2-forward-stacked-conductors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cbd8fc39-68c1-4ba9-a378-d8ad8f63a51a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#\n",
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f42b025-3565-4106-9ba0-282e46efcfff",
   "metadata": {},
   "source": [
    "# Forward simulation of stacked conductors on a cylindrical mesh\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": "a898d22d-ae71-461b-a331-9e99c29a98aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm, Normalize, SymLogNorm\n",
    "import time\n",
    "import ipywidgets\n",
    "\n",
    "import discretize\n",
    "from simpeg.electromagnetics import time_domain as tdem\n",
    "from simpeg import maps\n",
    "\n",
    "# solver \n",
    "from simpeg.utils.solver_utils import get_default_solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f0050dc-bf03-47a3-80ce-cc9d76f06b6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pymatsolver.direct.mumps.Mumps"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Solver = get_default_solver()\n",
    "Solver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4bedee2-8727-493c-8cd7-bc833d180fb0",
   "metadata": {},
   "source": [
    "## define survey and model parameters\n",
    "\n",
    "- specifiy conductivity of background, targets, air\n",
    "- define target geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98859137-4f52-48a7-a9fd-a0331add65f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# conductivity values\n",
    "rho_back = 500\n",
    "sigma_back = 1/rho_back\n",
    "sigma_air = 1e-8\n",
    "\n",
    "sigma_targets = np.r_[1e3, 1e1] #np.r_[1e1, 1e3]\n",
    "\n",
    "# target geometry\n",
    "target_radii = np.r_[50, 50]\n",
    "target_thickness = np.r_[20, 10] #np.r_[10, 20]\n",
    "target_depths = np.r_[220, 250]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff8ae6d6-705b-4662-ac1b-a2fd40891e00",
   "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": "2023d8a9-3d62-433b-bd91-919683dc3555",
   "metadata": {},
   "outputs": [],
   "source": [
    "def diffusion_distance(sigma, t):\n",
    "    \"\"\"\n",
    "    Compute the diffusion distance based on the conductivity and time\n",
    "    \"\"\"\n",
    "    return 1260 * np.sqrt(t/sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5ec9c0b-88eb-47e4-ba1c-6562b6846a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.3984469851812158)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# minimum diffusion distance (max conductivity, earliest time) \n",
    "diffusion_distance(np.max(sigma_targets), rx_times.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99e71ae2-6608-449d-b0d6-caa055a091e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(2817.4456516497353)"
      ]
     },
     "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": "0b7f9615-e4fa-49e5-bd13-38b13c0d6986",
   "metadata": {},
   "source": [
    "## design a mesh\n",
    "\n",
    "- using the cylindrically symmetric mesh\n",
    "- similar to setting up a tensor mesh (define x, z plane and rotate about axis of symmetry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc8c401d-b0eb-4a79-aca8-5438d37038eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26988"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_size_x = 1\n",
    "cell_size_z = 1\n",
    "padding_factor = 1.3\n",
    "\n",
    "n_cells_x = int(np.max(target_radii) / cell_size_x)\n",
    "n_cells_z = int((np.max(np.abs(target_depths)) + np.max(target_thickness)/2) / cell_size_z)\n",
    "n_cells_above_ground = int(tx_height / cell_size_z)\n",
    "n_padding_x = 28\n",
    "n_padding_z = 28\n",
    "\n",
    "mesh = discretize.CylindricalMesh(\n",
    "    [\n",
    "        [(cell_size_x, n_cells_x), (cell_size_x, n_padding_x, padding_factor)], # x-direction \n",
    "        [np.pi*2],  # theta-direction\n",
    "        [(cell_size_z, n_padding_z, -padding_factor), (cell_size_z, n_cells_z+n_cells_above_ground), (cell_size_z, n_padding_z, padding_factor)]  # z-direction\n",
    "    ], \n",
    "    origin = \"000\"\n",
    ")\n",
    "mesh.origin = np.r_[0, 0, -mesh.h[2][:n_padding_z+n_cells_z].sum()]\n",
    "mesh.n_cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a4302d80-3a57-4188-bc7d-1db5b9688189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='x', ylabel='z'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "mesh.plot_grid(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b64dff17-bbdc-4715-87ba-a7dda155aca4",
