{
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   "source": [
    "# A-scan from a metal cylinder (2D)\n",
    "\n",
    "This example is the GPR modelling equivalent of 'Hello World'! It demonstrates how to simulate a single trace (A-scan) from a metal cylinder buried in a dielectric half-space. The input needed to create the model is:\n",
    "\n",
    "### my_cylinder_Ascan_2D.in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ../../user_models/cylinder_Ascan_2D.in\n"
     ]
    }
   ],
   "source": [
    "%%writefile ../../user_models/cylinder_Ascan_2D.in\n",
    "#title: A-scan from a metal cylinder buried in a dielectric half-space\n",
    "#domain: 0.240 0.210 0.002\n",
    "#dx_dy_dz: 0.002 0.002 0.002\n",
    "#time_window: 3e-9\n",
    "\n",
    "#material: 6 0 1 0 half_space\n",
    "\n",
    "#waveform: ricker 1 1.5e9 my_ricker\n",
    "#hertzian_dipole: z 0.100 0.170 0 my_ricker\n",
    "#rx: 0.140 0.170 0\n",
    "\n",
    "#box: 0 0 0 0.240 0.170 0.002 half_space\n",
    "#cylinder: 0.120 0.080 0 0.120 0.080 0.002 0.010 pec\n",
    "\n",
    "#geometry_view: 0 0 0 0.240 0.210 0.002 0.002 0.002 0.002 cylinder_half_space n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geometry of a metal cylinder buried in a dielectric half-space\n",
    "<img style=\"float: left\" src=\"cylinder_half_space_geo.png\" width=\"50%\" height=\"50%\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The geometry of the scenario is straightforward - the transparent area around the boundary of the domain represents the PML region. The red cell is the source and the blue cell is the receiver.\n",
    "\n",
    "For this initial example a detailed description of what each command in the input file does and why each command was used is given. The following steps explain the process of building the input file:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Determine the constitutive parameters for the materials\n",
    "\n",
    "There will be three different materials in the model representing air, the dielectric half-space, and the metal cylinder. Air (free space) already exists as a built-in material in gprMax which can be accessed using the ``free_space`` identifier. The metal cylinder will be modelled as a Perfect Electric Conductor, which again exists as a built-in material in gprMax and can be accessed using the ``pec`` identifier. So the only material which has to be defined is for the dielectric half-space. It is a non-magnetic material, i.e. $\\mu_r=1$ and $\\sigma_*=0$ and with a relative permittivity of six, $\\epsilon_r=6$, and zero conductivity, $\\sigma=0$. The identifier ``half_space`` will be used.\n",
    "\n",
    "    #material: 6 0 1 0 half_space\n",
    "\n",
    "### 2. Determine the source type and excitation frequency\n",
    "\n",
    "These should generally be known, often based on the GPR system or scenario being modelled. Low frequencies are used where significant penetration depth is important, whereas high frequencies are used where less penetration and better resolution are required. In this case a theoretical Hertzian dipole source fed with a Ricker waveform with a centre frequency of $f_c=1.5~\\textrm{GHz}$ will be used to simulate the GPR antenna (see the bowtie antenna example model for how to include a model of the actual GPR antenna in the simulation).\n",
