LoraSim-python2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# LoRaSim-AIM: simulate collisions in LoRa using the additive interference model\n",
    "Extension to the original LoRaSim by Thiemo Voigt <thiemo@sics.se> and Martin Bor <m.bor@lancaster.ac.uk>\n",
    "\n",
    "This work is licensed under the Creative Commons Attribution 4.0\n",
    "International License. To view a copy of this license,\n",
    "visit http://creativecommons.org/licenses/by/4.0/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import modules\n",
    "import simpy\n",
    "import random\n",
    "import numpy as np\n",
    "import math\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hardware Sensitivity\n",
    "\n",
    "Modelling the changes in receiver sensitivity with changes in bandwidth and spreading factor.\n",
    "\n",
    "These values are derived from Utz's experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Scaled down configuratons for testing purposes\n",
    "nrBS = 1\n",
    "avgSendTime = 1200000\n",
    "simtime = 24*3600*1000 # time in ms\n",
    "\n",
    "maxNrNodes = 2000\n",
    "maxNrBS = 1\n",
    "\n",
    "# define the maximum simulation area\n",
    "maxX = 10000 # in m\n",
    "maxY = 10000 # in m\n",
    "\n",
    "# # log shadow model parameters\n",
    "# Ptx = 16 # in dBm\n",
    "# gamma = 2.08\n",
    "# # d0 = 40.0 # in m\n",
    "# d0 = 380 # place-holder value - Adwait\n",
    "# var = 0           # variance ignored for now\n",
    "# # Lpld0 = 127.41\n",
    "# Lpld0 = 109 # place-holder value - Adwait\n",
    "# GL = 0\n",
    "\n",
    "# log shadow model parameters\n",
    "Ptx = 30 # in dBm\n",
    "gamma = 2.2394\n",
    "# d0 = 40.0 # in m\n",
    "d0 = 40.0\n",
    "var = 0           # variance ignored for now\n",
    "# Lpld0 = 127.41\n",
    "Lpld0 = 95.0038\n",
    "GL = 0\n",
    "\n",
    "full_collision = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAEcCAYAAAC/Gw8ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nj6fI4pWgx+PxeIosXgl6PB6Pp8jy/48qBuJUixCZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1187d5450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this is an array with measured values for sensitivity\n",
    "# see LoRa scaling paper, Table 1\n",
    "sf7 = np.array([7,-126.5,-124.25,-120.75])\n",
    "sf8 = np.array([8,-127.25,-126.75,-124.0])\n",
    "sf9 = np.array([9,-131.25,-128.25,-127.5])\n",
    "sf10 = np.array([10,-132.75,-130.25,-128.75])\n",
    "sf11 = np.array([11,-134.5,-132.75,-128.75])\n",
    "sf12 = np.array([12,-133.25,-132.25,-132.25])\n",
    "\n",
    "# array of sensitivity values\n",
    "sensi = np.array([sf7,sf8,sf9,sf10,sf11,sf12])\n",
    "# sensi = sensi - 11 # correction for changes from 1272 to 1276\n",
    "\n",
    "# plotting\n",
    "def plotSensitivity(sensi):\n",
    "    sensi_fig = plt.figure()\n",
    "\n",
    "    s125, = plt.plot(sensi[:,0], sensi[:,1],\"bo-\", label=\"125 kHz\")\n",
    "    s250, = plt.plot(sensi[:,0], sensi[:,2],\"ko:\", label=\"250 kHz\")\n",
    "    s500, = plt.plot(sensi[:,0], sensi[:,3],\"ro--\", label=\"500 kHz\")\n",
    "\n",
    "    plt.legend(title=\"Bandwidth\", handles = [s125, s250, s500])\n",
    "    plt.grid()\n",
    "    plt.xlabel(\"Spreading Factor (2^)\")\n",
    "    plt.ylabel(\"Base-station Receiver\\nSensitivity (dBm)\")\n",
    "    plt.xticks([6,7,8,9,10,11,12])\n",
    "\n",
    "    plt.rcParams[\"font.family\"] = \"sans-serif\"\n",
    "    plt.rcParams[\"font.size\"] = 20\n",
    "    plt.show()\n",
    "    \n",
    "plotSensitivity(sensi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packet Airtime\n",
    "\n",
    "Packets of the same size can have varying airtime due to choices of spreading factors and coding rates. The preamble must also be a part of each packet which also adds to the airtime.\n",
    "\n",
    "The actual formulas cann be seen in the LoRa Design Guide document\n",
    "\n",
    "**TODO: Add direct formulae from the document**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAasAAAEcCAYAAAB53pugAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcVNX7wPHPAVHEHTXNFHBJzCV3Lcsld03LMn8ZlFIa\nWWZufUvThL5qLqVmVpq5VVJ9tbTEUlMTy1TKpczdREBTS0UERBCZ5/fHHQhkgBlgmGE879frvnDO\nPXPnOYoczrnnPkeJCJqmaZrmzNwcHYCmaZqm5Ud3VpqmaZrT052Vpmma5vR0Z6VpmqY5Pd1ZaZqm\naU5Pd1aapmma09OdlaZpmub0dGelaZqmOT3dWWmapmlOr5SjA3AV1apVEz8/vwK99+rVq5QrV65o\nA3IQV2mLq7QDdFuclau0pTDtqFatGps2bdokIr3zq6s7qyLi5+fHnj17CvTeiIgIunTpUrQBOYir\ntMVV2gG6Lc7KVdpS2HYopapZU09PA2qapmlOT3dWmqZpmtPTnZWmaZrm9HRnpWmapjk93VlpmqZp\nTk93VpqmaZrNwsLAzw+6du2Mn5/x2p700nVN0zTNJmFhEBwMyckAipgY4zVAYKB9PlOPrDRN0zSb\nTJqU0VH9KznZKLcXPbJygNTUVOLi4khMTCQ9PZ1KlSpx5MgRR4dVJFylLc7WDnd3dypUqIC3tzdl\nypRxdDjaLS421rbyoqA7q2KWmppKbGwsVapUwc/PDw8PD5KSkqhQoYKjQysSiYmJLtEWZ2qHiJCW\nlkZCQgKxsbH4+PjoDktzKB8fiImxXG4vehqwmMXFxVGlShWqVatG6dKlUUo5OiTNySmlKF26NNWq\nVaNKlSrExcU5OiTtFjd5cs4yLy+YPt1+n6k7q2KWmJhIxYoVHR2GVkJVrFiRxMRER4eh3eKOHTO+\n1qwJSgm+vrB4sf0WV4DurIpdeno6Hh4ejg5DK6E8PDxIT093dBjaLez4cZg/H555Bs6dgx9+2E50\ntH07KtCdlUPoqT+toPT3juZoY8dC2bLw5pvF+7l6gYWmaZpmlW+/he++g7ffhho1ivez9chK0zRN\ny9f168aoyt8fRo0q/s/XIytN0zQtX/Pnw4kTsGEDlC5d/J+vR1YuLDQ0FKVUjsPNzY1q1arx8MMP\n89tvvzlFjBEREYW+VpcuXVBKER8fn2/doKAglFJER0cX2TU1zVWdPw9Tp0K/ftA73w3o7UOPrG4B\nPXr0oHHjxpmvr169yt69e1m3bh1bt25l9+7dNG3a1IERFo3HHnuMFi1aFPiBWaUUzZs3d3gHrmnO\nZuJESEmBuXMdF4PurG4BAQEBBAUF5SgfM2YM8+fPZ+bMmaxcubL4AytiL774oqND0DSXExkJK1bA\nK6/AnXc6Lg49DXgLGzZsGIAeSWiaZpHJBC+9ZDz8aylrRXHSndUt7MaNGwB4e3tnll25coXQ0FDu\nuusuvLy8uO222+jcuTOffvpptveuWLECpRQrV65k7dq1tG/fnrJly1K3bl2GDRuW4x7P9evXCQkJ\noW7dunh6etKoUSMWL16crc7+/ftRSvHggw9mK589ezZKKbp27Zqt/Ouvv0YpxTvvvANYvr+UkJDA\n6NGjqVWrFmXLlqVly5asXbvWYlsAfv/9d5RSvHnTQySJiYmMGjWK22+/PdfraJqr+fRT+OUXmDUL\nHJ0q06ZpQKVUbaAz0AioAVQE4oALwHFgs4j8U9RBakVPRHj//fcBGDx4MAAmk4kePXrw66+/0qZN\nG5566imuXLnChg0bGDJkCKdPn+a1117Ldp3Fixfz888/07NnT4YOHcr333/PsmXLSExMZNWqVZn1\nAgMD+fLLL6lXrx5DhgwhOjqaESNGUK9evcw6LVq04LbbbmPnzp2ISGYH8tNPPwEQGRlJWlpaZgaQ\njEUZvXr1stjGtLQ0evfuza5du2jWrBn9+/fn4MGDPPbYY/j6+mbWa9y4MaNHj2b+/PlUq1aNwMBA\n2rZtm+1aDz30EGfOnKF3794kJSWxfv16Bg0aRGRkJK1bt7b571/TnF1iIkyYAO3bw5NPOjoajB9a\neR2AAoYAO4F082HK5UgH9gJPA+75XduVjtatW4s1Dh8+nKMsISHBqvfaKiQkRADp0aOHjB49OvN4\n5plnpFGjRqKUkpEjR8qNGzdERCQyMlIACQoKEpPJlHmdEydOiFJKmjRpklm2fPlyAcTd3V02bNiQ\nWR4bGyve3t7i4eEhSUlJIiISHh6eGUdqampm3S+++EIAAWTbtm0iIhIQECCA/PHHHyIiYjKZxNvb\nWypXriyA7N69O/P9LVq0kDp16mS+7ty5swBy+fJlERFZsGBBZnvS09Mz682aNSvzc0+dOpVZDkjz\n5s1F5N9/k4xrtm3bVi5dupRZd+HChQLISy+9ZMs/SZGw9D2Ul4y/W1eg21J8XnlFBEQiI/OuV9h2\nAHvEip+xeU4DKqU6Ab8BK4DbgI+B4UBPoDVwJ9AK6AoEAYuB0sBS4LBSqn8B+1CtCG3evJn58+dn\nHsuWLePo0aOICMePH+fPP/8EoEqVKsyYMYMJEyZkS+tTv359ypcvT1JSUo5rDxo0iN5Z1rJWrlyZ\njh07kpaWxrlz5wBYsmQJAHPmzKF0lgc0Hn/8cXr06JHtej179gRgx44dABw6dIi4uDheeukl4N9R\n1uXLlzlw4ECuo6qMz/X09GT27Nm4uf37rf7KK69wp413ikNCQrJNl2ZMVUZFRdl0HU0rCY4fh3nz\nICgI2rVzdDSGXDsrpdRK4HtgH9BWRBqIyDMislxEtojIfhE5KSK/iUiEiHwiIs+LSDOgJbADWK2U\nCi+epmi5Wb58ebbfUK5fv87JkyeZPHkyW7ZsoWvXrsTHx3PnnXcyYcIE6tSpw/bt21m2bBlTpkyh\nR48euWb6bt68eY6yypUrA8YSeYDdu3dTuXJlmjVrlqPuvffem+11Rmf1888/A/92Ws8//zzVq1fP\n7Kx+/PFHTCZTrp1VcnIyf/zxB40aNaJ69er5fm5+WrRoke11xl5XljpwTSvpxo0DT0+YMcPRkfwr\nr5FVInCniDwtInttuaiI/C4iw4B6wMnCBKgVPQ8PD+rVq8fUqVMJCgri7NmzfPLJJ9y4cYPx48dT\npUoVunTpwsiRI1m7dm3mYgtLPD09c/0cY4QPly5dombNmhbrlCtXLtvr22+/nWbNmmV2Uj/99BMN\nGzakZs2adOrUiR07diAibN++HXd3d7p3727xupcvX8ZkMln9ufnJrX5GGzXNVWzYYOQAnDLFWAXo\nLHLtrMyjpNOFubiInBWRMYW5hmZf7cxj/OPHjzNjxgzmzp3Lo48+ypEjRzJHJwsWLKBUqYI/kleu\nXDkuXLhg8dzZs2dzlPXs2ZPo6GjOnj3Ljh076NSpE2Cs9ouLi+Pw4cNERETQrl27zFGcpc8EbPpc\nTbvVXb8OY8ZAw4bGknVnYvPSdaXUA0qpGuY/l1JKTVJKfaeUWqSUalD0IWr2FGPem7pGjRqsW7eO\nUqVKsWzZMho1apR53+rixYskJCQU+DOaN2/OpUuXOHjwYI5zP/74Y46yjKnAzz//nNjY2GydFUB4\neDi///57nverKleujI+PDwcPHuTixYvZzqWmphIZGVnQ5miay3r33X/vVzki/19erO6slFKVlVK7\ngS1AE3PxVPPRGwgGflFKNc7lEpqTiYqKYsmSJbi5uTFw4EBKly7NjRs3OH363wH11atXef755wv1\nOUOGDAFg8uTJ2TYO/Pzzz9m/f3+O+p06dcLT05O55twuGZ1VkyZNqF69Ou+8806e96uyfm5qaipv\nvPFGtvK5c+dy/vz5HPWVUpnPnmnareb8efjvf6FvX+NwNrbM7YQC7YD5wG9KKXfgWeAA0ANoC6wF\nQoDHizZMrTA+++yzbFkqRIS//vqLDRs2kJycTEhICI0bN+aZZ55h586ddOjQgYcffphr166xadMm\n6tatS9OmTTly5AjPPPMMy5Yts+nzn376aVatWsU333xD06ZN6dy5M+fOnWP9+vX06dOHDRs2ZKvv\n6elJx44d2bx5Mz4+PpnPRCml6NSpE1999RVVqlTJ8SzUzSZOnEh4eDjvvfceu3btol27dhw/fpwf\nf/yR7t27s2XLlmz169Spw5EjR3j66afp168fAwcOtKmdmlaSvfaakf9v3jxHR2KZLdOADwO/iMhY\nEYnD6Li8gQ9F5IKIfAdsxnhoWHMiNy9dX7BgAdu2baNDhw6sXbuW0NBQwEi/tHDhQqpVq8Znn33G\n3r17GTNmDDt27GDatGlUqlSJ8HDbF3e6ubnxzTffMHHiRBISEvj44485c+YMX331FY8++qjF92SM\nmjJGVRkypgK7deuGu7t7np/r5eXFtm3bGDFiBDExMXz88cekpqaydetW2rdvn6P+zJkzqVGjBitX\nruT48eM2t1PTSqpff4Xly/+9X+WUrHkYy7ziKQVYlOX1KxgPAd+ZpWwhcN3aa7rS4YwPBTuCq7TF\nmduhHwp2Dc7SlvR0kXvuEalRQ+TKFdvfX1wPBdsyDXgeI71Sht7AWRE5kaXMD2PJu6ZpmlYCrFwJ\nu3cbI6uKFfOv7yi2dFY7gIeVUo8CGTkCMzORKqU6A92BH4o0Qk3TNM0uEhPh1VeNLBXmdVBOy5bO\n6nXgQWA1Rr7AFGAegFJqIcZiCwHezO0CmqZpmvOYPt1YBfj11+Dm5HtwWN1ZicgppVQLjE6pMvCJ\niGTchU4BvgRmiMjvRR+mpmmaVpROnDBW/g0damRWd3Y2pSUQkRggxxZcIjK2yCLSNE3T7G7cOOPB\nX2fK/5cXm3PoKKXKA7WAXJPCiciBwgSlaZqm2c/GjbB+vbGp4u23Ozoa61jdWSmlqmFs/dEHyPsB\nl/zPa5qmaQ6Qkf/vzjth9GhHR2M9W0ZWC4D+wGGMbUPS866uaZqmOZv33oNjx4yRVZkyjo7GerZ0\nVr2BPcA9ImKyUzyapmmanfz9N7zxBvTpA+b9Q0sMWxYrugP7dUelaZpWMr32GiQnO2/+v7zY0lnt\nBO5XSjn5anxN0zTtZnv2GFkqRo8Gf39HR2M7Wzqe/wB3AGuUUvcopaorpSpaOuwUq6ZpmlYAIsZm\nirfdZuwAXBLZcs/qLHAIY5FF/zzqiY3X1TRN0+woLAx27YJly5w7/19ebF0NeC9wGTgGpNolIu2W\n88EHHzBy5EguX75scZv65ORkpk+fzpdffklsbCyenp60bt2aiRMn0q1bt2x116xZY3H3YYC77rqL\n5557Ls9Y/Pz8APjjjz/yrBcaGsobb7xBSEhI5hYrN4uOjqZu3br4+voSHR2d5/U0zV4SE+GVV6Bt\nWyNbRUllS2fVCzgKtBWRq3aKR7vFmEymPDdzvH79Og888AC//PILzZo1Y8iQIZw+fZqNGzcSERHB\nmjVreOihhzLrh4WFsWbNGovX6tWrV76dlaa5mjffhHPnYM0a58//lxdbQ/9Zd1RaYYkIBw4c4OOP\nP6Zr167s3bs317pLly7ll19+ITAwkN9//50PP/yQ7777jq+++or09HReeOEF0tLSMutHRUXRp08f\ni/vhbNy4sTiap2lO488/Ye5cI6P6Pfc4OprCsaWz+g64x96rAZVSLyilRCmVYz5IKVVKKTVOKXVY\nKZWslIpWSs1VSlXI5VpBSqn9SqmrSqm/lFJLlFI1c6nbTym1UymVqJT6Ryn1P6WUs+6ZWaJdvXqV\n5s2bExQUxPbt2/Os+9VXXwEwbdo0lFKZ5Y888gjt2rXjr7/+YteuXZnlJ0+epEGDBvYJXNNKmPHj\njfx/M2c6OpLCs6XjeRkoA+T6A7+wzB3hM3lUWQTMwdii5FPgJDAW2K6UyvYstlJqMrAcqAqEYWTd\nCAIizamjstZ9CggHGmJsgRIBPGyuWyI7rLAw8PMzhv1+fsZrZ+Hp6cnnn3+eedx111251j116hRV\nqlTJvJeUlY+PDwBnzpwB4MKFCyQmJlK/fv0ijffQoUNUr16dSpUq8euvvxbqWkFBQSilcj1yu/+l\nabbatAnWrYPJk0tO/r+82HLPahdQHhgKDFVKXQQsTQmKiFj900IZvy43A1oCTwOtc6nXBRgGbAH6\nikiauTwECMVYWj/NXFbfXHYQuE9EEszlQ4EVwExguLmsEsbikfNAaxE5m+XztgEfYGwqWWKEhUFw\nsPHwH0BMjPEaIDDQcXFlKFWqFIMHD858vWjRIo4cOWKx7nvvvYe7e85Ukzdu3GDfvn0A1KhRAzCm\nAAFq165NWFgYBw8epHTp0tx777306tUr28jMWseOHaNbt26kpKSwadMm2rZta/M1surZs2eORSSp\nqaksXrwYk8lE3bp1C3V9TQNISzPy/zVoYHx1BbZ0Vm4YnVPWDsrS/35bfyKUA6zZA2uk+euEjI7K\n7G1gEsY+W9PMZSMwMm6EZnRUZp9gbA4ZqJR6SUSSgSeBSsDUjI4KQEQilFK7gG5KqXoiEmVju2w2\nZgz89lvhr7N7N6TetFYzORmGDYOPPirctVu0gHfeKdw1bNGnTx+L5ZMmTSIqKorq1atz3333AcYU\nIEBwcDBxcXHZ6t97772sXbs2s2OzRlRUFN26dSMxMZENGzbQoUOHHHU2btxIfHy8xfcnJCTkKAsI\nCCAgICBbWXBwMCaTiUGDBjG0JC/X0pzGe+/B0aPGyKok5f/Liy2bL/rZKYYU4Iksr6cAluaFOgH/\niEi2u/EiclUptQ9or5TyE5FooDNgAjbdVFeUUj8BjwPtMKb7OptPb7DwmT9iLNfvBNi9syoqN3dU\n+ZWXJBcuXGDEiBGsWbMGNzc3Fi5ciKensVtNxsiqbdu2zJo1C39/f06cOMHkyZNZt24dAwYMYOfO\nnVaNsE6fPk3fvn25dOkS4eHhdOrUyWK9yMhIIiMjC9yeFStW8NFHH+Hv78/SpUsLfB1Ny/DPPxAa\nCr17Q79+jo6mCFlaNSUiAG65nbPlAErZWD8C48HiylnK7jCXReTynmXm8z3Nr68D0bnUnWKuG2x+\nfQK4AbhbqDvEXPfN/OJu3bq1WOPw4cM5yhISEqx6r7V8fUWMZ9azH76+RfoxFhWkLZ07dxZALl++\nnGsdk8kkS5cuFW9vbwHE29tb1q1bl63OgQMH5Ouvv5br169nK09LS5NWrVoJIN9//32esfj6+krV\nqlWlXr16Aoi/v3+O64mIhISECCAhISG5XuvUqVMCiG8uf/H79++XsmXLipeXlxw8eDDPuLKy9D2U\nl23bttlU35nptuRv+HCRUqVEjhyxy+VzKGw7gD1iRd+Q1wKLo+bVdAXKRqGU8lBKBQPHC/L+m1Q1\nf/07l/MZ8zDe5nRPHtbUzXLtSyJiacuTm+uWCNOng5dX9jIvL6O8JIqLi6NPnz4MGzaMuLg4Bg8e\nzOHDh+nfP3silWbNmvHwww/j4eGRrbxUqVI88sgjAOzfvz/fz7t06RJnzpyhffv2HDt2jDlz5hRd\nY8zi4+MZOHAg165dY/HixTRp0qTIP0O79ezdC0uXGqmVGjVydDRFK6+O6G3zMU0ptQj4TKy4b6OU\nagz8HxCMMRU3rgjizEgQkttEVsZ9tFI21s249lkr62Zj7oyDwbjJHxERkctl/lWpUiUSExOzlaWn\np+coK4yHHoKUlFK88UYZzpxR1K4thISk8tBDNyjCj7GoIG1JTzd+T0hMTMyxmCIpKYmePXty8OBB\nfH19mT9/Pl27ds2sb62MDiy/+EQEDw8PPvnkE1q3bk2rVq2YOnUq/fr1w9fXN7NeqnlONTU1Ndfr\nJSUlZV4zax0RISAggKioKIYNG8ZDDz1kU1tSUlKs+l7LGoct9Z2ZbkvuRGDUqJZUrlyWBx6IJCKi\neLYcLLZ/k7yGXUAVYB5wDWOzxb+Ar4C5GNNp4zBW3b0LfANcMNe7hrHEvII1w7ubPjOCnNOA7c1l\nq3N5zyzz+UeBGuY//5pL3efN58eZX18DLuRSt4+57rv5xe1M04COVNTTgBMmTBBAWrVqJZcuXcr1\nGsnJyaKUksaNG1s8/9hjjwkgW7ZsyTMWX19f8fX1zWzHW2+9JYD069cvW73CTANOmzZNAGnbtq2k\npqbmGY8lehrQNRR1W1auNKb7ly4t0svmyxmmARGRyyIyFqgHvAHEYjx/NMbcSb1t7rReBB7EeO7p\nNcBXRMaLSFH9Hn/B/LVKLuczpgnPAnEYHaY1dTOuXUlZvut+c12tmH322Wd4eHiwdu1avL1zn40t\nW7YsHTt25PDhw3z++efZzmWkZapfvz5dunSx6fNHjx6Nv78/69evZ+3atQVpQjZbt25lypQpeHt7\ns3r1akqXLl3oa2paUpKR/69NGwgKcnQ09mHV/SgROQf8F/ivObNEfaA6xr2cZMwZ2cV+qZhizJ/T\nNJfzd2JMOR4WkTSl1EmgvlKqvIgkWagLxjNYAEeAOkAj85/zqqsVo9OnTxMbG8vtt9/O3Llzc633\n4osv0qBBA+bNm0eXLl0ICAhg6dKl+Pv7c/r0ab777jvKlCnD8uXLLT6zlRcPDw/effddevXqxejR\no+nRowfly5cvUHsSEhJ44oknMJlMNGzYkHkWdsBr165djqXtmpafN9+Es2fhyy9Ldv6/vNi8eEJE\n4oHck7nZgYikK6W2A32UUneLyIGMc+bOsx0QKf8+U7UVIxtFV2Bdlrru5rKM7U4y6vYEepCzs+qJ\nce/Lchpvza7Onz8PwLlz55g/f36u9QYMGECDBg1o1aoVBw8eZNq0aWzZsoWffvoJb29vHnvsMV57\n7TXuvvvuAsXRs2dPBgwYwNdff01ISEiBF1zExcVx4YIxSbB79252796do87QoUN1Z6XZ5ORJmDMH\nnnwS7r3X0dHYkTVzhcV5YOGelbn8IXP5d5iXmWM8gPyhuTwgS90WGCOt/YBXlvJJ5rqvZSmrgTFq\nOw3clqU80Fx3sTVx63tWBldpizO3Q9+zcg1F1ZaHHxYpV07kr7+K5HI2K657ViVmk0QRWaeUWoWx\n0nC/UmonRmqmNkC4iHyWpe5vSqm3gFeAg0qpLRhTel2APRgLRDLq/q2UGo+RVumAUmo9UBPoi/Eg\n8KTiaJ+maZqtNm+Gb76BGTOgVi1HR2NfJW12MxCYDHhhJKX1BkKAx26uKCKvAi9grPZ7CuM+2ztA\nNxFJuanuQoxO8AwQgNEJLgfuF5ELaJqmOZm0NBg9GurXh7FjHR2N/TndyEpEuuRx7gYw3XxYc62F\nwEIr667GyLiuaZrm9N5/H44cMUZWrpL/Ly8lbWSlaZp2y7twwcj/17Mn3JTIxWXpzkrTNK2EmTQJ\nrl41dkAowM43JZJNnZVSyk0p9YxS6jul1Eml1N/m8vFKqSH2CVHTNE3LsG8fLFkCo0ZBHvuWuhyr\n71mZn1EKB3qZi1IAT/OfWwABSqlHgUfMyxE1TdO0IiRiJKmtVg2mTHF0NMXLlpHVi0BvjOeaagBZ\nt/F7AWOb+f7AqCKLTtM0Tcv0+efw889GxoqbNpx2ebZ0Vk8DB0TkBRG5iPHALABi5AB8FuO5JL3V\nqaZpWhG7etXI/9eqFTz9tKOjKX62dFYNgZ25nRRjq/kdQIPCBqVpmqZlN2MG/PUXvPsu2Jji0iXY\n0lklYkz/5aUiWUZcmqZpWuFFRcHbb0NgINx3n6OjcQxbOqutGIlk/S2dVErVA7qRx+hL0zRNs934\n8VCqFMya5ehIHMeWzioEuAHsVEq9hnn7DKVUf6XUFIxOqizGvleapmlaEdiyBb7+Gl57De64w9HR\nOI7VnZWInMDYRiMemIaR6FUBX2NsxFgGGCwikUUfpuZqLl26xKhRo6hbty5lypShWrVqPPzww+zb\nty9H3SVLljBmzBiLx+rVOTNkrVixgpYtW1KuXDnuuOMOhg8fnrndSH78/Pzw8/PLt15oaChKKUJD\nQ3OtEx0djVLKqutpmiUZ+f/q1YNx4xwdjWPZlBtQRCKVUg0xdgW+F2Mn3avA78Aa+Xc/KU3L1eXL\nl2nbti2nTp3innvuoWfPnhw9epR169axefNmtm/fTtu2bTPrL1y40GInBpCSksKgQYMyX0+bNo3X\nX3+dOnXqEBgYyLlz51ixYgWbN29m7969VKtWze7t07SisnAhHD5sjKw8PfOv78oKsvliOsaGhuvy\nq6tplsycOZNTp04xceJE3nzzzczyd955h7FjxzJy5Eh++eWXzPKoqCief/55Pvjggzyve/LkSUJD\nQ2natCk///wzFStWBODjjz8mKCiICRMmsGTJEvs0StOK2IULxoO/PXrAQw85OhrHszk3oFLqLqVU\nT6XUQ7kd9ghUcx1fffUVZcuW5fXXX89WPnr0aGrWrMmvv/5KbGwsYOyuGx8fT4MG+T8RsWjRItLT\n0wkNDc3sqACGDBlCrVq1CAsLIzk5uWgbo2l2MnkyJCXdWvn/8mJ1Z6WUqquU+h04CGwA1lo4vjZ/\n1RwsLCwMPz8/3Nzc8PPzIywszNEhAcbO1DExMTRq1IiyZctmO6eUonbt2gCcOXMGMEZLAPXr18/3\n2tu3b8fNzY1evXplK1dK0bFjR1JSUrKN2Kx16NAhqlevTqVKlfj1119tfn9WQUFBKKVyPfK6B6bd\nOvbvh48+ghdfhMaNHR2Nc7BlGvB9oBmwCfgVSLdLRFqhhYWFERwcnDmKiImJITg4GIDAwEBHhobJ\nZGL16tUW7x0lJCRw7NgxAGrUMB7pi4qKAqB69eosXbqUEydOUK5cOR544AHuv//+bO//7bffqFOn\nDuXLl89x7cbm//HHjx+nS5cuVsd77NgxunXrRkpKCps2bcp2L60gevbsSeWb8uSkpqayePFiTCYT\ndevWLdT1tZIvI/9f1arGNiCawZbOqiOwTUT62CsYjcwfpB9++CH+/v68/fbbrF+/nn79+vHyyy9z\n7NgxnnvuOQAiIiIACA4O5vjx44wfP57+/fszduzYHNNdycnJDBs2jDZt2hT4uuHh4fQv5OY57u7u\nDBgwIEd5eno6I0aMIDExkWbNmmWOpDJGVv379ycuLi7be/r3789nn31G+fLlSUhIIC0tLbOTu1lG\nB3HzNfKGq6MGAAAgAElEQVQSFRVFt27dSExMZMOGDXTo0CFHnY0bNxIfH2/x/QkJOdcbBQQEEBAQ\nkK0sODgYk8nEoEGDGDpUZyu71X3xBezYAYsX33r5//JiS2d1Azhlr0C0onPx4kWL5ampqcUciXVO\nnTpFUFAQP/74I56ennz44YeZ5zJGVn379s1c5XfgwAHGjx9PeHg4w4YN43//+19mx1Amly1Ty5Ur\nB8CNGzesiun06dP07duXS5cuER4eTqdOnSzWi4yMJDKy4E9rrFixgo8++gh/f3+WLl1a4OtoruHq\nVfjPf6BlS3jmGUdH42RExKoDWIXRWXla+55b6WjdurVY4/DhwznKEhISrHqvtXx9fQUj7VW2w9fX\nt0g/xxJb2pKWliazZs0SLy8vAcTHx0d27tyZrc7u3bvl22+/zfHeK1euSO3atQWQ48ePy/nz5wWQ\nNm3aWPysDz74QACZM2dOnjH5+vpK1apVpV69egKIv7+/XL9+PUe9kJAQASQkJCTXa506dSrPv/f9\n+/dL2bJlxcvLSw4ePJhnXFlZ+h7Ky7Zt22yq78xcvS2TJ4uAyE8/FX88BVXYfxNgj1jxM9aW1YAv\nA+7Aj0qpx5VSbZVSd1s6iqYb1Qpq+vTpeHl5ZSvz8vJi+vTpDooop5iYGDp06MCrr75KSkoKL774\nIgcPHuTee+/NVq99+/b07ds3x/srVqyYuZBi//79eHt74+7uzuXLly1+3qVLlwCoVatWvrFdunSJ\nM2fO0L59e44dO8acOXNsbV6+4uPjGThwINeuXWPx4sU0adKkyD9DK1lOnYK33oKAALjpdqyGbdOA\n7sAVoA3wmRV1NQfJWEQxadIkYmNj8fHxYfr06Q5fXJHh3Llz3H///Zw5c4bmzZuzdOlSWrdubfN1\nKlSoAECpUqXw8PCgfv36nDx5kqSkpByLLE6cOAFA06ZN872uh4cHK1eupGPHjjRs2JCpU6cyePDg\nIstEISIMGTIk8/kxZ/l30Rzr5ZeNbOq3cv6/vNgysloANAaOAv8DPsnj0BwsMDCQ6OhoTCYT0dHR\nTvUDcdy4cZw5c4bevXuze/fuXDuqo0ePopTiwQcftHh+x44dADRv3hyAbt26kZ6ezg8//JCtXkZZ\nrVq1rBrB1KpVi969e1OzZk2mTJlCcnIyo0YV3Z6ib775JuHh4bRt25Z33nmnyK6rlVxbt8KaNUb+\nP/PTG9pNbOmsOgL7gGYiEiAiT+d22ClWzQVcvXqVr7/+murVq7Nq1So888gh4+/vj5+fH5s2beKn\nn37Kdu6TTz5hz549dOnSJXPlYHBwMEopQkJCsq2GnDlzJmfOnGHkyJEoG5+uHD16NP7+/qxfv561\nawv/COHWrVuZMmUK3t7erF69mtKlSxf6mlrJduOGkf+vbl0ju7pmmS3TgNeA38VIt6RpBbJv3z5S\nUlLw9fXNkcEiq4wf6B988AEPP/wwXbt2pXfv3vj4+HDs2DG2bt1K1apVWbhwYeZ7WrRowX/+8x9m\nz55N06ZN6d69OydOnCAiIoI2bdowrgCZQD08PHj33Xfp1asXo0ePpkePHhaf47JGQkICTzzxBCaT\niYYNGzJv3rwcddq1a5djabvm2hYuhEOHjJHVrZ7/L0/WrMIwFmywADiJXg3o9KsBHSm/tqxatcri\nSsWbj1OnTmW+59ChQzJ48GCpXbu2lC5dWurUqSPDhg2TqKgoi5/xwQcfSOPGjcXT01Pq1KkjY8aM\nkStXrlgVv6+vr/j6+uZox4ABAwSQcePGiUjBVgNmvM7rGDp0aL4x6tWArmHbtm1y4YJI5coi3buL\nmEyOjqhgims1oDLq5k8pVR7YjLFFSAjwh4hcK3x36RratGkje/bsybfekSNHuOuuu7KVJSYmZi4W\nKOlcpS3O3A5L30N5iYiIsClrhzNztbZ88UUXliyBAwdKblqlwv6bKKX2ikib/OrZMg14JePaQE/z\nh1iqJyJiczZ3TdO0W8mff5Zn8WIYNarkdlTFyZZO5SeMqQpN0zStgMLCjFV/sbGtcXMD/Yiddazu\nrESkix3j0DRNc3lhYRAcDMZiVYXJBGPHQrly4ERPlzglm/ez0jRN0wpm0qSMjupfyclGuZa3XEdW\nSqk1QLiILM/y2hoiIgOLIjhN0zRXYt5T1Opy7V95TQMOAKJvem0NfV9L0zTtJiYTlC2bc2QF4ONT\n/PGUNHl1VnWBhJtea5qmaTYSMbJUJCeDhwekpf17zssLnCjHtNPKtbMSkZibi4AEEbG80xyglKoM\nlM3tvKZp2q3ozTfhvfeMdEotWxr3qGJjBR8fxfTpenGFNWxZYHEKyD0/juEN4HDBw9E0TXMtH30E\nkyfDk0/C7NlGxxQdDT/8sJ3oaN1RWSvPpetKqZeyvgRa3VR287UeRm8PommaBsDXX8OIEdCnDyxb\nBm56/XWB5fec1TsY03/K/LUT0Dmf93xeBHFpmqaVaD/+CIMHQ9u2sHq1ca9KK7j8OquM7T4UsAz4\nntw7IxNwGtheNKFpmqaVTAcOwEMPGdt+fPut8dCvVjh5dlYi8nHGn5VSQ4F1Wcs0TdO07E6dgt69\noXx52LQJqlZ1dESuwaoZVKWUJ1ARcM401FqJM3v2bMaMGWPx2Lp1a7a6N27cYO7cuTRu3BgvLy/8\n/PwYN24ciYmJFq+9YsUKWrZsSbly5bjjjjsYPnw458+ftyouPz8/q7avDw0NRSlFaGhornWio6NR\nSll1Pc01XLgAvXrBtWtGR6Wfnyo6VuUGFJEUpVQ5jHtWs+wbknYrmDVrFnFxcRbP1axZk27dumW+\nHjFiBEuXLqVRo0Y89dRT/Pnnn8ybN4+IiAh27dpFmTJlMutOmzaN119/nTp16hAYGMi5c+dYsWIF\nmzdvZu/evVSrVs3ubdNuTYmJ0LcvnD4NW7a4foLasLAwJk2aRGxsLD4+PkyfPp1AOy5ttGVtyhSg\nh1LqYXsFo90arly5QlxcHLNmzbK4ydqECRMy60ZERLB06VK6d+/OgQMH+PDDD9m6dSuhoaHs37+f\nt956K7PuyZMnCQ0NpWnTphw8eJDFixcTHh7O0qVLiY2NzXZdTStK16/DwIGwfz+sWgX33efoiOwr\nLCyM4OBgYmJiEBFiYmIIDg4mLCzMbp9pS2fVAPgdWKOU2q6Uek8pNdfCMcdOsWou4uTJkwA0aNAg\n37rvv/8+ADNnzsQjy3Kql19+GQ8PDz766KPMskWLFpGenk5oaCgVK1bMLB8yZAi1atUiLCyMZEu5\nbjStEEwmCAqCzZuNZ6r693d0RPY3adKkHP+XkpOTmWTHjLy2dFbTgNYYKwM7Ai8AY3I5NEcLCwM/\nP+PBDj8/47WTyOis6tevn2/dH3/8kdtuu43WrVtnKy9XrhytWrUiNjaW6OhoALZv346bmxu9evXK\nVlcpRceOHUlJSeGXX36xOd5Dhw5RvXp1KlWqxK+//mrz+7MKCgpCKZXrkdc9MM35iBhbfHz+Ocyc\nCU8/nf97XEFsLpl3cysvCrZsvviA3aLQilb2TXMgJsZ4DU7xuHxUVBQA5cuX5/333ycmJgZvb296\n9+5NixYtMuv99ddf/PPPP3TubPnRvsaNGxMZGcnx48fx8/Pjt99+o06dOpQvX95iXYDjx4/btAX3\nsWPH6NatGykpKWzatIm2bdva0NKcevbsSeXKlbOVpaamsnjxYkwmE3Xr6hScJcnMmfDuuzBmDLzy\niqOjKR4JCQnUqlWLv/76K8c5HzuuKLFl80X9/FRxsPSDtF8/ePll68/v3g2pqdnrZGyak2XarEDX\nLwInT57Ezc2N1q1bc+XKlczyiRMnMnz4cBYtWoS7uzuXLl0CoEaNGhavk/FDPy4ujoSEBNLS0qyq\na62oqCi6detGYmIiGzZsoEOHDjnqbNy4kfh4y+kyExIScpQFBAQQEBCQrSw4OBiTycSgQYMYOnSo\n1fFpjrV0qbHjb0AAzJkDSjk6IvuLj4+nT58+mEwmvLy8sk0Fenl5Md2OGXnz2s+qIpAqIqlZXltF\nRHL+L9WKz80dVYbYWGNK0MGioqIwmUwMHz6cl156iapVq7J7925eeukllixZgre3N7Nmzcr8YZ91\ntV9W5cxPWt64ccOmutY4ffo0ffv25dKlS4SHh9OpUyeL9SIjI4mMjLTqmpasWLGCjz76CH9/f5Yu\nXVrg62jFa906Y7KiVy9YvvzWSaMUExPDyZMnWbx4MVevXi3W1YB5jawuY6RbGm9+HY91e1VJPtfV\n8hIRUfjzfn7G1N/NfHwKf/0iMGnSJCZNmsQDD/w7s9ytWzc2bNhAkyZNmD9/Pq+//nrmgopr165Z\nvM7169cB4zc6W+rmJykpif79+xMTE4O/v3+u05AAISEhud5nio6OznNa77fffuOFF17Ay8uLr776\nigoV9GOMJcGOHfD449C6NXz5JZQu7eiI7C8uLo6yZcvSvHlzoqKiMqfaAwMDiYiIsGlqvaDy6lR+\nAk5mef0jemPFkmH69Oz3rMCpNs3J2kll5ePjQ/v27dm6dSuHDx+mevXqAFy+fNli/Yxpwlq1auHt\n7Y27u7tVdfNz6dIlEhMTad++PZGRkcyZM6fIl73Hx8czcOBArl27xsqVK2ni6g/luIg//jBW+/n4\nGGmULNwedTkXLlygW7du1K9fn7Vr11q8J1wc8trPqkterzUnljEUNzbNMf5nlZBNczJGF6VKlcLX\n1xcvLy8OHjxose6JEydwc3OjcePGeHh4UL9+fU6ePElSUlKO/1AnTpwAoGnTpvnG4OHhwcqVK+nY\nsSMNGzZk6tSpDB48uMgyUYgIQ4YMISoqiueff96uUyda0YmJMdIoeXnB99+D+Xcpl7djxw6ioqKY\nO3euQ+Mo0plWpVQLpZTOcOEMMjbNMZlwpk1zNm7ciFKKkSNH5jh3/fp1fvnlF0qXLk2jRo1wd3en\nc+fO/P333xw4cCBb3fj4eH755Rfat2+f+UxVt27dSE9P54cffshWN6OsVq1aVo1gatWqRe/evalZ\nsyZTpkwhOTmZUaNGFaLV2b355puEh4fTtm1b3nnnnSK7rmY/Fy8a96eSk2HjRvD1dXRE9nflyhVE\nhEceeYSoqCi6d+/u0HgK3VkppRoqpaYopY4Ae4GiWzamuZz77ruPcuXKERYWxtGjR7OdmzlzJmfP\nniUgICDz3tKIESMAmDBhAunp6YAxMnn11VdJSUnhxRdfzHx/cHAwSilCQkKyrVKaOXMmZ86cYeTI\nkSgbl2yNHj0af39/1q9fz9q1awvU5qy2bt3KlClT8Pb2ZvXq1ZS+FW54lHBJSfDgg8bIat06aNbM\n0RHZ3+nTp2nTpg3//e9/AbjtttscHFEBF0IopWoDg4EngIwHYxRwDviqaELTXFGFChWYN28ewcHB\ntGrVir59+3Lbbbexf/9+du/eTd26dZk169/B+UMPPcT//d//sWrVKlq2bEmHDh3Yu3cve/bsoX//\n/tmWgbdo0YL//Oc/zJ49m6ZNm9K9e3dOnDhBREQEbdq0Ydy4cTbH6+HhwbvvvkuvXr0YPXo0PXr0\nKPCcfUJCAk888QQmk4mGDRsyb968HHXatWuXY2m75jjXr8Njj8GePbB2LXTs6OiIisfy5cv5559/\n6NGjh6ND+Zel3GyWDqA6RtaKH4EbQDrGHlYXgPnA/YCy9nqudrRu3Vqscfjw4RxlCQkJVr23JLC2\nLTt37pR+/fpJjRo1pEyZMlK/fn0ZM2aM/PPPPznqpqWlybRp06R+/fpSpkwZqVevnrzxxhuSmppq\n8doffPCBNG7cWDw9PaVOnToyZswYuXLlilVx+fr6iq+vb452DBgwQAAZN26ciIiEhIQIICEhIble\n69SpUwKIr69vttd5HUOHDs03RkvfQ3nZtm2bTfWdWXG2JT1dJDBQBESWLCn66zvjv0tKSoqIiKSn\np8uff/5p1XsK2w5gj1jTB+V50tgSZCiwEbiepYO6AoSZ/7zMmg9y9UN3VgZXaYszt0N3VvZnMomM\nHWv8hJw+3T6f4Wz/LsePHxc/Pz9Zu3atTe8rrs4qr4eC1wC9gTIYU3xxwDqMab7NInJdKfVE4cd2\nmqZpzuWtt2DePHjpJZg40dHRFI/JkyeTlJTktCm/8rpnNQBjauIA8Arwg4ikF0tUmqZpDrJiBbz6\nKgwebHRYrp5GyWQy4ebmxpIlS/jrr79o1KiRo0OyKK/VgKcwRlR3A8uAWUqpNsUSlaZpmgOsXw/D\nh0P37vDxx66fRumPP/7g7rvv5tChQ1SoUMFpOyrIo7MSkfrAfcCHgCcwDohUSv2plJqmlLoFFnBq\nmnar2LkT/u//oGVLWLPG9dMoiRgPp8fHx5eIRyjyXLouIruAXUqpl4C+wJNAP+A1YCLGNGFDpVQD\nEfnT3sECKKVeAXLLmRMuIlvN9UoBLwHDAT/gH2ANECIiiRauGwSMBhpi5EHcAEwWkfNF3ARN05zM\noUPG5gK1axtplFw9TaOIoJRi1apVuLm5WbW3nKNZ9ZyViNzAWFyxTilVARiE0XF1AjoAx5RSe4CV\nwP9E5B87xQvwKuCdy7nzwFbznxcBw4CjwKcYOx2PBboope4VczZ5AKXUZGAqcBpjlePtQBDQQynV\nWkQu2qEdmqY5gdhYIztFmTKwaRM4wfOvdhUZGcmkSZNYvXo1d955p6PDsZrNM7Iikigiy0SkK+AL\nTAAOAW0xnrc6XbQh/kspVQmjo3pVRJSFY6a5XheMjmoLcLeIPCci3YBQoCXwnyzXrG8uPwg0FZFg\nEelvfr8PMLOo22Gs1tQ02+nvnaJ16ZLRUSUmGh2Vky6EKzJXr16lf//+REdHk5SU5OhwbFKo24ci\n8peIzBaRu4HmwNvA30USmWUZY9X8phwzEs9NEJG0LOVvA2nAs1nKRgDuQKhk34frE+AsEKiUyn9f\nCSu5u7uTlpaWf0VNsyAtLQ13d3dHh+ESrl410iidOgXh4XD33Y6OyP7KlSvHZ599xvbt26lTp46j\nw7FJka11EZE/ROQVjNGWvWR0VifzrGVMT/4jInuzForIVWAf4KOU8jMXd8Z4uHnTTXUFY5sUT6Bd\noaLOokKFChZ3kNU0ayQkJOh9r4pAWhoMGgS//gpffAG57K3pMn744QdGjBhBeno63bt354477nB0\nSDYr8oWZYt95inrmr0lKqZFKqdlKqQlKqYz8hCil7gBuA47kco3D5q8NzV9bAKdFxNKY+Oa6hebt\n7c3ly5e5ePEi169f19M6Wr5EhOvXr3Px4kUuX76Mt3dut2w1a5hMMGwYbNgAixbBgAGOjsi+YmJi\nePDBB/n5559L9C/KJW1H3/oYo6C9QKUs5TOUUkswpvSqmstym46MN3/1VkpVBDysqVvgiG9SpkwZ\nfHx8iIuLIzo6mvT0dFJSUvD09Cyqj3AoV2mLs7XD3d2dChUq4OPjQ5kyZRwdTon26qvw6acwdSo8\n+2z+9Us6X19fFixYwIABA6hSpYqjwymwktZZ1cMYDS4B3gUuAfeY/zwcIyVUuLluqqULAFfNX0sB\nFW2oW2TKlCnD7bffzu233w5AREQELVu2LMqPcBhXaYurtEPL7u23jWPkSGNvUle2bt06Tp8+zciR\nIxk+fLijwym0ktZZTQemi8i2LGVblVJ9MFYkjga+M5eXzeUaGU+/JWMstrC2bg5KqWAgGKBGjRpE\nRETkF79FSUlJBX6vs3GVtrhKO0C3JcOmTTWYOfMuunT5h0ceOcz27UUbm63s+e9y8OBBxowZQ8OG\nDfH396dUKfv9qC+27y9rst2WhANjmbpgLIYQYEsu9ZaYz9+DMQV4A/gzl7qTzXUH5/f51mZdt8TZ\nsi8Xhqu0xVXaIaLbIiLy7bci7u4i3bqJmHfBcDh7/rtcv35dXn/9dau3ximM4sq6bvUCC6XUMqXU\n4HzqBCil5tjaYRaRjKwU6Rgjoaa51LsT477XYTGWtZ8E/JRSlnbUy3hi7mBRBqppWvHZvdvYQLF5\ncyONkivf8stYlu7h4cF///tfKlasmP+bSghbVgMGYTz4m5cBwIv51CkQpVRvpZQopd63cK40xojq\nOsYqwO1ADaXU3TfVq2yuFyn/PlO1FeM5q6431c0oO4sxxahpWglz5IjxLFWtWvDdd+BCP7tzWLNm\nDU8++SRz5jhqvGBfeXZWSqm4jMNc9ELWspuOBGAg9stg8TPGgodApdTNqYEnYOQL/ExEkjFSLQHM\nNHc6KKUUMAvjuan3srx3McZU3xs3Pfw7AagNvG8eqmqaVoKcPg09e4KHB3z/PdSo4eiI7Ov+++/n\nueee44svvnB0KHaR3123BIwf5GAsFb+OsUuwJSaMB25DiySym4hIolJqLEbnsk8p9R1GctqWGPef\nTmHkDURE1imlVgH/B+xXSu0EWgNtMJLdfpblur8ppd7C2LProFJqC8b0XxdgDzDXHu3RNM1+4uKg\nd29ISIDt26FevfzfU1J98sknPPDAA9SpU4eFCxc6Ohy7yXNkJSJ+IlJXROpi7G21NOO1haO+iHQR\nkQh7BSsiH2Ekzt0K3A88A1QH3gHaS/YEuoEYCyS8MKYwvYEQ4DEL130VeAG4BjyF8TzXO0A3EUmx\nU3M0TbOD5GQjg/qff8I330CLFvm/p6RatGgRQ4cOZdasWY4Oxe5sWc/4AHDGXoFYS4xtS/pbUe8G\n5qXuVl53IeC6v5Zo2i0gLc3Yk2r3bli9Grp0cXRE9tW6dWuGDh3K3LmuPwFk9QILEdkuIieVUncq\npV5RSi1VSv0PQCnVVynVwH5hapqm5U3EyEjx7bfwwQcwcKCjI7KfL774gpSUFNq2bcuKFStKxOaJ\nhWVTbkCl1CSMfHkzgaf5d0rtCeCQUmp80YanaZpmnQkTjK3oQ0NhxAhHR2M/06ZN44knnuD993Ms\njHZptjxn1Q9jg8LfMTZf/DTL6QUYndhspVTfIo1Q0zQtH3PnwuzZ8PzzMGWKo6Oxrzp16hAUFMSY\nMWMcHUqxsmVkNQ7jmaPOIvIVRh4+AETkF6A3cBljN15N07RisXIljB9vPPi7YAEo5eiIip6IsGHD\nBkSEoUOHsnz58ltuXzNbOqs2wCYx9oTKQUT+xlil58JrbzRNcyYbN8LTT8MDDxidliv+/BYRXn75\nZfr27Ut4eHj+b3BRtnRWgpFLLy+pQLmCh6NpmmadyEhjEUXTpvD1166dRiklJYVRo0bRv3++C6Fd\nli1L1/cA3ZVSFUQk8eaT5uwPndCpiTRNs7OjR400SjVrGpsoumIaJZPJxL59+2jTpg0LFixAKYVy\nxTlOK9kyspoF1AQ2KqU6YN5WQylVQSnVCfgWqIOxt5SmaZpdnDkDvXoZU37ff290WK7GZDLx3HPP\ncc8993Dw4EHc3Nxu6Y4KbBhZicj3SqkXgXnAT1lOZeymq4B5IvJpjjdrmqYVQliYsVlibGxnSpUC\nNzfYtQvq13d0ZPaRkpLC0aNHee2112jSpImjw3EKNu3IJSIfmHPnjQDuxdhC/irGcvblIuLg7cw0\nTXM1YWEQHGykUQJFWppxf+rwYXC1zZxv3LjB6dOnqVu3Llu2bKGMK9+Is5FNDwUDiMhxERknIveK\nSEMRaSkiQRkdlVLKBQflmqY5yqRJGR3Vv1JTXW9b+rS0NAIDA2nfvj2XLl3SHdVNbHko+BOVx6Sp\nMrwEHC2SyDRNu+XFxUFMjOVzsbHFG4u9/f333/z888+88sorVK1a1dHhOB1bpgGfBDyUUoEiYsp6\nQinVGmMPqVYYW4VomqYVWGoqvPceTM8jDbWPT/HFY0+pqakkJydTu3ZtDh06RKVKlRwdklOyZRpw\nEfA4sEopVQoyVwK+B+zG2C9qF8ZOvJqmaTYzmeDzz6FRI3j5ZWjfHmbMAC+v7PW8vPLuyEqKlJQU\nHnnkEXr27MmNGzd0R5UHW7KuvwDMBx4F1iilAoFjGPtA/QMMEZH7RWS/XSLVNM2lbd9udE4BAVC5\nMmzebDxDNWECLF4Mvr6glODra7wODHR0xAUTFhaGn58fXbt2pW7dumzevJng4GBKlbJpvZvjhYWB\nnx+du3YFPz/jtR3ZuhpwrFLqGsaW7w8CN4C3gKkikmSH+DRNc3FHjsCrr0J4ONSubWROf/JJY3l6\nhsBA44iI2E6XErxJVVhYGMHBwSSbV4ycP3+esmXL4nXz0NHZZVmiqcC4sRgcbJyz028RBVkN+BrG\n1vUKI6vFFN1RaZpmq/Pnja08mjUzRlUzZsDx4zBkSPaOypVMmjQps6PKcO3aNSaVtKWNlpZoJifb\ndYlmriMrpdSQPN4XDUQC9wDfKKU+y3pSRD4pkug0TXM5V6/CnDnGlh6pqfDCC/D661C9uqMjs5/0\n9HQuX75MbC5LGHMrd0oiuS/FtGM78poGXIGRvPZmylyesYy9J9DjpnO6s9I0LZsbN2D5cmO/qfPn\njSS0M2bAnXc6OjL7+uabbxg7dixNmjTBx8eHGAtr8X1KwtJGEfjuO5g61fizJXZsR16d1dN2+1RN\n024