             Byeong Gi Lee's, 1D RADIX-2 Fast Cosine Transform.
             --------------------------------------------------

  Like many Fast algorithms of their Discrete counterparts, a RADIX-2 version
  is about the _easiest_, term used loosely, one to fulfill. This is version
  no exception.
  It is recursive so offers a huge saving in computation time, O(N*log2(N)),
  as opposed to the standard Discrete, O(N**2).

  It is also much easier to have a separate function that applies the
  scaling, and, in this DEMO case it follows scipy's DEFAULT settings, of
  "type=2"; and, "norm=None", or, "norm='backward'". This DEMO follows the
  same rules as written inside code and 'DFT_Multipliere.txt' file.
  Just change the two commented lines to this:-

def DFT_Multiplier(x, N=0):
	for i in range(0, N, 1):
		# Coefficient at position data 0...
		if i==0: Coefficient=(1.0/math.sqrt(2))
		# All other data positions,,,
		if i>=1: Coefficient=1.0
		FCT_Result[i]=(Coefficient*FCT_Result[i])
	return(FCT_Result)

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