    "\n",
    "    #waveform: ricker 1 1.5e9 my_ricker\n",
    "    #hertzian_dipole: z 0.100 0.170 0 my_ricker\n",
    "\n",
    "The Ricker waveform is created with the ``#waveform`` command, specifying an amplitude of one, centre frequency of 1.5 GHz and picking an arbitrary identifier of ``my_ricker``. The Hertzian dipole source is created using the ``#hertzian_dipole`` command, specifying a z direction polarisation (perpendicular to the survey direction if a B-scan were being created), location on the surface of the slab, and using the Ricker waveform already created.\n",
    "\n",
    "### 3. Calculate a spatial resolution and domain size\n",
    "\n",
    "In the guidance section it was stated that a good *rule-of-thumb* was that the spatial resolution should be one tenth of the smallest wavelength present in the model. To determine the smallest wavelength, the highest frequency and lowest velocity present in the model are required. The highest frequency is not the centre frequency of the Ricker waveform!  \n",
    "\n",
    "You can use the following code to plot builtin waveforms and their FFTs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from gprMax.waveforms import Waveform\n",
    "from tools.plot_builtin_wave import check_timewindow, mpl_plot\n",
    "\n",
    "w = Waveform()\n",
    "w.type = 'ricker'\n",
    "w.amp = 1\n",
    "w.freq = 1.5e9\n",
    "timewindow = 3e-9\n",
    "dt = 1.926e-12\n",
    "\n",
    "timewindow, iterations = check_timewindow(timewindow, dt)\n",
    "plt = mpl_plot(w, timewindow, dt, iterations, fft=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By examining the spectrum of a Ricker waveform it is evident much higher frequencies are present, i.e. at a level -40dB from the centre frequency, frequencies 2-3 times as high are present. In this case the highest significant frequency present in the model is likely to be around 4 GHz. To calculate the wavelength at 4 GHz in the half-space (which has the lowest velocity) use:\n",
    "\n",
    "$$\\lambda = \\frac{c}{f \\sqrt{\\epsilon_r}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
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   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "\n",
    "# Speed of light in vacuum (m/s)\n",
    "c = 299792458\n",
    "\n",
    "# Highest relative permittivity present in model\n",
    "er = 6\n",
    "\n",
    "# Maximum frequency present in model\n",
    "fmax = 4e9\n",
    "\n",
    "# Minimum wavelength\n",
    "wmin = c / (fmax * sqrt(er))\n",
    "\n",
    "# Maximum spatial resolution\n",
    "dmin = wmin / 10\n",
    "\n",
    "print('Minimum wavelength: {:g} m'.format(wmin))\n",
    "print('Maximum spatial resolution: {:g} m'.format(dmin))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This would give a minimum spatial resolution of 3 mm. However, the diameter of the cylinder is 20 mm so would be resolved to 7 cells. Therefore a better choice would be 2 mm which resolves the diameter of the rebar to 10 cells.\n",