   "metadata": {},
   "source": [
    "## put model on the mesh\n",
    "- use a dictionary to store our model vectors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4d67e129-459a-4d4e-a7ee-4c2976fbf4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_inds_target(radius, depth, thickness):\n",
    "    \"\"\"\n",
    "    find the cell center indices where the target is located \n",
    "    based on its radius, depth, and thickness\n",
    "    \"\"\"\n",
    "    target_z = -depth + thickness/2 * np.r_[-1, 1]\n",
    "    return (\n",
    "        (mesh.cell_centers[:, 0] < radius) &\n",
    "        (mesh.cell_centers[:, 2] < target_z.max()) &\n",
    "        (mesh.cell_centers[:, 2] > target_z.min()) \n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "45fad095-6158-45e9-980b-a4346e488881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['background', 'target1', 'target2', 'both_targets']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = {}\n",
    "\n",
    "background = sigma_air * np.ones(mesh.n_cells)\n",
    "background[mesh.cell_centers[:, 2] < 0] = 1./rho_back\n",
    "models[\"background\"] = background\n",
    "\n",
    "target1 = background.copy()\n",
    "target1_inds = get_inds_target(target_radii[0], target_depths[0], target_thickness[0])\n",
    "target1[target1_inds] = sigma_targets[0]\n",
    "models[\"target1\"] = target1\n",
    "\n",
    "target2 = background.copy()\n",
    "target2_inds = get_inds_target(target_radii[1], target_depths[1], target_thickness[1])\n",
    "target2[target2_inds] = sigma_targets[1]\n",
    "models[\"target2\"] = target2\n",
    "\n",
    "both_targets = background.copy()\n",
    "both_targets[target1_inds] = sigma_targets[0]\n",
    "both_targets[target2_inds] = sigma_targets[1]\n",
    "models[\"both_targets\"] = both_targets\n",
    "\n",
    "model_keys = list(models.keys())\n",
    "model_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dcc52e88-846b-4c0e-8a99-deed524c06e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, len(models), figsize=(3*len(models), 2))\n",
    "\n",
    "xlim = 500*np.r_[-1, 1]\n",
    "zlim = np.r_[-600, 100]\n",
    "\n",
    "for i, key in enumerate(models.keys()):\n",
    "    plt.colorbar(mesh.plot_image(\n",
    "        models[key], ax=ax[i], \n",
    "        pcolor_opts={\"norm\":LogNorm(vmin=sigma_air, vmax=sigma_targets.max()), \"cmap\":\"Spectral_r\"},\n",
    "        mirror=True\n",
    "    )[0], ax=ax[i])\n",
    "    ax[i].set_xlim(xlim)\n",
    "    ax[i].set_ylim(zlim)\n",
    "    ax[i].set_title(key)\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b39ca3d-464a-444a-a335-10a29a3f059d",
   "metadata": {},
   "source": [
    "## set up the survey\n",
    "\n",
    "- central loop system, step-off waveform \n",
    "- look at $b_z$, $db_z/dt$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7c32e138-6cef-4b63-a427-93ec27bfa87e",
   "metadata": {},
   "outputs": [],
   "source": [
    "waveform=tdem.sources.StepOffWaveform()\n",
    "\n",
    "rx_dbdt = tdem.receivers.PointMagneticFluxTimeDerivative(\n",
    "    np.r_[0, 0, tx_height], times=rx_times, orientation=\"z\"\n",
    ")\n",
    "\n",
    "rx_b = tdem.receivers.PointMagneticFluxDensity(\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_dbdt, rx_b], waveform=waveform\n",
    ")\n",
    "\n",
    "survey = tdem.Survey([src])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c1ce7e00-9967-4809-8c61-f063f57893f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "nsteps = 20\n",
    "time_steps = [\n",
    "    (1e-5, nsteps), (3e-5, nsteps), (1e-4, nsteps), (3e-4, nsteps+10)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4d0d5f2e-7d12-4572-a661-1a74377a6fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "simulation = tdem.simulation.Simulation3DMagneticFluxDensity(\n",
    "    mesh=mesh, survey=survey, time_steps=time_steps, solver=Solver,\n",
    "    sigmaMap=maps.IdentityMap(mesh)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8dead231-8e66-42af-95be-45aad0406491",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.02, 0.015)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(5, 2))\n",
    "\n",
    "ax.semilogx((rx_times)*1e3, np.zeros_like(rx_times), \"|k\", label=\"rx times\")\n",
    "ax.semilogx(simulation.times*1e3, -0.01*np.ones_like(simulation.times), \"|r\", label=\"simulation\")\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"time (ms)\")\n",
