{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Dopplerov efekat i Brza Fourierova transformacija\n", "\n", "Izračunavanje brzine kretanja izvora zvuka pomoću Dopplerovog efekta korištenjem Python programskog jezika i algoritma Brze Fourierove transformacije" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#INPUT: zvučni fajl u formatu (.wav)\n", "#OUTPUT: Brzina kretanja izvora zvuka" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Učitavanje potrebnih bibilioteka i paketa\n", "\n", "%matplotlib inline\n", "from __future__ import division\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from numpy.fft import fft, ifft\n", "import scipy.io.wavfile as wav\n", "from IPython.core.display import HTML, display\n", "\n", "# Parametri za crtanje grafova:\n", "newparams = {'axes.labelsize': 11, 'axes.linewidth': 1, 'savefig.dpi': 300, \n", " 'lines.linewidth': 1.0, 'figure.figsize': (12, 4),\n", " 'ytick.labelsize': 10, 'xtick.labelsize': 10,\n", " 'ytick.major.pad': 5, 'xtick.major.pad': 5,\n", " 'legend.fontsize': 10, 'legend.frameon': True, \n", " 'legend.handlelength': 1.5}\n", "plt.rcParams.update(newparams)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Funkcija koja učitava i računa FFT\n", "\n", "def fft_doppler():\n", " \"\"\"\n", " Output\n", " P1 : 1D vector\n", " P2 : 1D vector\n", " f : 1D vector\n", " \n", " Kako se funkcija poziva\n", " (P1,P2,f) = fftwrapper()\n", " \"\"\"\n", " \n", " N=80000 # Broj uzoraka (tacaka) u uzorku1 i uzorku2\n", " shift1=168000 # Prvi indeks za izdvanjanje uzorka1 (priblizavanje)\n", " shift2=280000 # Prvi indeks za izdavanje uzorka2 (udaljavanje)\n", " fcutoff=880 # Maksimalna dopuštena frekvencija u rezultujućem uzorku\n", " \n", " # Učitvanje zvučnog fajla i pretvaranje u mono format\n", " Fs, ystereo = wav.read('Doppler_Demo.wav', 'r')\n", " ymono = (ystereo[:,0] + ystereo[:,1])/2\n", " ymono = ymono/max(abs(ymono))\n", " deltat = 1/Fs # Frekvencija uzorkovanja, Fs=Bitrate\n", " \n", " sample1 = ymono[shift1:N+shift1-1]\n", " sample2 = ymono[shift2:N+shift2-1]\n", " \n", " # FFT transformacija\n", " p1 = fft(sample1)\n", " p2 = fft(sample2)\n", " P1 = np.absolute(p1)**2\n", " P2 = np.absolute(p2)**2\n", " f = np.linspace(0,N-1,N)/(N*deltat)\n", " \n", " # Redukovanje veličine vektora na željenu veličinu - priprema za crtanje\n", " ifcutoff= np.nonzero(abs(f-fcutoff)==min(abs(f-fcutoff)))[0]-1\n", " ifcutoff=int(ifcutoff)\n", " f = f[0:ifcutoff]\n", " P1 = P1[0:ifcutoff]\n", " P2 = P2[0:ifcutoff]\n", " \n", " \n", " return (P1, P2, f)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Pozivanje i izvršavanje prethodne funkcije\n", "(P1, P2, f) = fft_doppler()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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txW1PnnxzxMDk+37VYXRYIrtS7u5FrfgohDhaCLFICLFUCHFxmuVOEkJIIcSk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\nZ+QIqIR7ndO53LKG5+M+Bzt0jA3jWEU9HWPDSGkXDwHPngB41TWFMh3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Crtanje grafa P1 i P2 kao funkcije frekvencije u frekventnom opsegu (0,fcutoff)\n", "plt.plot(f, P1/max(P1), f, P2/max(P2))\n", "plt.xlabel(r\"$f$ (Hz)\");\n", "plt.ylabel(r\"$P/P_{max}$\")\n", "plt.title(\"60 km/h\")\n", "plt.legend(['Priblizavanje','Udaljavanje','Location','NorthWest'], loc=2);\n", "plt.savefig(\"60kmh.png\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 = 848.925000, f2 = 776.711250\n", "Brzina automobila (t=0°C) je 52.98 km/h.\n", "Brzina automobila pri 30°C je 55.82 km/h.\n" ] } ], "source": [ "# Biranje pikova u oba uzorka\n", "f1 = f[P1==max(P1)]\n", "f2 = f[P2==max(P2)]\n", "\n", "print(\"f1 = %f, f2 = %f\" % (f1, f2))\n", "\n", "# Izračunavanje brzine kretanja izvora prema forumuli (1):\n", "\n", "c = 331.3 # Brzina zvuka na 0°C [m/s]\n", "v = (f1-f2)/(f1+f2)*c # Brzina izvora [m/s]\n", "v = 3.6*v # Brzina izvora [km/h]\n", "\n", "print(\"Brzina automobila (t=0°C) je %0.2f km/h.\" % v)\n", "\n", "temp=30.\n", "c = 331.3*np.sqrt(1+temp/273) # Brzina zvuka na temperaturi t [m/s]\n", "v = (f1-f2)/(f1+f2)*c # Brzina izvora [m/s]\n", "v = 3.6*v # Brzina izvora [km/h]\n", "\n", "print(\"Brzina automobila pri %0.2i°C je %0.2f km/h.\" % (temp, v))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }