diff --git a/.ipynb_checkpoints/sound_profile-checkpoint.py b/.ipynb_checkpoints/sound_profile-checkpoint.py
new file mode 100644
index 0000000..7881691
--- /dev/null
+++ b/.ipynb_checkpoints/sound_profile-checkpoint.py
@@ -0,0 +1,48 @@
+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()
+
+
+
+
+
diff --git a/brg_freq_mesh.py b/brg_freq_mesh.py
index f073054..c1d441c 100644
--- a/brg_freq_mesh.py
+++ b/brg_freq_mesh.py
@@ -1,38 +1,39 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.widgets import Slider, Button
-
+import random
 
 soundscape = np.empty((1000,1))
 for i in range(1000):
-	soundscape[(i,0)] = -.003*i-60
+	soundscape[(i,0)] = -.03*i-60
 brgs = np.ones((1,360))
 brg_freq = np.matmul(soundscape, brgs)
 
-targ_brg = [60]
-brg_width = [5]
-tonals = [20,40,100,500, 800]  	#build our threat object here
-tonal_widths = [2,2,2,2,2]		#how diffuse is each tonal
-decr = [.8,.8,.8,.90,.90]		#sound decrement from ambient
+
+targ_brg = [60,220]
+brg_width = [5, 5]
+tonals = [[20,40,100,500, 800], [30,60,200,250,500]]  	#build our threat object here
+tonal_widths = [[2,2,2,2,2], [6,5,4,3,2]]		#how diffuse is each tonal
+decr = [[.8,.8,.8,.90,.90], [.7,.6,.5,.4,.3]]		#sound decrement from ambient
+
+i = 0
+while i < 50000:
+	brg_freq[random.randint(0,999),random.randint(0,359)] = random.randint(-60,-50)
+	i+=1	
 
 
-for azim,spread in zip(targ_brg,brg_width):
+for azim,spread,x,y,z in zip(targ_brg,brg_width, tonals, tonal_widths, decr):
 	brg = azim - spread//2
 	while brg <= azim + spread//2:	
-		for tone,wide,loud in zip(tonals, tonal_widths, decr):#right now the source is based on bkgd
+		for tone,wide,loud in zip(x,y,z):
 			freq = tone - wide//2			#strength, but we will need to just
 			while freq < tone + wide//2:		#assign values
-				brg_freq[(freq,brg)] = -50	#
+				brg_freq[(freq,brg)] = -35	#
 				freq += 1
 		brg += 1
 
-axtime = plt.axes()
-time = Slider(axtime, "Time", 0, 60)
-def update(brg):
-	t = time.val
-time.on_changed(update)
 
-plt.imshow(brg_freq)
+plt.imshow(brg_freq, origin="lower", aspect=.25)
 plt.colorbar()
 plt.show()
 
@@ -40,3 +41,5 @@ plt.show()
 
 
 
+
+
diff --git a/mesh.png b/mesh.png
new file mode 100644
index 0000000..3b08468
Binary files /dev/null and b/mesh.png differ
diff --git a/sound_profile.py b/sound_profile.py
index 7881691..e744eb6 100644
--- a/sound_profile.py
+++ b/sound_profile.py
@@ -4,37 +4,55 @@ import random
 import acoustics.decibel as db
 
 
-freq_upper = 10000	#size of the frequency spectrum we want to investigate
-freq_div = 10
+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] = np.log((x[i]+1)/freq_div)  #input model for background here. each freq is assigned an SNR
+	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 = [.5,.5,.5,.95,.98]		#sound decrement from ambient
+decr = [.8,.8,.8,.90,.90]		#sound decrement from ambient
 
 
-for i in range(len(source)):
-	source[i] = random.randint(0,10)/200
+#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
+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)		#
+		source[freq] = bkgd[i]*(1-loud)	#
 		freq += 1				#
 
 		
-received = db.dbadd(bkgd, source)	
+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/freq_div,received, label = "Legacy")
-plt.plot(x/freq_div,source, label = "Distilled")
+plt.plot(x, received, label = "Legacy")
+plt.plot(x, source, label = "Distilled")
+
 plt.xlabel("freq (Hz)")
 plt.ylabel("SNR")
 plt.title("Concept")
@@ -42,7 +60,10 @@ 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()
+"""
 
 
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..e69de29