6a_Fullspace.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    "# OUTDATED, the examples moved to the gallery\n",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 homogeneous fullspace\n",
    "\n",
    "Example how to use emg3d to model time-domain data, using FFTLog and DLF.\n",
    "\n",
    "The example is based on the first example (Figures 3-4) of Mulder et al., 2008, Geophysics: \"Time-domain modeling of electromagnetic diffusion with a frequency-domain code\". These results are shown at the end of the notebook.\n",
    "\n",
    "The published example took roughly **3.75 hours**, whereas here we need less than **2 minutes**. There are two main reasons for the speed gain:\n",
    "\n",
    "1. gridding and\n",
    "2. frequency selection.\n",
    "\n",
    "Note re first point, gridding: We implemented here an adaptive gridding with various number of cells. Our calculation uses meshes between 36,864 (64x24x24) and 102,400 (64x40x40) cells, whereas Mulder et al., 2008, used 2,097,152 (128x128x128) for all frequencies.\n",
    "\n",
    "Note re second point, frequency selection: We only used 14 frequencies from 0.05-20 Hz, whereas Mulder et al., 2008, used 26 frequencies from 0.01-100 Hz. Have a look at the repo https://github.com/empymod/frequency-selection .\n",
    "\n",
    "**Requires**\n",
    "- **emg3d >= 0.9.0**\n",
    "- `empymod`, `discretize`\n",
    "\n",
    "\n",
    "#### References\n",
    "\n",
    "> Mulder, W. A., M. Wirianto, and E. Slob, 2008, Time-domain modeling of electromagnetic diffusion with a frequency-domain code: Geophysics, 73, F1-F8; DOI: [10.1190/1.2799093](https://doi.org/10.1190/1.2799093)."
   ]
  },
  {
   "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",
    "\n",
    "# Name is used to store the data for each frequency.\n",
    "name = 'Fullspace'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Style adjustments\n",
    "%matplotlib notebook\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model and Survey\n",
    "\n",
    "### Model\n",
    "- Homogeneous fullspace of 1 Ohm.m.\n",
    "\n",
    "### Survey\n",
    "- Source at origin.\n",
    "- Receiver at an inline-offset of 900 m.\n",
    "- Both source and receiver are x-directed electric dipoles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = [0, 0, 0]\n",
    "rec = [900, 0, 0]\n",
    "res = 1\n",
    "depth = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fourier Transforms parameters\n",
    "\n",
    "We only calculate frequencies $0.05 < f < 21$ Hz, which yields enough precision for our purpose.\n",
    "\n",
    "This means, instead of 30 frequencies from 0.0002 - 126.4 Hz, we only need 14 frequencies from 0.05 - 20.0 Hz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   time        [s] :  0.01 - 10 : 201  [min-max; #]\n",
      "   Fourier         :  FFTLog\n",
      "     > pts_per_dec :  5\n",
      "     > add_dec     :  [-2.  1.]\n",
      "     > q           :  0.0\n",
      "   Req. freq  [Hz] :  0.000200364 - 126.421 : 30  [min-max; #]\n",
      "   Calc. freq [Hz] :  0.0503292 - 20.0364 : 14  [min-max; #]\n"
     ]
    }
   ],
   "source": [
    "# Define desired times.\n",
    "time = np.logspace(-2, 1, 201)\n",
    "\n",
    "# Initiate a Fourier instance\n",
    "Fourier = emg3d.utils.Fourier(\n",
    "    time=time,\n",
    "    fmin=0.05,\n",
    "    fmax=21,\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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To store the info of each frequency.\n",
    "values = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===============  1/14 ===============  20.036420 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 112\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -775 - 1675\n",
      "   Final extent        [m] : -816 - 1853\n",
      "   Min/max cell width  [m] : 20 / 20 / 138\n",
      "   Alpha survey/calc       : 1.000 / 1.160\n",
      "   Number of cells (s/c/r) : 80 (55/24/1)\n",
      "\n",
      "   Skin depth          [m] : 112\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -675 - 675\n",
      "   Final extent        [m] : -699 - 699\n",
      "   Min/max cell width  [m] : 20 / 20 / 124\n",
      "   Alpha survey/calc       : 1.000 / 1.180\n",