ZGT/jXn0VDh2Ce++Fr76CDh0cHZn9HD58mKtXr9K2bVu8vLyoXbs2QUFBpKSkZLtnBeDl5cV0\nZ17aaDLBunVGJ7Vvn7HS5Zln4Isvsk8F2nmJZq6dlYh8bLdP1TTtlrBvn7EEfds2aNAAvvwSHn3U\nNTdIzDB79mxeffVVHnjgAX744Qd69OhBjx49stWZNGkSsbGx+Pj4MH36dAKdeWljejqMHWtkDl62\nzFj94uEBXbvCpElIbCzKx8foqOzYDptWAyql/IHxQBkRGWouKw1sA8KBBbltzqhp2q0jJsa41x4W\nBlWrwrvvwnPPQenSjo6s6MXHx7Ns2TLuuOMOHn/8cXr16kV6ejrPPvusxfqBgYEEBgYSERHhnCsb\nb9yA//3P6Ji+/RY8PY3nCPz8IOvyevMSze3F1A5b0i01Bn4FhgO3ZTklQHNgOvCrUko/1aZpt6j4\neOEw3Z8AACAASURBVHjlFfD3N6b6JkyAkydh1CjX66jEfN9m2rRpjB8/nk2bNgHQvHlzJk6cSLVq\n1RwZnu3S0mDFCmjc2Bg9/fMPnD5tnGvQIHtH5QC2LBANAbyAJ0WkT0ahiKQB1YHZQCNgYpFGqGma\n07t+Hd55x9iy4+234fHHjWXoM2aAqyVl2LJlCz169GDGjBkAjBkzhn379rFs2TIHR1YIMTHGbxhP\nPw3ly8OaNfD77061+sWWzqoD8K2IfHbzCRG5JiITgZ+BAUUVnKZpzk0EVq2Cu+4ybmu0amXcp/r4\nY6hTx9HRFZ34+HguXrwIwK5duzhy5EhmstnatWvTsiTuVZKSAnv2GH+uUwfuu894MnvvXnjkEad7\n2M2WaKoCZ/Op8wdQAtZgappWWDt2GCv7Hn8cypUzUiN9/z20aOHoyIrWzJkzqV27duZIavz48Zw6\ndYrnnnvOwZEVUHKyMQyuVw969oSkJKNj+vRT6NfPaVe/2NJZHQc6K6Xc86jTCvi7cCFpmubMjh0z\nfvHu2NG4pbFsGezfD717O+3POZukp6ezfv16jhw5AkC1atUYNGgQTz31FGAsNffw8HBkiAWTlARv\nvQV16xrDYH9/Y3lmuXKOjswqtnRWH2LckwpXSt2d9YRSqrZS6kOgLfB5EcanaZqT+OcfI9tEkyaw\nZQtMmwYnThi3Odzz+hW2hAkICKB///4sWrQIgOHDh7N8+XJalPQh486dxuqX5s3hxx+N5wm6di0x\nv2FYvbxDRBYqpVoAzwK9lFJxGKOoSsDtGB3fdmCqPQLVNM0xkpNh7lyYNQuuXTOWoIeEwG235f/e\nkuDIkSO89957DBo0iC5duvDss88ycOBAHnnkEUeHVjiXL8P8+cZvEq+/Dj16GEPgEtrp2pp1/Tml\n1DdAMMYo6k4gFSNP4CfARzdvzKhpWsmUnm4slHj9dTh7FgYMgJkzjdmjks5kMiEiuLu78/LLL7Nl\nyxYaNWpEly5d6N69u6PDK5yLF2HePFiwAP6/vTMPj6LKHvZ7AgQIBkIkgqAkyCayiLKJilFEEWYc\ncBsVXEAUXGYUFRdERRFcGBfkU2dUFkeDoiMOKoIOjrKqCCogPwTZg8MWtiAJgUDO98epTjpNdxYI\n6e5w3+epp9L33qo6t6tTp+65557z+++2HkrVRlBRqqjgyKKuT1fV3qraQFWrqmpNVT1PVV93isrh\niH5U4fPP4ayzYMAAcxSbMwf+/e+Koahee+01mjVrxkcffQTAmDFj2LhxI3/961/DLFkZMHGiLd59\n5hmbRFyyBNLSosbUVxRl6psoIuNFZEtZntPhcJQfixebg1iPHhYd/f334dtvzZkimlmxYgVZWRZc\n57vvvqNevXr5rudNmzblpGi2aW7aZJGBwd4mevWCZctsTUGbNkUfG0WUSlmJyMMislRE1gbbgJuA\nCjTV6nAcH2zcCDffXLBO6qWXYPly+POfo/+l/JprrqFFixakpaUB8OabbzJv3jy6du0aZsmOkvR0\nuOsuc0F/4gkrO/dci3F1xhlhFe1YUOI5KxG5AXgaC6+0F4gHsrFswTW9Zj8Dj5WxjA6H4xiRmWnz\nUGPGmPlvyBB45BFLKx+tZGZmMnHiRG644Qbq1KlD27Ztadu2bb7DRNWqVcMs4VGybp2Z+d56yz73\n62defhWc0jhY3ApkAu2AdcB/gOWqeo+I1AH+DiQDn5e5lA6Ho0w5cABefx1GjLD5+BtuMFf05ORw\nS3Z0qCodOnRg1apV1KpVi/79+0dfFt7iGDrUJhBvu83yrkRDLqwyoDRmwBbADFVdqxbB8RvgQgBV\n3Q70A04DhpSxjA6Ho4xQtQCzLVvC3XfblMYPP1jwgmhUVHl5eUyfPp0ePXqwbt06RIS//e1vLFq0\niP79K0hKvuXL7W1i+XL7/OyzsHYtvPrqcaOooHTK6gRsZOVjPdDE98FLDfIFcGOZSOZwOI6KSZPM\nMaxr11RSUmxa4/zz4eqroWpVy/7w5Zc2TxXpTJo0iZSUFLp27UpKSkqhoLGDBw9myZIlrFmzBoBe\nvXrRrl27cIladixdapOGrVrB1Kn2GeymNmgQVtHCQWnMgBuwcEo+1gDVRKSpqq7yyvbhYgM6HGFn\n0iQYONCXyFXYsAGefNIioL/5pk1zhDnjQ4mZNGlSoey6GzZsYMCAAWzZsoVHHnmE6dOnk5ycHJ0h\nkIKhagEX//UviI83s9+990K0pRwpY0ozsvoA6CAi40SkKZbbKhcYLiKxItIE6IUpNYfDESby8uCB\nBwpnHPdRsybcemv0KCpV5YEHHiiUBt7Hq6++CkCTJk0qhqLyYhEiYovbhg+31B2jRh33igpKp6xe\nBBYBtwBXqOo+4FWgD/A7sBKLzD6mrIV0OByhOXjQ5p1efNGW2NSpA5s3B2/722/lK9uRsG3bNubP\nnw/A9u3b2RyiM6HKIx7PPpvatauZ9B5/HLp3N3fz77+3Ni+8YHbb2rXDKWlEUZrYgHtEpBPQHtjl\nFT8I7AYuBw4AE1Q1ijOQORyRT26upSGaMwdmz7ZUHb//bnVNmsCVV9oUx44dhx8bifPx+/fvZ8+e\nPSQlJfHJJ5/Qq1cvkpKS2LJlC0lJSSQlJZGRkXHYcQ0jsTPF4WefFbCR01NPmblv9OgKuT6qrCjV\nomA1Fqrqau/zIVV9SlU7qur5TlE5HGVPTo4ppqeeslikCQm29vPhh2H9egv99u67NmpatQrGjbP4\npXFxhc8TF2cWpXCjquTlWWS20aNHc+KJJzJkiDkRd+rUiZEjRzJ9+nTEW4380ksvERfQmbi4OEZF\nQmdKw44doe2zCQlWd8IJ5S9XlFDsyMpLB3IBUANYhWUL3n+sBXM4jleysy3E0ezZpqS++w7277ep\njNatLV5faqqFQAoVJahvX9sPGwbp6UrDhsKoUQXl4eLBBx/kgw8+4LnnnuPaa6+lWbNm9OvXj969\nLcF43bp1D1sX1dcTetiwYaSnp9OwYUNGjRqVXx6x/PQTzJ1rpr0FC2D16tBto8E+G2ZCKiux15rx\nwM0BVZtF5HpVnXtMJXM4jhP27LFUQ7Nn27Zwoc1DxcSYW/ldd5lyOv98SEws+Xn79rVt1qzZXHjh\nhcdM/lDs3buX559/npkzZ/Lpp5+SmJjIzp07Ofvss/Nj8fXu3TtfURVF37596du3L7NmzQpLX4pE\nFdasMYW0dq2FqQd7U5gxA+rXh06d7C1jzBjYGiQ/bTSaNMuZokZWg7GFvhnAFCALSwtyAfCJ57K+\n/ZhL6HBUMHbtshdu35zTjz+aB1/lytChA9x/vymn884z771oYdu2bUydOpUNGzYwatQoqlWrxtix\nY2nWrBlbtmwhMTGRcePGhVvMsuNf/7Io599/XzBBeMIJdgPj4sxJ4o034JRTCo459VT/NQVGpNhn\nI5yilNWtmCNFW1XNd7sRkReBe4CrsOzBDoejCDIyTDH5lNPSpfYyXrWqvXAPGwYXXACdO0dNhnHA\nRk6zZs0iNjaWSy+9lBUrVjBo0CAaNWrE8OHDiY2N5X//+x/Vq1cPt6hHzv79lmZjwYICc97cuVC3\nrjlHbNxoib46drSb2bJlwbqAFi0OP5+ffVbT05GGDYkI+2wUUJSyagx85K+oPF7GRl2NjplUDkcU\ns3lzgUlvzpyCKDnVq5tjxJNP2sipY0eoVi28spaGvLw8Fi9eTL169ahfvz4jR47kueeeo1u3blx6\n6aV07tyZX375hebNm+c7R0SVolI1M15Skg1pJ0+2UPQHDlj9ySebQtq715TV/fdb5N/S4tlnZ0ei\nSTOCKUpZxWJp6wNJ96t3OI570tMLlNPs2QXz6PHxNs90442mnNq1g9go+6/ZvHkz9erVQ0S4+OKL\nmTVrFs8++ywPPfQQAwYM4NJLL+W8884DoEqVKpx++ulhlrgUZGWZ3/+CBQUjp+3bLQ/UNdeYN8vd\nd5uC6tTJzHn++VKiPXdKlFGcN6AeVqCq4m6So4IyaZLPgy6VYBYa31y6z6Q3e7ZZg8DWb3bpArff\nbsqpbdvoiRThIy8vj5iYGLKysujcuTM///wzS5cupXXr1gwaNChfQYElLWzatGmYJS4hBw4UmPPa\ntrW3iNWrLZuuiK1v+tOfbLh7zjl2TMuW8Le/hVduRz5R9q/kcBw7gsXTGzjQErHGxxcoqE2brH1S\nks013X+/7Vu3Ng++aOTnn39myJAhrFu3jpUrV1KjRg06dOiQnxMK4LrrrguzlKUkO9uScy1YYG7k\n+70VNw88YMqqZUv473+hffvo8mQ5TnHKyuHweOSRw9drZmcX5LU7+WQbMfm200+PbEvQpEmTQq5N\nmjt3LhMmTKBBgwaMHDmSmjVrkp6eTs+ePcnJyaF69eqMHz8+zD3wwxvypqanc9iQd9euAueHBQvM\nseH5522S8MMPoVEj+MtfCsx5p55qx1WuDNGeLfg4ojhldbWItC1lnarqxUcpl8NxTNi/3xKtrl5t\n5jzftnq1zT2FYtUqaNw4spWTP8Eilffr149ffvmFkSNHsmzZMj7++GNuuukmAJKTk/nFF0g10ggW\nomjgQKt74w0b8kKBOa9jx4LP6enRO9x1FKI4ZXWKt5Wm7rB5LoejPMnMLKyIfMpozRoLFKB+v9D4\neFNCrVvDli22QDeQ5GSLuRfJ5OTksGvXLk4++WRycnK48847D4tUfvDgQV599VVGjhxJ//79GThw\nIJUqVQqTxEWgWvBWMG0a3Hln8CHvsGEwaJDNO3XqFNyc5xRVhaEoZXVRuUnhcJQCVQsCEKiIfNv2\ngKXqJ51kCik11ZRO48YFW1JSwXOx8JyVESnrNffu3csJXty4yZMn89NPP9GzZ09SU1OZMGECAwYM\n4KyzzuLHH38kNjaWPcG0LpCZaflTq0WKz/yiRWbC87+RmZm2fgkgLS34GwTYqGno0PKT1RFWQior\nVZ1dnoI4HP4cPGjPq2DmurVrzevYR0yMTUP4Io77K6PGjW30VBLCFU9PVVFVYmJiWLx4MbNnzyYp\nKYk+ffqwfv162rVrx86dO9m1axcJCQlMnDiRWbNmUb9+fVJTU2nfvj0jRozIdxuPiYmhYcOGpAex\na5Z7pPL162Hx4sI3csMGW3xWqZJF3X39dZtfOu20gpt28KDNKf397xYoMZiN1oUoOr7w/aO47ei2\ndu3a6ZHy9ddfH/GxkUJammpysqpIniYn2+fiyM5WXbZM9eOPVV98UfWuu1S7d1dt0kS1cmVVG0PZ\nVrWqaosWqn/8o+o996iOHas6fbrqypWq+/eXfX+OxT3Zvn27fvDBB/raa6/ll3Xr1k3j4+N1ypQp\nqqr67LPPKqCXXHKJqqpmZ2fr7bffrs8884zu3r1bVVX37Nmjhw4dKvJaaWlpGhcXp5hZXgGNi4vT\ntJLcmNKwe7fqd9+pTpqkOmKE6s03q55/vmpmptU//HDBTaxdW7VDB9XrrrPjVFV/+031f/9Tzcsr\nqjOqcXGFfxBxcSX7kUUwFeH/XvXo+wEs0hI8Y8P+kK8o2/GsrIp6luzcqbpwoerkyaojR6r27696\nwQWqDRoUbg+qtWqpnn226jXXqA4dqjpunOrXX6ump6sW82wuw76kaXJysoqIJicnl/jhnpeXp/Pm\nzdO0tDTNyMhQVVM8zZs31yFDhqiq6oIFCxTQmJgYPXDggKqqPvDAA3r33XfrokWLVFV1586dmpGR\noXlFPbyPcV8KceiQ3YCvv7YbMnSo6p//rLppk9U/80zhm9iggd3g9HSrX7PGfgA7dx5tZ1STkzVP\nREv8NhThRPv/vY/yUlbOdd1xxGRlwbZtFnEm2Pz3TTdZgFZ/6tUzc123boVNdU2aWETxcHrbBfOg\nG+h5naWmprJ69Wrq169Ps2bNmDt3LsOHD6dq1arMmDEDgO7du5OVlcXMmTPp1q0bNWvWpFWrVpzh\nJdRr3bo1S5YsITk5OT8N++jRowvJULsMM8P29TYFivxaDxwwc52/rfWee8zle9w4c2LwUbmyZbfN\nyDBf/iuvNA+8xo3NjBcYXum002w76s64EEXHO05ZOfLZt8+UT0ZGyfb79hV9vrw8CwDgc2o47bTI\nCNR68OBBdu/eTfXq1alRowarVq1i4cKF3HvvvYd50GVnZzNs2DBat27NtGnTGDp0KE8//TQxMTHk\n5ORwmvcgFhFmzJhBnTp18svuuOMO7rjjjvxzVa9enTZt2pRPJ4O5e996qzkzNGhgwVebNYNPPoEr\nrij8VhEXB5dfbsrqwgtt3sj3RnHqqYXDcjRrZpvDcYxxyqoCk5NjiqWkysffacGfqlXNoy4pyfYt\nWhT+/NBDdo5AkpOPLM5nSVFVfvvtN3bu3EmzZs2oXr06X331FT/88AOtWrWiR48eLFu2jMGDB5OT\nk8O8efMAG+GsWLGCt99+mxtvvJHp06czePDgkNdJT0/nvffeY/DgwfmjpPPOO49vvvmmULsuXboc\nu86COR1kZsLu3TYMrV3bbty0aVa2e3dB/RdfHD7czcmBsWPt7/r1Tcm0bGkeJT5l1LixBWn1DXGd\nMnJECE5ZeYhIHDAUuB5oAGwE0oBnVDX3WFyzuDh0gRw4YG7ZJVE827bB778HP0+VKoWVTdOmhT8H\n7k84oWjzXGws9O8/idzcYVic44ZUqTKKUaOKd6NTVUSE7Oxsli5dyq5du+jRowcA//jHP1ixYgVX\nXXUVXbp0YcqUKTz66KOkpKQwY8YMDh06lO/dtmjRItq1a8f777/PG2+8wW233UaPHj2oVKkS+/bt\no3bt2vlx74YMGUJ2djYdOnQAoE+fPlx22WV069aNLr/9xtNAQ68njwDfNGxI586di+1LMR01ZeFT\nKnXq2Be8fTtMmVJY0ezeDf37Ww77pUuhZ08r83+beP11Gzlt3GhJ/XzUqmUp0rdtCy6HiEV8qFXL\nPjduDCNGHF3fHI5ywCkrQEQqAx8B3YFFwJdAW+BJoJOI/NGbCCwzgsWhu+UWeyFu2DC4Etq9O/i5\nKlUqrGA6dgyteE46ydZNluXcUG7uW4jcCfjsghtQvYWFCxfSt+8YAO68804yMjJ48sknOeOMM3j0\n0UcZO3YsV111FRMnTmTdunX5CiE7O5vq1avz7rvvsmTJElq2bEmXLl2oXbs2rVu3pnnz5gBUrlyZ\niRMnUrNmTRo1sow1o0eP5oUXXqCGZ29s0aIF8+fPLyTvAP+HO5CUlERSUhLvXX45Z/397/gslSnA\nm8BPPXuamWzv3gKlkpRkE3A7d8K77xYoGV/9jTdaYNQVKyxwYGZmQaoJgJdftojeW7da5FvrkCma\nhAT4wx+s7MQToXv3gvKEBFM0PuXZqpXNN9WqZT76vkW+KSkFEXb9adiwQFE5HFGElPEzOCoRkX7A\nROCfQH+fYhKRiVi25BtVNa2oc7Rv314XLVpU4mvas2QSUDAagVFAX2Ji7MW7qNGO/z4hIfRCfd/9\nFRG2bt3Krl27SExM5KSTTmLTpk18++23xMTEcMUVVwD2sN+xYwcDBgygWbNmpKWlMXnyZDp16sRj\njz1Geno6qampZGVl8euvv5KQkEC1atW4cv/+w0YkH1SqxMGDBwHyzWfjx4+nc+fOTJkyhblz53LO\nOedw3XXXkZWVxZw5c0hMTKRdu3ZUrlw5fyRUJKqQm2sTaL4tJ8ce8iedZG8DX311eP0551jUg+3b\n4YknrOy99w43ndmXV3AtH6NHW0DUNWsKwlvExRWMbB58EPr1szeN4cMLyn3Kpn17G9Lm5tqbSEKC\nOSeU1VtEqBXOb7wR1Yn+IjKt/RFSUfpytP0QkR9UtX2xDUviMljRN2AhcBCoG1DeDHOmml3cOUrr\nug5pej1VdB3oIdB1oNdTRSFNV65cpfPmzdO1a9eqquqGDRv09ddf13HjxuUfP3ToUL3lllt08eLF\nqqo6fvx4Peecc/Tee+9VVdWNGzdqfHy8xsTE6NatW1VV9bLLLlNAR4wYoaqqU6dOVUDr16+ff966\ndetq1apVdcaMGaqq+sorr+jZZ5+tDz30kKqq7tixQ2+44QYdNGiQZnprafqA7g3wQ98L2gdsEdSi\nRarz5qnOnKn6ySeq77+vunSpXTAzU/Xpp1Ufe0x1yBBbbDVggC2+UlXdskU1NVW1Y0fVNm1UmzZV\nPeUU1Zdesvr16w/3gQfVp55S78sLXu99B7pxo63/qV8/eDvf9thjqs8/b+7bH36oumqVHX/woGpG\nhqrnih5ROHfviKai9MW5rpcTIhIPnAX8qKqFkk2q6q8isgXoLCKxqnog6EmOgL5yD69rboDJKZcY\n7mbo/efyybRpPPzoozz1xBOsnjWLZwYNom5CAgM6dYLcXL6bMoWVe/dybe/esGULKT//zGXZ2bRM\nT4d33qF2o0YMGDCAhCpViH/zTahUiVfq1WNf797U27ABvviC1NRUfp47l4Zjx5rZKjeXzZ07IwcP\n5scsuuvaa7nrnXfgP/+Bzz4j8cAB3snNtSjWNWvC5s28AwSOf2oAz1WqZOdpH+Sl6fHHLSBfVpaF\nOwcbWVSrZvuWLa2scmVTF7VrF65v3NjqExNh5Egr823VqsGZZ1p9vXrmAedfX716gVviKaeYKQ9C\nm86Sk0PP61SqZMPgSMS5ezsqEMe9sgLaAJWA5SHqf8HiJKYAv5bVRUfqDgK9uGsAaeyEadNY06cP\n0r8/bN9O15tvZh3YfEjr1gB8NWyYPaS3boV69egKdAWbkJ8yhRrDhvHSSy+ZGapuXQAag5mZYmOh\nYUMSuncn4fTTLZxNbCxUqYJUqWJ/+7wzfPMoXj2++pQUqz/hhJBreBocOmTmuI8/LqxIqlc3JQK2\n37fPXA6DmcBOPNGSSIUiPt68VEIRGwueI0WxjBoVucEBHY7jHKes4ERvvzVEvc+tIbEsLxoqqpkC\n8vTTND7vPFuYlJNj8ww+ZREba5vPnTgxEebPLyj3tUn0xK1Tx5Scrz4wynadOgVBQ4ORkACffx66\nPj4eSU4OOiKR5GRTQn/6U+jjRUyBRQJ+wQE1PR0piYumw+EoF457BwsRuQF4B3hKVR8PUv8OcAPQ\nRVXnBdQNBAYC1K1bt93kyZNLfN0ze/WidpBo0rtq1mTJxx+Xqg/h5qQvv6T5889TyZeJFThUtSor\nhwxhW7duYZTsyPGPch7tuL5EJhWlL0fbj4suusg5WJRkA67FBjR/C1H/vld/dlHnKXVswLQ0zY2N\nLTSJnxsbG72T4BVsMr+iTH6rur5EKhWlL+XlYOEyk4Ev9kKooGw+M+GmMr1q375UnjABkpNREUhO\nts/RanLq2xfWr2f2V1/Zup9o7YfD4YhInLIyBwqA1iHqmwLbVXVLmV/ZPeAdDoejRBz3ykpVN2MK\nq62IFPJBFpHmmC/EzHDI5nA4HA7juFdWHv8AYrEQEgCISBXgRe/jq+EQyuFwOByGc103XsMcLQaK\nSBtgCZAKnA68oqrzizrY4XA4HMcWN7ICVPUgcAk2kjoFuBnzALzb2xwOh8MRRtzIykNVs4H7vc3h\ncDgcEcRxvyi4rBCRDCBIYLkSUQfYXobihJOK0peK0g9wfYlUKkpfjqYf2wFU9bLiGjplFQGIyCIt\nyQruKKCi9KWi9ANcXyKVitKX8uqHm7NyOBwOR8TjlJXD4XA4Ih6nrCKDN8ItQBlSUfpSUfoBri+R\nSkXpS7n0w81ZORwOhyPicSMrh8PhcEQ8TlmFERG5XETmi0imiOwRkTkickm45TpeEZF6IvKmiGwS\nkQMislFEXhGRUBH5IxYROU1EJonIVhHZJyIrRORxEYmQTJfFIyJ3ioiKSEKQusoicp+ILBeRbBFZ\nLyIvikh8OGQtiqL6EdCup9eubXnJVlqKuSdxIjJKRFZ6v7ldIvKliFxcFtd2yipMiMgg4BPgNOBD\nYDZwDjBdREqYhz28iMiF3g+3uO2JcMtaHCKSCHwD3Ar8H/BPbA3IXcBcEYmaLHki0gj4DuiD9WUS\nkAs8CUwTkUpFHB4RiEgMcEsRTf4BvAAIljx1DXAvMFtEqh57CUtGCfrhz63HUpajpai+iEgs8DXw\nCLAfeBv4FugKfCEiRaQLLyElSXrltjJP+HgKcABYDNT2K/8DFubps3DLWMJ+NAHGFLH96vXninDL\nWoK+POfJer9fmQATvPKh4ZaxFH352JO5v19ZJb++DAy3jCHkFqANFu5slierAgkB7S70ymcCVfzK\nh3vlj0ZDP7y2p2NxSaf6tWsb7ntxBPfkDq88Dc8Xwiu/wiv/zf9eHZEs4f4yjsfN78HYOUjdHGB9\nuGUsgz52xt7mXwq3LCWU9yfgEFAtoLypd6+mh1vGEvajttePH4PU1fH68m245Qwh+wl+D0P/LfDB\n+C+vvF1AeQ3sJXBDNPTDa7s9SLtIUlYlvSdfeuUpQc6xwKu74GhkcbEBw0N37B/q28AKVb0gDPKU\nKSJSE5gMrAAeCrM4JSUG+4cKpIq331uOshwNzbG+/BRYoarbvbBgHUQkXlV/L3fpiiYHuN7v8+NA\niyDtLgC2qeoP/oWqmiUiPwKdRCRFVdcfM0mLpqT9ALgN8Jkt78D6FkmUtC+NgF0hvvN0oCNmUTpi\nnLIqZ0SkBtAK+NSbO7gcaI89KOcDX6j3OhLFPAWcCnRR1QPhFqaEzMbMHXcDoyHfRv+wV//fMMlV\nWg55+9gQ9bGYSfBkIKKUlVr2g8m+zyJyOwEPRhFpAJyE3a9gLAc6Ac2A9cdE0GIoST/82v7br91l\nRJiyKkVf/kLBby8fEakMnO193Ho0sjhlVf6cjD0s9mMmv3MD6ueLSG9VjcoAl54n011AmkZXHrAn\nsHvxnIhcjmWP7gC0BT4DJoZPtFLxC2Z+7SIiVVQ111fhOe7U8j6eGA7hygCf3KEefLu9fWI5yOLw\nUNUZIapGYU5kGdjL+BHjvAHLH58b9J+xOYRLMbtwMuaBdp63j1Z82ZZHhFWK0rMfWOj9fT5mhYIL\n+wAADJFJREFUnvG5EKdjE80Rj6ruBd7C+z2JSBMRqSEif6Bgrgesv9FITW8fSv4sb+9exMOIiCSJ\nyBTgQSAPuENVc47mnE5ZlT8+80we5iU3U1WzVDUde0D+BvQUkVPDJuER4r259wQ+UNXV4ZanlHwA\n3I65eTfHJus7AF9hcwnPh0+0UnMfJvf1wCpsvm0asBP4j9cmKkfu2KgRoHqIet//V3Y5yOIIQIxb\nsPnqK7HfXG9VnXK053bKqvzxTdSvUdXl/hWeyeYz7+Pp5SpV2XCPt38trFKUEhE5E1Oyi4CbVPVX\nVc1W1UVAL2AzcHskrd8pCm901Q24DJs/fAZzjz4HqId5zB3V/EEYyfD2oRZq+8yEm8pBFocf3lrF\nGcB4zAw7GThDVT8ti/O7oXL5s97bh/IuywpRHtGIyInA1cBqVZ0XbnlKie/FYI6q5vlXqOpeEVkA\n9MZMa7+Wt3ClxYtSkaeqXwBf+JXHY84981U1Ws2AG7BRU6sQ9U0xq8XyEPWOY4C3aP5rzElpPbaW\nb2ZZXsONrMoZVc3E/pFO91y8A/ElMVtWflKVCb0xF9wPwy3IEeDzijs5RL3vLX5XOchyVHiRBLKx\nCBaBXI0594SaDI94VPUQ5glYV0Ta+Nd5IYA6AgtUdU845DuOGYYpqh+x9W9lqqjAKatw8SYQB7zo\nH/pGRK7AXFc/U9XN4RLuCLnc238eVimOjHmYwrpaRAp5Z3qxGrsAc1U1I9jBkYS3VGARcKaItPOV\ni0hDzCSYiYUqimZ88j/r+/8REcEW21cDXgmXYMcxfbD5xCtUdeexuIAzA4aH/4fNJwwAzhWR74D6\nwCXAFmzNQtTgPShSsR/r92EWp9So6h5v/cjbwBwR+RIzNzXC5n4ygTvDKGJpGYa9NMwRkanY+pde\nmNfptaq6u6iDIx1V/UREPsA8an8SkW+AdphV4lNVfTesAh5neM5gDbG53fvscRCUV47G8copqzCg\nqoe8wI73YYEhbwB2YA/L4Z5nYDTRBEjAQvzsC7cwR4Kqvisiv2IRN1KBizGPuUnAk9Hk3aiqM0Xk\nUuAx4E/YHM5PwAhV/SqswpUdfYGlQH+gH/A/LDbgs2GU6Xilnrc/mQInq2BMBY74/8glX3Q4HA5H\nxOPmrBwOh8MR8Thl5XA4HI6Ixykrh8PhcEQ8Tlk5HA6HI+JxysrhcDgcEY9TVg6Hw+GIeJyycjgc\nDkfE45SVI6oRkXNF5EMR2SQiuSKSKSLfisj9IhIXbvnKChF5S0RURFK8z/28z4PDKNMsT4bitrbF\nn83hKBoXwcIRtYjIAOANIAf4EotiUBOLQPE8MEhEuqrqb+GT8pixHHgZi0wRbqZgedhCccxiKorI\nW8DNwFmquvhYXccRfpyyckQlIlIfC1i6DujiH/hXRCoDY7GkiS9iMeQqFKr6PZETh/EVVZ0VbiEc\nFRtnBnREK3/AImy/EBihXlUPAvcDe4DLpYjImqVBRGJFxP3PVHBEJFQWYkcYcf94jmiljrcPOi/l\nBdT9K/AkUAUKzfPcKiLXichPIrJPRNaJyKPeiCwfr+2XItJWROZiCTNr+tVf782P7RWRXSIyTUTO\nD5RFRGqJyBMi8ouIZIvINhGZLSI3BmlbU0Re9ubg9nkyXhGk3WFzVn7yJovIuyKyw7veNyHkaici\nM0Uky5NpvIgkeed5K+i3fpSIyJUi8pWIbPe+t19E5BkRSQrS9iwR+VREdntzkQtE5Ab//mImQLDo\n6+sDju8vIt97/dvjXbdHQJsUr7/jRKSriPwEbCz7njuOGlV1m9uibsOSPSqWh2oAkFCCY/p5xyzA\nUrt/BIzD0oEo8F5AewVWYilCFmF5lKp7dc959euAiVh09p1YhPOb/c4Rg5nrFFgIvI6l+870yh7x\na1sF+MYrX+q1nY+l+FjrlacE9GVwEHm3YHNab2KJFhUbZdbza3sulqQxB5tzehtL8fB/Xvu3SvB9\nzvLaXljCe/ZXr/1W7/sa53e95UAlv7ZdgX3YC8K7wD+9finwmNdmjHeceud73O/4V73y9d738DY2\nd6bAg37tUryyxd5vYjY2Wg/7b9xtAb+fcAvgNrcdyYZlvP3Ce9AocNBTBi9hGXETgxzje8DnAal+\n5TU8ZaTAZX7lvnO/HHCei73yj4FqfuX1MMW3B6jtlXX02k7Ey3LglTfx5FjmV/YXv7YxfuUP+smS\nEtCXQGWl3oPZ//jxXvlt3mcBfvEezh392tUBfj4CZfWhpziCbU382i8HtgFJfmUxWDp0Bdp7ZbGY\nw8Ze4IwA+TZhLyjVvLK3vGPb+rW7zCubHXB/EjHlmAe08spS/L63+8L9u3ZbEb+3cAvgNrcd6YY5\nCN2CeQLu83vo+JTXv4GGfu19D/jPgpzrcq9unF+Zeuc9IaDtR17dKUHOM8Sru8773BR4GGge0E4w\npbber2yxd72kIOf9tYTKKgeoGXCsbxQ60vvcyfucFuQ6Vx6Bsipqu9Cv/WDg6iDnecW/rZ+8Lwdp\nezfwne/7DKGspnpl5wc5/nqv7mnvs09ZbcBPwbst8jbnDeiIWtQcKSYAE0QkFjgbOA97s74Ie+i1\nEZHWqprtd+j8IKf7xts3Dyhfp6p7A8rOxZThkCC+Gyn+51HVVVj69TgRSQUae23OBeIx0yHemrDW\nwFJVDebq/S2m+IpjvaruCSjzZQau4e3P9vbBvocj8TC8SEvgDaiqY8RoBZyBfQ9NsZTo/nT09v8N\nco6xmKdnUXQCflfVeUHqFnj7lgHl/6eqecWc1xFGnLJyVAhU9QD2xv0d8IKINMXMhKdh2XIn+zUP\npgx8Cik2oPxAkLYnYv87RWVFjYd8N/rnMBNfLDbyWY2NSjr7ta+NmcS2hDhfVhHX8ieniDqfZk30\n9tuDtNlWwuuUGhHpCfwdS4GumKnvJ+AHoItfU5/zTKjvojjqAGtC1PleWgI9/oLdZ0cE4bwBHVGJ\n5+G1LlS9N6L5h/exUUB1MNfk+t5+ZwkunwVkqqoUsQ3x2g4F7sNMhy2AOFVtrap/xUZn/ucEOMwr\nLkC+smC/t68VpC7U9Y8KEWmGOXIAdMdMqw1VtReHL2wOKZ+IVBKRasUsIcimQCEH0sDbHzOl7Dg2\nOGXliFaWAQ1FJFAR+eOzHASOpDoGNsSiXkDJIkIsAWr5Qh/5IyJ/EJE0EenkFf0JU0q3qOoKVZso\nEZE6+LnBq+puIB1o5dX5n7MqZtoqK9Z6+zOD1HUrw+v4cwm2Lm6kqv4nwCx7WkDbn719sD6/ic3r\nFWUS/RlIEpEWQep8ruvfFS+yI5JwysoRrbyG/X7TRKRuYKWIdMDMdDnA9IDqa0TkbL+2icCjmGnq\ngxJce6K3f9Z/bZaINMHMXH/EHCLAzEuVgVP92tXw2gXyNlAVGB5Qfh/maVhWfIWNPvqJyOl+ctUL\ncu2ywmdmS/EvFJE+FCgQH1M8+e7wX38lImdiDhKrVHWlV+ybZ/Kf0njH2wfen7pYVJPfKWwWdkQB\nbs7KEZWo6j89hXQXsFZEZmEeXbHYiKE9Zk7qr6qbAg7/HzBfRD7F1jtdDtQFxmjJ4sv9E1NI1wJt\nReRbIAG4FFM2N6rqLq/tBMyZ4hsR+RgzQXbH1mctA1qIyARVvQV4xpPlLyLSGXN2aAZcgHk8lsmo\nR1V3i8jDmKPCAhGZDuR6ffItiC1rZ4NPsP4N9ZTOJmyE2xxzfb8WGC4iuao631vs/AawTESmYYu/\ne2Fr0fyD9/rk/X8i8rmqPomt37oKG9UuF5Gvse+9J2Ye7KeqwebrHJFMuN0R3ea2o9mwB9BUbEFr\nLuYosRgYDSQHtO2H5+6NjbrWYG/wS4Bb8VsH5bVXYHGI61YC7sXWDu3HFrp+SnB36duBFZj5aiXw\nGGYS6wXsADL82tbGRl0Z2DzWXMz5YCQlc10/TF7gQq9uTED5Vdj6sv2Ys8VEoI3X9sUSfPezKN2i\n4DaY0t2DzRm9B5yOOawsxEbBV/i1vwLz3tuHOVtMAzoEnLMBZtLbD/zgV14VGy2v8Op2Yg433QKO\nT/H6MDXcv2W3Fb2Jd8McjgqPiPTDHsj3quqYMIsTkYjIhdgi3ftU9aUwi+Nw5OPmrByO4wwRiReR\n/SKyRESq+JVXAR7wPn4WHukcjuC4OSuH4zhDVX8XkYnAIGCFiMzG5qguwjzzxqrqr0Wdw+Eob5yy\ncjiOT+7C5s9uAq7B5m1WAM+q6pvhFMzhCIabs3I4HA5HxOPmrBwOh8MR8Thl5XA4HI6Ixykrh8Ph\ncEQ8Tlk5HA6HI+JxysrhcDgcEY9TVg6Hw+GIeP4/cLVGZqIbC9EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11842d150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this function computes the airtime of a packet\n",
    "# according to LoraDesignGuide_STD.pdf\n",
    "#\n",
    "def airtime(sf,cr,pl,bw):\n",
    "    H = 0        # implicit header disabled (H=0) or not (H=1)\n",
    "    DE = 0       # low data rate optimization enabled (=1) or not (=0)\n",
    "    Npream = 8   # number of preamble symbol (12.25  from Utz paper)\n",
    "\n",
    "    if bw == 125 and sf in [11, 12]:\n",
    "        # low data rate optimization mandated for BW125 with SF11 and SF12\n",
    "        DE = 1\n",
    "    if sf == 6:\n",
    "        # can only have implicit header with SF6\n",
    "        H = 1\n",
    "\n",
    "    Tsym = (2.0**sf)/bw\n",
    "    Tpream = (Npream + 4.25)*Tsym\n",
    "    payloadSymbNB = 8 + max(math.ceil((8.0*pl-4.0*sf+28+16-20*H)/(4.0*(sf-2*DE)))*(cr+4),0)\n",
    "    Tpayload = payloadSymbNB * Tsym\n",
    "    return Tpream + Tpayload\n",
    "\n",
    "def plotAirtime():\n",
    "    \n",
    "    cr = 1 # fixed coding rate\n",
    "    pl = 20 # fixed payload length in bytes\n",
    "    \n",
    "    bw_list = [125, 250, 500]\n",
    "    sf_list = [6, 7, 8, 9, 10, 11, 12]\n",
