6b_Marine-1D.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OUTDATED, the examples moved to the gallery\n",
    "## See https://empymod.github.io/emg3d-gallery\n",
    "\n",
    "----\n",
    "\n",
    "# Transient CSEM for a layered, marine model\n",
    "\n",
    "Example how to use `emg3d` to model time-domain data using FFTLog. \n",
    "\n",
    "\n",
    "**Requires**\n",
    "- **emg3d >= 0.9.0**\n",
    "- `empymod`, `discretize`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbpresent": {
     "id": "4cf9c8a2-cf5f-45cd-b5da-0b1c7fe23108"
    }
   },
   "outputs": [],
   "source": [
    "import emg3d\n",
    "import empymod\n",
    "import discretize\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "# Name is used to store the data for each frequency.\n",
    "name = 'Marine-1D'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Style adjustments\n",
    "%matplotlib notebook\n",
    "plt.style.use('ggplot')\n",
    "clim = np.log10([0.2, 200])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model and Survey\n",
    "\n",
    "### Model\n",
    "- 1 km water depth, 0.3 Ohm.m.\n",
    "- Target of 100 Ohm.m, 500 m thick, 1 km below seafloor.\n",
    "- Air set to 1e8 Ohm.m, background is 1 Ohm.m.\n",
    "\n",
    "### Survey\n",
    "- Source at origin, 50 m above seafloor.\n",
    "- Receiver on the seafloor at an inline-offset of 4 km.\n",
    "- Both source and receiver are x-directed electric dipoles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = [0, 0, 950]\n",
    "rec = [4000, 0, 1000]\n",
    "res = np.array([1e8, 0.3, 1, 100, 1])\n",
    "depth = np.array([0, 1000, 2000, 2500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a dummy mesh with one cell in x- and y-directions, and our 1D model in z-direction. From this, we can interpolate the model to our varying meshes afterwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
       "\n",
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       "    fig.ondownload(fig, format);\n",
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       "\n",
       "\n",
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       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
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       "\n",
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       "\n",
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       "    // Updates the figure title.\n",
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       "\n",
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       "\n",
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       "\n",
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       "    // Request the server to send over a new figure.\n",
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       "\n",
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       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
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       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the mesh.\n",
    "orig_mesh = discretize.TensorMesh(\n",
    "    [[1,], [1,], np.r_[1000, np.diff(depth)[::-1], 1000]],\n",
    "    x0=('C', 'C', -(depth[-1]+1000)))\n",
    "\n",
    "# Create a resistivity model using the 1D model and the above mesh.\n",
    "orig_model = emg3d.utils.Model(orig_mesh, res_x=np.array(res)[::-1])\n",
    "\n",
    "# QC.\n",
    "orig_mesh.plot_3d_slicer(np.log10(orig_model.res_x), zlim=[-3000, 500], clim=clim)\n",
    "\n",
    "# Get figure and axes\n",
    "fig = plt.gcf()\n",
    "axs = fig.get_children()\n",
    "\n",
    "fig.suptitle(r'Resistivity model')\n",
    "\n",
    "# Adjust the y-labels on the first subplot (XY)\n",
    "axs[1].set_yticks([-0.4, -0.2, 0, 0.2, 0.4])\n",
    "axs[1].set_yticklabels(['', '', '0.0', '', ''])\n",
    "axs[1].set_ylabel('y-direction (m)')\n",
    "\n",
    "# Adjust x- and y-labels on the second subplot (XZ)\n",
    "axs[2].set_xticks([-0.4, -0.2, 0, 0.2, 0.4])\n",
    "axs[2].set_xticklabels(['', '', '0.0', '', ''])\n",
    "axs[2].set_xlabel('Easting (m)')\n",
    "\n",
    "#plt.setp(axs[2].yaxis.get_majorticklabels(), rotation=90)\n",
    "axs[2].set_yticks([0, -1000, -2000, -2500])\n",
    "axs[2].set_yticklabels(['$0.0$', '-1.0', '-2.0', '-2.5'])\n",
    "axs[2].set_ylabel('Elevation (km)')\n",
    "\n",
    "# Adjust x-labels on the third subplot (ZY)\n",
    "axs[3].set_xticks([400, 0, -1000, -2000, -2500, -3000])\n",
    "axs[3].set_xticklabels(['', '$0.0$', '-1.0', '-2.0', '-2.5'])\n",
    "\n",
    "# Adjust colorbar\n",
    "axs[4].set_ylabel(r'$\\rm{log}_{10}-$resistivity ($\\Omega\\,$m)')\n",
    "\n",
    "# Ensure sufficient margins so nothing is clipped\n",
    "plt.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fourier Transform parameters\n",
    "\n",
    "We only calculate frequencies $0.003 < f < 5$ Hz, which yields enough precision for our purpose.\n",
    "\n",
    "This means, instead of 30 frequencies from 0.00002 - 12.6 Hz, we only need 16 frequencies from 0.003 - 3.2 Hz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   time        [s] :  0.1 - 100 : 201  [min-max; #]\n",
      "   Fourier         :  FFTLog\n",
      "     > pts_per_dec :  5\n",
      "     > add_dec     :  [-2.  1.]\n",
      "     > q           :  0.0\n",
      "   Req. freq  [Hz] :  2.00364E-05 - 12.6421 : 30  [min-max; #]\n",
      "   Calc. freq [Hz] :  0.00317556 - 3.17556 : 16  [min-max; #]\n"
     ]
    }
   ],
   "source": [
    "# Define desired times.\n",
    "time = np.logspace(-1, 2, 201)\n",
    "\n",
    "# Initiate a Fourier instance\n",
    "Fourier = emg3d.utils.Fourier(\n",
    "    time=time,\n",
    "    fmin=0.003,\n",
    "    fmax=5,\n",
    "    ft='fftlog',  # Fourier transform to use\n",
    "    ftarg={'pts_per_dec': 5, 'add_dec': [-2, 1], 'q': 0},\n",
    ")\n",
    "\n",
    "# Dense frequencies for comparison reasons\n",
    "freq_dense = np.logspace(np.log10(Fourier.freq_req.min()), np.log10(Fourier.freq_req.max()), 301)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency-domain calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To store the info of each frequency.\n",
