import matplotlib.pyplot as plt
import numpy as np
import random
import acoustics.decibel as db


freq_upper = 10000	#size of the frequency spectrum we want to investigate
freq_div = 10

x = np.array(range(freq_upper))	#initialize frequencies (x-axis)
bkgd = np.zeros(freq_upper)
source = np.zeros(freq_upper)

for i in range(len(bkgd)):
	bkgd[i] = np.log((x[i]+1)/freq_div)  #input model for background here. each freq is assigned an SNR
	
tonals = [20,40,100,500, 800]  	#build our threat object here
widths = [2,2,2,2,2]		#how diffuse is each tonal
decr = [.5,.5,.5,.95,.98]		#sound decrement from ambient


for i in range(len(source)):
	source[i] = random.randint(0,10)/200
	
	
for tone,wide,loud in zip(tonals, widths, decr):		#right now the source is based on bkgd
	freq = tone - wide//2				#strength, but we will need to just
	while freq < tone + wide//2:			#assign values
		source[freq] = bkgd[freq]*(1-loud)		#
		freq += 1				#

		
received = db.dbadd(bkgd, source)	
	
	
plt.plot(x/freq_div,received, label = "Legacy")
plt.plot(x/freq_div,source, label = "Distilled")
plt.xlabel("freq (Hz)")
plt.ylabel("SNR")
plt.title("Concept")

plt.legend()
plt.show()