    "    \n",
    "    time_in_air = np.zeros((len(bw_list), len(sf_list)))\n",
    "    \n",
    "    for i,bw in enumerate(bw_list):\n",
    "        for j,sf in enumerate(sf_list):\n",
    "            time_in_air[i,j] = airtime(sf, cr, pl, bw)\n",
    "            \n",
    "    airtime_fig = plt.figure()\n",
    "    \n",
    "    at125, = plt.plot(sf_list, time_in_air[0,:], \"bo-\", label=\"125 kHz\")\n",
    "    at250, = plt.plot(sf_list, time_in_air[1,:], \"ko:\", label=\"250 kHz\")\n",
    "    at500, = plt.plot(sf_list, time_in_air[2,:], \"ro--\", label=\"500 kHz\")\n",
    "\n",
    "    plt.legend(title=\"Bandwidth\", handles = [at125, at250, at500])\n",
    "    plt.grid()\n",
    "    plt.xlabel(\"Spreading Factor\")\n",
    "    plt.ylabel(\"Packet Airtime (ms)\")\n",
    "    plt.xticks([6,7,8,9,10,11,12])\n",
    "\n",
    "    plt.rcParams[\"font.family\"] = \"sans-serif\"\n",
    "    plt.rcParams[\"font.size\"] = 20\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "plotAirtime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# node placement\n",
    "\n",
    "Lpl_matrix = Ptx - (sensi[:,1:4])\n",
    "maxDist = np.dot(d0, np.exp(np.divide((Lpl_matrix - Lpld0),(10*gamma))))\n",
    "bestDist = np.amax(maxDist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== Location generation =====\n",
      "generated locations for 1 base-stations\n",
      "generated locations for 2000 nodes\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgwAAAFSCAYAAACe6ocVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiLi6/8DSwPlfylttVUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1187b2390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BSLoc = np.zeros((maxNrBS, 3))\n",
    "nodeLoc = np.zeros((maxNrNodes, 3))\n",
    "\n",
    "def placeRandomly(number, locArray, xRange, yRange):\n",
    "    for n in xrange(number):\n",
    "        x = random.uniform(xRange[0], xRange[1])\n",
    "        y = random.uniform(yRange[0], yRange[1])\n",
    "        locArray[n,:] = [n, x, y]\n",
    "        \n",
    "def placeRandomlyInRange(number, locArray, xRange, yRange, refLoc):\n",
    "    for n in xrange(number):\n",
    "#         rounds = 0\n",
    "        while True:\n",
    "            # This could technically turn into an infinite loop but that shouldn't ever practically happen.\n",
    "            # add check here later\n",
    "            x = random.uniform(xRange[0], xRange[1])\n",
    "            y = random.uniform(yRange[0], yRange[1])\n",
    "            if np.any(np.sum(np.square(refLoc[:,1:3] - np.array([x,y]).reshape(1,2)), axis=1) <= bestDist**2):\n",
    "                locArray[n,:] = [n, x, y]\n",
    "                break\n",
    "        \n",
    "print \"===== Location generation =====\"\n",
    "\n",
    "random.seed(1)\n",
    "\n",
    "if maxNrBS == 1:\n",
    "    BSLoc[0] = [0, maxX*0.5, maxY*0.5]\n",
    "else:\n",
    "    placeRandomly(maxNrBS, BSLoc, [maxX*0.1, maxX*0.9], [maxY*0.1, maxY*0.9])\n",
    "print \"generated locations for {} base-stations\".format(maxNrBS)\n",
    "\n",
    "placeRandomlyInRange(maxNrNodes, nodeLoc, [0, maxX], [0, maxY], BSLoc)\n",
    "print \"generated locations for {} nodes\".format(maxNrNodes)\n",
    "\n",
    "# np.save(\"bsList_bs{}_nodes{}.npy\".format(maxNrBS, maxNrNodes), BSLoc)\n",
    "# np.save(\"nodeList_bs{}_nodes{}.npy\".format(maxNrBS, maxNrNodes), nodeLoc)\n",
    "\n",
    "def plotLocations():\n",
    "    loc_plot = plt.figure(figsize = (5,5))\n",
    "    loc_ax = plt.gca()\n",
    "    \n",
    "    bestDist = np.amax(maxDist)\n",
    "    \n",
    "    bestDist = np.amax(maxDist)\n",
    "    for b in BSLoc[:,1:3]:\n",
    "        loc_ax.add_artist(plt.Circle((b[0], b[1]), bestDist, fill=False, hatch='//', color = 'red'))\n",
    "    \n",
    "    nodePoints, = plt.plot(nodeLoc[:,1], nodeLoc[:,2], \"b.\", mfc='none', label=\"end device\")\n",
    "    bsPoints, = plt.plot(BSLoc[:,1], BSLoc[:,2], \"r^\", label=\"base station\")\n",
    "    \n",
    "    plt.xticks([0, 5000, 10000, 15000, 20000], [0,5,10,15,20])\n",
    "    plt.yticks([0, 5000, 10000, 15000, 20000], [0,5,10,15,20])\n",
    "    \n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    plt.grid()\n",
    "    \n",
    "    plt.axis('equal')\n",
    "    plt.xlim((0, maxX))\n",
    "    plt.ylim((0, maxY))\n",
    "    \n",
    "    plt.xlabel('distance (km)')\n",
    "    plt.ylabel('distance (km)')\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "plotLocations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packet Collisions\n",
    "\n",
    "A number of things determine if 2 or more packets would collide in the air: overlapping spectrum of transmissions, spreading factor leakages, timing collisions etc.\n",
    "\n",
    "The traditional method of modelling this is using the independent interference model, which checks for the interference between any two packets currently in the air (and not more). This is the method currently used by the original LoRaSim.\n",
    "\n",
    "Another method is the more complex additive interference model. The next few section will test both of these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# collision checks using the independent interference model\n",
    "# the collision functions are heavily dependent on the object definitions\n",
    "\n",
    "#\n",
    "# check for collisions at base station\n",
    "# Note: called before a packet (or rather node) is inserted into the list\n",
    "#\n",
    "# conditions for collions:\n",
    "#     1. same sf\n",
    "#     2. frequency, see function below (Martins email, not implementet yet):\n",
    "def checkcollision(packet):\n",
    "    col = 0 # flag needed since there might be several collisions for packet\n",
    "    # lost packets don't collide\n",
    "    if packet.lost:\n",
    "        return 0\n",
    "    if packetsAtBS[packet.bs]:\n",
    "        for other in packetsAtBS[packet.bs]:\n",
    "            if other.id != packet.nodeid:\n",
    "                # simple collision\n",
    "                if frequencyCollision(packet, other.packet[packet.bs]) \\\n",
    "                 and sfCollision(packet, other.packet[packet.bs]):\n",
    "                    if full_collision:\n",
    "                        if timingCollision(packet, other.packet[packet.bs]):\n",
    "                            # check who collides in the power domain\n",
    "                            c = powerCollision(packet, other.packet[packet.bs])\n",
    "                            # mark all the collided packets\n",
    "                            # either this one, the other one, or both\n",
    "                            for p in c:\n",
    "                                p.collided = 1\n",
    "                        else:\n",
    "                            # no timing collision, all fine\n",
    "                            pass\n",
    "                    else:\n",
    "                        packet.collided = 1\n",
    "                        other.packet[packet.bs].collided = 1  # other also got lost, if it wasn't lost already\n",
    "                        col = 1\n",
    "        return col\n",
    "    return 0\n",
    "\n",
    "#\n",
    "# frequencyCollision, conditions\n",
    "#\n",
    "#        |f1-f2| <= 120 kHz if f1 or f2 has bw 500\n",
    "#        |f1-f2| <= 60 kHz if f1 or f2 has bw 250\n",
    "#        |f1-f2| <= 30 kHz if f1 or f2 has bw 125\n",
    "def frequencyCollision(p1,p2):\n",
    "    if (abs(p1.freq-p2.freq)<=120 and (p1.bw==500 or p2.freq==500)):\n",
    "        return True\n",
    "    elif (abs(p1.freq-p2.freq)<=60 and (p1.bw==250 or p2.freq==250)):\n",
    "        return True\n",
    "    else:\n",
    "        if (abs(p1.freq-p2.freq)<=30):\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "def sfCollision(p1, p2):\n",
    "    if p1.sf == p2.sf:\n",
    "        # p2 may have been lost too, will be marked by other checks\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "def powerCollision(p1, p2):\n",
    "    powerThreshold = 6 # dB\n",
    "    if abs(p1.rssi - p2.rssi) < powerThreshold:\n",
    "        # packets are too close to each other, both collide\n",
    "        # return both packets as casualties\n",
    "        return (p1, p2)\n",
    "    elif p1.rssi - p2.rssi < powerThreshold:\n",
    "        # p2 overpowered p1, return p1 as casualty\n",
    "        return (p1,)\n",
    "    # p2 was the weaker packet, return it as a casualty\n",
    "    return (p2,)\n",
    "\n",
    "def timingCollision(p1, p2):\n",
    "    # assuming p1 is the freshly arrived packet and this is the last check\n",