    "ax.set_yticklabels(\"\")\n",
    "ax.set_ylim([-0.02, 0.015])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c17e9e-b99b-4e43-99f9-7e5d74349079",
   "metadata": {},
   "source": [
    "## solve maxwell's equations \n",
    "- `fields` stores the computed fields at every timestep (we will use this for visualization)\n",
    "- `dpred` stores our predicted data as an object "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "26b78c3f-cae2-4634-86a7-17fabcab6392",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done background...  2.07e+00s\n",
      "done target1...  2.53e+00s\n",
      "done target2...  2.39e+00s\n",
      "done both_targets...  2.32e+00s\n"
     ]
    }
   ],
   "source": [
    "fields = {}\n",
    "dpred = {}\n",
    "\n",
    "for key, val in models.items():\n",
    "    t = time.time()\n",
    "    fields[key] = simulation.fields(val)\n",
    "    dpred[key] = simulation.make_synthetic_data(val, f=fields[key])\n",
    "    print(f\"done {key}... {time.time() - t: 1.2e}s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c8193ff8-2bb7-4ce6-ae27-5df3ce544c34",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(keys_to_plot=list(dpred.keys()), rx_type=\"dbdt\", ax=None, difference=False): \n",
    "    \"\"\"\n",
    "    A function for plotting the data corresponding to the provided keys_to_plot\n",
    "    on a log-log plot \n",
    "    \"\"\"\n",
    "    if ax is None: \n",
    "        fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "    if rx_type == \"dbdt\":\n",
    "        rx = rx_dbdt\n",
    "        ylabel = \"db/dt (V/m$^2$)\"\n",
    "    elif rx_type == \"b\": \n",
    "        rx = rx_b\n",
    "        ylabel = \"B (T)\"\n",
    "        \n",
    "    for i, key in enumerate(keys_to_plot):\n",
    "        val = dpred[key][src, rx]\n",
    "\n",
    "        if difference is True: \n",
    "            val = val - dpred[model_keys[0]][src, rx]\n",
    "            if key == model_keys[0]:\n",
    "                pass\n",
    "        \n",
    "        if key == model_keys[0]: \n",
    "            color = \"--k\"\n",
    "            label=\"background\"\n",
    "        else:\n",
    "            color_ind = model_keys.index(key) - 1\n",
    "            color = f\"C{i-1}\"\n",
    "            label = f\"{key}\"\n",
    "            \n",
    "        ax.loglog(rx_times*1e3, np.abs(val), color, label=label)\n",
    "    ax.legend()\n",
    "    ax.set_xlabel(\"time (ms)\")\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.grid(alpha=0.7)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "daa4e4e9-1052-4606-a1d0-d91660539210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(8, 3))\n",
    "\n",
    "plot_data(rx_type=\"dbdt\", ax=ax[0])\n",
    "plot_data(rx_type=\"b\", ax=ax[1])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc2be310-1205-46cc-8735-062f8829eb3d",
   "metadata": {},
   "source": [
    "## plot the fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b9fa8ce0-b74e-4e60-9059-ddb734eacd6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_current_density(\n",
    "    key, ti, ax=None, xlim=400*np.r_[-1, 1], zlim=np.r_[-500, 80],\n",
    "    colorbar=False, ylabels=True, vmax=None\n",
    "):\n",
    "    \"\"\"\n",
    "    A function for plotting the current density on a cyl mesh. This assumes the \"fields\" \n",
    "    are defined earlier as a dictionary and you are interested in plotting the current \n",
    "    density due to the model corresponding with the provided \"key\" at time ti. \n",
    "    \"\"\"\n",
    "    if ax is None: \n",
    "        fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
    "    jplt = mesh.average_edge_y_to_cell * fields[key][:, \"j\", ti]\n",
    "    \n",
    "    if vmax is not None: \n",
    "        pcolor_opts={\"norm\":Normalize(vmin=-vmax, vmax=vmax), \"cmap\":\"coolwarm\"}\n",
    "    else: \n",
    "        pcolor_opts = {\"cmap\":\"coolwarm\"}\n",
    "    out = mesh.plot_image(\n",
    "        jplt, ax=ax, \n",
    "        mirror=True, mirror_data = -jplt,\n",
    "        pcolor_opts=pcolor_opts\n",
    "    )\n",
    "    \n",
    "    ax.plot(np.r_[0], tx_height, \"ko\", ms=2)\n",
    "    ax.set_xlim(xlim)\n",
    "    ax.set_ylim(zlim)\n",
    "    ax.set_aspect(1)\n",
    "    \n",
    "    if colorbar is True: \n",
    "        cb = plt.colorbar(out[0], ax=ax, orientation=\"horizontal\")\n",
    "        cb.set_label(\"current density\")\n",
    "    \n",
