blob: 5ac094cbdfc2122c9de740888c1e1218a43a62ad [file] [log] [blame]
#!/usr/bin/env python3
# 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 as 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 = int(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)