n",
    "        ax[3].plot(\n",
    "            inversion_results[\"phi_m\"][iteration], inversion_results[\"phi_d\"][iteration], \"s\", ms=6, color=\"k\"\n",
    "        )\n",
    "\n",
    "    if log is True: \n",
    "        for a in ax:\n",
    "            a.set_yscale('log')\n",
    "        ax[3].set_xscale('log')\n",
    "    \n",
    "    # plt.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c8f0b27a-5874-4ec4-b985-49a85f02ae8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x250 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tikhonov_curves(save_iteration.inversion_results, iteration=iter_target, log=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2f95c790-2d75-4778-b727-8fbc8bbc043a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_inversion_results_by_iteration(iteration, log=False):\n",
    "    fig, axd = plt.subplot_mosaic(\n",
    "        [['a', 'b', 'c', 'd'],\n",
    "         ['e', 'f', 'f', 'g']],\n",
    "        figsize=(10, 6), layout=\"constrained\",\n",
    "        gridspec_kw={\"height_ratios\": [1, 2]}\n",
    "    )\n",
    "    \n",
    "    ax_tikhonov = plot_tikhonov_curves(\n",
    "        save_iteration.inversion_results, iteration, ax=[axd[k] for k in [\"a\", \"b\", \"c\", \"d\"]], log=log\n",
    "    )\n",
    "    \n",
    "    plot_recovered_model(save_iteration.inversion_results[\"model\"][iteration], ax=axd[\"e\"])\n",
    "    plot_obs_pred_data(recovered_model, save_iteration.inversion_results[\"dpred\"][iteration], ax=axd[\"f\"])\n",
    "\n",
    "    axd[\"g\"].axis(\"off\")\n",
    "    # plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2ae2acb9-5bb7-4775-9935-45bf706227a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4002b2befa7460aaaa8ebba6a97982d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='iteration', max=29), Checkbox(value=False, description='…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_inversion_results_by_iteration(iteration, log=False)>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipywidgets.interact(\n",
    "    plot_inversion_results_by_iteration, \n",
    "    iteration=ipywidgets.IntSlider(min=0, max=save_iteration.inversion_results[\"iteration\"][-2], value=0)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bcc2de7-f951-41ef-a8fe-c82b006bf2df",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}