      "   Number of cells (s/c/r) : 24 (2/22/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:35:45 ::\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  24 x  24     => 46,080 cells\n",
      "   Coarsest grid  :   5 x   3 x   3     => 45 cells\n",
      "   Coarsest level :   4 ;   3 ;   3   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 4\n",
      "   > Final rel. error : 5.443e-07\n",
      "\n",
      ":: emg3d END   :: 11:35:48 :: runtime = 0:00:03\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 4/1 iteration(s); 3 s; alpha = 1.000 / 1.180\n",
      "\n",
      "===============  2/14 ===============  12.642126 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 142\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -949 - 1849\n",
      "   Final extent        [m] : -985 - 2077\n",
      "   Min/max cell width  [m] : 20 / 20 / 192\n",
      "   Alpha survey/calc       : 1.000 / 1.190\n",
      "   Number of cells (s/c/r) : 80 (55/24/1)\n",
      "\n",
      "   Skin depth          [m] : 142\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -849 - 849\n",
      "   Final extent        [m] : -897 - 897\n",
      "   Min/max cell width  [m] : 20 / 20 / 178\n",
      "   Alpha survey/calc       : 1.000 / 1.220\n",
      "   Number of cells (s/c/r) : 24 (2/22/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:35:48 ::\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  24 x  24     => 46,080 cells\n",
      "   Coarsest grid  :   5 x   3 x   3     => 45 cells\n",
      "   Coarsest level :   4 ;   3 ;   3   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 2.293e-09\n",
      "\n",
      ":: emg3d END   :: 11:35:53 :: runtime = 0:00:05\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 5 s; alpha = 1.000 / 1.220\n",
      "\n",
      "===============  3/14 ===============   7.976643 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 178\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -1169 - 2069\n",
      "   Final extent        [m] : -1195 - 2360\n",
      "   Min/max cell width  [m] : 20 / 20 / 265\n",
      "   Alpha survey/calc       : 1.000 / 1.220\n",
      "   Number of cells (s/c/r) : 80 (55/24/1)\n",
      "\n",
      "   Skin depth          [m] : 178\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -1069 - 1069\n",
      "   Final extent        [m] : -1084 - 1084\n",
      "   Min/max cell width  [m] : 20 / 20 / 233\n",
      "   Alpha survey/calc       : 1.000 / 1.250\n",
      "   Number of cells (s/c/r) : 24 (2/22/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:35:53 ::\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  24 x  24     => 46,080 cells\n",
      "   Coarsest grid  :   5 x   3 x   3     => 45 cells\n",
      "   Coarsest level :   4 ;   3 ;   3   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 8.640e-09\n",
      "\n",
      ":: emg3d END   :: 11:35:57 :: runtime = 0:00:04\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 4 s; alpha = 1.000 / 1.250\n",
      "\n",
      "===============  4/14 ===============   5.032921 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 224\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -1446 - 2346\n",
      "   Final extent        [m] : -1455 - 2719\n",
      "   Min/max cell width  [m] : 20 / 20 / 364\n",
      "   Alpha survey/calc       : 1.000 / 1.250\n",
      "   Number of cells (s/c/r) : 80 (55/24/1)\n",
      "\n",
      "   Skin depth          [m] : 224\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -1346 - 1346\n",
      "   Final extent        [m] : -1396 - 1396\n",
      "   Min/max cell width  [m] : 20 / 20 / 329\n",
      "   Alpha survey/calc       : 1.000 / 1.290\n",
      "   Number of cells (s/c/r) : 24 (2/22/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:35:57 ::\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  24 x  24     => 46,080 cells\n",
      "   Coarsest grid  :   5 x   3 x   3     => 45 cells\n",
      "   Coarsest level :   4 ;   3 ;   3   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 2.885e-08\n",
      "\n",
      ":: emg3d END   :: 11:36:02 :: runtime = 0:00:05\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 5 s; alpha = 1.000 / 1.290\n",
      "\n",
      "===============  5/14 ===============   3.175559 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 282\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -1795 - 2695\n",