    "values = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===============  1/16 ===============   3.175559 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 155 / 2824\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -17046 - 21046\n",
      "   Final extent        [m] : -17840 - 21840\n",
      "   Min/max cell width  [m] : 100 / 100 / 5521\n",
      "   Alpha survey/calc       : 1.000 / 1.440\n",
      "   Number of cells (s/c/r) : 64 (42/22/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 155 / 2824\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -16946 - 16946\n",
      "   Final extent        [m] : -17840 - 17840\n",
      "   Min/max cell width  [m] : 100 / 100 / 5521\n",
      "   Alpha survey/calc       : 1.000 / 1.440\n",
      "   Number of cells (s/c/r) : 24 (2/22/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 155 / 282 / 2824\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -4195 - 16946\n",
      "   Final extent        [m] : -4478 - 17000\n",
      "   Min/max cell width  [m] : 100 / 100 / 5767\n",
      "   Alpha survey/calc       : 1.000 / 1.500\n",
      "   Number of cells (s/c/r) : 40 (25/15/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:04:19 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  64 x  24 x  40     => 61,440 cells\n",
      "   Coarsest grid  :   2 x   3 x   5     => 30 cells\n",
      "   Coarsest level :   5 ;   3 ;   3   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 5.712e-08\n",
      "\n",
      ":: emg3d END   :: 12:04:26 :: runtime = 0:00:07\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 7 s; alpha = 1.000 / 1.500\n",
      "\n",
      "===============  2/16 ===============   2.003642 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 195 / 3556\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -21434 - 25434\n",
      "   Final extent        [m] : -21450 - 25450\n",
      "   Min/max cell width  [m] : 100 / 100 / 6926\n",
      "   Alpha survey/calc       : 1.000 / 1.470\n",
      "   Number of cells (s/c/r) : 64 (42/22/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 195 / 3556\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -21334 - 21334\n",
      "   Final extent        [m] : -21450 - 21450\n",
      "   Min/max cell width  [m] : 100 / 100 / 6926\n",
      "   Alpha survey/calc       : 1.000 / 1.470\n",
      "   Number of cells (s/c/r) : 24 (2/22/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 195 / 356 / 3556\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -4633 - 21334\n",
      "   Final extent        [m] : -4700 - 25715\n",
      "   Min/max cell width  [m] : 100 / 100 / 5881\n",
      "   Alpha survey/calc       : 1.000 / 1.290\n",
      "   Number of cells (s/c/r) : 48 (25/23/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:04:26 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  64 x  24 x  48     => 73,728 cells\n",
      "   Coarsest grid  :   2 x   3 x   3     => 18 cells\n",
      "   Coarsest level :   5 ;   3 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 5.796e-08\n",
      "\n",
      ":: emg3d END   :: 12:04:35 :: runtime = 0:00:09\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 9 s; alpha = 1.000 / 1.470\n",
      "\n",
      "===============  3/16 ===============   1.264213 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 245 / 4476\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -26958 - 30958\n",
      "   Final extent        [m] : -30360 - 34360\n",
      "   Min/max cell width  [m] : 100 / 100 / 5957\n",
      "   Alpha survey/calc       : 1.000 / 1.240\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 245 / 4476\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -26858 - 26858\n",
      "   Final extent        [m] : -28745 - 28745\n",
      "   Min/max cell width  [m] : 100 / 100 / 7207\n",
      "   Alpha survey/calc       : 1.000 / 1.330\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 245 / 448 / 4476\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -5186 - 26858\n",
      "   Final extent        [m] : -6043 - 28645\n",
      "   Min/max cell width  [m] : 100 / 100 / 7207\n",
      "   Alpha survey/calc       : 1.000 / 1.330\n",
      "   Number of cells (s/c/r) : 48 (25/23/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:04:35 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  32 x  48     => 122,880 cells\n",
      "   Coarsest grid  :   5 x   2 x   3     => 30 cells\n",
      "   Coarsest level :   4 ;   4 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 1.163e-07\n",
      "\n",
      ":: emg3d END   :: 12:04:50 :: runtime = 0:00:15\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 15 s; alpha = 1.000 / 1.330\n",
      "\n",
      "===============  4/16 ===============   0.797664 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 309 / 5635\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -33912 - 37912\n",
      "   Final extent        [m] : -34294 - 38294\n",
      "   Min/max cell width  [m] : 100 / 100 / 6939\n",
      "   Alpha survey/calc       : 1.000 / 1.250\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 309 / 5635\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -33812 - 33812\n",
      "   Final extent        [m] : -34490 - 34490\n",
      "   Min/max cell width  [m] : 100 / 100 / 9016\n",
      "   Alpha survey/calc       : 1.000 / 1.350\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 309 / 564 / 5635\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -5881 - 33812\n",
      "   Final extent        [m] : -6370 - 34390\n",
      "   Min/max cell width  [m] : 100 / 100 / 9016\n",
      "   Alpha survey/calc       : 1.000 / 1.350\n",
      "   Number of cells (s/c/r) : 48 (25/23/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:04:50 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  32 x  48     => 122,880 cells\n",
      "   Coarsest grid  :   5 x   2 x   3     => 30 cells\n",
      "   Coarsest level :   4 ;   4 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 2.432e-07\n",
      "\n",
      ":: emg3d END   :: 12:05:07 :: runtime = 0:00:17\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 17 s; alpha = 1.000 / 1.350\n",
      "\n",
