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Top Forums Shell Programming and Scripting awk to compare diff output by fields Post 302470591 by vgersh99 on Wednesday 10th of November 2010 12:29:55 PM
Old 11-10-2010
Code:
#!/bin/ksh

nawk '
  # create an variable "idx" which is a concatenation of the first 5 fields in a record/line
  # as we read the file line by line
  {idx=$1 FS $2 FS $3 FS $4 FS $5}
  {
    # see if "idx" is in array "a" - array "a" is indexed by the value of "idx"
    if (idx in a)
      # if "idx" is already in "a", check if the stored value (a[idx]) is less than the
      # last field ($NF) of the current record/line. If it's, we see the "increase" in value
      # If so, output the current line (print $0)
      if (a[idx]<$NF)
         print $0
    # store the last field ($NF) of the current record in aarray "a" indexed by "idx"
    a[idx]=$NF
  }' myDiffFile | while read line
do
  # read the output of "nawk" and do "doSomething" with the read line
  echo "doSomething with [$line]"
done


Last edited by vgersh99; 11-10-2010 at 01:54 PM.. Reason: comments
This User Gave Thanks to vgersh99 For This Post:
 

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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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