      "   Final extent        [m] : -1953 - 2847\n",
      "   Min/max cell width  [m] : 24 / 24 / 290\n",
      "   Alpha survey/calc       : 1.000 / 1.170\n",
      "   Number of cells (s/c/r) : 80 (48/32/0)\n",
      "\n",
      "   Skin depth          [m] : 282\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -1695 - 1695\n",
      "   Final extent        [m] : -1749 - 1749\n",
      "   Min/max cell width  [m] : 24 / 24 / 422\n",
      "   Alpha survey/calc       : 1.000 / 1.300\n",
      "   Number of cells (s/c/r) : 24 (2/22/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:36:02 ::\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  24 x  24     => 46,080 cells\n",
      "   Coarsest grid  :   5 x   3 x   3     => 45 cells\n",
      "   Coarsest level :   4 ;   3 ;   3   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 3.395e-08\n",
      "\n",
      ":: emg3d END   :: 11:36:06 :: runtime = 0:00:04\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 4 s; alpha = 1.000 / 1.300\n",
      "\n",
      "===============  6/14 ===============   2.003642 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 356\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -2233 - 3133\n",
      "   Final extent        [m] : -2297 - 3186\n",
      "   Min/max cell width  [m] : 30 / 30 / 437\n",
      "   Alpha survey/calc       : 1.000 / 1.230\n",
      "   Number of cells (s/c/r) : 64 (38/26/0)\n",
      "\n",
      "   Skin depth          [m] : 356\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -2133 - 2133\n",
      "   Final extent        [m] : -2202 - 2202\n",
      "   Min/max cell width  [m] : 30 / 30 / 531\n",
      "   Alpha survey/calc       : 1.000 / 1.300\n",
      "   Number of cells (s/c/r) : 24 (2/22/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:36:07 ::\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  24     => 36,864 cells\n",
      "   Coarsest grid  :   2 x   3 x   3     => 18 cells\n",
      "   Coarsest level :   5 ;   3 ;   3   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 3.825e-08\n",
      "\n",
      ":: emg3d END   :: 11:36:10 :: runtime = 0:00:03\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 3 s; alpha = 1.000 / 1.300\n",
      "\n",
      "===============  7/14 ===============   1.264213 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 448\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -2786 - 3686\n",
      "   Final extent        [m] : -2903 - 3799\n",
      "   Min/max cell width  [m] : 37 / 37 / 401\n",
      "   Alpha survey/calc       : 1.000 / 1.150\n",
      "   Number of cells (s/c/r) : 64 (30/34/0)\n",
      "\n",
      "   Skin depth          [m] : 448\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -2686 - 2686\n",
      "   Final extent        [m] : -2773 - 2773\n",
      "   Min/max cell width  [m] : 37 / 37 / 669\n",
      "   Alpha survey/calc       : 1.000 / 1.300\n",
      "   Number of cells (s/c/r) : 24 (2/22/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:36:10 ::\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  24     => 36,864 cells\n",
      "   Coarsest grid  :   2 x   3 x   3     => 18 cells\n",
      "   Coarsest level :   5 ;   3 ;   3   \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 3.232e-08\n",
      "\n",
      ":: emg3d END   :: 11:36:14 :: runtime = 0:00:03\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 3 s; alpha = 1.000 / 1.300\n",
      "\n",
      "===============  8/14 ===============   0.797664 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 564\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -3481 - 4381\n",
      "   Final extent        [m] : -3608 - 4488\n",
      "   Min/max cell width  [m] : 40 / 40 / 495\n",
      "   Alpha survey/calc       : 1.000 / 1.150\n",
      "   Number of cells (s/c/r) : 64 (28/36/0)\n",
      "\n",
      "   Skin depth          [m] : 564\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -3381 - 3381\n",
      "   Final extent        [m] : -3498 - 3498\n",
      "   Min/max cell width  [m] : 40 / 40 / 616\n",
      "   Alpha survey/calc       : 1.000 / 1.200\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:36:14 ::\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  32 x  32     => 65,536 cells\n",
      "   Coarsest grid  :   2 x   2 x   2     => 8 cells\n",