      "===============  5/16 ===============   0.503292 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 389 / 7094\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -42667 - 46667\n",
      "   Final extent        [m] : -43757 - 47757\n",
      "   Min/max cell width  [m] : 100 / 100 / 9381\n",
      "   Alpha survey/calc       : 1.000 / 1.270\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 389 / 7094\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -42567 - 42567\n",
      "   Final extent        [m] : -45265 - 45265\n",
      "   Min/max cell width  [m] : 100 / 100 / 12537\n",
      "   Alpha survey/calc       : 1.000 / 1.380\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 389 / 709 / 7094\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -6757 - 42567\n",
      "   Final extent        [m] : -6914 - 45165\n",
      "   Min/max cell width  [m] : 100 / 100 / 12537\n",
      "   Alpha survey/calc       : 1.000 / 1.380\n",
      "   Number of cells (s/c/r) : 48 (25/23/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:05:08 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  32 x  48     => 122,880 cells\n",
      "   Coarsest grid  :   5 x   2 x   3     => 30 cells\n",
      "   Coarsest level :   4 ;   4 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 8.640e-07\n",
      "\n",
      ":: emg3d END   :: 12:05:22 :: runtime = 0:00:14\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 14 s; alpha = 1.000 / 1.380\n",
      "\n",
      "===============  6/16 ===============   0.317556 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 489 / 8931\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -53688 - 57688\n",
      "   Final extent        [m] : -55811 - 59811\n",
      "   Min/max cell width  [m] : 100 / 100 / 12624\n",
      "   Alpha survey/calc       : 1.000 / 1.290\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 489 / 8931\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -53588 - 53588\n",
      "   Final extent        [m] : -54199 - 54199\n",
      "   Min/max cell width  [m] : 100 / 100 / 15557\n",
      "   Alpha survey/calc       : 1.000 / 1.400\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 489 / 893 / 8931\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -7859 - 53588\n",
      "   Final extent        [m] : -7983 - 70774\n",
      "   Min/max cell width  [m] : 100 / 100 / 21382\n",
      "   Alpha survey/calc       : 1.000 / 1.430\n",
      "   Number of cells (s/c/r) : 48 (25/23/0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ":: emg3d START :: 12:05:22 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  32 x  48     => 122,880 cells\n",
      "   Coarsest grid  :   5 x   2 x   3     => 30 cells\n",
      "   Coarsest level :   4 ;   4 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 2\n",
      "   > MG prec. steps   : 10\n",
      "   > Final rel. error : 9.162e-08\n",
      "\n",
      ":: emg3d END   :: 12:05:39 :: runtime = 0:00:17\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 10/2 iteration(s); 17 s; alpha = 1.000 / 1.430\n",
      "\n",
      "===============  7/16 ===============   0.200364 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 616 / 11244\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -67563 - 71563\n",
      "   Final extent        [m] : -71138 - 75138\n",
      "   Min/max cell width  [m] : 100 / 100 / 16910\n",
      "   Alpha survey/calc       : 1.000 / 1.310\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 616 / 11244\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -67463 - 67463\n",
      "   Final extent        [m] : -70874 - 70874\n",
      "   Min/max cell width  [m] : 100 / 100 / 21382\n",
      "   Alpha survey/calc       : 1.000 / 1.430\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 616 / 1124 / 11244\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -9246 - 67463\n",
      "   Final extent        [m] : -12212 - 68499\n",
      "   Min/max cell width  [m] : 100 / 100 / 22001\n",
      "   Alpha survey/calc       : 1.000 / 1.470\n",
      "   Number of cells (s/c/r) : 48 (25/23/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:05:39 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  32 x  48     => 122,880 cells\n",
      "   Coarsest grid  :   5 x   2 x   3     => 30 cells\n",
      "   Coarsest level :   4 ;   4 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 2\n",
      "   > MG prec. steps   : 13\n",
      "   > Final rel. error : 1.897e-08\n",
      "\n",
      ":: emg3d END   :: 12:06:01 :: runtime = 0:00:21\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 13/2 iteration(s); 21 s; alpha = 1.000 / 1.470\n",
      "\n",
      "===============  8/16 ===============   0.126421 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 775 / 14155\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -85031 - 89031\n",
      "   Final extent        [m] : -90588 - 94588\n",
      "   Min/max cell width  [m] : 100 / 100 / 22552\n",
      "   Alpha survey/calc       : 1.000 / 1.330\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 775 / 14155\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -84931 - 84931\n",
      "   Final extent        [m] : -92442 - 92442\n",
      "   Min/max cell width  [m] : 100 / 100 / 29194\n",
      "   Alpha survey/calc       : 1.000 / 1.460\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 775 / 1416 / 14155\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -10993 - 84931\n",
      "   Final extent        [m] : -13733 - 87279\n",
      "   Min/max cell width  [m] : 100 / 100 / 29193\n",
      "   Alpha survey/calc       : 1.000 / 1.500\n",
      "   Number of cells (s/c/r) : 48 (25/23/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:06:01 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  32 x  48     => 122,880 cells\n",
      "   Coarsest grid  :   5 x   2 x   3     => 30 cells\n",
      "   Coarsest level :   4 ;   4 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 2\n",
      "   > MG prec. steps   : 13\n",
      "   > Final rel. error : 5.032e-08\n",
      "\n",
      ":: emg3d END   :: 12:06:25 :: runtime = 0:00:23\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 13/2 iteration(s); 23 s; alpha = 1.000 / 1.500\n",
      "\n",
      "===============  9/16 ===============   0.079766 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 976 / 17820\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -107022 - 111022\n",
      "   Final extent        [m] : -115221 - 119221\n",
      "   Min/max cell width  [m] : 100 / 100 / 29946\n",
      "   Alpha survey/calc       : 1.000 / 1.350\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 976 / 17820\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -106922 - 106922\n",
      "   Final extent        [m] : -110186 - 110186\n",
      "   Min/max cell width  [m] : 100 / 100 / 35804\n",
      "   Alpha survey/calc       : 1.000 / 1.480\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 976 / 1782 / 17820\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -13192 - 106922\n",
      "   Final extent        [m] : -13369 - 131849\n",
      "   Min/max cell width  [m] : 100 / 100 / 26470\n",
      "   Alpha survey/calc       : 1.000 / 1.250\n",
      "   Number of cells (s/c/r) : 64 (25/39/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:06:25 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  32 x  64     => 163,840 cells\n",
      "   Coarsest grid  :   5 x   2 x   2     => 20 cells\n",
      "   Coarsest level :   4 ;   4 ;   5   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 2\n",
      "   > MG prec. steps   : 13\n",
      "   > Final rel. error : 2.049e-07\n",
      "\n",
      ":: emg3d END   :: 12:07:10 :: runtime = 0:00:45\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 13/2 iteration(s); 45 s; alpha = 1.000 / 1.480\n",
      "\n",
      "=============== 10/16 ===============   0.050329 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 1229 / 22435\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -134707 - 138707\n",
      "   Final extent        [m] : -146355 - 150355\n",
      "   Min/max cell width  [m] : 100 / 100 / 39600\n",
      "   Alpha survey/calc       : 1.000 / 1.370\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 1229 / 22435\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -134607 - 134607\n",
      "   Final extent        [m] : -146355 - 146355\n",
      "   Min/max cell width  [m] : 100 / 100 / 39600\n",
      "   Alpha survey/calc       : 1.000 / 1.370\n",
      "   Number of cells (s/c/r) : 40 (2/38/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 1229 / 2243 / 22435\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -15961 - 134607\n",
      "   Final extent        [m] : -18992 - 145320\n",
      "   Min/max cell width  [m] : 100 / 100 / 30995\n",
      "   Alpha survey/calc       : 1.000 / 1.270\n",
      "   Number of cells (s/c/r) : 64 (25/39/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:07:11 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  40 x  64     => 204,800 cells\n",
      "   Coarsest grid  :   5 x   5 x   2     => 50 cells\n",
      "   Coarsest level :   4 ;   3 ;   5   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 2\n",
      "   > MG prec. steps   : 13\n",
      "   > Final rel. error : 4.734e-08\n",
      "\n",
      ":: emg3d END   :: 12:08:00 :: runtime = 0:00:49\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 13/2 iteration(s); 49 s; alpha = 1.000 / 1.370\n",
      "\n",
      "=============== 11/16 ===============   0.031756 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 1547 / 28243\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -169560 - 173560\n",
      "   Final extent        [m] : -185623 - 189623\n",
      "   Min/max cell width  [m] : 100 / 100 / 52153\n",
      "   Alpha survey/calc       : 1.000 / 1.390\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 1547 / 28243\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -169460 - 169460\n",
      "   Final extent        [m] : -185623 - 185623\n",
      "   Min/max cell width  [m] : 100 / 100 / 52153\n",
      "   Alpha survey/calc       : 1.000 / 1.390\n",
      "   Number of cells (s/c/r) : 40 (2/38/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 1547 / 2824 / 28243\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -19446 - 169460\n",
      "   Final extent        [m] : -20587 - 170580\n",
      "   Min/max cell width  [m] : 100 / 100 / 37414\n",
      "   Alpha survey/calc       : 1.000 / 1.280\n",
      "   Number of cells (s/c/r) : 64 (25/39/0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ":: emg3d START :: 12:08:01 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  40 x  64     => 204,800 cells\n",
      "   Coarsest grid  :   5 x   5 x   2     => 50 cells\n",
      "   Coarsest level :   4 ;   3 ;   5   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 2\n",
      "   > MG prec. steps   : 13\n",
      "   > Final rel. error : 2.548e-07\n",
      "\n",
      ":: emg3d END   :: 12:08:42 :: runtime = 0:00:40\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 13/2 iteration(s); 40 s; alpha = 1.000 / 1.390\n",
      "\n",
      "=============== 12/16 ===============   0.020036 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 1948 / 35556\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -213438 - 217438\n",
      "   Final extent        [m] : -235043 - 239043\n",
      "   Min/max cell width  [m] : 100 / 100 / 68417\n",
      "   Alpha survey/calc       : 1.000 / 1.410\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 1948 / 35556\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -213338 - 213338\n",
      "   Final extent        [m] : -235043 - 235043\n",
      "   Min/max cell width  [m] : 100 / 100 / 68417\n",
      "   Alpha survey/calc       : 1.000 / 1.410\n",
      "   Number of cells (s/c/r) : 40 (2/38/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 1948 / 3556 / 35556\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -23834 - 213338\n",
      "   Final extent        [m] : -24247 - 234780\n",
      "   Min/max cell width  [m] : 100 / 100 / 54280\n",
      "   Alpha survey/calc       : 1.000 / 1.300\n",
      "   Number of cells (s/c/r) : 64 (25/39/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:08:42 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  40 x  64     => 204,800 cells\n",
      "   Coarsest grid  :   5 x   5 x   2     => 50 cells\n",
      "   Coarsest level :   4 ;   3 ;   5   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 2\n",