    "    if ylabels is False: \n",
    "        ax.set_ylabel(\"\")\n",
    "        ax.set_yticklabels(\"\")\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aba01a43-0a5c-4d07-8306-7c014ad66588",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dbdt(\n",
    "    key, time_ind, field_type=\"dbdt\", ax=None, xlim=400*np.r_[-1, 1], zlim=np.r_[-500, 80],\n",
    "    colorbar=False, ylabels=True, vmax=None, vmin=None\n",
    "):\n",
    "    \"\"\"\n",
    "    A function for plotting the db/dt or b on a cyl mesh. This assumes the \"fields\" \n",
    "    are defined earlier as a dictionary and you are interested in plotting the current \n",
    "    density due to the model corresponding with the provided \"key\" at time index (time_ind). \n",
    "    \"\"\"\n",
    "    if ax is None: \n",
    "        fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
    "    field_to_plot = fields[key][:, field_type, :]\n",
    "\n",
    "    if vmax is None: \n",
    "        vmax = 1e-3 * np.max(np.abs(field_to_plot))\n",
    "    if vmin is None: \n",
    "        if field_type == \"dbdt\":\n",
    "            vmin = 1e-9 * vmax\n",
    "        else: \n",
    "            vmin = 1e-6 * vmax\n",
    "    \n",
    "    dbdtplt = mesh.average_face_to_cell_vector * field_to_plot[:, time_ind]\n",
    "    out = mesh.plot_image(\n",
    "        dbdtplt, \"CCv\", view=\"vec\", ax=ax, \n",
    "        mirror=True,\n",
    "        pcolor_opts={\"norm\":LogNorm(vmin=vmin, vmax=vmax)},\n",
    "        range_x=xlim, \n",
    "        range_y=zlim,\n",
    "        stream_threshold=vmin\n",
    "    )\n",
    "    ax.plot(np.r_[0], tx_height, \"ko\", ms=2)\n",
    "    ax.set_xlim(xlim)\n",
    "    ax.set_ylim(zlim)\n",
    "    ax.set_aspect(1)\n",
    "    if ylabels is False: \n",
    "        ax.set_ylabel(\"\")\n",
    "        ax.set_yticklabels(\"\")\n",
    "    if colorbar is True: \n",
    "        cb = plt.colorbar(out[0], ax=ax, orientation=\"horizontal\")\n",
    "        cb.set_label(\"db/dt (V/m$^2$)\" if field_type==\"dbdt\" else \"B (T)\")\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a616161d-e797-4562-b093-1ca2fec7922d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fields_and_data(key, time_ind, field_type=\"dbdt\", xlim=400*np.r_[-1, 1], zlim=np.r_[-450, 50]):\n",
    "    \"\"\"\n",
    "    Function to plot the current density, magnetic field (db/dt or b) and data for a given model\n",
    "    (specified by the key) and a specific time index (time_ind). \n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "    t = simulation.times[time_ind]-waveform.off_time\n",
    "    if t*1e3 > 0.99: \n",
    "        time_label = f\"{t*1e3:1.1f} ms\"\n",
    "    else: \n",
    "        time_label = f\"{t*1e3:1.2f} ms\"\n",
    "        \n",
    "    fig.suptitle(f\"{key}, t={time_label}\")\n",
    "                 \n",
    "    plot_current_density(key, time_ind, ax=ax[0], colorbar=True, xlim=xlim, zlim=zlim)\n",
    "    plot_dbdt(key, time_ind, field_type=field_type, ax=ax[1], colorbar=True, xlim=xlim, zlim=zlim)\n",
    "    plot_data([model_keys[0], key], rx_type=field_type, ax=ax[2])\n",
    "    if simulation.times[time_ind] > rx_times[0]: \n",
    "        ylim = ax[2].get_ylim()\n",
    "        ax[2].plot(1e3*simulation.times[time_ind]*np.r_[1, 1], ylim, \":k\")\n",
    "        ax[2].set_ylim(ylim)\n",
    "    plt.tight_layout()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dfac1510-2baf-4715-9f57-3b383a4c9827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29c2179d72c549b18918af54ab515ab4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='key', options=('background', 'target1', 'target2', 'both_targets')…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_fields_and_data(key, time_ind, field_type='dbdt', xlim=array([-400,  400]), zlim=array([-450,   50]))>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlim=400*np.r_[-1, 1] \n",
    "# zlim=np.r_[-300, -100]\n",
    "zlim=np.r_[-400, 0]\n",
    "\n",
    "ipywidgets.interact(\n",
    "    plot_fields_and_data, \n",
    "    key = model_keys, \n",
    "    field_type = ipywidgets.ToggleButtons(options=[\"dbdt\", \"b\"], value=\"b\"),\n",
    "    time_ind = ipywidgets.IntSlider(min=0, max=len(simulation.times)-1, value=0),\n",
    "    xlim = ipywidgets.fixed(xlim), \n",
    "    zlim = ipywidgets.fixed(zlim)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
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