frequency analysis without numpy

[debug]

Hi-

I've been using python now for about 2 months for plugin development
within Maya (a commercial 3d application). I'm currently in the
process of writing a sound analysis plugin for maya and have completed
a good portion of it including the ability to retrieve the amplitude
at certain intervals from a wav file.

The next part of the project however consists of analysing the wav
file and outputting the amplitude at certain frequencies. Due to the
need of distributing this to many computers after it has been finished
I'm reluctant to use an external python module for FFT'ing. Accuracy
and speed aren't really issues as just a generalisation of the
amplitude of low, medium and high frequencies is required.

Do you either know of any generic FFT functions written in python that
could just be inserted into my processing class (open source
licensed).

So far i've managed to put together a chunk of code but I'm not sure
its returning the right values, any ideas?

import wave
import sys
import array
import struct
from cmath import pi, exp


def nextpow2(i):
   n = 2
   while n < i: n = n * 2
   return n

def bitrev(x):
   N, x = len(x), x[:]
   if N != nextpow2(N): raise ValueError, 'N is not power of 2'
   for i in range(N):
  k, b, a = 0, N>>1, 1
  while b >= a:
 if b & i: k = k | a
 if a & i: k = k | b
 b, a = b>>1, a<<1
   if i < k:  # important not to swap back
  x[i], x[k] = x[k], x[i]
   return x



def fft(x, sign=-1):
   N, W = len(x), []
   for i in range(N):  # exp(-j...) is default
  W.append(exp(sign * 2j * pi * i / N))
   x = bitrev(x)
   m = 2
   while m <= N:
  for s in range(0, N, m):
 for i in range(m/2):
n = i * N / m
a, b = s + i, s + i + m/2
x[a], x[b] = x[a] + W[n % N] * x[b], x[a] - W[n % N] * x
[b]
  m = m * 2
   return x



def ifft(X):
N, x = len(X), fft(X, sign=1)   # e^{j2\pi/N}
for i in range(N):
x[i] = x[i] / float(N)
return x




fp = wave.open("hankuncom.wav","rb")
sample_rate = fp.getframerate()
total_num_samps = fp.getnframes()
fft_length = 4096
num_fft = (total_num_samps / fft_length ) - 2


temp = []

for i in range(num_fft):
   tempb = fp.readframes(fft_length)
   moo = struct.unpack("%uh"%(fft_length),tempb)
   temp.append(moo)


listed = list(temp[0])

freq_pwr  = fft(listed)

print(freq_pwr)

fp.close()

Quick warning this code snippet does run slowly
Thanks!

The wav file referenced in the script is here: 
www.reality-debug.co.uk/hankuncom.zip
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Re: frequency analysis without numpy

[debug]

On Jan 21, 11:31 am, sturlamolden  wrote:
> On Jan 21, 12:13 am, sturlamolden  wrote:
>
> > Apart from that, an FFT in pure python is going to be atrociously slow
> > for anything but the shortest signals. I cannot imagine why you want
> > to do this.
>
> Just to elaborate on this:
>
> The whole purpose of using FFT is speed. That pretty much excludes the
> use of Python.
>
> If you don't care about speed, you could just as well compute the DFT
> directly. The FFT is just a O(n lon n) algorithm for computing the
> DFT. Here is a DFT with O(N**2) behavior:
>
> from math import sin, cos, pi
>
> def real_dft(x):
>    ''' DFT for a real valued sequence x '''
>    r = []
>    N = len(x)
>    M = N//2 + 1 if N%2 else N//2
>    for n in range(M):
>       s = 0j
>       for k in range(N):
>          tmp = 2*pi*k*n/N
>          s += x[k] * (cos(tmp) - 1j*sin(tmp))
>       r.append(s)
>    return r
>
> S.M.

Thanks for the quick reply, so what do I pass the real_dft function
(obviously a list) but do I pass it the unpacked binary data?

Dom
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