{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation of Impulse Response Measurements\n",
    "The software (https://github.com/franzpl/sweep) has been written in the context of my bachelor thesis with the topic \"On the influence of windowing of sweep signals for room impulse measurements\" at the University of Rostock.\n",
    "Impulse responses are an important tool to determine acoustic properties of a Device Under Test. The main requirement is that all desired frequencies cover the interesting frequency range with sufficient energy. Therefore, sweep signals and white noise are usally favored to excite DUT's. In this context sweep signals and LTI-Systems were used. However, the design of sweep signals in time domain causes ripple in the excitation spectrum at the start and stop frequency. It is possible to reduce ripple with the use of convenient windows. With this software, you can evaluate the effect of windowing of sweep signals on impulse responses under the influence of noise. This Ipython3 Notebook shows an examplary impulse response measurement (Sweep -> DUT -> System Response -> Impulse Response -> Quality of Impulse Response). You can also use the software for real measurements, because measurement module and simulation module are seperated strictly. <br>\n",
    "Let's start the simulation of an impulse response measurement! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports\n",
    "First, you need imports from Python and the software."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Software Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import generation\n",
    "import plotting\n",
    "import ir_imitation\n",
    "import calculation\n",
    "import windows\n",
    "import measurement_chain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Excitation\n",
    "Than, you have to design the excitation signal.\n",
    "## Excitation Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fs = 44100\n",
    "fstart = 1\n",
    "fstop = 22050\n",
    "duration = 1  # seconds\n",
    "pad = 5  # attach 5 seconds zeros to excitation signal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excitation Signal\n",
    "Generate a excitation signal with the excitation parameters above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "excitation = generation.log_sweep(fstart, fstop, duration, fs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Time Domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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vqt0PCXBNkZxGEW+jyOwpoDnRKJoIt197KsgNT5mfH/+c/iGE/MLWhJDvojxZ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\nHAbg9wD+rnHLhFzKrj9VJZ+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8AbjySnX95JOb+bxXvCJqF60DMff2yBIN815SP71dbJw9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\n8lDkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe2f1a69ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting.plot_time(excitation, fs);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Frequency Domain\n",
    "As shown in this figure, the excitation spectrum is characterized by ripple at the start and stop frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lINIx06aFSmDhQhgxAv75T+jePemopFI61U0E/NnMdi1xTJKA/Hl2pH2qLWeb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe2f1b6ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting.plot_freq(excitation, fs, scale='db')\n",
