AutoCorrAbitrary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoCorrelation of Arbitrary Signal Shapes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "delta = 50  # Set step size here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Autointeracplot(x1,x2,x3,x4,x5,x6,x7,x8):\n",
    "    \n",
    "  \n",
    "    y1 = np.linspace(x1, x2, delta)\n",
    "    y2 = np.linspace(x2, x3, delta)\n",
    "    y3 = np.linspace(x3, x4, delta)\n",
    "    y4 = np.linspace(x4, x5, delta)\n",
    "    y5 = np.linspace(x5, x6, delta)\n",
    "    y6 = np.linspace(x6, x7, delta)\n",
    "    y7 = np.linspace(x7, x8, delta)\n",
    "    y  = np.concatenate((y1,y2,y3,y4,y5,y6,y7))\n",
    "    \n",
    "       \n",
    "    plt.figure(1, figsize=(12, 3), dpi=80, facecolor='w', edgecolor='k')\n",
    "    plt.plot(y,linewidth=1,c='b')\n",
    "    plt.title(\"Signal Synthesised\")\n",
    "    plt.axvline(0,linewidth=3,c='r')\n",
    "    plt.ylim([-2, 6])\n",
    "    plt.grid(1)\n",
    "    \n",
    "    z = np.correlate(y,y,\"full\")\n",
    "    plt.figure(2, figsize=(12, 3), dpi=80, facecolor='w', edgecolor='k')\n",
    "    plt.title(\"AutoCorrelation of Synthesised Signal\")\n",
    "    plt.plot(z)\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from IPython.html.widgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3f44a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "7ty5o7/MUOPPhVSIiIiIiEqgQpRwEBERERGZCibQREREREQlwASaiIiIiKgEmEATEREREZUAE2gi\n",
       "IiIiohJgAk1EREREVAJMoImIiIiISuD/Af6fgTbnxdVKAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbd8def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(Autointeracplot, x1 =2, x2 =2, x3 = 2, x4 = 2, x5 = 2, x6 = 2, x7 = 2, x8 = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
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