#Nature of code - Gaussian Noise
#**********************************
# The graph shows consecutive random numbers with a normal distribution, meaning that they tend to cluster around the mean with a certain variance.
# A large veriance will approach pure random noice, while a small variance will produce a lot less small or large values
# Where in BasicNoise, we remap the rand values 0 to 1 to height/2 plus or minus 150, here we create values with a mean of height/2 and variance 50
# Because we use seed(123456), every time you run this program, you will get the same values
# Change the seed or completely omit it to get different random values at every run. Change the variance to influence the 'spread' of returned values 
#

#Setup
gosub ScreenSetup
gosub RandomSetup
gosub GraphObjectSetup

#Draw
while 1
  gosub UpdateGraphObject
end while

ScreenSetup:
  Fastgraphics
  width=800
  height=400 
  graphsize width,height 
  color black
  rect(0,0,width-1,height-1)
return  

RandomSetup: 
  seed(123456)
  dim randmov(width/2)
  mean = height/2
  sigma = 50        # variance / spread of the bell curve
return
 
GraphObjectSetup:
  oldx=0
  oldy= height/2
  for i = 0 to width/2-1
    u1 = rand
    u2 = rand
    z = sqrt(-2*log(u1)) * cos(2*pi*u2)
    randmov[i] = mean + z * sigma
    color white
    line(oldx,oldy,i*2,randmov[i])
    oldx = i*2
    oldy = randmov[i]
  next i
  refresh
return 

UpdateGraphObject:
  oldi=0
  oldy=randmov[0]
  #shift values left
  for i = 0 to 398
    randmov[i] = randmov[i+1]
  next i
  #create new Gaussian value
  u1 = rand
  u2 = rand
  z = sqrt(-2*log(u1)) * cos(2*pi*u2)
  randmov[399] = mean + z * sigma
  #draw graph
  for i = 0 to width/2-1
    color white
    line(oldi,oldy,i*2,randmov[i])
    oldi = i*2
    oldy = randmov[i]
  next i    
  refresh
  pause 0.05
  color black
  rect(0,0,width-1,height-1)
return