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Full Discussion: Nawk Split
Top Forums UNIX for Dummies Questions & Answers Nawk Split Post 97218 by steveramsey on Friday 27th of January 2006 08:28:40 AM
Old 01-27-2006
Thanks Penguin. I did something like that. The input file will have comments embedded so first I get rid of all the junk and then dynamically create arrays and populate.

Code:
idx=1
while read entry
do
  if [ ${entry:0:1} == "#" ]; then
    continue
  else
    eval "set$idx=( `echo $entry | nawk ' { v=split($0,a,"::"); if ( v > 0 ) { print a[1] " " a[2] " " a[3] " " a[4]; } } '`)"
    idx=$(($idx+1))
    setIdx=$(($idx-1))
  fi
done < manifest

The problem I am having now is referencing the created arrays. When I try to dump the content like this I get a bad substitution error. Any ideas? Thanks.

Code:
a=1
while (( $a <= $setIdx ))
do
 echo "${set$a[@]}"
 ((a += 1))
done

 

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spectral-analysis(7)						  Numm Tutorials					      spectral-analysis(7)

NAME
spectral analysis - perform realtime spectral analysis SYNOPSIS
numm-run FILE DESCRIPTION
Frequency makes for a meaningful description of many audio signals. We can use numpy's fourier analysis to compute spectra from the micro- phone and display the results visually. We will break down the process into smaller parts: baby steps... First, create and save a skeletal file that moves a line across the screen: idx = 0 def video_out(a): global idx a[:,idx] = 255 idx = (idx + 1) % a.shape[1] def audio_in(a): pass Save this snippet and run it with numm-run. We will use the numpy.fft module for our analysis. First we define a function to get a particular frequency from the fourier transform: import numpy as np def get_freq(fourier, frequency): freqs = np.fft.fftfreq(len(fourier), 1/44100.0) nearest = (abs(freqs - frequency)).argmin() return abs(fourier[nearest]) Next, we hook up this function to audio input from the microphone. A frequency bin is chosen on a log scale for each row on the screen to display a spectogram. In total: import numpy as np idx = 0 recent_audio = np.zeros(4096, np.int16) recent_video = np.zeros((240,320,3), np.uint8) freq_bins = np.exp2(np.linspace(np.log2(27000),np.log2(27),240)) def get_freq(fourier, frequency): freqs = np.fft.fftfreq(len(fourier), 1/44100.0) nearest = (abs(freqs - frequency)).argmin() return abs(fourier[nearest]) def video_out(a): global idx fourier=np.fft.fft(recent_audio) values =np.array([get_freq(fourier,X) for X in freq_bins]) recent_video[:,idx,1] = (values/10000).clip(0,255) idx = (idx + 1) % a.shape[1] a[:] = np.roll(recent_video, -idx, axis=1) def audio_in(a): recent_audio[:] = np.roll(recent_audio, len(a)) recent_audio[:len(a)] = a.mean(axis=1) SEE ALSO
numm-run(1), numm.getting-started(7), numm.one-bit-instrument(7) numm February 2012 spectral-analysis(7)
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