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#!/usr/bin/python
import argparse
from matplotlib import pyplot as plt
from movingaverage import movingaverage
from scipy.interpolate import interp1d
from numpy import linspace
def smooth(l):
return movingaverage(l, 3, data_is_list=True, avoid_fp_drift=False)
def plot(filename, subtitle=''):
# config the plot
plt.xlabel('time (s)')
plt.ylabel('usage (%)')
title = 'soledad sync'
if subtitle != '':
title += '- %s' % subtitle
plt.title(title)
x = []
ycpu = []
ymem = []
ypcpu = []
ypmem = []
ys = [
(ycpu, 'total cpu', 'r'),
(ymem, 'total mem', 'b'),
(ypcpu, 'proc cpu', 'm'),
(ypmem, 'proc mem', 'g'),
]
# read data from file
with open(filename, 'r') as f:
line = f.readline()
while True:
line = f.readline()
if line.startswith('#'):
continue
if line == '' or line is None:
break
time, cpu, mem, pcpu, pmem = tuple(line.strip().split(' '))
x.append(float(time))
ycpu.append(float(cpu))
ymem.append(float(mem))
ypcpu.append(float(pcpu))
ypmem.append(float(pmem))
smoothx = [n for n in smooth(x)]
#xnew = linspace(0.01, 19, 100)
for y in ys:
kwargs = {
'linewidth': 1.0,
'linestyle': '-',
# 'marker': '.',
'color': y[2],
}
#f = interp1d(x, y[0], kind='cubic')
plt.plot(
smoothx,
[n for n in smooth(y[0])],
#xnew,
#f(xnew),
label=y[1], **kwargs)
#plt.axes().get_xaxis().set_ticks(x)
#plt.axes().get_xaxis().set_ticklabels(x)
# annotate max and min values
plt.xlim(0, 20)
plt.ylim(0, 100)
plt.grid()
plt.legend()
plt.show()
if __name__ == '__main__':
# parse command line
parser = argparse.ArgumentParser()
parser.add_argument(
'-d', dest='datafile', required=False, default='/tmp/profile.log',
help='the data file to plot')
parser.add_argument(
'-s', dest='subtitle', required=False, default='',
help='a subtitle for the plot')
args = parser.parse_args()
plot(args.datafile, args.subtitle)
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