      "   Coarsest level :   5 ;   4 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 8.863e-09\n",
      "\n",
      ":: emg3d END   :: 11:36:20 :: runtime = 0:00:06\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 6 s; alpha = 1.000 / 1.200\n",
      "\n",
      "===============  9/14 ===============   0.503292 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 709\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -4357 - 5257\n",
      "   Final extent        [m] : -4491 - 5371\n",
      "   Min/max cell width  [m] : 40 / 40 / 675\n",
      "   Alpha survey/calc       : 1.000 / 1.170\n",
      "   Number of cells (s/c/r) : 64 (28/36/0)\n",
      "\n",
      "   Skin depth          [m] : 709\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -4257 - 4257\n",
      "   Final extent        [m] : -4599 - 4599\n",
      "   Min/max cell width  [m] : 40 / 40 / 893\n",
      "   Alpha survey/calc       : 1.000 / 1.230\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:36:20 ::\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  32 x  32     => 65,536 cells\n",
      "   Coarsest grid  :   2 x   2 x   2     => 8 cells\n",
      "   Coarsest level :   5 ;   4 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 2.186e-08\n",
      "\n",
      ":: emg3d END   :: 11:36:28 :: runtime = 0:00:08\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 8 s; alpha = 1.000 / 1.230\n",
      "\n",
      "=============== 10/14 ===============   0.317556 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 893\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -5459 - 6359\n",
      "   Final extent        [m] : -5607 - 6487\n",
      "   Min/max cell width  [m] : 40 / 40 / 916\n",
      "   Alpha survey/calc       : 1.000 / 1.190\n",
      "   Number of cells (s/c/r) : 64 (28/36/0)\n",
      "\n",
      "   Skin depth          [m] : 893\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -5359 - 5359\n",
      "   Final extent        [m] : -5524 - 5524\n",
      "   Min/max cell width  [m] : 40 / 40 / 1137\n",
      "   Alpha survey/calc       : 1.000 / 1.250\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:36:28 ::\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  32 x  32     => 65,536 cells\n",
      "   Coarsest grid  :   2 x   2 x   2     => 8 cells\n",
      "   Coarsest level :   5 ;   4 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 4.709e-08\n",
      "\n",
      ":: emg3d END   :: 11:36:36 :: runtime = 0:00:08\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 8 s; alpha = 1.000 / 1.250\n",
      "\n",
      "=============== 11/14 ===============   0.200364 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 1124\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -6846 - 7746\n",
      "   Final extent        [m] : -7014 - 7894\n",
      "   Min/max cell width  [m] : 40 / 40 / 1237\n",
      "   Alpha survey/calc       : 1.000 / 1.210\n",
      "   Number of cells (s/c/r) : 64 (28/36/0)\n",
      "\n",
      "   Skin depth          [m] : 1124\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -6746 - 6746\n",
      "   Final extent        [m] : -7275 - 7275\n",
      "   Min/max cell width  [m] : 40 / 40 / 1623\n",
      "   Alpha survey/calc       : 1.000 / 1.280\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:36:37 ::\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  32 x  32     => 65,536 cells\n",
      "   Coarsest grid  :   2 x   2 x   2     => 8 cells\n",
      "   Coarsest level :   5 ;   4 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 1.485e-07\n",
      "\n",
      ":: emg3d END   :: 11:36:44 :: runtime = 0:00:08\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 8 s; alpha = 1.000 / 1.280\n",
      "\n",
      "=============== 12/14 ===============   0.126421 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 1416\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -8593 - 9493\n",
      "   Final extent        [m] : -8788 - 9668\n",
      "   Min/max cell width  [m] : 40 / 40 / 1661\n",
      "   Alpha survey/calc       : 1.000 / 1.230\n",
      "   Number of cells (s/c/r) : 64 (28/36/0)\n",
      "\n",
      "   Skin depth          [m] : 1416\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -8493 - 8493\n",
      "   Final extent        [m] : -8739 - 8739\n",
      "   Min/max cell width  [m] : 40 / 40 / 2047\n",
