{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compatibility of wind turbines with RAS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License\n",
    "\n",
    "```\n",
    "Wind turbine compatibility with RAS observations.\n",
    "Copyright (C) 2015+  Benjamin Winkel (bwinkel@mpifr.de)\n",
    "\n",
    "This program is free software; you can redistribute it and/or\n",
    "modify it under the terms of the GNU General Public License\n",
    "as published by the Free Software Foundation; either version 2\n",
    "of the License, or (at your option) any later version.\n",
    "\n",
    "This program is distributed in the hope that it will be useful,\n",
    "but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
    "GNU General Public License for more details.\n",
    "\n",
    "You should have received a copy of the GNU General Public License\n",
    "along with this program; if not, write to the Free Software\n",
    "Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy import units as u\n",
    "from pycraf import conversions as cnv\n",
    "from pycraf import pathprof, protection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Wind turbines are not radio services in the strict sense, because they don't emit wanted radio emission. However, having large heights they are visible over large distances and electrical devices (e.g., the generator, or digital control units) may be placed in the hub. Even when radio telescopes are put to a remote location, ideally well-shielded by terrain, the wind turbine might be above the local horizon. In this example, we will show how one can calculate separation distances (aka exclusion zones) to protect RAS observations from emission.\n",
    "\n",
    "For a case, where no frequency allocation is applicable, we have to find some other way to define protection criteria. For industrial devices there is CISPR standard, which contains permitted emission limits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CISPR-11\n",
    "\n",
    "For an industrial plant the electrical field limits are defined for a reference distance of 30 m; see Table 17 of the European standard CISPR-11 (= EN 55011 in Germany) for devices of group 1 class A:\n",
    "\n",
    "$$\n",
    "E_\\mathrm{lim}~\\left[\\mathrm{dB}_{\\mu \\mathrm{V} / \\mathrm{m}}\\right]= \\begin{cases}\n",
    "30 & \\text{for}\\,~ f < 230~\\mathrm{MHz} \\\\\n",
    "37 & \\text{for}\\,~ 230~\\mathrm{MHz} < f < 1~\\mathrm{GHz}\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "No limits are provided for $f > 1~\\mathrm{GHz}$, unfortunately. Therefore, we will simply use the limit of $37~\\mathrm{dB(\\mu V/m)}$ for all $f > 1~\\mathrm{GHz}$.\n",
    "\n",
    "The CISPR standard detector is a quasi-peak (QP) detector with a bandwidth of 120 kHz. How the QP detector converts to an RMS (which is more suitable to compare with the RA.769 thresholds) or AV (average) detector depends strongly on the input signal. For a continouus-wave signal (CW), the QP and RMS produce the same results. For stochastic signals, to convert to an RMS detector one has to subtract 5.5 dB. As we have no further information, we assume that the signals are CW-like, which is the expectation for the kind of equipment used in wind turbines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, that the CISPR detector has only a bandwidth of 120 kHz in contrast to the RAS bandwidths of several MHz (continuum). This would be OK, if we assumed that only one peak would be present in the protected bands (which may be reasonable for wind turbine equipment, which could be more of a line spectrum). However, there is the possibility that the full RAS band is polluted and we will assume this for our calculations of the separation distances, as a worst-case scenario. It would be straight-forward to calculate the limits for a one-channel (120 kHz) interference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Minimal coupling loss\n",