      "   > MG prec. steps   : 13\n",
      "   > Final rel. error : 8.471e-07\n",
      "\n",
      ":: emg3d END   :: 12:09:28 :: runtime = 0:00:46\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 13/2 iteration(s); 46 s; alpha = 1.000 / 1.410\n",
      "\n",
      "=============== 13/16 ===============   0.012642 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 2452 / 44763\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -268677 - 272677\n",
      "   Final extent        [m] : -297107 - 301107\n",
      "   Min/max cell width  [m] : 100 / 100 / 89410\n",
      "   Alpha survey/calc       : 1.000 / 1.430\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 2452 / 44763\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -268577 - 268577\n",
      "   Final extent        [m] : -297107 - 297107\n",
      "   Min/max cell width  [m] : 100 / 100 / 89410\n",
      "   Alpha survey/calc       : 1.000 / 1.430\n",
      "   Number of cells (s/c/r) : 40 (2/38/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 2452 / 4476 / 44763\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -29358 - 268577\n",
      "   Final extent        [m] : -31145 - 377845\n",
      "   Min/max cell width  [m] : 100 / 100 / 93851\n",
      "   Alpha survey/calc       : 1.000 / 1.330\n",
      "   Number of cells (s/c/r) : 64 (25/38/1)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:09:29 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  40 x  64     => 204,800 cells\n",
      "   Coarsest grid  :   5 x   5 x   2     => 50 cells\n",
      "   Coarsest level :   4 ;   3 ;   5   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 3\n",
      "   > MG prec. steps   : 16\n",
      "   > Final rel. error : 1.531e-07\n",
      "\n",
      ":: emg3d END   :: 12:10:29 :: runtime = 0:00:59\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 16/3 iteration(s); 59 s; alpha = 1.000 / 1.430\n",
      "\n",
      "=============== 14/16 ===============   0.007977 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 3087 / 56353\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -338218 - 342218\n",
      "   Final extent        [m] : -374878 - 378878\n",
      "   Min/max cell width  [m] : 100 / 100 / 116410\n",
      "   Alpha survey/calc       : 1.000 / 1.450\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 3087 / 56353\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -338118 - 338118\n",
      "   Final extent        [m] : -374878 - 374878\n",
      "   Min/max cell width  [m] : 100 / 100 / 116410\n",
      "   Alpha survey/calc       : 1.000 / 1.450\n",
      "   Number of cells (s/c/r) : 40 (2/38/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 3087 / 5635 / 56353\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -36312 - 338118\n",
      "   Final extent        [m] : -36890 - 517550\n",
      "   Min/max cell width  [m] : 100 / 100 / 134280\n",
      "   Alpha survey/calc       : 1.000 / 1.350\n",
      "   Number of cells (s/c/r) : 64 (25/38/1)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:10:30 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  40 x  64     => 204,800 cells\n",
      "   Coarsest grid  :   5 x   5 x   2     => 50 cells\n",
      "   Coarsest level :   4 ;   3 ;   5   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 3\n",
      "   > MG prec. steps   : 16\n",
      "   > Final rel. error : 7.544e-07\n",
      "\n",
      ":: emg3d END   :: 12:11:23 :: runtime = 0:00:54\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 16/3 iteration(s); 54 s; alpha = 1.000 / 1.450\n",
      "\n",
      "=============== 15/16 ===============   0.005033 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 3886 / 70944\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -425765 - 429765\n",
      "   Final extent        [m] : -472121 - 476121\n",
      "   Min/max cell width  [m] : 100 / 100 / 151018\n",
      "   Alpha survey/calc       : 1.000 / 1.470\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 3886 / 70944\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -425665 - 425665\n",
      "   Final extent        [m] : -472121 - 472121\n",
      "   Min/max cell width  [m] : 100 / 100 / 151018\n",
      "   Alpha survey/calc       : 1.000 / 1.470\n",
      "   Number of cells (s/c/r) : 40 (2/38/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 3886 / 7094 / 70944\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -45067 - 425665\n",
      "   Final extent        [m] : -53866 - 444900\n",
      "   Min/max cell width  [m] : 100 / 100 / 117868\n",
      "   Alpha survey/calc       : 1.000 / 1.360\n",
      "   Number of cells (s/c/r) : 64 (25/39/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 12:11:24 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  40 x  64     => 204,800 cells\n",
      "   Coarsest grid  :   5 x   5 x   2     => 50 cells\n",
      "   Coarsest level :   4 ;   3 ;   5   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 3\n",
      "   > MG prec. steps   : 19\n",
      "   > Final rel. error : 1.708e-07\n",
      "\n",
      ":: emg3d END   :: 12:12:19 :: runtime = 0:00:55\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 19/3 iteration(s); 55 s; alpha = 1.000 / 1.470\n",
      "\n",
      "=============== 16/16 ===============   0.003176 Hz ========================================\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 4892 / 89313\n",
      "   Survey domain       [m] : -100 - 4100\n",
      "   Calculation domain  [m] : -535981 - 539981\n",
      "   Final extent        [m] : -593444 - 597444\n",
      "   Min/max cell width  [m] : 100 / 100 / 195227\n",
      "   Alpha survey/calc       : 1.000 / 1.490\n",
      "   Number of cells (s/c/r) : 80 (42/38/0)\n",
      "\n",
      "   Skin depth (m/l-r)  [m] : 4892 / 89313\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -535881 - 535881\n",
      "   Final extent        [m] : -593444 - 593444\n",
      "   Min/max cell width  [m] : 100 / 100 / 195227\n",
      "   Alpha survey/calc       : 1.000 / 1.490\n",
      "   Number of cells (s/c/r) : 40 (2/38/0)\n",
      "\n",
      "   Skin depth (m/l/r)  [m] : 4892 / 8931 / 89313\n",
      "   Survey domain       [m] : -2500 - 0\n",