      "   Alpha survey/calc       : 1.000 / 1.300\n",
      "   Number of cells (s/c/r) : 32 (2/30/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:36:45 ::\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  32 x  32     => 65,536 cells\n",
      "   Coarsest grid  :   2 x   2 x   2     => 8 cells\n",
      "   Coarsest level :   5 ;   4 ;   4   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 4.473e-07\n",
      "\n",
      ":: emg3d END   :: 11:36:51 :: runtime = 0:00:06\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 6 s; alpha = 1.000 / 1.300\n",
      "\n",
      "=============== 13/14 ===============   0.079766 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 1782\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -10792 - 11692\n",
      "   Final extent        [m] : -11022 - 11902\n",
      "   Min/max cell width  [m] : 40 / 40 / 2220\n",
      "   Alpha survey/calc       : 1.000 / 1.250\n",
      "   Number of cells (s/c/r) : 64 (28/36/0)\n",
      "\n",
      "   Skin depth          [m] : 1782\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -10692 - 10692\n",
      "   Final extent        [m] : -10751 - 10751\n",
      "   Min/max cell width  [m] : 40 / 40 / 2043\n",
      "   Alpha survey/calc       : 1.000 / 1.230\n",
      "   Number of cells (s/c/r) : 40 (2/38/0)\n",
      "\n",
      "\n",
      ":: emg3d START :: 11:36:51 ::\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  40 x  40     => 102,400 cells\n",
      "   Coarsest grid  :   2 x   5 x   5     => 50 cells\n",
      "   Coarsest level :   5 ;   3 ;   3   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 2.508e-07\n",
      "\n",
      ":: emg3d END   :: 11:37:00 :: runtime = 0:00:09\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 9 s; alpha = 1.000 / 1.250\n",
      "\n",
      "=============== 14/14 ===============   0.050329 Hz ========================================\n",
      "\n",
      "   Skin depth          [m] : 2243\n",
      "   Survey domain       [m] : -100 - 1000\n",
      "   Calculation domain  [m] : -13561 - 14461\n",
      "   Final extent        [m] : -13830 - 14710\n",
      "   Min/max cell width  [m] : 40 / 40 / 2955\n",
      "   Alpha survey/calc       : 1.000 / 1.270\n",
      "   Number of cells (s/c/r) : 64 (28/36/0)\n",
      "\n",
      "   Skin depth          [m] : 2243\n",
      "   Survey domain       [m] : 0 - 0\n",
      "   Calculation domain  [m] : -13461 - 13461\n",
      "   Final extent        [m] : -13718 - 13718\n",
      "   Min/max cell width  [m] : 40 / 40 / 2776\n",
      "   Alpha survey/calc       : 1.000 / 1.250\n",
      "   Number of cells (s/c/r) : 40 (2/38/0)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ":: emg3d START :: 11:37:00 ::\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  40 x  40     => 102,400 cells\n",
      "   Coarsest grid  :   2 x   5 x   5     => 50 cells\n",
      "   Coarsest level :   5 ;   3 ;   3   \n",
      "\n",
      "\n",
      "   > CONVERGED\n",
      "   > Solver steps     : 1\n",
      "   > MG prec. steps   : 7\n",
      "   > Final rel. error : 3.781e-07\n",
      "\n",
      ":: emg3d END   :: 11:37:10 :: runtime = 0:00:09\n",
      "\n",
      "   > File exists, overwriting it.\n",
      "\n",
      "   CONVERGED; 7/1 iteration(s); 9 s; alpha = 1.000 / 1.270\n"
     ]
    }
   ],
   "source": [
    "gridinput = {\n",
    "    'min_width': [20., 40.],  # Restrict the cell width within the survey domain.\n",
    "    'return_info': True,      # To get back some information for later.\n",
    "    'pps': 12,                # Many points, to have a small min cell width.\n",
    "    'alpha': [1, 1.3, 0.01],  # Lower the alpha will improve the result, but slow down calculation.\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=res, fixed=src[0], domain=[-100, 1000], **gridinput)\n",
    "    yz, yz0, hiyz = emg3d.utils.get_hx_h0(\n",
    "        freq=frq, res=res, fixed=src[1], domain=[0, 0], **gridinput)\n",
    "\n",
    "    # Store values in log.\n",
    "    values[key]['alpha'] = [np.min([hix['amin'], hiyz['amin']]),\n",
    "                            np.max([hix['amax'], hiyz['amax']])]\n",
    "    values[key]['dminmax'] = [np.min([hix['dmin'], hiyz['dmin']]),\n",
    "                              np.max([hix['dmax'], hiyz['dmax']])]\n",
    "\n",