    "\n",
    "    #dx_dy_dz: 0.002 0.002 0.002\n",
    "\n",
    "The domain size should be enough to enclose the volume of interest, plus allow 10 cells (if using the default value) for the PML absorbing boundary conditions and approximately another 10 cells of between the PML and any objects of interest. In this case the plan is to take a B-scan of the scenario (in the next example) so the domain should be large enough to do that. Although this is a 2D model one cell must be specified in the infinite direction (in this case the z direction) of the domain.\n",
    "\n",
    "    #domain: 0.240 0.210 0.002\n",
    "\n",
    "### 4. Choose a time window\n",
    "\n",
    "It is desired to see the reflection from the cylinder, therefore the time window must be long enough to allow the electromagnetic waves to propagate from the source through the half-space to the cylinder and be reflected back to the receiver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = 0.090\n",
    "t = (2 * d) / (c / sqrt(6))\n",
    "print('Minimum time window: {:g} s'.format(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the minimum time required, but the source waveform has a width of 1.2 ns, to allow for the entire source waveform to be reflected back to the receiver an initial time window of 3 ns will be tested.\n",
    "\n",
    "    #time_window: 3e-9\n",
    "\n",
    "The time step required for the model is automatically calculated using the CFL condition (for this case in 2D).\n",
    "\n",
    "### 5. Create the objects\n",
    "\n",
    "Now physical objects can be created for the half-space and the cylinder. First the ``#box`` command will be used to create the half-space and then the ``#cylinder`` command will be given which will overwrite the properties of the half-space with those of the cylinder at the location of the cylinder.\n",
    "\n",
    "    #box: 0 0 0 0.240 0.170 0.002 half_space\n",
    "    #cylinder: 0.120 0.080 0 0.120 0.080 0.002 0.010 pec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the model\n",
    "\n",
    "You can now run the model using:\n",
    "    \n",
    "    python -m gprMax user_models/cylinder_Ascan_2D.in\n",
    "\n",
    "**Tip**: You can use the ``--geometry-only`` command line argument to build a model and produce any geometry views but not run the simulation. This option is useful for checking the geometry of the model is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********************************************************************\n",
      "\n",
      "Model input file: /Users/cwarren/Documents/Git-projects/gprMax/user_models/cylinder_Ascan_2D.in\n",
      "\n",
      "Constants/variables available for Python scripting: {'input_directory': '/Users/cwarren/Documents/Git-projects/gprMax/user_models', 'e0': 8.854187817620389e-12, 'z0': 376.73031346177066, 'm0': 1.2566370614359173e-06, 'number_model_runs': 1, 'c': 299792458.0, 'current_model_run': 1}\n",
      "\n",
      "Model title: A-scan from a metal cylinder buried in a dielectric half-space\n",