    "    # we've already determined that p1 is a weak packet, so the only\n",
    "    # way we can win is by being late enough (only the first n - 5 preamble symbols overlap)\n",
    "\n",
    "    # assuming 8 preamble symbols\n",
    "    Npream = 8\n",
    "\n",
    "    # we can lose at most (Npream - 5) * Tsym of our preamble\n",
    "    Tpreamb = 2**p1.sf/(1.0*p1.bw) * (Npream - 5)\n",
    "\n",
    "    # check whether p2 ends in p1's critical section\n",
    "    p2_end = p2.addTime + p2.rectime\n",
    "    p1_cs = env.now + Tpreamb\n",
    "    if p1_cs < p2_end:\n",
    "        # p1 collided with p2 and lost\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "# this function computes the airtime of a packet\n",
    "# according to LoraDesignGuide_STD.pdf\n",
    "#\n",
    "def airtime(sf,cr,pl,bw):\n",
    "    H = 0        # implicit header disabled (H=0) or not (H=1)\n",
    "    DE = 0       # low data rate optimization enabled (=1) or not (=0)\n",
    "    Npream = 8   # number of preamble symbol (12.25  from Utz paper)\n",
    "\n",
    "    if bw == 125 and sf in [11, 12]:\n",
    "        # low data rate optimization mandated for BW125 with SF11 and SF12\n",
    "        DE = 1\n",
    "    if sf == 6:\n",
    "        # can only have implicit header with SF6\n",
    "        H = 1\n",
    "\n",
    "    Tsym = (2.0**sf)/bw\n",
    "    Tpream = (Npream + 4.25)*Tsym\n",
    "    payloadSymbNB = 8 + max(math.ceil((8.0*pl-4.0*sf+28+16-20*H)/(4.0*(sf-2*DE)))*(cr+4),0)\n",
    "    Tpayload = payloadSymbNB * Tsym\n",
    "    return Tpream + Tpayload\n",
    "\n",
    "#\n",
    "# this function creates a BS\n",
    "#\n",
    "class myBS():\n",
    "    def __init__(self, id, position):\n",
    "        self.id = id\n",
    "        self.x, self.y = position\n",
    "\n",
    "#\n",
    "# this function creates a node\n",
    "#\n",
    "class myNode():\n",
    "    def __init__(self, id, position, period, packetlen):\n",
    "        global bs\n",
    "\n",
    "        self.id = id\n",
    "        self.period = period\n",
    "        self.x, self.y = position\n",
    "        self.packet = []\n",
    "        self.dist = []\n",
    "        \n",
    "        self.f, self.bw, self. sf, self.cr = self.getTransmissionParams()\n",
    "        \n",
    "        # create \"virtual\" packet for each BS\n",
    "        global nrBS\n",
    "        for i in range(0,nrBS):\n",
    "            d = np.sqrt((self.x-bs[i].x)*(self.x-bs[i].x)+(self.y-bs[i].y)*(self.y-bs[i].y))\n",
    "            self.dist.append(d)\n",
    "            self.packet.append(myPacket(self.id, packetlen, self.dist[i], self.f, i, self.sf, self.cr, self.bw))\n",
    "\n",
    "        self.sent = 0\n",
    "        \n",
    "    def getTransmissionParams(self):\n",
    "        # this function currently returns fixed parameters\n",
    "        freq = 902.3\n",
    "        bw = 125\n",
    "        sf = 10\n",
    "        cr = 1\n",
    "        return freq, bw, sf, cr\n",
    "    \n",
    "    def updateTransmissionParams(self):\n",
    "        # use this function to create a hopping node\n",
    "        pass\n",
    "\n",
    "#\n",
    "# this function creates a packet (associated with a node)\n",
    "# it also sets all parameters, currently random\n",
    "#\n",
    "class myPacket():\n",
    "    def __init__(self, nodeid, plen, distance, freq, bs, sf, cr, bw):\n",
    "        global experiment\n",
    "        global Ptx\n",
    "        global gamma\n",
    "        global d0\n",
    "        global var\n",
    "        global Lpld0\n",
    "        global GL\n",
    "        global sensi\n",
    "\n",
    "\n",
    "        # new: base station ID\n",
    "        self.bs = bs\n",
    "        self.nodeid = nodeid\n",
    "        \n",
    "        # randomize configuration values\n",
    "        self.sf = sf\n",
    "        self.cr = cr\n",
    "        self.bw = bw\n",
    "\n",
    "        # log-shadow\n",
    "        # note: transmit power is global variable\n",
    "        Lpl = Lpld0 + 10*gamma*math.log(distance/d0)\n",
    "#         print Lpl\n",
    "        Prx = Ptx - GL - Lpl\n",
    "\n",
    "        # transmission range, needs update XXX\n",
    "        self.transRange = 150\n",
    "        self.pl = plen\n",
    "        self.symTime = (2.0**self.sf)/self.bw\n",
    "        self.arriveTime = 0\n",
    "        self.rssi = Prx\n",
    "        self.freq = freq\n",
    "\n",
    "        self.rectime = airtime(self.sf,self.cr,self.pl,self.bw)\n",
    "        # denote if packet is collided\n",
    "        self.collided = 0\n",
    "        self.processed = 0\n",
    "\n",
    "        packet_sensitivity = sensi[sf - 7, int(bw // 250) + 1]\n",
    "        self.lost = self.rssi < packet_sensitivity\n",
    "#         if self.lost:\n",
    "#             print \"node {} bs {} lost (rssi = {} < sensi = {})\".format(self.nodeid, self.bs, self.rssi, packet_sensitivity)\n",
    "\n",
    "\n",
    "#\n",
    "# main discrete event loop, runs for each node\n",
    "# a global list of packet being processed at the gateway\n",
    "# is maintained\n",
    "#\n",
    "def transmit(env,node):\n",
    "    \n",
    "    global packetSeq\n",
    "    global nrBS\n",
    "    global nrLost\n",
    "    global nrTransmitted\n",
    "    global nrReceived\n",
    "    global nrCollisions\n",
    "    \n",
    "    while True:\n",
    "        yield env.timeout(random.expovariate(1.0/float(node.period)))\n",
    "\n",
    "        # time sending and receiving\n",
    "        # packet arrives -> add to base station\n",
    "\n",
    "        node.sent = node.sent + 1\n",
    "        \n",
    "        packetSeq = packetSeq + 1\n",
    "\n",
    "        for bs in range(0, nrBS):\n",
    "            if (node in packetsAtBS[bs]):\n",
    "                print \"ERROR: packet already in\"\n",
    "            else:\n",
    "                # adding packet if no collision\n",
    "                if (checkcollision(node.packet[bs])==1):\n",
    "                    node.packet[bs].collided = 1\n",
    "                else:\n",
    "                    node.packet[bs].collided = 0\n",
    "                packetsAtBS[bs].append(node)\n",
    "                node.packet[bs].addTime = env.now\n",
    "                node.packet[bs].seqNr = packetSeq\n",
    "\n",
    "        # take first packet rectime\n",
    "        yield env.timeout(node.packet[0].rectime)\n",
    "        \n",
    "        nrTransmitted += 1\n",
    "\n",
    "        # if packet did not collide, add it in list of received packets\n",
    "        # unless it is already in\n",
    "        \n",
    "        packetLostFlag = True\n",
    "        collisionFlag = False\n",
    "        for bs in range(0, nrBS):\n",
    "            if node.packet[bs].lost:\n",
    "                lostPackets.append(node.packet[bs].seqNr)\n",
    "            else:\n",
    "                if node.packet[bs].collided == 0:\n",
    "                    packetLostFlag = False\n",
    "                    packetsRecBS[bs].append(node.packet[bs].seqNr)\n",
    "                    if (recPackets):\n",
    "                        if (recPackets[-1] != node.packet[bs].seqNr):\n",
    "                            recPackets.append(node.packet[bs].seqNr)\n",
    "                    else:\n",
    "                        recPackets.append(node.packet[bs].seqNr)\n",
    "                else:\n",
    "                    # XXX only for debugging\n",
    "                    collidedPackets.append(node.packet[bs].seqNr)\n",
    "                    collisionFlag = True\n",
    "                    \n",
    "        if packetLostFlag:\n",
    "            nrLost += 1\n",
    "        else:\n",
    "            nrReceived += 1\n",
    "            \n",
    "        if collisionFlag:\n",
    "            nrCollisions += 1\n",
    "            \n",
    "        # complete packet has been received by base station\n",
    "        # can remove it\n",
    "        for bs in range(0, nrBS):\n",
    "            if (node in packetsAtBS[bs]):\n",
    "                packetsAtBS[bs].remove(node)\n",
    "                # reset the packet\n",
    "                node.packet[bs].collided = 0\n",
    "                node.packet[bs].processed = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 100\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 7205\n",
      "# received = 6922\n",
      "# lost = 283\n",
      "# collisions = 283\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 200\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 14340\n",
      "# received = 13304\n",
