#!/usr/bin/env python # Filename: ftqviz.py # Author: Darren Hart <dvhltc@us.ibm.com> # Description: Plot the time and frequency domain plots of a times and # counts log file pair from the FTQ benchmark. # Prerequisites: numpy, scipy, and pylab packages. For debian/ubuntu: # o python-numeric # o python-scipy # o python-matplotlib # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # Copyright (C) IBM Corporation, 2007 # # 2007-Aug-30: Initial version by Darren Hart <dvhltc@us.ibm.com> from numpy import * from numpy.fft import * from scipy import * from pylab import * from sys import * from getopt import * NS_PER_S = 1000000000 NS_PER_MS = 1000000 NS_PER_US = 1000 def smooth(x, wlen): if x.size < wlen: raise ValueError, "Input vector needs to be bigger than window size." # reflect the signal to avoid transients... ? s = r_[2*x[0]-x[wlen:1:-1], x, 2*x[-1]-x[-1:-wlen:-1]] w = hamming(wlen) # generate the smoothed signal y = convolve(w/w.sum(), s, mode='same') # recenter the the smoothed signal over the originals (slide along x) y1 = y[wlen-1:-wlen+1] return y1 def my_fft(x, sample_hz): X = abs(fftshift(fft(x))) freq = fftshift(fftfreq(len(x), 1.0/sample_hz)) return array([freq, abs(X)/len(x)]) def smooth_fft(timefile, countfile, sample_hz, wlen): # The higher the sample_hz, the larger the required wlen (used to generate # the hamming window). It seems that each should be adjusted by roughly the # same factor ns_per_sample = NS_PER_S / sample_hz print "Interpolated Sample Rate: ", sample_hz, " HZ" print "Hamming Window Length: ", wlen t = fromfile(timefile, dtype=int64, sep='\n') x = fromfile(countfile, dtype=int64, sep='\n') # interpolate the data to achieve a uniform sample rate for use in the fft xi_len = (t[len(t)-1] - t[0])/ns_per_sample xi = zeros(xi_len) last_j = 0 for i in range(0, len(t)-1): j = (t[i] - t[0])/ns_per_sample xi[j] = x[i] m = (xi[j]-xi[last_j])/(j-last_j) for k in range(last_j + 1, j): xi[k] = m * (k - last_j) + xi[last_j] last_j = j # smooth the signal (low pass filter) try: y = smooth(xi, wlen) except ValueError, e: exit(e) # generate the fft X = my_fft(xi, sample_hz) Y = my_fft(y, sample_hz) # plot the hamming window subplot(311) plot(hamming(wlen)) axis([0,wlen-1,0,1.1]) title(str(wlen)+" Point Hamming Window") # plot the signals subplot(312) ts = arange(0, len(xi), dtype=float)/sample_hz # time signal in units of seconds plot(ts, xi, alpha=0.2) plot(ts, y) legend(['interpolated', 'smoothed']) title("Counts (interpolated sample rate: "+str(sample_hz)+" HZ)") xlabel("Time (s)") ylabel("Units of Work") # plot the fft subplot(313) plot(X[0], X[1], ls='steps', alpha=0.2) plot(Y[0], Y[1], ls='steps') ylim(ymax=20) xlim(xmin=-3000, xmax=3000) legend(['interpolated', 'smoothed']) title("FFT") xlabel("Frequency") ylabel("Amplitude") show() def usage(): print "usage: "+argv[0]+" -t times-file -c counts-file [-s SAMPLING_HZ] [-w WINDOW_LEN] [-h]" if __name__=='__main__': try: opts, args = getopt(argv[1:], "c:hs:t:w:") except GetoptError: usage() exit(2) sample_hz = 10000 wlen = 25 times_file = None counts_file = None for o, a in opts: if o == "-c": counts_file = a if o == "-h": usage() exit() if o == "-s": sample_hz = long(a) if o == "-t": times_file = a if o == "-w": wlen = int(a) if not times_file or not counts_file: usage() exit(1) smooth_fft(times_file, counts_file, sample_hz, wlen)