    "    # Initiate mesh.\n",
    "    grid = discretize.TensorMesh([xx, yz, yz], x0=np.array([x0, yz0, yz0]))\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",
    "    if fi == 0:\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\n",
    "    model = emg3d.utils.Model(grid, res_x=res)\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",
    "                           exists=-1, path='data/time')\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": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                **** TOTAL RUNTIME :: 0:01:24 ****\n",
      "\n",
      "   20.036 Hz:  4/1 it;    3 s; a: 1.000 / 1.180 ; nC:   46,080; a:    20 /     138\n",
      "   12.642 Hz:  7/1 it;    5 s; a: 1.000 / 1.220 ; nC:   46,080; a:    20 /     192\n",
      "    7.977 Hz:  7/1 it;    4 s; a: 1.000 / 1.250 ; nC:   46,080; a:    20 /     265\n",
      "    5.033 Hz:  7/1 it;    5 s; a: 1.000 / 1.290 ; nC:   46,080; a:    20 /     364\n",
      "    3.176 Hz:  7/1 it;    4 s; a: 1.000 / 1.300 ; nC:   46,080; a:    24 /     422\n",
      "    2.004 Hz:  7/1 it;    3 s; a: 1.000 / 1.300 ; nC:   36,864; a:    30 /     531\n",
      "    1.264 Hz:  7/1 it;    3 s; a: 1.000 / 1.300 ; nC:   36,864; a:    37 /     669\n",
      "    0.798 Hz:  7/1 it;    6 s; a: 1.000 / 1.200 ; nC:   65,536; a:    40 /     616\n",
      "    0.503 Hz:  7/1 it;    8 s; a: 1.000 / 1.230 ; nC:   65,536; a:    40 /     893\n",
      "    0.318 Hz:  7/1 it;    8 s; a: 1.000 / 1.250 ; nC:   65,536; a:    40 /    1137\n",
      "    0.200 Hz:  7/1 it;    8 s; a: 1.000 / 1.280 ; nC:   65,536; a:    40 /    1623\n",
      "    0.126 Hz:  7/1 it;    6 s; a: 1.000 / 1.300 ; nC:   65,536; a:    40 /    2047\n",
      "    0.080 Hz:  7/1 it;    9 s; a: 1.000 / 1.250 ; nC:  102,400; a:    40 /    2220\n",
      "    0.050 Hz:  7/1 it;    9 s; a: 1.000 / 1.270 ; nC:  102,400; a:    40 /    2955\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": 8,
   "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": [
    "--------------------\n",
    "## 1. Using FFTLog\n",
    "\n",
    "### Interpolate missing frequecies and calculate analytical result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
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       "    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=\"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": 11,
   "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": 12,
   "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, 2])\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-6, 1e0])\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 the analytical fullspace solution in the time-domain.\n",
    "- The blue result was obtained with empymod, using the same 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`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------\n",
    "## 2. Using DLF\n",
    "\n",
    "We use the same frequencies and calculated data as in the FFTLog example, but apply the digital-linear-filter method for the transformation.\n",
    "\n",
    "### Fourier Transform parameters for DLF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   time        [s] :  0.01 - 10 : 201  [min-max; #]\n",
      "   Fourier         :  DLF (Sine-Filter)\n",
      "     > Filter      :  Key 201 CosSin (2012)\n",
      "     > DLF type    :  Lagged Convolution\n",
      "   Req. freq  [Hz] :  1.4626E-08 - 1.80659E+07 : 251  [min-max; #]\n",
      "   Calc. freq [Hz] :  0.0503292 - 20.0364 : 14  [min-max; #]\n"
     ]
    }
   ],
   "source": [
    "Fourier_dlf = emg3d.utils.Fourier(\n",
    "    time=time,\n",
    "    fmin=0.05,\n",
    "    fmax=21,\n",
    "    ft='sin',  # Fourier transform to use\n",
    "    ftarg={'pts_per_dec': -1},\n",
    "    freq_inp=Fourier.freq_req,  # Use same frequencies as in above example\n",
    ")\n",
    "\n",
    "\n",
    "# Dense frequencies for comparison reasons\n",
    "freq_dense_dlf = np.logspace(np.log10(Fourier_dlf.freq_req.min()), np.log10(Fourier_dlf.freq_req.max()), 301)\n",
    "\n",
    "# Get data\n",
    "data_int_dlf = Fourier_dlf.interpolate(data)\n",
    "\n",
    "# Calculate analytical result using empymod (epm)\n",
    "epm_req_dlf = empymod.dipole(src, rec, depth, res, Fourier_dlf.freq_req, verb=1)\n",
