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Top Forums Shell Programming and Scripting Read Field from file1 and find and replace in file2 Post 302511417 by Chubler_XL on Wednesday 6th of April 2011 06:28:12 PM
Old 04-06-2011
Not real clear what you are after here. Some sssumptions I've made:

1. The order the files are output should remain the same as defined in config file
2. Multiple replaces may appear for each file
3. Replace tag (eg @Phone@) may have different values for different files

Here is the solution in awk:

Code:
awk -F\| '{if(!($1 in F))P[++f]=$1;F[$1];R[$1,$2]=$3}
END{
   for(i=1;i<=f;i++)  {
     while((getline < P[i]) > 0) {
         for(idx in R) {
            split(idx,val,SUBSEP);
            if(val[1]==P[i]) gsub(val[2],R[idx]);
         }
         print $0
     }
     close(P[i])
   }
}' ConfigFile


Last edited by Chubler_XL; 04-06-2011 at 07:35 PM..
This User Gave Thanks to Chubler_XL 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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