{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 5 - FM demod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Refs [1]: http://ultra.sdk.free.fr/docs/DxO/FM%20DEMODULATION%20USING%20A%20DIGITAL%20RADIO%20AND%20DIGITAL%20SIGNAL%20PROCESSING%20Digradio.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The FM demodulation technique we use is called polar discrimination, from [1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "nb_dir = os.path.split(os.getcwd())[0]\n",
    "if nb_dir not in sys.path:\n",
    "    sys.path.append(nb_dir)\n",
    "    \n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from directdemod import fmDemod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chunking test\n",
    "\n",
    "The following is a result if the array 'a' is undergone FM demodulation,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.57079633  1.57079633 -1.57079633  1.57079633 -1.57079633]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1+1j, 2-2j, 3+3j, 4-4j, 5+5j, 6-6j])\n",
    "\n",
    "fm = fmDemod.fmDemod(storeState = False)\n",
    "print(fm.demod(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we do it in parts,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.57079633  1.57079633]\n",
      "[ 1.57079633 -1.57079633]\n"
     ]
    }
   ],
   "source": [
    "a1 = np.array([1+1j, 2-2j, 3+3j])\n",
    "a2 = np.array([4-4j, 5+5j, 6-6j])\n",
    "fm = fmDemod.fmDemod(storeState = False)\n",
    "print(fm.demod(a1))\n",
    "print(fm.demod(a2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of 5 values, we got 4. We lost one value because of chunking. It can be solved by using flag 'storeState = True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.57079633  1.57079633]\n",
      "[-1.57079633  1.57079633 -1.57079633]\n"
     ]
    }
   ],
   "source": [
    "a1 = np.array([1+1j, 2-2j, 3+3j])\n",
    "a2 = np.array([4-4j, 5+5j, 6-6j])\n",
    "fm = fmDemod.fmDemod(storeState = True)\n",
    "print(fm.demod(a1))\n",
    "print(fm.demod(a2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Now we see that this indeed solves the chunking problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation\n",
    "\n",
    "FM demodulation introduces a one sample delay, this must be taken into consideration. The solution to chunking is provided by enabling the flag 'storeState = True' "
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}