    "plt.xscale('log')\n",
    "plt.xlim([1, fs/2])\n",
    "plt.ylim([-55, -14]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Window Parameters\n",
    "A window reduces the ringing artifacts. Fade in and fade out parameters of the window can help to produce a smoother spectrum. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fade_in = 50  # ms\n",
    "fade_out = 10  # ms\n",
    "beta = 7  # kaiser window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Windowed Sweep\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "window = windows.window_kaiser(len(excitation), fade_in, fade_out, fs, beta)\n",
    "excitation_windowed = window * excitation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zeropadding\n",
    "Zeropadding makes space for the recording signal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "excitation_windowed_zeropadded = generation.zero_padding(excitation_windowed, pad, fs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Zeropadded Windowed Sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RicvijGsREanHKB/d1NewT7YbJWZ2iZk9YmYrY8dSBzNbbGY3lidV3mlmfxI7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\nIiEiIpVUJEREpNL/B1gXRQ5ceRSYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe2f120f940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting.plot_time(excitation_windowed_zeropadded, fs);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# System\n",
    "Now, you have to design the DUT.\n",
    "## FIR-Filter System\n",
    " For a better understanding, in this example a dirac impuls was used as filter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dirac = measurement_chain.convolution([1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Noise System\n",
    "In addition, you can define the system with additve noise. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "noise_level = -30  # RMS (dB)\n",
    "awgn = measurement_chain.additive_noise(noise_level)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combinate System Elements\n",
    "Finally, the system elements must be combined. Feel free to add more elements (lowpass, bandpass, gain, ...) to the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "system = measurement_chain.chained(dirac, awgn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# System Response\n",
    "To record the system response, you have to simply pass the excitation signal to the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "system_response = system(excitation_windowed_zeropadded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot System Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DU8iPSXRpPgkzmws0ufsiM9sGaHb33Tp4zrnACndfq6tLYxIiIuuuWmMS5wJH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\nEiIiUtb/ByPdt4gSnU6ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe2ff419e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting.plot_time(system_response,fs);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Impulse Response\n",