      "Number of threads: 4\n",
      "Spatial discretisation: 0.002 x 0.002 x 0.002m\n",
      "Domain size: 0.24 x 0.21 x 0.002m (120 x 105 x 1 = 12600 cells)\n",
      "Memory (RAM) usage: ~30.5MiB required, 32.0GiB available\n",
      "Time step (at 2D CFL limit): 4.71731e-12 secs\n",
      "Time window: 3e-09 secs (637 iterations)\n",
      "Waveform my_ricker of type ricker with amplitude 1, frequency 1.5e+09Hz created.\n",
      "Hertzian dipole with polarity z at 0.1m, 0.17m, 0m, using waveform my_ricker created.\n",
      "Receiver at 0.14m, 0.17m, 0m with output(s) Ex, Ey, Ez, Hx, Hy, Hz, Ix, Iy, Iz created.\n",
      "Material half_space with epsr=6, sig=0 S/m; mur=1, sig*=0 S/m created.\n",
      "Geometry view from 0m, 0m, 0m, to 0.24m, 0.21m, 0.002m, discretisation 0.002m, 0.002m, 0.002m, filename cylinder_half_space created.\n",
      "Box from 0m, 0m, 0m, to 0.24m, 0.17m, 0.002m of material(s) half_space created, dielectric smoothing is on.\n",
      "Cylinder with face centres 0.12m, 0.08m, 0m and 0.12m, 0.08m, 0.002m, with radius 0.01m, of material(s) pec created, dielectric smoothing is off.\n",
      "\n",
      "Input file processed in [HH:MM:SS]: 0:00:00\n",
      "\n",
      "PML slab (xminus direction) with 10 cells created.\n",
      "PML slab (yminus direction) with 10 cells created.\n",
      "PML slab (xplus direction) with 10 cells created.\n",
      "PML slab (yplus direction) with 10 cells created.\n",
      "\n",
      "Model built in [HH:MM:SS]: 0:00:00\n",
      "\n",
      "Materials:\n",
      "\n",
      "ID\tName\t\tProperties\n",
      "--------------------------------------------------\n",
      "  0\tpec         \tepsr=1, sig=0 S/m; mur=1, sig*=0 S/m; dielectric smoothing not permitted.\n",
      "  1\tfree_space  \tepsr=1, sig=0 S/m; mur=1, sig*=0 S/m; dielectric smoothing permitted.\n",
      "  2\thalf_space  \tepsr=6, sig=0 S/m; mur=1, sig*=0 S/m; dielectric smoothing permitted.\n",
      "  3\tfree_space+free_space+half_space+half_space\tepsr=3.5, sig=0 S/m; mur=1, sig*=0 S/m; dielectric smoothing permitted.\n",
      "\n",
      "Geometry file(s) written in [HH:MM:SS]: 0:00:00\n",
      "\n",
      "Output to file: /Users/cwarren/Documents/Git-projects/gprMax/user_models/cylinder_Ascan_2D.out\n",
      "Estimated runtime [HH:MM:SS]: 0:00:03\n",
      "Solving for model run 1 of 1...\n",
      "|##################################################| 100.0%\n",
      "\n",
      "Solving took [HH:MM:SS]: 0:00:04\n",
      "Peak memory (approx) used: 58.4MiB\n",
      "\n",
      "Total simulation time [HH:MM:SS]: 0:00:04\n",
      "\n",
      "Simulation completed.\n",
      "********************************************************************\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from gprMax.gprMax import api\n",
    "\n",
    "filename = os.path.join(os.pardir, os.pardir, 'user_models', 'cylinder_Ascan_2D.in')\n",
    "api(filename, n=1, geometry_only=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View the results\n",
    "\n",
    "### Plot the A-scan\n",
    "\n",
