83 lines
3.0 KiB
Python
Executable File
83 lines
3.0 KiB
Python
Executable File
#!/usr/bin/python3
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print('*** Preprost zvočni spektralni analizator - S53MV 28.09.2020 ***')
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print('*** popravek odziva za EliteBook 8540w - Andrej 11.2.2012 ***')
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print('*** meritev faznega suma (interpolacija in povprecenje v frekvenci-sai) - Andrej 15.2.2012 ***')
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fs=192000 #Frekvenca vzročenja zvočnega signala (max 192000Hz)
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pts=2048 #Število točk spektra 0...fs/2 (potenca 2 za učinkovit FFT)
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avg=128 #Število povprečenj spektra
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import sounddevice as sd #Poiskati na spletu in naložiti s pip3!
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import numpy as np #Uporaba učinkovitih funkcij numpy
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import matplotlib.pyplot as plt #Risanje rezultata z matplotlib
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import matplotlib.animation as animation
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sd.default.device = 0
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fskala=np.linspace(0,fs/2,2*pts+1)[:-1] #Izračunaj frekvenčno skalo [Hz]
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### podatki za kalibracijo sistema
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dSB = -65.36+14.33 #dB
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L = 1256 #metrov
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tau = L*1.4676/3e8
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T = 0.5*1.4676/3e8
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### popravek odziva
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odziv = np.loadtxt("5mhz-polinom-2048tock.csv", delimiter=',')
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a = odziv[:,1]
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popravek = np.max(a[4:-1])-a[4:-1]
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def spekter(fs,pts,avg,fskala): #Zajem signala z zvočno kartico in povprečenje spektra [dB]
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s=sd.rec(pts*(1+avg),samplerate=fs,channels=1) #Vzorčenje ADC zvočne kartice, 2D polje!
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sd.wait() #Počakaj do konca vzorčenja
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nz=1.e-12 #Dodatek za neničelni argument logaritma
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s2=np.square(s) #Izpis jakosti/max [dB]
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print('Jakost:',"%.1f"%(10*np.log10(nz+np.mean(s2))),'dB Vrh:',"%.1f"%(10*np.log10(nz+np.amax(s2))),'dB',end=' ')
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o=1+np.cos(np.linspace(-np.pi,np.pi,2*pts)) #Okno dvignjeni kosinus
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k=0
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s2=np.zeros(2*pts)
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while k<pts*avg: #Seštevanje moči FFT
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s2=s2+np.square(np.abs(np.fft.rfft(o*s.reshape(-1)[k:k+2*pts],n=4*pts)[:-1]))
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k+=pts
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s=10*np.log10(nz+0.5*s2/pts/pts/avg) #Povprečje [dB]
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print(' Max:',"%.1f"%(np.amax(s[4:-1])),'dB @',"%.1f"%(fskala[np.argmax(s[200:-1])]),'Hz')
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return s[4:-1]+popravek
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### izris z animacijo
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f=fskala[4:-1]
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rbw = np.diff(f)[0]
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print(rbw)
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initsum = spekter(fs,pts,avg,fskala)
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fig,ax = plt.subplots()
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raw,pn, = ax.plot(f,initsum,f,initsum)
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ax.set_xlim(100, 100000)
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ax.set_ylim(-130,-30)
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ax.set_ylabel("Fazni šum [dBc/Hz]")
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ax.set_xlabel("Frekvenčni odmik [Hz]")
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plt.yticks(np.arange(-150,-29,10))
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plt.xscale('log')
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ax.grid(True)
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def meritev(nic):
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s = spekter(fs,pts,avg,fskala)
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Pcal = np.amax(s)
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K = Pcal - 3 - dSB - 20*np.log10(np.pi*T*84e3)
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fsum = s - K - 3 - 6 - 20*np.log10(np.sin(f*np.pi*tau)) -10*np.log10(rbw)
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raw.set_data(f,s)
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pn.set_data(f,fsum)
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ax.figure.canvas.draw()
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return raw,pn
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ani = animation.FuncAnimation(fig,meritev,interval=50)
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plt.show()
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#shrani csv file
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s = spekter(fs,pts,avg,fskala)
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Pcal = np.amax(s[200:-1])
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K = Pcal - 3 - dSB - 20*np.log10(np.pi*T*84e3)
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fsum = s - K - 3 - 6 - 20*np.log10(np.sin(f*np.pi*tau)) -10*np.log10(rbw)
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data = np.column_stack((f,fsum,s))
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np.savetxt('floor.csv',data,delimiter=',')
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print('*** Konec ***')
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