0,1,0
0.1220703125,1,0
0.244140625,1,0
0.3662109375,1,0
0.48828125,1,0
0.6103515625,1,0
.....
-0.6103515625,1,0
-0.48828125,1,0
-0.3662109375,1,0
-0.244140625,1,0
-0.1220703125,1,0

0.000,819,0
0.001,805.600848087199,4.11892742135933E-12
0.002,766.191083986647,7.80009390410896E-12
0.003,703.07869137741,1.07704956064936E-11
0.004,619.930866652693,1.26768595620774E-11
0.005,521.519198753447,1.32877042702262E-11
0.006,413.390428478234,1.26576527037514E-11
0.007,301.48763797423,1.08400510789863E-11
8,0.008,191.750273447113,7.79765141345479E-12
....
8.186,413.390428478235,7.79385628257856E-09
8.187,521.519198753448,9.84392145575441E-09
8.188,619.930866652693,1.17116023545805E-08
8.189,703.07869137741,1.32913150485692E-08
8.19,766.191083986645,1.44921696865197E-08
8.191,805.600848087196,1.52439926792702E-08

SF: Sample Frequency
Samples: is array with samples 0..255
N: is number of samples (8192)
NP: is root of 2^NP = N (13)
CF: cutoff freq of filter (30)
W: number of coefficients in filter (40)
dare: real data input
daim: imaginary data input

for i = 0 To N – 1
{
// time axis for graphing results
time(i) = i / SF

// freq axis for graphing, note symetric FFT so need -ve freq
if i <= N / 2 then // Positive frequencies
freq(i) = i * SF / N
else // Negative frequencies
freq(i) = i * SF / N - SF

// copy input data to local array
// and normalise to 127 = 0, centering the input/transform

dare(i) = Samples(i) – 127
daim(i) = 0
}

// fft the input data
FFT(NP, N, dare, daim)

// Create the impulse response of the filter in the frequency domain
// this could be any filter but I use a simple box here: 1 in the passband and 0 elsewhere. Could use any filter here:

// Note that I used symmetric complex FFT: it is necessary
// fill in the negative frequencies as well.

for i = 0 To N - 1
{
re(i) = 0
if i <= (CF * N / SF) or i >= (N - CF * N / SF)
re(i) = 1
im(i) = 0
}

// Inverse filter into the time domain
// Note: imaginary parts should be ~ 0 after iFFT
IFFT(NP, N, re, im)

// Hann window to truncate the filter series at say +/-20
// can use other window formulas (e.g. bartlett) if desired
for i = 0 To N – 1
{
window(i) = 0
if i <= Won2 then
window(i) = (0.5 + 0.5 * Cos(i * 2pi / (W + 1)))
else if i >= N - Won2 then
window(i) = (0.5 + 0.5 * Cos((N - i) * 2pi / (W + 1)))
}

// Apply the Hann window to the filter
for i = 0 To N – 1
{
//careful here if complex multiply required
re(i) = re(i) * window(i)
im(i) = 0
}

// can IFFT and check for freq response and back again

// Shift the filter to make it i.e. +ve times only
//shift lower coefs up
for i = Won2 To 0 Step -1
{
re(i + Won2) = re(i) //
}
//wrap -ve coeffs around
for i = 0 To Won2 – 1
{
re(i) = re(N - Won2 + i) //wrap N -> 0, N - 1 -> 1 etc
re(N - Won2 + i) = 0
}

// Transform filter back into the frequency domain
FFT(NP, N, re, im)

//multiply input data by filter COMPLEX MULT REQUIRED!!
// may be able to shorten this step if desired
for i = 0 To N – 1
{
tmpre = dare(i) * re(i) - daim(i) * im(i)
tmpim = dare(i) * im(i) + daim(i) * re(i)
dare(i) = tmpre
daim(i) = tmpim
}

//inverse back to time domain
IFFT(NP, N, dare, daim)

// copy back to input normalised and re centered
// may need to normalise/scale at this stage
for i = 0 To N – 1
{
Samples(i) = dare(i) + 127
}