    "You should have produced an output file ``cylinder_Ascan_2D.out``. You can view the results using:\n",
    "\n",
    "    python -m tools.plot_Ascan user_models/cylinder_Ascan_2D.out\n",
    "    \n",
    "You can use the following code to experiment with plotting different field/current components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "image/png": 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IvJDIxJiJTOkiHdlCCCFEUWLUGFNRUVGsXLmSVatWER4eXvC8l5cXAwYMoF27\ndjYt0prkWm4hhGpcpV3bu3cvCQkJvP3227z77rsFz3t5eREWFka5cuXsVourbPOiqtFnjTh04RC/\nP/c7rfxaWW25MckxhC0Io3319mx/1vI7e1mD3qDnuejnmJ8wH7jZeVahZAX2n9tPjj4HN50bL7d9\nmUkPTcLDzXU6aAcuH8iSv5bQq24vogdGo9Pp7jl/7KlY2s9tj7ubO4deOESd8nXsVKnzkHbN/mSb\nCyFUY267ZlTHVL7Y2Fjatm1r8kqKEvkCEEKoxtXatdzcXDw9PR1ag6tt86Ik40YGPlN98HDzIPO1\nTIp7FL//m4yUfCWZmjNq4uflx+n/nrbaci3xxqY3mLx9MqU8S7GgzwIea/gYOp2OC9cuMOm3Sczc\nNRODZqBzzc781O8nypW0Xweto2xN3kroglBKepTk4AsHC850u59no55lXsI8nmj8BEsfX2rbIp2Q\nM7ZrO3bsoH379vd9rqhyxm0uhBD3Ym67ZtIYUz/88ANjxowp9HjrrbeIiooyecXCOlS6JlWlLKBW\nHpWygHp5XE1wcDCBgYGFHh06dGDs2LFcvHjR0eU5JWfaJ3af2Y2GRrMqze7YKWVJFn9vf9x17pzJ\nPMONvBsWVGkdW5O3Mvm7ybjp3IgeGM3jjR4vODOoYqmKfNL9E7ZGbMW3tC+bT2wmbEFYwaV+RZE1\nfs80TeP1za8DMOHBCUZ3SgG8G/YuJTxKsOzAMnal7rK4Fmfab1Q1evRoo54T9qHSPqFSFlArj0pZ\nQL085jLpnO8bN26QmJhYMOD58uXLqVmzJnv37mXLli18+umnNilSCCGEyNejRw/c3d158sknAViy\nZAnXrl2jSpUqREREEB0d7eAKhS3ZanwpAA83D/y9/UlJT+FUximHXu6lN+gLxlB6s8ObdK7Z+Y7z\nPfjAg+x6fhddv+vK3rS9dJjXgY3PbKR62er2LNdu1h1bx85TO6lYqiJj24w16b3+3v682PpFpu2Y\nxnu/vUf0QGkrnFVsbCw7d+7k/PnzfPzxxwXPZ2RkoNfrHViZEEIIc5h0KV+bNm3YsWMH7u7uAOTl\n5dGhQwe2b99O06ZNOXjwoM0KtRY5ZVYIoRpXa9eCg4OJj4+/43NNmzZl//79Nq/B1bZ5UdLvx378\ndPAn5v1nHhFBEVZffuj8ULambGXDMxvoUquL1ZdvrPkJ8xkSNYSAsgEkjkqkhEeJe86fdjWNbt93\nY1/aPmp4eQjEAAAgAElEQVT41GDzoM3ULFfTTtXah0Ez0OKrFuw5u4cPu37IuHam33XtfNZ5Aj4N\nIDsvm4ThCTSr0swGlTonZ2rXtm7dSkxMDF9++SUjRowoeN7Ly4vevXtTt25dB1ZnPGfa5kIIYQxz\n2zWTzpi6fPkyV69epWzZsgBkZWVx6dIl3N3dKV7cemM8CCGEEHej1+vZtWsXrVrdHPT6jz/+KPgL\nudydT335l2DZ4owpgACfAEiBlCuOuzOfQTPwwY4PAJgYOvG+nVIAvmV8iRkcQ/dF3dmVuotO8zux\nefBmpQb5XnFoBXvO7qGaVzVGthxp1jIqla7EsJBhzPh9BpO3T5axppxUp06d6NSpExEREQQEBDi6\nHCGEEBYyaYypV199laCgIIYMGUJERATNmzfnlVdeISsriy5dHPdXRVem0jWpKmUBtfKolAXUy+Nq\nvvnmG4YOHUrNmjWpUaMGQ4cO5euvvyYrK4vXXnvN0eU5JWfZJ85lneNk+klKe5amQcUGd5zH0iw1\nytYAbg6E7ijrj63n0IVD+Hv743fRz+j3lStZjg3PbKB99facyjhFx3kdSbyQaMNKTWPJZ6M36Hl7\ny9vAzUsbS3qWNHtZL7d7GU83T3488COHLxw2eznOst+o7MaNGwwbNoxu3brRuXPngodwDJX2CZWy\ngFp5VMoC6uUxl0l/Wh46dCg9e/Zk166bf62cPHky1apVA2D69OnWr04IIYT4l5YtW7J//37S09MB\nCs7iBXjiiSccVZawgz9Sb44v1aJaC9zd3G2yjvzBtJPTk22yfGPM3TMXgJEtRuKhN+0sQO/i3qx/\nej29F/cmJjmGTvM7sWnQJppUbmKLUu1m0f5FHLpwiBo+NRgaPNSiZfl7+xMRFMHX8V/z4c4P+Tr8\naytVKeytX79+jBgxgueee65gqBEhhBDOx6QxpgBSU1NJSUkhLy+v4LmOHTtavTBbkWu5hRCqcbV2\n7caNGyxfvpzk5ORC30Vvv/223WpwtW1eVLy5+U0mbZvEK+1e4YOuH9hkHVtObKHzws48+MCDbBuy\nzSbruJdL2Zeo+lFVcvW5nBp7Cj9v48+YutW13Gv0WdKHDUkbqFiqIhuf2ei04ynl6HNoMLsBJ66c\nYP5/5jM4aLDFyzxy8QgNZjfA092T5BeTqepV1QqVOjdnbNdCQkLYvXu3o8swmzNucyGEuBe7jDE1\nfvx4li5dSuPGjXFzcytYsTN1TAkhhHBu//nPfyhbtiwhISEyvqGL2X5yO3DzTnS2kn/G1InLJ2y2\njntZ+tdScvQ5dK3V1exOKYBSnqVYNXAVjy17jLVH1xK6IJQf+/3o0AHdzTV3z1xOXDlBg4oNeDrw\naasss16Fejza8