      "# lost = 1036\n",
      "# collisions = 1036\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 300\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 21688\n",
      "# received = 19141\n",
      "# lost = 2547\n",
      "# collisions = 2547\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 400\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 29006\n",
      "# received = 24707\n",
      "# lost = 4299\n",
      "# collisions = 4299\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 500\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 36197\n",
      "# received = 29562\n",
      "# lost = 6635\n",
      "# collisions = 6635\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 600\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 43017\n",
      "# received = 33965\n",
      "# lost = 9052\n",
      "# collisions = 9052\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 700\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 50062\n",
      "# received = 38038\n",
      "# lost = 12024\n",
      "# collisions = 12024\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 800\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 57430\n",
      "# received = 41779\n",
      "# lost = 15651\n",
      "# collisions = 15651\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 900\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 65522\n",
      "# received = 45769\n",
      "# lost = 19753\n",
      "# collisions = 19753\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 1000\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 71505\n",
      "# received = 48418\n",
      "# lost = 23087\n",
      "# collisions = 23087\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 1100\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 79004\n",
      "# received = 51369\n",
      "# lost = 27635\n",
      "# collisions = 27635\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 1200\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 86142\n",
      "# received = 53938\n",
      "# lost = 32204\n",
      "# collisions = 32204\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 1300\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 93259\n",
      "# received = 56363\n",
      "# lost = 36896\n",
      "# collisions = 36896\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 1400\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 100781\n",
      "# received = 58795\n",
      "# lost = 41986\n",
      "# collisions = 41986\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 1500\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 108371\n",
      "# received = 60850\n",
      "# lost = 47521\n",
      "# collisions = 47521\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 1600\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 115337\n",
      "# received = 62705\n",
      "# lost = 52632\n",
      "# collisions = 52632\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 1700\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 122282\n",
      "# received = 64131\n",
      "# lost = 58151\n",
      "# collisions = 58151\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 1800\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 129804\n",
      "# received = 65763\n",
      "# lost = 64041\n",
      "# collisions = 64041\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 1900\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 136826\n",
      "# received = 66921\n",
      "# lost = 69905\n",
      "# collisions = 69905\n",
      "===== Setup parameters =====\n",
      "grid size = 10000 m x 10000 m\n",
      "# base-stations = 1\n",
      "# nodes = 2000\n",
      "minimum sensitivity (dBm) = -134.5\n",
      "calculated max transmission distance (m) =\n",
      "[[ 623.27  563.69  482.13]\n",
      " [ 644.5   630.27  557.43]\n",
      " [ 770.54  673.93  651.73]\n",
      " [ 823.92  736.89  689.15]\n",
      " [ 890.89  823.92  689.15]\n",
      " [ 842.52  805.73  805.73]]\n",
      "===== simulation objects created =====\n",
      "# transmitted = 144330\n",
      "# received = 68249\n",
      "# lost = 76081\n",
      "# collisions = 76081\n",
      "nodes: [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000]\n",
      "Transmitted: [7205, 14340, 21688, 29006, 36197, 43017, 50062, 57430, 65522, 71505, 79004, 86142, 93259, 100781, 108371, 115337, 122282, 129804, 136826, 144330]\n",
      "Received: [6922, 13304, 19141, 24707, 29562, 33965, 38038, 41779, 45769, 48418, 51369, 53938, 56363, 58795, 60850, 62705, 64131, 65763, 66921, 68249]\n",
      "Collisions: [283, 1036, 2547, 4299, 6635, 9052, 12024, 15651, 19753, 23087, 27635, 32204, 36896, 41986, 47521, 52632, 58151, 64041, 69905, 76081]\n"
     ]
    }
   ],
   "source": [
    "simSetting = list(np.linspace(100,2000,20,endpoint = True, dtype=int))\n",
    "\n",
    "totalTransmitted = []\n",
    "totalLost = []\n",
    "totalReceived = []\n",
    "totalCollided = []\n",
    "\n",
    "for i,n in enumerate(simSetting):\n",
    "    # global stuff\n",
    "    nrNodes = n\n",
    "    nodes = []\n",
    "    bs = []\n",
    "    packetsAtBS = []\n",
    "    packetsRecBS = []\n",
    "\n",
    "    env = simpy.Environment()\n",
    "\n",
    "\n",
    "    # max distance: 300m in city, 3000 m outside (5 km Utz experiment)\n",
    "    # also more unit-disc like according to Utz\n",
    "    nrLost = 0\n",
    "    nrCollisions = 0\n",
    "    nrReceived = 0\n",
    "    nrTransmitted = 0\n",
    "\n",
    "    # global value of packet sequence numbers\n",
    "    packetSeq = 0\n",
    "\n",
    "    # list of received packets\n",
    "    recPackets=[]\n",
    "    collidedPackets=[]\n",
    "    lostPackets = []\n",
    "\n",
    "    # some feedback on what's going on\n",
    "\n",
    "    print \"===== Setup parameters =====\"\n",
    "    print \"grid size = {} m x {} m\".format(maxX, maxY)\n",
    "    print \"# base-stations = {}\".format(nrBS)\n",
    "    print \"# nodes = {}\".format(nrNodes)\n",
    "    print \"minimum sensitivity (dBm) = {}\".format(np.amin(sensi))\n",
    "    np.set_printoptions(precision=2, suppress=True)\n",
    "    print \"calculated max transmission distance (m) =\\n{}\".format(maxDist)\n",
    "\n",
    "    ## create all the base-stations\n",
    "\n",
    "    for b_loc in BSLoc:\n",
    "        b = myBS(int(b_loc[0]), (b_loc[1], b_loc[2]))\n",
    "        bs.append(b)\n",
    "        packetsAtBS.append([])\n",
    "        packetsRecBS.append([])\n",
    "    \n",
    "    for node_loc in nodeLoc[0:nrNodes, :]:\n",
    "        n = myNode(int(node_loc[0]), (node_loc[1], node_loc[2]), avgSendTime, 20) # send a packet of 20 bytes every 20 min\n",
    "        nodes.append(n)\n",
    "        env.process(transmit(env, n))\n",
    "\n",
    "    print \"===== simulation objects created =====\"\n",
    "\n",
    "    env.run(until=simtime)\n",
    "\n",
    "    # print \"nr received packets\", len(recPackets)\n",
    "    # print \"nr collided packets\", len(collidedPackets)\n",
    "    # print \"nr lost packets\", len(lostPackets)\n",
    "\n",
    "    print \"# transmitted = {}\".format(nrTransmitted)\n",
    "    print \"# received = {}\".format(nrReceived)\n",
    "    print \"# lost = {}\".format(nrLost)\n",
    "    print \"# collisions = {}\".format(int(nrCollisions))\n",
    "    \n",
    "    totalTransmitted.append(nrTransmitted)\n",
    "    totalReceived.append(nrReceived)\n",
    "    totalLost.append(nrLost)\n",
    "    totalCollided.append(nrCollisions)\n",
    "    \n",
    "print \"nodes: {}\".format(simSetting)\n",
    "print \"Transmitted: {}\".format(totalTransmitted)\n",
    "print \"Received: {}\".format(totalReceived)\n",
    "print \"Collisions: {}\".format(totalCollided)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEFCAYAAADnpECTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11898df10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def plotIIMPacketReception():\n",
    "    \n",
    "#     print totalReceived\n",
    "#     print totalTransmitted\n",
    "    \n",
    "#     print np.divide(np.array(totalReceived, dtype=float), np.array(totalTransmitted, dtype=float))\n",
    "\n",
    "    iimDerPlot = plt.figure()\n",
    "    plt.plot(simSetting, np.divide(np.array(totalReceived, dtype=float), np.array(totalTransmitted, dtype=float)), 'bo-')\n",
    "    plt.show()\n",
    "    \n",
    "plotIIMPacketReception()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:lorasim-env]",
   "language": "python",
   "name": "conda-env-lorasim-env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}