    "We will start by calculating the *minimal coupling loss*, i.e., the least amount of path attenuation (propagation loss), which would be necessary to ensure compatibility. Since the path attenuation is dependend on the distance between interferer and victim service, the MCL translates to a separation distance, which we will infer further below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we query the RA.769 limits for continuum in the lower-frequency bands. It is unlikely, that a wind turbine will be problematic at high frequencies only, so we restrict ourselves to the low frequencies for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=5&gt;\n",
       "<table id=\"table140613217244048\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>frequency</th><th>bandwidth</th><th>T_A</th><th>T_rx</th><th>T_rms</th><th>P_rms_nu</th><th>Plim</th><th>Plim_nu</th><th>Slim</th><th>Slim_nu</th><th>Efield</th><th>Efield_norm</th></tr></thead>\n",
       "<thead><tr><th>MHz</th><th>MHz</th><th>K</th><th>K</th><th>mK</th><th>dB(W / Hz)</th><th>dB(W)</th><th>dB(W / Hz)</th><th>dB(W / m2)</th><th>dB(W / (Hz m2))</th><th>dB(uV2 / m2)</th><th>dB(uV2 / m2)</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>325</td><td>7</td><td>40</td><td>60</td><td>0.870</td><td>-259.2</td><td>-201.0</td><td>-269.2</td><td>-189.3</td><td>-257.5</td><td>-43.5</td><td>-51.7</td></tr>\n",
       "<tr><td>408</td><td>4</td><td>25</td><td>60</td><td>0.962</td><td>-258.8</td><td>-202.9</td><td>-268.8</td><td>-189.2</td><td>-255.1</td><td>-43.4</td><td>-49.3</td></tr>\n",
       "<tr><td>611</td><td>6</td><td>20</td><td>60</td><td>0.730</td><td>-260.0</td><td>-202.2</td><td>-270.0</td><td>-185.0</td><td>-252.8</td><td>-39.2</td><td>-47.0</td></tr>\n",
       "<tr><td>1414</td><td>27</td><td>12</td><td>10</td><td>0.095</td><td>-268.8</td><td>-204.5</td><td>-278.8</td><td>-180.1</td><td>-254.4</td><td>-34.3</td><td>-48.6</td></tr>\n",
       "<tr><td>1665</td><td>10</td><td>12</td><td>10</td><td>0.156</td><td>-266.7</td><td>-206.7</td><td>-276.7</td><td>-180.8</td><td>-250.8</td><td>-35.0</td><td>-45.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=5>\n",
       "frequency bandwidth   T_A   ...     Slim_nu        Efield    Efield_norm \n",
       "   MHz       MHz       K    ... dB(W / (Hz m2)) dB(uV2 / m2) dB(uV2 / m2)\n",
       " float64   float64  float64 ...     float64       float64      float64   \n",
       "--------- --------- ------- ... --------------- ------------ ------------\n",
       "      325         7      40 ...          -257.5        -43.5        -51.7\n",
       "      408         4      25 ...          -255.1        -43.4        -49.3\n",
       "      611         6      20 ...          -252.8        -39.2        -47.0\n",
       "     1414        27      12 ...          -254.4        -34.3        -48.6\n",
       "     1665        10      12 ...          -250.8        -35.0        -45.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra769_tab_db = protection.ra769_limits(mode='continuum')[4:9]\n",
    "ra769_tab_db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate CISPR power limit for these frequencies:\n",
    "\n",
    "1. Assuming a single line polluting the RAS band\n",
    "2. Assuming pollution over the full RAS band\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 37.  37.  37.  37.  37.] dB(uV2 / m2) @ 120.0 kHz\n"
     ]
    }
   ],
   "source": [
    "freqs = ra769_tab_db['frequency']\n",
    "ras_bw = ra769_tab_db['bandwidth']\n",
    "\n",
    "detector_dist=30 * u.m\n",
    "# we query the QP values and assume that they equal 'RMS';\n",
    "# as discussed above\n",
    "detector_type='QP'  \n",
    "\n",
    "# case 1\n",
    "cispr11_lim, cispr11_bw = protection.cispr11_limits(\n",
    "    freqs, detector_type=detector_type, detector_dist=detector_dist\n",
    "    )\n",
    "print(cispr11_lim, '@', cispr11_bw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert this to the emitted power levels for both cases. In the first case, it's straight forward. In the second case, we assume that each 120-kHz channel in the RAS band is \"using\" the maximum CISPR-11 level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "case 1: [-68.22578194 -68.22578194 -68.22578194 -68.22578194 -68.22578194] dB(W)\n",
      "case 2: [-50.82215504 -53.10694833 -51.23608189 -44.70395675 -49.0175944 ] dB(W)\n"
     ]
    }
   ],
   "source": [
    "cispr11_pow_lim_1 = cnv.ptx_from_efield(cispr11_lim, detector_dist, 0 * cnv.dBi)\n",
    "print('case 1:', cispr11_pow_lim_1.to(cnv.dB_W))\n",
