chirpfinder_real.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy.fft import fft, ifft, fftshift\n",
    "import scipy.signal\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from rfutils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SAMP_RATE = 50e6 # samples per second\n",
    "FFT_SIZE = 65536*4*4*4 # bins\n",
    "TIME = 0.1 # seconds of recording\n",
    "\n",
    "raw = np.fromfile('./data/own/IQREC-02-03-19-13h38m31s479.iq', dtype='int16')\n",
    "raw = raw[0 : len(raw) : 2] + 1.0j * raw[0 : len(raw) : 2]\n",
    "raw = raw[0:int(TIME * SAMP_RATE)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rytse/anaconda3/envs/irss/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "NOISE_DB = 30 # dB\n",
    "TX_DB = -10 # dB\n",
    "ts = np.linspace(0, TIME, TIME*SAMP_RATE, endpoint=False)\n",
    "chirp = scipy.signal.chirp(ts, f0=2e6, t1=TIME*20, f1=20e6, method='linear')\n",
    "sig = np.power(10, TX_DB / 10)*(chirp.copy() / np.max(chirp)) + np.power(10, NOISE_DB / 10)*(raw / np.max(raw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "500000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor1 = xcorr(sig, chirp)\n",
    "delayed = delay_sig(sig, 5000*100)\n",
    "cor2 = xcorr(delayed, chirp)\n",
    "\n",
    "plt.plot(fftshift(cor1), c='r')\n",
    "plt.plot(fftshift(cor2), c='b')\n",
    "plt.show()\n",
    "\n",
    "np.argmax(cor2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cfreq = (2e6 + 20e6) / 2\n",
    "# passw = (3e6 - 2e6) / 2\n",
    "# stop_l = cfreq - 1.2 * passw\n",
    "# stop_h = cfreq + 1.2 * passw\n",
    "\n",
    "cfreq = 2e6 + (20e6 - 2e6) / 20 / 2 \n",
    "passw = (0.06e6) / 2\n",
    "stop_l = cfreq - 1.2 * passw\n",
    "stop_h = cfreq + 1.2 * passw\n",
    "\n",
    "bpf = design_remez(10000, cfreq, passw, stop_l, stop_h, SAMP_RATE)\n",
    "freq, response = scipy.signal.freqz(bpf)\n",
    "plt.semilogy(0.5 * SAMP_RATE * freq / np.pi, np.abs(response), 'b-')\n",
    "plt.show()\n",
    "\n",
    "filtd = scipy.signal.lfilter(bpf, [1], delayed)\n",
    "cor3 = xcorr(filtd, chirp)\n",
    "plt.plot(fftshift(cor3), c='r')\n",
    "plt.show()\n",
    "\n",
    "np.argmax(cor3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = scipy.signal.chirp(ts, f0=2e6, t1=TIME*20, f1=20e6 * 1.00, method='linear')\n",
    "cor1 = xcorr(sig, template)\n",
    "delayed = delay_sig(sig, 5000*100)\n",
    "cor2 = xcorr(delayed, template)\n",
    "plt.plot(fftshift(cor1), c='r')\n",
    "plt.plot(fftshift(cor2), c='b')\n",
    "plt.show()\n",
    "\n",
    "ed = np.argmax(cor2)\n",
    "vm = delayed[ed]\n",
    "ptot = np.sum(delayed * delayed) / len(delayed)\n",
    "\n",
    "print('Peak Power Ratio Correct: ' + str(vm / ptot))\n",
    "\n",
    "template = scipy.signal.chirp(ts, f0=2e6, t1=TIME*20, f1=20e6 * 1.001, method='linear')\n",
    "cor1 = xcorr(sig, template)\n",
    "delayed = delay_sig(sig, 5000*100)\n",
    "cor2 = xcorr(delayed, template)\n",
    "plt.plot(fftshift(cor1), c='r')\n",
    "plt.plot(fftshift(cor2), c='b')\n",
    "plt.show()\n",
    "\n",
    "ed = np.argmax(cor2)\n",
    "vm = delayed[ed]\n",
    "ptot = np.sum(delayed * delayed) / len(delayed)\n",
    "\n",
    "print('Peak Power Ratio Offset: ' + str(vm / ptot))\n",
    "\n",
    "template = scipy.signal.chirp(ts, f0=2e6, t1=TIME*20, f1=20e6 * 0.995, method='linear')\n",
    "cor1 = xcorr(sig, template)\n",
    "delayed = delay_sig(sig, 5000*100)\n",
    "cor2 = xcorr(delayed, template)\n",
    "plt.plot(fftshift(cor1), c='r')\n",
    "plt.plot(fftshift(cor2), c='b')\n",
    "plt.show()\n",
    "\n",
    "ed = np.argmax(cor2)\n",
    "vm = delayed[ed]\n",
    "ptot = np.sum(delayed * delayed) / len(delayed)\n",
    "\n",
    "print('Peak Power Ratio Offset: ' + str(vm / ptot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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