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


freq_upper = 1000	#size of the frequency spectrum we want to investigate
#freq_div = 10
#freq_bin = freq_upper/freq_div

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

for i in range(len(bkgd)):
	bkgd[i] = -.003*x[i]-60  #input model for background here. each freq is assigned an SNR
	bkgd[i] = 10**(bkgd[i]/20)
	
tonals = [20,40,100,500, 800]  	#build our threat object here
widths = [2,2,2,2,2]		#how diffuse is each tonal
decr = [.8,.8,.8,.90,.90]		#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[i]*(1-loud)	#
		freq += 1				#

		
received = bkgd + source
for i in range(freq_upper):
	received[i] = 20*np.log10(received[i])
	
for i in range(freq_upper):
	source[i] = 20*np.log10(source[i])

for i in range(freq_upper-1):
	fav_received[i] = abs((received[i]-received[i+1])**3)

for i in range(freq_upper):
	source[i] = db.dbsub(received[i],bkgd[i]/2)

for i in range(freq_upper-1):
	fav_source[i] = abs((source[i]-source[i+1])**3)

	
plt.plot(x, received, label = "Legacy")
plt.plot(x, source, label = "Distilled")

plt.xlabel("freq (Hz)")
plt.ylabel("SNR")
plt.title("Concept")

plt.legend()
plt.show()

"""plt.plot(x, fav_received, label = "Traces")
plt.plot(x, fav_source+1, label = "Distilled Traces")
plt.legend()
plt.show()
"""