    "\n",
    "# case 2\n",
    "cispr11_pow_lim_2 = cispr11_pow_lim_1 * ras_bw / cispr11_bw\n",
    "print('case 2:', cispr11_pow_lim_2.to(cnv.dB_W))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, in case 1 all numbers are equal (because the electrical field limit was also the same for all these frequencies)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MCL follows directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-68.22578194, -68.22578194, -68.22578194, -68.22578194, -68.22578194])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cispr11_pow_lim_1.to(cnv.dB_W).value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-201.00659898, -202.85480637, -202.18266254, -204.52327303,\n",
       "       -206.68009185])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Plim = ra769_tab_db['Plim']\n",
    "Plim.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 132.78081705  134.62902443  133.9568806   136.2974911   138.45430992] dB\n",
      "[ 150.18444394  149.74785804  150.94658065  159.81931628  157.66249746] dB\n"
     ]
    }
   ],
   "source": [
    "MCL_1 = (\n",
    "    cispr11_pow_lim_1.to(cnv.dB_W).value - \n",
    "    ra769_tab_db['Plim'].data\n",
    "    ) * cnv.dB\n",
    "MCL_2 = (\n",
    "    cispr11_pow_lim_2.to(cnv.dB_W).value - \n",
    "    ra769_tab_db['Plim'].data\n",
    "    ) * cnv.dB\n",
    "\n",
    "print(MCL_1)\n",
    "print(MCL_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+/ZkzZw5btmxh4cKFvPvuu+WmVEeOHElOTg6bN2/mrbfeol+/fuW+m5OTw/bt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3042a5080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax1 = fig.add_axes((0.1, 0.5, 0.8, 0.4))\n",
    "ax2 = fig.add_axes((0.1, 0.1, 0.8, 0.4))\n",
    "ax1.plot(freqs, cispr11_pow_lim_2.to(cnv.dB_W), 'o', label='CISPR-11, full RAS band')\n",
    "ax1.plot(freqs, cispr11_pow_lim_1.to(cnv.dB_W), 'o', label='CISPR-11, single chan')\n",
    "ax1.plot(freqs, ra769_tab_db['Plim'].data, 'o', label='RA 769')\n",
    "ax2.plot(freqs, MCL_2.to(cnv.dB), 'o', label='CISPR-11, full RAS band')\n",
    "ax2.plot(freqs, MCL_1.to(cnv.dB), 'o', label='CISPR-11, single chan')\n",
    "ax1.legend(*ax1.get_legend_handles_labels(), loc='center right', numpoints=1, fontsize=12)\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.grid()\n",
    "    ax.set_xlim((301, 1690))\n",
    "\n",
    "ax1.tick_params(axis='x', which='both', labelbottom='off')\n",
    "ax2.set_xlabel('Frequency [MHz]', fontsize=12)\n",
    "ax1.set_ylabel('Power [dB(Watt)]', fontsize=12)\n",
    "ax2.set_ylabel('MCL [dB]', fontsize=12)\n",
    "ax1.set_ylim((-219, -30))\n",
    "ax2.set_ylim((125, 165))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen, that the CISPR-11 levels refer to relatively high permitted transmission power levels. As a consequence, the MCL is large."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Path attenuation and separation distances\n",
    "Calculating the path propagation loss is fairly simple with pycraf, and since it has been explained in the other notebooks in detail, we will just post the necessary code without much explanation here.\n",
    "\n",
    "In this notebook, we'll start with a generic (aka flat-Earth) analysis. In [the next notebook](https://github.com/bwinkel/pycraf/blob/master/notebooks/B02_wind_turbine_terrain.ipynb) a site-specific study is performed. For this example, we will look at the 610 MHz band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCL: 150.9 dB\n"
     ]
    }
   ],
   "source": [
    "MCL_610 = MCL_2[2]  # 2nd scenario (full RAS band) as worst-case\n",
    "print('MCL: {:.1f}'.format(MCL_610))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define dummy coordinates\n",
    "lon_t, lon_r = 0 * u.deg, 0 * u.deg\n",
    "lat_t, lat_r = 50 * u.deg, 50 * u.deg\n",
    "h_tg, h_rg = 160 * u.m, 50 * u.m  # Tx and Rx are well above clutter\n",
    "\n",
    "frequency = 610 * u.MHz  # choose any frequency, you like\n",
    "temperature = 293.15 * u.K\n",
    "pressure = 1013. * u.hPa\n",
    "\n",
    "time_percent = 2 * u.percent\n",
    "hprof_step = 30 * u.m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hprof_data = pathprof.height_path_data_generic(\n",
    "    200 * u.km, hprof_step, 0. * u.deg, lat_t,\n",