FFWHFrBjN9nMLXLVKssV9hX7969+fzzz3n00UcLfR+UL1/egVUJIYQwlUljTK1c\nuZLDhw+zZs0aoqOjiY6OZtWqVbaqTRhBpWtSVcoCauVRKQuol8fVnD59mqVLl/Lqq68ybty4gocw\nnzPsEzn6nIKBz9tVb3fX+SzN4u/tj5vOjTOZZ8jR51i0LHN8v/97AAY3u3lWkCV5SniUYMUTK+jb\nsC9Xrl+h+/fd+WzXZ9Yo0yzmZMnOzea9394D4N3Qd616Ceer7V4F4Is/vyDjRobJ73eG/UZ1CxYs\nYPr06bRr146QkBBCQkJo0aKFo8tyWSrtEyplAbXyqJQF1MtjLpM6pmrVqkVubq6tahFCCCHuq127\nduzfv9/RZQg72/P3HrLzsmlQsQEVS1W02Xo83T2p5lUNDY3TGadttp47SbuaRuypWEp4lKBPgz5W\nWWZxj+L82O9HJrSfgF7TM2rdKAb9PIj06+lWWb6tffHnF5zJPENQlSAea/SYVZfd2r81nQI6kXEj\ngzl/zrHqsoV9nDhx4rZHUlKSo8sSQghhIpPGmHrsscfYu3cvDz30UKHTZWfOnGmT4mxBruUWQqjG\n1dq1Ro0acezYMWrWrEnx4sXRNA2dTse+ffvsVoOrbfOi4KOdH/HyhpcZ2nwo34R/Y9N1PTj3QXac\n2sHmQZsJqxlm03Xdau6euQxdNZRedXux+snVVl/+9/u+Z1j0MLLzsgkoG8DCRxfSMaDoDseQfj2d\nWjNrcSn7EmueXEPPuj2tvo51R9fR84eeVPOqRtKYJIp7uO7lwc7Yrl27do2PP/6YkydP8tVXX3H0\n6FEOHz7MI4884ujSjOKM21wIIe7F3HbNpDOmwsPDeeuttwqdLhsSEmLySu9m/fr1NGjQgHr16jFt\n2rQ7zjNmzBjq1q1LUFAQCQkJJr1XCCGE81u3bh1Hjx7l119/JTo6mtWrVxMdHW215cv3SdG049QO\nwLbjS+UL8AkAICU9xebrulX0kZu/x4/Us81B9dOBTxM/PJ7gqsGkpKfQaX4nBv08iL8z/7bJ+iz1\nwY4PuJR9iY4BHelRp4dN1tG9TneaVG7Cmcwz/LD/B5usQ9jOkCFDKFasGDt37gTAz8+PN9980yrL\nluMSIYSwI81E165d0xITE019233p9Xqtdu3aWnJyspaTk6M1a9ZMO3ToUKF51q5dq/Xs2VPTNE2L\ni4vTWrdubfR785kRuUjbsmWLo0uwGpWyaJpaeVTKomnq5VGtXTPGtm3btLlz52qapmnnzp3TkpKS\nrLJcY79PVNvmRX2f0Bv0WsUPKmpEoh29ePSe81ojy+sbX9eIRIvcEmnxsoyVnZutlZpUSiMS7VT6\nqYLnbfHZ3Mi7ob29+W2t2HvFNCLRykwuo7256U3t4rWLVl/XrUzJkpqRqpV8v6RGJFrsqVjbFaVp\n2sKEhRqRaA1mN9D0Br3R7yvq+42pnLFdCwkJ0TRN04KCggqeCwwMtHi5clxiHpX2CZWyaJpaeVTK\nomnq5TG3XTPpjKno6GiCgoLo3r07AAkJCYSHh1ulg2zXrl3UrVuXgIAAPD09GTBgAFFRUYXmiYqK\nYtCgQQC0bt2a9PR00tLSjHqvEEIINUycOJFp06YxZcoUAHJzc3n6aevcqUu+T4qmfWn7uHDtAg+U\nfYDa5WrbfH2OOGMqJjmGa7nXaF6lOf7e/jZdVzH3YkwMm8jBkQfpXa83V3Ou8v6296nxaQ1e3/R6\nkTiDamLMRLLzsunbsC9t/NvYdF0Dmgygund1Ei8ksvqI9S+htBZN07ied50beTfQG/RyCRhQrFgx\nsrOz0el0ABw/ftwqd2uV4xIhhLAvD1NmjoyMZNeuXYSGhgIQFBRktQEGU1NTqV69esG0v78/u3bt\nuu88qampRr23kP99eakg1NEFWFGoowuwslBHF2BFoY4uwMpCHV2AsMjPP//Mnj17CA4OBqBatWpk\nZmZaZdmmfJ/0XtwbHTe/T/IPinToCv2c/5ol85m0DCOW66Zzo0yxMgUPr2JelKlQhs0nNuPn5Ye/\ntz+li5U2fePZ0MakjQB0qdmlIN/d5P8fxRIBZe3fMbX5xGYAutXuVuh5a+S5m9rla7Nq4Cq2n9zO\nu1vfZUPSBqZsn8KHOz/kicZP8GLrF2np19Jq6zM2y96ze/l2z7e469yZ3Hmy1dZ/N57unvy37X8Z\n+8tYpu2YRnh94/7oau3PJv1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zWef4JO4TAF5p94oty7yv/DvzJV9JtsnyY0/fvIyvrb9z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\nu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TduXJ7f2Wy2Qh3/JXeZ/8aKU4BfAB/2/ZDwt8P5Yu8XvLb5NcZ2GOvw/BbvWQzAvU3u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\nZs0aa968eQ1+/uXmh7q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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cae2160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "from gprMax.receivers import Rx\n",
    "from tools.plot_Ascan import mpl_plot\n",
    "\n",
    "filename = os.path.join(os.pardir, os.pardir, 'user_models', 'cylinder_Ascan_2D.out')\n",
    "outputs = Rx.availableoutputs\n",
    "#outputs = ['Ez']\n",
    "plt = mpl_plot(filename, outputs, fft=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}