{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## RUN THIS FUNCTION BEFORE YOU START THIS PROBLEM\n",
    "## This function will generate the gold code associated with the satellite ID using linear shift registers\n",
    "## The satellite_ID can be any integer between 1 and 24\n",
    "def Gold_code_satellite(satellite_ID):\n",
    "    codelength = 1023\n",
    "    registerlength = 10\n",
    "    \n",
    "    # Defining the MLS for G1 generator\n",
    "    register1 = -1*np.ones(registerlength)\n",
    "    MLS1 = np.zeros(codelength)\n",
    "    for i in range(codelength):\n",
    "        MLS1[i] = register1[9]\n",
    "        modulo = register1[2]*register1[9]\n",
    "        register1 = np.roll(register1,1)\n",
    "        register1[0] = modulo\n",
    "    \n",
    "    # Defining the MLS for G2 generator\n",
    "    register2 = -1*np.ones(registerlength)\n",
    "    MLS2 = np.zeros(codelength)\n",
    "    for j in range(codelength):\n",
    "        MLS2[j] = register2[9]\n",
    "        modulo = register2[1]*register2[2]*register2[5]*register2[7]*register2[8]*register2[9]\n",
    "        register2 = np.roll(register2,1)\n",
    "        register2[0] = modulo\n",
    "    \n",
    "    delay = np.array([5,6,7,8,17,18,139,140,141,251,252,254,255,256,257,258,469,470,471,472,473,474,509,512,513,514,515,516,859,860,861,862])\n",
    "    G1_out = MLS1;\n",
    "    G2_out = np.roll(MLS2,delay[satellite_ID - 1])\n",
    "    \n",
    "    CA_code = G1_out * G2_out\n",
    "    \n",
    "    return CA_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## PART A CODE HERE\n",
    "def array_correlation(array1,array2):\n",
    "    \"\"\" This function should return two array or a matrix with one row corresponding to \n",
    "    the offset and other to the correlation value\n",
    "    \"\"\"\n",
    "    ## INSERT YOUR CODE HERE\n",
    "    ## Use np.correlate with \"FULL\". Check out the helper page for it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## PART B CODE HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## PART C CODE HERE\n",
    "## THIS IS A HELPER FUNCTION FOR PART C\n",
    "def integernoise_generator(length_of_noise):\n",
    "    noise_array = np.random.randint(2,size = length_of_noise)\n",
    "    noise_array = 2*noise_array - np.ones(size(noise_array))\n",
    "    return noise_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## PART D CODE HERE\n",
    "## THIS IS A HELPER FUNCTION FOR PART D\n",
    "def gaussiannoise_generator(length_of_noise):\n",
    "    noise_array = np.random.normal(0,1,length_of_noise)\n",
    "    return noise_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## PART E CODE HERE\n",
    "## USE 'LOAD' FUNCTION TO LOAD THE DATA\n",
    "## USE DATA1.NPY AS THE SIGNAL ARRAY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## PART F CODE HERE\n",
    "## USE DATA2.NPY AS THE SIGNAL ARRAY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## PART G CODE HERE\n",
    "## USE DATA3.NPY AS THE SIGNAL ARRAY"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}