      "   Calculation domain  [m] : -56088 - 535881\n",
      "   Final extent        [m] : -64966 - 598482\n",
      "   Min/max cell width  [m] : 100 / 100 / 164899\n",
      "   Alpha survey/calc       : 1.000 / 1.380\n",
      "   Number of cells (s/c/r) : 64 (25/39/0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ":: emg3d START :: 12:12:19 ::\n",
      "\n",
      "   MG-cycle       : 'F'                 sslsolver : 'bicgstab'\n",
      "   semicoarsening : True [1 2 3]        tol       : 1e-06\n",
      "   linerelaxation : True [4 5 6]        maxit     : 50 (3)\n",
      "   nu_{i,1,c,2}   : 0, 2, 1, 2          verb      : 2\n",
      "   Original grid  :  80 x  40 x  64     => 204,800 cells\n",
      "   Coarsest grid  :   5 x   5 x   2     => 50 cells\n",
      "   Coarsest level :   4 ;   3 ;   5   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 4\n",
      "   > MG prec. steps   : 22\n",
      "   > Final rel. error : 1.822e-07\n",
      "\n",
      ":: emg3d END   :: 12:13:27 :: runtime = 0:01:07\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 22/4 iteration(s); 67 s; alpha = 1.000 / 1.490\n"
     ]
    }
   ],
   "source": [
    "gridinput = {\n",
    "    'min_width': 100.,    # We fix the cell width within the survey domain to 100 m.\n",
    "    'return_info': True,  # To get back some information for later.\n",
    "}\n",
    "\n",
    "# Start the timer.\n",
    "runtime = emg3d.utils.Time()\n",
    "\n",
    "# Loop over frequencies, going from high to low.\n",
    "for fi, frq in enumerate(Fourier.freq_calc[::-1]):\n",
    "    print(f\"\\n{15*'='} {fi+1:2}/{Fourier.freq_calc.size} {15*'='} {frq:10.6f} Hz {40*'='}\\n\")\n",
    "    \n",
    "    # Key is used to store the data etc.\n",
    "    key = int(frq*1e6)\n",
    "          \n",
    "    # Initiate log for this frequency.\n",
    "    values[key] = {}\n",
    "    values[key]['freq'] = frq\n",
    "\n",
    "    # Get cell widths and origin in each direction\n",
    "    # - source location is grid center.\n",
    "    # - We use 100 Ohm.m for skin depth for each direction where there is air.\n",
    "    #   Should be fine, as we have 1 km of water column.\n",
    "    xx, x0, hix = emg3d.utils.get_hx_h0(\n",
    "        freq=frq, res=[0.3, 100.], fixed=src[0], domain=[-100, 4100], **gridinput)\n",
    "    yy, y0, hiy = emg3d.utils.get_hx_h0(\n",
    "        freq=frq, res=[0.3, 100.], fixed=src[1], domain=[0, 0], **gridinput)\n",
    "    zz, z0, hiz = emg3d.utils.get_hx_h0(\n",
    "        freq=frq, res=[0.3, 1., 100.], domain=[-2500, 0], **gridinput,\n",
    "        fixed=[-depth[1], -depth[0], -depth[-1]])\n",
    "    \n",
    "    # Store values in log.\n",
    "    values[key]['alpha'] = [np.min([hix['amin'], hiy['amin'], hiz['amin']]),\n",
    "                            np.max([hix['amax'], hiy['amax'], hiz['amax']])]\n",
    "    values[key]['dminmax'] = [np.min([hix['dmin'], hiy['dmin'], hiz['dmin']]),\n",
    "                              np.max([hix['dmax'], hiy['dmax'], hiz['dmax']])]\n",
    "\n",
    "    # Initiate mesh.\n",
    "    grid = discretize.TensorMesh([xx, yy, zz], x0=np.array([x0, y0, z0]))\n",
    "    values[key]['nC'] = grid.nC  # Store number of cells in log.\n",
    "\n",
    "    # Interpolate the starting electric field from the last one (can speed-up the calculation).\n",
    "    # Only for lower frequencies.\n",
    "    if fi == 0 or frq > 1:\n",
    "        efield = emg3d.utils.Field(grid, freq=frq)\n",
    "    else:\n",
    "        efield = emg3d.utils.grid2grid(old_grid, efield, grid, method='cubic', extrapolate=False)\n",
    "        efield = emg3d.utils.Field(grid, efield, freq=frq)\n",
    "\n",
    "    # Generate model (interpolate on log-scale from our coarse model).\n",
    "    res_x = 10**emg3d.utils.grid2grid(orig_mesh, np.log10(orig_model.res_x), grid, 'volume')\n",
    "    res_y = 10**emg3d.utils.grid2grid(orig_mesh, np.log10(orig_model.res_y), grid, 'volume')\n",
    "    res_z = 10**emg3d.utils.grid2grid(orig_mesh, np.log10(orig_model.res_z), grid, 'volume')\n",
    "    model = emg3d.utils.Model(grid, res_x, res_y, res_z)\n",
    "    # grid.plot_3d_slicer(np.log10(model.res_x), zlim=[-3000, 500], clim=clim)  # QC\n",
    "\n",
    "    # Define source.\n",
    "    sfield = emg3d.utils.get_source_field(\n",
    "        grid, [src[0], src[1], -src[2], 0, 0], frq, strength=0)\n",
    "\n",
    "    # Solve the system.\n",
    "    info = emg3d.solver.solver(\n",
    "        grid, model, sfield, efield=efield, verb=2, return_info=True,\n",
    "        sslsolver=True,  semicoarsening=True, linerelaxation=True,\n",
    "    )\n",
    "\n",
    "    # Store info\n",
    "    values[key]['info'] = info\n",
    "\n",
    "    # Save result for this frequency.\n",
    "    grid._vol = None  # Saves space, not needed.\n",
    "    emg3d.utils.data_write(name+'_'+str(key), ['grid', 'efield'], [grid, efield],\n",
    "                           path='data/time', exists=-1)\n",
    "    \n",
    "    print(f\"\\n   {info['exit_message']}; {info['it_mg']:g}/{info['it_ssl']:g} iteration(s); \"\n",
    "          f\"{info['time']} s; alpha = {values[key]['alpha'][0]:.3f} / {values[key]['alpha'][1]:.3f}\")\n",
    "\n",
    "    # Store the grid for the interpolation.\n",
    "    old_grid = grid\n",
    "\n",
    "# Stop the timer.\n",
    "total_time = runtime.runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                **** TOTAL RUNTIME :: 0:09:08 ****\n",
      "\n",
      "    3.176 Hz:  7/1 it;    7 s; a: 1.000 / 1.500 ; nC:   61,440; a:   100 /    5767\n",
      "    2.004 Hz:  7/1 it;    9 s; a: 1.000 / 1.470 ; nC:   73,728; a:   100 /    6926\n",
      "    1.264 Hz:  7/1 it;   15 s; a: 1.000 / 1.330 ; nC:  122,880; a:   100 /    7207\n",
      "    0.798 Hz:  7/1 it;   17 s; a: 1.000 / 1.350 ; nC:  122,880; a:   100 /    9016\n",
      "    0.503 Hz:  7/1 it;   14 s; a: 1.000 / 1.380 ; nC:  122,880; a:   100 /   12537\n",
      "    0.318 Hz: 10/2 it;   17 s; a: 1.000 / 1.430 ; nC:  122,880; a:   100 /   21382\n",
      "    0.200 Hz: 13/2 it;   21 s; a: 1.000 / 1.470 ; nC:  122,880; a:   100 /   22001\n",