    "epm_calc_dlf = empymod.dipole(src, rec, depth, res, Fourier_dlf.freq_calc, verb=1)\n",
    "epm_dense_dlf = empymod.dipole(src, rec, depth, res, freq_dense_dlf, verb=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpolate missing frequecies and calculate analytical result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_int_dlf = Fourier_dlf.interpolate(data)\n",
    "\n",
    "# Calculate analytical result using empymod (epm)\n",
    "epm_req_dlf = empymod.dipole(src, rec, depth, res, Fourier_dlf.freq_req, verb=1)\n",
    "epm_calc_dlf = empymod.dipole(src, rec, depth, res, Fourier_dlf.freq_calc, verb=1)\n",
    "epm_dense_dlf = empymod.dipole(src, rec, depth, res, freq_dense_dlf, verb=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot frequency-domain result"
   ]
  },
  {
   "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=\"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_dlf, 1e9*epm_dense_dlf.real, 'C1')\n",
    "plt.plot(Fourier_dlf.freq_req, 1e9*data_int_dlf.real, 'k--', label='interpolated')\n",
    "plt.plot(Fourier_dlf.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_dlf, 1e9*epm_dense_dlf.real, 'C1')\n",
    "plt.plot(Fourier_dlf.freq_req, 1e9*data_int_dlf.real, 'k--')\n",
    "plt.plot(Fourier_dlf.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_dlf.real-epm_req_dlf.real) /\n",
    "                             epm_req_dlf.real), 0.01, 10)\n",
    "err_cal_r = np.clip(100*abs((data.real-epm_calc_dlf.real) /\n",
    "                            epm_calc_dlf.real), 0.01, 10)\n",
    "\n",
    "plt.ylabel('Rel. error %')\n",
    "plt.plot(Fourier_dlf.freq_req, err_int_r, 'k.')\n",
    "plt.plot(Fourier_dlf.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_dlf, 1e9*epm_dense_dlf.imag, 'C1')\n",
    "plt.plot(Fourier_dlf.freq_req, 1e9*data_int_dlf.imag, 'k--', label='interpolated')\n",
    "plt.plot(Fourier_dlf.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_dlf, 1e9*epm_dense_dlf.imag, 'C1')\n",
    "plt.plot(Fourier_dlf.freq_req, 1e9*data_int_dlf.imag, 'k--')\n",
    "plt.plot(Fourier_dlf.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_dlf.imag-epm_req_dlf.imag) /\n",
    "                             epm_req_dlf.imag), 0.01, 10)\n",
    "err_cal_i = np.clip(100*abs((data.imag-epm_calc_dlf.imag) /\n",
    "                            epm_calc_dlf.imag), 0.01, 10)\n",
    "\n",
    "plt.plot(Fourier_dlf.freq_req, err_int_i, 'k.')\n",
    "plt.plot(Fourier_dlf.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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate corresponding time-domain signal.\n",
    "data_time_dlf = Fourier_dlf.freq2time(data, rec[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot time-domain result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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_dlf*1e9, 'C0')\n",
    "plt.ylabel('$E_x$ (nV/m)')\n",
    "plt.xlim([0, 2])\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_dlf*1e9, 'C0', label='emg3d, DLF')\n",
    "plt.plot(-1, 1e9, 'k>', label=f\"Peak error: {100*(max(data_time_dlf)-max(epm_time_precise))/max(epm_time_precise):.2f} %\")\n",
    "plt.xlim([1.5e-2, 2e0])\n",
    "plt.ylim([1e-6, 1e0])\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_dlf-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_dlf.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Results from Mulder et al., 2008, Geophysics\n",
    "\n",
    "Total calculation time (CPU) is 13,632 s, which corresponds to 3 h 47 min 12 s.\n",
    "\n",
    "![Figures 3-4 and Table 1](./data/figures/Mulder2008_Figs_3-4_Tab_1.png)\n",
    "\n",
    "Figures 3 and 4 and Table 1, page F5 of Mulder et al., 2008, Geophysics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style='border: 3px solid #ddd;'>\n",
       "  <tr>\n",
       "     <td style='text-align: center; font-weight: bold; font-size: 1.2em; border: 2px solid #fff;' colspan='6'>Fri Nov 22 18:11:58 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;'>0.9.1</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 22 18:11:58 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",
       "             0.9.1 : 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": 18,
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     "output_type": "execute_result"
    }
   ],
   "source": [
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