Weighted Integration validity test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19af9f6bc08>]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Create monitor input for I and Q values (siginput1 and siginput2)\n",
    "\n",
    "length = 4096\n",
    "#Iones = np.ones(4016)\n",
    "Iones = np.full(4075, 736e-3)\n",
    "Izeros = np.zeros(length-4075)\n",
    "I = np.concatenate((Iones, Izeros))\n",
    "Q = np.concatenate((Iones, Izeros))\n",
    "sample = np.arange(0, length, 1)\n",
    "\n",
    "plt.plot(sample, I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19af9fd0a08>]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Create weight function\n",
    "smpFreq = 1.8e9\n",
    "freq = 50e3\n",
    "period = 1/freq\n",
    "\n",
    "samplesCos = period*smpFreq\n",
    "\n",
    "freqaxis = np.linspace(-(2*np.pi), 2*np.pi, int(samplesCos*1.75)) #multiplied by 1.75 as np.cos function creates 1.75 of a cosine wave\n",
    "\n",
    "weight1 = np.cos(freqaxis)\n",
    "\n",
    "plt.plot(freqaxis[:4096], weight1[:4096])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The integration value is : 2679.79 V\n"
     ]
    }
   ],
   "source": [
    "## Code weighted integration formula\n",
    "Vi = np.zeros(4096)\n",
    "Vi[0] = weight1[0] * I[0]\n",
    "for n in range(1, 4096):\n",
    "    Vi[n] = weight1[n] * I[n]\n",
    "\n",
    "print('The integration value is : %.2f V' % np.sum(Vi))"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}