      "    0.126 Hz: 13/2 it;   23 s; a: 1.000 / 1.500 ; nC:  122,880; a:   100 /   29194\n",
      "    0.080 Hz: 13/2 it;   45 s; a: 1.000 / 1.480 ; nC:  163,840; a:   100 /   35804\n",
      "    0.050 Hz: 13/2 it;   49 s; a: 1.000 / 1.370 ; nC:  204,800; a:   100 /   39600\n",
      "    0.032 Hz: 13/2 it;   40 s; a: 1.000 / 1.390 ; nC:  204,800; a:   100 /   52153\n",
      "    0.020 Hz: 13/2 it;   46 s; a: 1.000 / 1.410 ; nC:  204,800; a:   100 /   68417\n",
      "    0.013 Hz: 16/3 it;   59 s; a: 1.000 / 1.430 ; nC:  204,800; a:   100 /   93851\n",
      "    0.008 Hz: 16/3 it;   54 s; a: 1.000 / 1.450 ; nC:  204,800; a:   100 /  134280\n",
      "    0.005 Hz: 19/3 it;   55 s; a: 1.000 / 1.470 ; nC:  204,800; a:   100 /  151018\n",
      "    0.003 Hz: 22/4 it;   67 s; a: 1.000 / 1.490 ; nC:  204,800; a:   100 /  195227\n"
     ]
    }
   ],
   "source": [
    "print(f\"\\n                **** TOTAL RUNTIME :: {total_time} ****\\n\")\n",
    "for key, value in values.items():\n",
    "    print(f\"  {value['freq']:7.3f} Hz: {value['info']['it_mg']:2g}/{value['info']['it_ssl']:g} it; \"\n",
    "          f\"{value['info']['time']:4.0f} s; \"\n",
    "          f\"a: {value['alpha'][0]:.3f} / {value['alpha'][1]:.3f} ; \"\n",
    "          f\"nC: {value['nC']:8,.0f}; \"\n",
    "          f\"a: {value['dminmax'][0]:5.0f} / {value['dminmax'][1]:7.0f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data, interpolate at receiver location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We initiate with zeros.\n",
    "data = np.zeros(Fourier.freq_calc.size, dtype=complex)\n",
    "\n",
    "# Loop over frequencies.\n",
    "for fi, frq in enumerate(Fourier.freq_calc):\n",
    "    key = int(frq*1e6)\n",
    "    grid, efield = emg3d.utils.data_read(name+'_'+str(key), ['grid', 'efield'], path='data/time')\n",
    "    data[fi] = emg3d.utils.get_receiver(grid, efield.fx, (rec[0], rec[1], -rec[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpolate missing frequecies and calculate analytical result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_int = Fourier.interpolate(data)\n",
    "\n",
    "# Calculate analytical result using empymod (epm)\n",
    "epm_req = empymod.dipole(src, rec, depth, res, Fourier.freq_req, verb=1)\n",
    "epm_calc = empymod.dipole(src, rec, depth, res, Fourier.freq_calc, verb=1)\n",
    "epm_dense = empymod.dipole(src, rec, depth, res, freq_dense, verb=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot frequency-domain result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "# Real, log-lin\n",
    "ax1 = plt.subplot(321)\n",
    "plt.title('(a) log-lin Real')\n",
    "plt.plot(freq_dense, 1e9*epm_dense.real, 'C1')\n",
    "plt.plot(Fourier.freq_req, 1e9*data_int.real, 'k.', label='interpolated')\n",
    "plt.plot(Fourier.freq_calc, 1e9*data.real, 'C0*')\n",
    "plt.ylabel('$E_x$ (nV/m)')\n",
    "plt.xscale('log')\n",
    "\n",
    "# Real, log-symlog\n",
    "ax3 = plt.subplot(323, sharex=ax1)\n",
    "plt.title('(c) log-symlog Real')\n",
    "plt.plot(freq_dense, 1e9*epm_dense.real, 'C1')\n",
    "plt.plot(Fourier.freq_req, 1e9*data_int.real, 'k.')\n",
    "plt.plot(Fourier.freq_calc, 1e9*data.real, 'C0*')\n",
    "plt.ylabel('$E_x$ (nV/m)')\n",
    "plt.xscale('log')\n",
    "plt.yscale('symlog', linthreshy=1e-5)\n",
    "\n",
    "# Real, error\n",
    "ax5 = plt.subplot(325, sharex=ax3)\n",
    "plt.title('(e) clipped 0.01-10')\n",
    "\n",
    "# Calculate the error\n",
    "err_int_r =  np.clip(100*abs((data_int.real-epm_req.real) /\n",
    "                             epm_req.real), 0.01, 10)\n",
    "err_cal_r = np.clip(100*abs((data.real-epm_calc.real) /\n",
    "                            epm_calc.real), 0.01, 10)\n",
    "\n",
    "plt.ylabel('Rel. error %')\n",
    "plt.plot(Fourier.freq_req, err_int_r, 'k.')\n",
    "plt.plot(Fourier.freq_calc, err_cal_r, 'C0*')\n",
    "plt.axhline(1, color='.4')\n",
    "\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.ylim([0.008, 12])\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "\n",
    "# Imaginary, log-lin\n",
    "ax2 = plt.subplot(322)\n",
    "plt.title('(b) log-lin Imag')\n",
    "plt.plot(freq_dense, 1e9*epm_dense.imag, 'C1')\n",
    "plt.plot(Fourier.freq_req, 1e9*data_int.imag, 'k.', label='interpolated')\n",
    "plt.plot(Fourier.freq_calc, 1e9*data.imag, 'C0*')\n",
    "plt.xscale('log')\n",
    "\n",
    "# Imaginary, log-symlog\n",
    "ax4 = plt.subplot(324, sharex=ax2)\n",
    "plt.title('(d) log-symlog Imag')\n",
    "plt.plot(freq_dense, 1e9*epm_dense.imag, 'C1')\n",
    "plt.plot(Fourier.freq_req, 1e9*data_int.imag, 'k.')\n",
    "plt.plot(Fourier.freq_calc, 1e9*data.imag, 'C0*')\n",
    "\n",
    "plt.xscale('log')\n",
    "plt.yscale('symlog', linthreshy=1e-5)\n",
    "\n",
    "# Imaginary, error\n",
    "ax6 = plt.subplot(326, sharex=ax2)\n",
    "plt.title('(f) clipped 0.01-10')\n",
    "\n",
    "# Calculate error\n",
    "err_int_i =  np.clip(100*abs((data_int.imag-epm_req.imag) /\n",
    "                             epm_req.imag), 0.01, 10)\n",
    "err_cal_i = np.clip(100*abs((data.imag-epm_calc.imag) /\n",
    "                            epm_calc.imag), 0.01, 10)\n",
    "\n",
    "plt.plot(Fourier.freq_req, err_int_i, 'k.')\n",
    "plt.plot(Fourier.freq_calc, err_cal_i, 'C0*')\n",
    "plt.axhline(1, color='.4')\n",
    "\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.ylim([0.008, 12])\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('./'+name+'_freq.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fourier Transform\n",
    "\n",
    "Carry-out Fourier transform, calculate analytical result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate corresponding time-domain signal.\n",
    "data_time = Fourier.freq2time(data, rec[0])\n",
    "\n",
    "# Analytical result\n",
    "epm_time_precise = empymod.dipole(src, rec, depth, res, time, signal=0, verb=1)\n",
    "epm_time = empymod.dipole(src, rec, depth, res, time, signal=0, ft=Fourier.ft, ftarg=Fourier.ftarg, verb=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot time-domain result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "\n",
    "# lin-lin\n",
    "plt.subplot(221)\n",
    "plt.title('(a) lin-lin')\n",
    "plt.plot(time, epm_time_precise*1e9, 'k', lw=2)\n",
