48 lines
1.1 KiB
Python
48 lines
1.1 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import random
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import acoustics.decibel as db
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freq_upper = 1000 #size of the frequency spectrum we want to investigate
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freq_div = 10
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x = np.array(range(freq_upper)) #initialize frequencies (x-axis)
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bkgd = np.zeros(freq_upper)
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source = np.zeros(freq_upper)
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for i in range(len(bkgd)):
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bkgd[i] = np.log((x[i]+1)/freq_div) #input model for background here. each freq is assigned an SNR
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tonals = [20,40,100,500, 800] #build our threat object here
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widths = [2,2,2,2,2] #how diffuse is each tonal
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decr = [.5,.5,.5,.95,.98] #sound decrement from ambient
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for i in range(len(source)):
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source[i] = random.randint(0,10)/200
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for tone,wide,loud in zip(tonals, widths, decr): #right now the source is based on bkgd
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freq = tone - wide//2 #strength, but we will need to just
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while freq < tone + wide//2: #assign values
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source[freq] = bkgd[freq]*(1-loud) #
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freq += 1 #
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received = db.dbadd(bkgd, source)
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plt.plot(x/freq_div,received, label = "Legacy")
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plt.plot(x/freq_div,source, label = "Distilled")
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plt.xlabel("freq (Hz)")
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plt.ylabel("SNR")
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plt.title("Concept")
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plt.legend()
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plt.show()
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