    "Via the FFT and IFFT, the impulse response is calculated. That's it! A Plot with linear and dB scale show you the characteristics of the IR. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ir = calculation.deconv_process(excitation_windowed_zeropadded, system_response, fs)[:len(excitation_windowed_zeropadded)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Impulse Response (linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xM/CD2vgk4IHhK36OdkinmZnlQzs9gfm1yeEVo2F6Sg7tdE/AzGziOtITkNTfsYjMzKyr\njNsTiIh3RsQ3IuLHEXFT7bHNtX1s+7knkF9Fzg2cXxm00xP4CXAVcBPJeQJQ0ks+m5kVTTs9gfsk\nzZiieMaKwT0BM7MJ6tR5ApdFxBxgEfDK8ExJv5xceGZmlrV2zhPYj+ROYl/G1w5KhXsC+VXk3MD5\nlUE7nwROAt7h6wWZmRVPOz2Bn5BcK+ipqQlp1BjcEzAzm6BO9QTeDCyLiPup9wTaup+AmZl1t3Z6\nAhcCHwUuxj2BVLgnkF9Fzg2cXxm0c8ZwtXEcEX8GzAZuTykmMzObIm3dYzgiDiJ54/+vwBPADZIu\nSzm2xp/vnoCZ2QRN9h7D7yJ54z8ZeBr4IUnRqHQySDMzy06rnsBS4CDgGEl/XvvPf3BqwioX9wTy\nq8i5gfMrg1ZF4C+ATcAdEXFFRBxJchlpMzMriHbOE3gjcCLJrqGZwHeAH0v61/TD2xqDewJmZhPU\nTk9g3ENEJb0o6buSTgCmAw8BX+hQjGZmlqF2zhPYStJ6Sd+SdERaAZWRewL5VeTcwPmVwYSKgJmZ\nFUtb5wlkzT0BM7OJ60hPwMzMistFoAu4J5BfRc4NnF8ZpF4EImJWRCyLiMci4txRln8iIh6OiEci\n4q6I2D/tmMzMLJFqTyAipgHLgaOAJ4H7gdmSljas86fAEknPRcQsYI6kw5q2456AmdkEdUNPYAaw\nQtJKSZuB60hOPNtK0t2SnqsN7wX2SjkmMzOrSbsI7Amsahivrs0by/8Abk41oi7knkB+FTk3cH5l\n0M6dxSaj7X04ETET+Cvgg6Mt7+/vp7e3F4Cenh76+vqoVCpA/YnM2xiS8YsvDlCtZh9PWuOBgYGu\nisdjj4s6rlarzJ8/H2Dr++V40u4JHEayj39WbXweMCTpkqb19gduBGZJWjHKdtwTMDOboG7oCTwA\n7BsRvRGxE8m9CRY0rhARbycpAH85WgEwM7P0pFoEJG0BzgIWAUuA6yUtjYjTI+L02mr/i+Rm9v8c\nEQ9FxH1pxtSN3BPIryLnBs6vDNLuCSDpFuCWpnnzGqZPBU5NOw4zM9uWrx2UIfcEzCxN3dATMDOz\nLuYi0AXcE8ivIucGzq8MXATMzErMPYEMuSdgZmlyT8DMzFpyEegC7gnkV5FzA+dXBi4CZmYl5p5A\nhtwTMLM0uSdgZmYtuQh0AfcE8qvIuYHzKwMXATOzEnNPIEPuCZhZmtwTMDOzllwEuoB7AvlV5NzA\n+ZWBi4CZWYm5J5Ah9wTMLE3uCZiZWUsuAl3APYH8KnJu4PzKwEXAzKzE3BPIkHsCZpYm9wTMzKwl\nF4Eu4J5AfhU5N3B+ZeAiYGZWYu4JZMg9ATNLk3sCZmbWkotAF3BPIL+KlNvg4LbzFi+ucvvtsGXL\n1MczFYr0/G0vF4ECePbZrCOYuC1bYM2azm4zAo45prPbnApDQyPHa9fCunXJtAQvvrj923755fbW\nmzULdtgh+R3ecQe8+moy/4gjoFKBHXdsbzvPPDP2Mgk2b279/e95TxJD895fCc48sx7XRAwOto4L\n4Lnntv2ZeXbwwXDooW2uLCm1BzALWAY8Bpw7xjqX1pY/DBw4xjoqosWLJZD22Ue66CLpueekW2+V\nHnxQ+uxn6+tVq9IXv5is+93vJvNWrpQefzyZN/wYtnatNDSUTA8NSf390rx50qZNyfiUU6Q770yW\nP/ts8nXTpvp21q7ddrunnSbtvrv00kvS9dcn21mxov5zbrwxmX700frPHf7+d79bWrhQOvPMkdsF\n6YILknXvvjsZP/64tHlzPZcnnpDWravHePXV0m23ScuXJ7+vdeukZcukvr76Nj/3ueTrjBnb/s6H\n47rmmuTnDA0lX199VdqyJVlncFD6/e+lj32svk5z3N/5TvJcfOlLoz+33/ymdNRRyfQrr0hve1v9\ne3/6U+mcc6SNG7fd7ssv16fPP78+ffvt0po1yfTJJ9fnL1iQfL322vpzePnl2742hh8bNki//W39\neR422rqjPQYHk+e4ed7g4Mh5L7448jW4ZUt92aZN9fnXXFOfv3TpyG3cdtvYcdx9d7KNgYFk/Oyz\nye+8+W+hMbfTTpP22EN6+OFtX6ONj7vu2va5efbZ5O9w+GcPDta3/8ILydclS+rrX3tt/XmTpOef\nr/9dfe1r9d/NsFtvTZbdckvymtuyRXrmmfrfwt/+bX3bV121bcxnn52su3HjyN917b2z5ft0ao3h\niJgGLAeOAp4E7gdmS1rasM5xwFmSjouI9wPflHTYKNtSWnFmabgxbGaWjmwbwzOAFZJWStoMXAec\n2LTOR4BvA0i6F+iJiN1TjKmrPP308FQ1wyimQjXrAFJUzTqAlFWzDiBl1awDyFyaRWBPYFXDeHVt\n3njr7JViTF3lK1/JOgIzK7sdUtx2u/tvmj+qjPp9/f399Pb2AtDT00NfXx+VSgWod/jzNt64sdKQ\nYRWoNExToPHwvG6Jp5PjSpfF0+lxpcvi6fS40mXxTHZcBebXxr20I82ewGHAHEmzauPzgCFJlzSs\ncwVQlXRdbbwMOFzSU03bKmRPYN48+Ou/zjoKMyuubHsCDwD7RkRvROwEnAwsaFpnAfBJ2Fo0NjQX\ngCL71KeGp6oZRjEVqlkHkKJq1gGkrJp1ACmrZh1A5lIrApK2AGcBi4AlwPWSlkbE6RFxem2dm4HH\nI2IFMA84M614ulEE3Hff2MuXL4eFC7edf+mlo6/f1wfr1yfTV18NL7wAjz8+cp2DD65Pz5mTfP3h\nD+Gtbx07jssvr08ff/zIZffcA7vumkzvvHPy9d//HW64Yeztjebtbx857u+H1avr40WLkstrTNSV\nVybHia9fDx/60LbLTzgBPvpR6O2Fo49OjmN/y1vqy7/wBXjwwdY/Y9my5LyHEQfmteGLX2w7DQ45\npP11J2L9+okdH/8nf7LtvIUL4bLLRs674AKYPRsuvHDb9RctSp7bXXaZWKyN3ve+kedX7L//yOUX\nXQSHHQYbN47+/UuXwsUXj75st91Gj7vZ2rXJ3w7Ar3899nrDn/Y/8Yn6vH33hWuuSV4Dzb//886D\nT386mT7qqPHPrRh22WXJ3+pTE/03erxjSLvhQUHPE5ioZ57Zdt6DDyZvOwsXTn77Q0P145c3bUqO\nhZ+M1auT494bDQ4m5zicemr9GPNhw8fpb49XXx15jHjzcdiTtXhxcs7C2rXS7363/dtpjmv4OPcb\nb5QeeyyZ15jHDTdIhx468vckSffeK33ve6Nvc9i6ddIHPpCcE/LKK/Xj2UfTfNz5Pfc0H28+fm4r\nVybr/eY3oy//6lelk07adv7GjcnvtjGONWuS8fB5GmeckczfY4+R55E0GhxMzgUY/t5hxx8/dvxP\nPimtWpUs/9GPRr4Gh4aS33Hza7iVoaHktShJ69e3/33D8bdy8831+F55JTnX5KWXkveF5tdAV5wn\n0ElF7QmYdYstW+Af/gHOOQfe+Mb6/CVLYL/94FvfgtNOm5pYBgdh2rSp+VlF5wvI5UTRr19S5PyK\nktsOO8Df/d3IAgDwhz9UkaauAMDUFoCiPH+T4SJgZlZi3h1kZlZQ3h1kZmYtuQh0gaLvlyxyfkXO\nDZxfGbgImJmVmHsCZmYF5Z6AmZm15CLQBYq+X7LI+RU5N3B+ZeAi0AUGBgayDiFVRc6vyLmB8ysD\nF4EusGHDhqxDSFWR8ytybuD8ysBFwMysxFwEusDKlSuzDiFVRc6vyLmB8yuD3BwimnUMZmZ5NN4h\norkoAmZmlg7vDjIzKzEXATOzEstNEYiIkyLi1xExGBEHZR1PJ0TErIhYFhGPRcS5WcfTSRFxTUQ8\nFRGPZh1LGiJiekQsrr0mfxURn8k6pk6KiNdFxL0RMRARSyLiH7OOqdMiYlpEPBQRN2UdS6dFxMqI\neKSWX4s7meeoCACPAh8F7sg6kE6IiGnA5cAs4L3A7Ih4T7ZRddS1JLkV1WbgbyTtBxwG/M8iPX+S\nXgZmSuoD9gdmRsR/zjisTjsbWAIUsTEqoCLpQEkzWq2YmyIgaZmk32QdRwfNAFZIWilpM3AdcGLG\nMXWMpDuBZ7OOIy2S1koaqE2/CCwF9sg2qs6S9FJtcidgGrA+w3A6KiL2Ao4DrgJaHj2TY23llZsi\nUEB7Aqsaxqtr8yxnIqIXOBC4N9tIOisiXhMRA8BTwGJJS7KOqYO+AZwDDGUdSEoE/DQiHoiIlneI\n3mGKAmpLRNwGvG2URedLKtp+uyJ+BC2diHgj8CPg7NongsKQNAT0RcSbgEURUZFUzTisSYuIE4A/\nSHooIipZx5OSD0r6fUT8B+C2iFhW+3S+ja4qApKOzjqGKfQkML1hPJ3k04DlRETsCNwA/Iukn2Qd\nT1okPRcRC4FDgGrG4XTCB4CPRMRxwOuAXSPiO5I+mXFcHSPp97WvT0fEj0l2P49aBPK6O6gI+/Ae\nAPaNiN6I2Ak4GViQcUzWpogI4GpgiaS5WcfTaRGxW0T01KZfDxwNPJRtVJ0h6XxJ0yW9A/hvwM+L\nVAAiYueI2KU2/QbgQyQH1owqN0UgIj4aEatIjsRYGBG3ZB3TZEjaApwFLCI5QuF6SUuzjapzIuL7\nwC+A/xQRqyLilKxj6rAPAn9JctTMQ7VHkY6G+mPg57WewL3ATZJ+lnFMaSnartndgTsbnrv/J+lf\nx1rZl40wMyux3HwSMDOzznMRMDMrMRcBM7MScxEwMysxFwEzsxJzETAzKzEXAbMxRMSbIuKMcda5\nIiI+MFUxmXWai4DZ2N4MnDnOOu8H7p6CWMxS4SJgNrYvA3vXzga+pHlh7f4Bv1HTGZe1GyA9Wrsh\ny+1TFazZ9vAZw2ZjiIj/SHLK/fvGWP5ZYL2k+U3zHwGOqV3FcVdJz6cfrdn28ScBs7GNd6HCDwG3\njjL/LuDbEXEqXXalXrNmLgJm2yEidgZ6JK1tXibpDOBLJJcHfzAi3jLV8Zm1y/+lmI3tBWCXMZbN\nBH4+2oKI2FvSfcB9EXEssBcFujWjFYs/CZiNQdIzwF21Jm9zY/hYRt8VBPCViHgkIh4F7pL0SKqB\nmk2CG8Nm2yEiHgRmSBrMOhazyXARMDMrMe8OMjMrMRcBM7MScxEwMysxFwEzsxJzETAzKzEXATOz\nEnMRMDMrsf8Pu+X9dCS9yzYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe2f1b7feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting.plot_time(ir, fs)\n",
    "plt.xlim([-1, 5])\n",
    "plt.ylim([-0.1, 1.1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Impulse Response (dB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6YEZuj/5NDA11dzcsEQmnmw4MWRG8u6e77+TuT1VNn+nuU919V3fv8pqJgydu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\nLtbLdKmd3cgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe2f1230b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting.plot_time(ir, fs, scale='db')\n",
    "plt.xlim([-1, 5])\n",
    "plt.ylim([-60, 2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Impulse Response Quality\n",
    "The 'Peak to Noise Ratio' provides information about the quality of the IR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44.4698202175 dB\n"
     ]
    }
   ],
   "source": [
    "pnr = calculation.pnr_db(ir[0], ir[fs:pad*fs])\n",
    "print(str(pnr), 'dB')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}