    "    )\n",
    "distances = hprof_data['distances']\n",
    "\n",
    "results = pathprof.atten_path_fast(\n",
    "    frequency, temperature, pressure,\n",
    "    h_tg, h_rg,\n",
    "    time_percent,\n",
    "    hprof_data,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# look at path attenuation as a function of distance\n",
    "\n",
    "attens = np.zeros(\n",
    "    distances.shape, \n",
    "    dtype=np.dtype([\n",
    "        ('LOS', 'f8'), ('Diffraction', 'f8'), ('Troposcatter', 'f8'), \n",
    "        ('Ducting', 'f8'), ('Total', 'f8')\n",
    "        ])\n",
    "    )\n",
    "\n",
    "for fname, k in zip(\n",
    "        attens.dtype.names,\n",
    "        ['L_bfsg', 'L_bd', 'L_bs', 'L_ba', 'L_b']\n",
    "        ):\n",
    "    attens[fname] = results[k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c8a5OZNzr2dVDzVs11H9QT/2mehobGmlpb+HS6ZeSuS2TQFuAOyN38g/+sc+5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JUNQfx2L9zGUq4ADrRq6W1QIiIiIiIih4mOe8CCQxA3bNnIZ/jYjFGvpRIwEREREREJL1FRASsb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe303fbcb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "fig = plt.figure(figsize=(14, 8))\n",
    "ax = fig.add_axes((0.1, 0.1, 0.8, 0.8))\n",
    "for (name, style) in zip(\n",
    "        attens.dtype.names, \n",
    "        ['b-', 'r-', 'c-', 'm-', 'k--']\n",
    "        ):\n",
    "    ax.plot(distances, attens[name], style, label=name, lw=2)\n",
    "\n",
    "ax.legend(\n",
    "    *ax.get_legend_handles_labels(), \n",
    "    loc='lower right', fontsize=8, handlelength=3\n",
    "    )\n",
    "ax.set_xlim((0, 199))\n",
    "ax.set_ylim((71, 179))\n",
    "ax.set_xlabel('Distance [km]')\n",
    "ax.set_ylabel('Path attenuation [dB]')\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The path attenuation is very sensitive to the wind turbine hub height:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "heights = [80, 120, 160, 200]\n",
    "attens_dict = {}\n",
    "\n",
    "for _h_tg in heights:\n",
    "    \n",
    "    results = pathprof.atten_path_fast(\n",
    "        frequency, temperature, pressure,\n",
    "        _h_tg * u.m, h_rg,\n",
    "        time_percent,\n",
    "        hprof_data,\n",
    "        )\n",
    "    \n",
    "    attens_dict[_h_tg] = results['L_b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the margin tells us, how large the necessary separation distance would need to be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Uv+lp513MQoufftrMkH79zD60X5W8uLdmjXk3z88PBg2CO+6AyEissDC25eWxMCOD+IwM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\nlzT7W2oq7deto3J2NquvvZZyOT8Y5Ez6O5Y3XZ/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SKet277ZH41qWHfbO8sxeRnY20w4e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\nVe/zAPtw3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe30401ad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_axes((0.1, 0.1, 0.8, 0.8))\n",
    "for _h_tg, style in zip(heights, ['g-', 'b-', 'r-', 'c-']):\n",
    "    ax.plot(\n",
    "        distances,\n",
    "        -MCL_610.to(cnv.dB).value + attens_dict[_h_tg].to(cnv.dB).value,\n",
    "        style, label='{:d} m'.format(_h_tg)\n",
    "        )\n",
    "\n",
    "ax.axhline(0., c='0.3', ls='--', lw=2, label='CISPR')\n",
    "ax.axhline(-10., c='0.5', ls='--', lw=2, label='CISPR $-$ 10 dB')\n",
    "ax.axhline(-20., c='0.7', ls='--', lw=2, label='CISPR $-$ 20 dB')\n",
    "# ax.axvline(P_lim, c='r', lw=2, ls='--', label='RA.769')\n",
    "ax.legend(*ax.get_legend_handles_labels(), loc='lower right')\n",
    "ax.set_xlim((0, 199))\n",
    "ax.set_ylim((-69, 19))\n",
    "ax.set_xlabel('Distance [km]')\n",
    "ax.set_ylabel('Margin [dB]')\n",
    "ax.text(\n",
    "    0.05, 0.1, 'Frequency: {:.0f}'.format(frequency), \n",
    "    va='top', ha='left', transform=ax.transAxes\n",
    "    )\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "So, if the CISPR-11 levels were used to the maximum, the necessary protection zone would need to have a radius of about 150 km! If the wind turbine's emission would fall 20 dB below CISPR-11 levels, the zero-margin lies at about 75-100 km separation, depending on the hub height."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}