{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EE120 Fall 2016 PS 8, Quadrature amplitude modulation for 4 channels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To ouput python notebook to printable format, use ``ipython nbconvert file.ipynb'' from command prompt. You may need to install pandoc first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "print 4+5  # check to see if iPython is running..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt4Agg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "print 'imported numpy and scipy'\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from scipy.io import wavfile\n",
    "print 'numpy version is', np.__version__\n",
    "print 'finished importing'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Graphing helper function\n",
    "def setup_graph(title='', x_label='', y_label='', fig_size=None):\n",
    "    fig = plt.figure()\n",
    "    if fig_size != None:\n",
    "        fig.set_size_inches(fig_size[0], fig_size[1])\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_ylabel(y_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Write signal to .wav files after down sampling by 16 and scaling\n",
    "def wavwrite(name,sig,length):\n",
    "    rate1 = 44100 # CD audio quality,\n",
    "    out = np.zeros(length/16)\n",
    "    out=sig.real[0:length:16] # downsample\n",
    "    scale = 32767.0/(max(np.max(out),-np.min(out)))\n",
    "    out = np.multiply(scale,out)\n",
    "    wavfile.write(name, rate1, out.astype(np.int16)) # 16 bit integer\n",
    "    print 'wrote file',name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# import file\n",
    "from scipy.io import wavfile\n",
    "rate,data= wavfile.read('xmit-signal.wav')  # 16 bit data from transmitter\n",
    "print 'rate =', rate\n",
    "print 'data =', data\n",
    "length = np.size(data)\n",
    "print 'length  = ', length\n",
    "k = np.linspace(0,length-1,length)\n",
    "\n",
    "####################\n",
    "# parameters used:\n",
    "dt = 1.0/(44.1*16)*1e-3  # sample period\n",
    "print 'Ts =', dt\n",
    "time = dt * np.linspace(0,length-1,length)\n",
    "signal = np.zeros(length)\n",
    "omega_c = 2.0 * np.pi * 3e5\n",
    "omega_d = 2.0 * np.pi * 3.16e5  # use channel spacing of 16 kHz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to specify $k_{min}$, $k_{max}$, $k_{c}$, $k_{d}$ parameters in next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "##################\n",
    "## PARAMETERS TO BE DETERMINED\n",
    "\n",
    "kc =1 # CHANGE THIS\n",
    "kd =2 # change this\n",
    "print 'k at wc=', kc\n",
    "print 'k at wd=', kd\n",
    "\n",
    "k_min = 0  # change this value\n",
    "k_max = 500000  # CHNAGE THIS VALUE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# plot FFT of modulated signal near carrier frequency\n",
    "kcarrier = kc\n",
    "Y = np.fft.fft(data)  \n",
    "setup_graph(title='FFT of input data', x_label='$k$', y_label='$Y[k]$', fig_size=(6,3))\n",
    "_ = plt.plot(k[kcarrier-k_min:kcarrier+k_max],Y[kcarrier-k_min:kcarrier+k_max])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify digital low pass filter $H[k]$ with cutoff of 8 kHz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# specify low pass filter\n",
    "H = np.zeros(length)\n",
    "k_cutoff =  0 # CHANGE THIS\n",
    "print 'reconstruction k_cutoff =', k_cutoff\n",
    "# \n",
    "H[0:1]=1  ### CHANGE THIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next cell enter code for calculating recovered signals s1[n], s2[n], s3[n], s4[n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# code here to extract s1....s4 from ``data''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# this cell writes all data for you, and plots S1[k]\n",
    "wavwrite('sig1.wav',s1,length)\n",
    "wavwrite('sig2.wav',s2,length)\n",
    "wavwrite('sig3.wav',s3,length)\n",
    "wavwrite('sig4.wav',s4,length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# plot FFT of s1\n",
    "Sig1 = np.fft.fft(s1)   \n",
    "setup_graph(title='FFT of s1', x_label='$k$', y_label='$S1[k]$', fig_size=(12,3))\n",
    "plt.subplot(1,2,1)\n",
    "_ = plt.plot(k[0:2*k_cutoff],Sig1.real[0:2*k_cutoff],'b')\n",
    "ylabel('real')\n",
    "plt.subplot(1,2,2)\n",
    "_ = plt.plot(k[0:2*k_cutoff],Sig1.imag[0:2*k_cutoff],'g')\n",
    "ylabel('Imag')\n",
    "xlabel('k')"
   ]
  }
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