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Top Forums Shell Programming and Scripting Performing aggregation in File Post 302837269 by vgersh99 on Thursday 25th of July 2013 12:39:33 PM
Old 07-25-2013
something along these lines:
awk -f vf.awk myInputFile | sort -t '|' -k1,1 -k2,2 where vf.awk is:
Code:
BEGIN {
  FS=OFS="|"
  flags="A|I"
  n=split(flags, flagsA, FS)
}

{
  idx= $1 OFS $2
  lb[idx]
  sum[idx,$3]+=$4
}
END {
  for (i in lb) {
    printf i
    for(j=1;j<=n;j++)
      printf("%s%s%s", OFS, sum[i,flagsA[j]], (j==n)?RS:"")
  }
}

 

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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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