    "plt.plot(time, epm_time*1e9, 'C1')\n",
    "plt.plot(time, data_time*1e9, 'C0')\n",
    "plt.ylabel('$E_x$ (nV/m)')\n",
    "plt.xlim([-0.1, 12])\n",
    "plt.xlabel('Time (s)')\n",
    "\n",
    "# log-log\n",
    "ax2 = plt.subplot(222)\n",
    "plt.title('(b) log-log')\n",
    "plt.plot(time, epm_time_precise*1e9, 'k', lw=2, label='empymod, analytical')\n",
    "plt.plot(time, epm_time*1e9, 'C1', label='empymod, same FT as emg3d')\n",
    "plt.plot(time, data_time*1e9, 'C0', label='emg3d, FFTLog')\n",
    "plt.plot(-1, 1e9, 'k>', label=f\"Peak error: {100*(max(data_time)-max(epm_time_precise))/max(epm_time_precise):.2f} %\")\n",
    "#plt.xlim([1.5e-2, 2e0])\n",
    "plt.ylim([1e-8, 1e-3])\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "\n",
    "# Error\n",
    "ax4 = plt.subplot(224, sharex=ax2)\n",
    "plt.title('(c) clipped 0.01-10 %')\n",
    "\n",
    "# Calculate error\n",
    "err = np.clip(100*abs((data_time-epm_time_precise)/epm_time_precise), 0.01, 10)\n",
    "err2 = np.clip(100*abs((epm_time-epm_time_precise)/epm_time_precise), 0.01, 10)\n",
    "\n",
    "plt.loglog(time, err2, 'C1.')\n",
    "plt.loglog(time, err, 'C0.')\n",
    "plt.hlines(1, 0, 100)\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Rel. error %')\n",
    "plt.ylim([0.008, 12])\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Plot peak error\n",
    "ax2.legend(bbox_to_anchor=(-0.5, -0.5))\n",
    "\n",
    "#plt.savefig('./'+name+'_time.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further explanations to the results in the above figure:\n",
    "\n",
    "- The black line is a very accurate result using empymod and the following characteristics for the Fourier transform:\n",
    "  - Filter: Key 201 CosSin (2012)\n",
    "  - DLF type: Lagged Convolution\n",
    "  - Required frequencies: 251, from 1.5e-9 to 1.8e6 Hz\n",
    "- The blue result was equally obtained with empymod, but with the Fourier-transform parameters as used for `emg3d`, hence FFTLog with 5 pts per decade. However, in contrary to the red response, all frequencies are calculated, with a very high precision.\n",
    "- The red result is the result obtain with `emg3d`. \n",
    "\n",
    "## Plot result for a single frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency: 0.00503 Hz\n"
     ]
    }
   ],
   "source": [
    "pfreq = Fourier.freq_req[12]\n",
    "print(f\"Frequency: {pfreq:.5f} Hz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid, efield = emg3d.utils.data_read(name+'_'+str(int(pfreq*1e6)), ['grid', 'efield'], path='data/time')\n",
    "grid.plot_3d_slicer(\n",
    "    efield.fx.ravel('F'), view='abs', vType='Ex', clim=[1e-14, 1e-9],\n",
    "    xlim=[-100, 4100], ylim=[-2500, 2500], zlim=[-4000, 200],\n",
    "    zslice=-2000,\n",
    "    pcolorOpts={'norm': LogNorm()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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'>Fri Nov 08 12:13:30 2019 CET</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Linux</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>OS</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>4</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>CPU(s)</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>x86_64</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Machine</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>64bit</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Architecture</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>15.5 GB</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>RAM</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Jupyter</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Environment</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; border: 2px solid #fff;' colspan='6'>Python 3.7.4 (default, Aug 13 2019, 20:35:49) \n",
       "[GCC 7.3.0]</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.10.2</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>empymod</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.4.10</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>discretize</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.17.3</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>numpy</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.3.1</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>scipy</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.46.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>numba</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.9.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>emg3d</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>7.9.0</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>IPython</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>3.1.1</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>matplotlib</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) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications</td>\n",
       "  </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "\n",
       "--------------------------------------------------------------------------------\n",
       "  Date: Fri Nov 08 12:13:30 2019 CET\n",
       "\n",
       "             Linux : OS\n",
       "                 4 : CPU(s)\n",
       "            x86_64 : Machine\n",
       "             64bit : Architecture\n",
       "           15.5 GB : RAM\n",
       "           Jupyter : Environment\n",
       "\n",
       "  Python 3.7.4 (default, Aug 13 2019, 20:35:49)  [GCC 7.3.0]\n",
       "\n",
       "            1.10.2 : empymod\n",
       "            0.4.10 : discretize\n",
       "            1.17.3 : numpy\n",
       "             1.3.1 : scipy\n",
       "            0.46.0 : numba\n",
       "             0.9.0 : emg3d\n",
       "             7.9.0 : IPython\n",
       "             3.1.1 : matplotlib\n",
       "\n",
       "  Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for\n",
       "  Intel(R) 64 architecture applications\n",
       "--------------------------------------------------------------------------------"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emg3d.Report([empymod, discretize])"
   ]
  }
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