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#!/usr/bin/python
# Create a plot of the results of running the ./benchmark-storage.py script.
import argparse
from matplotlib import pyplot as plt
from sets import Set
def plot(filename, subtitle=''):
# config the plot
plt.xlabel('doc size (KB)')
plt.ylabel('operation time (s)')
title = 'soledad 1000 docs creation/retrieval times'
if subtitle != '':
title += '- %s' % subtitle
plt.title(title)
x = Set()
ycreate = []
yget = []
ys = []
#ys.append((ycreate, 'creation time', 'r', '-'))
#ys.append((yget, 'retrieval time', 'b', '-'))
# read data from file
with open(filename, 'r') as f:
f.readline()
for i in xrange(6):
size, y = f.readline().strip().split(' ')
x.add(int(size))
ycreate.append(float(y))
f.readline()
for i in xrange(6):
size, y = f.readline().strip().split(' ')
x.add(int(size))
yget.append(float(y))
# get doc size in KB
x = list(x)
x.sort()
x = map(lambda val: val / 1024, x)
# get normalized results per KB
nycreate = []
nyget = []
for i in xrange(len(x)):
nycreate.append(ycreate[i]/x[i])
nyget.append(yget[i]/x[i])
ys.append((nycreate, 'creation time per KB', 'r', '-.'))
ys.append((nyget, 'retrieval time per KB', 'b', '-.'))
for y in ys:
kwargs = {
'linewidth': 1.0,
'marker': '.',
'color': y[2],
'linestyle': y[3],
}
# normalize by doc size
plt.plot(
x,
y[0],
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, 1100)
#plt.ylim(0, 350)
plt.grid()
plt.legend()
plt.show()
if __name__ == '__main__':
# parse command line
parser = argparse.ArgumentParser()
parser.add_argument(
'datafile',
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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