def main():
[(_ignore_stat, first), (_ignore_stat, second)] = load_stats(sys.argv[1:])
# Attempt to increase robustness by dropping the outlying 10% of values.
first = trim(first, 0.1)
second = trim(second, 0.1)
fmean = stats.mean(first)
smean = stats.mean(second)
p = ttest_1samp(second, fmean)[1]
if p >= 0.95:
# rejected the null hypothesis
print(sys.argv[1], 'mean of', fmean, 'differs from', sys.argv[2], 'mean of', smean, '(%2.0f%%)' % (p * 100,))
else:
# failed to reject the null hypothesis
print('cannot prove means (%s, %s) differ (%2.0f%%)' % (fmean, smean, p * 100,))
def main():
[(stat, first), (stat, second)] = load_stats(sys.argv[1:])
# Attempt to increase robustness by dropping the outlying 10% of values.
first = trim(first, 0.1)
second = trim(second, 0.1)
fmean = stats.mean(first)
smean = stats.mean(second)
p = 1 - ttest_1samp(second, fmean)[1][0]
if p >= 0.95:
# rejected the null hypothesis
print sys.argv[1], 'mean of', fmean, 'differs from', sys.argv[2], 'mean of', smean, '(%2.0f%%)' % (p * 100,)
else:
# failed to reject the null hypothesis
print 'cannot prove means (%s, %s) differ (%2.0f%%)' % (fmean, smean, p * 100,)
def main():
fig = pyplot.figure()
ax = fig.add_subplot(111)
data = [samples for (_ignore_stat, samples) in load_stats(sys.argv[1:])]
bars = []
color = iter('rgbcmy').next
w = 1.0 / len(data)
xs = numpy.arange(len(data[0]))
for i, s in enumerate(data):
bars.append(ax.bar(xs + i * w, s, width=w, color=color())[0])
ax.set_xlabel('sample #')
ax.set_ylabel('seconds')
ax.legend(bars, sys.argv[1:])
pyplot.show()
def main():
fig = pyplot.figure()
ax = fig.add_subplot(111)
data = [samples for (_ignore_stat, samples) in load_stats(sys.argv[1:])]
bars = []
color = iter('rgbcmy').next
w = 1.0 / len(data)
xs = numpy.arange(len(data[0]))
for i, s in enumerate(data):
bars.append(ax.bar(xs + i * w, s, width=w, color=color())[0])
ax.set_xlabel('sample #')
ax.set_ylabel('seconds')
ax.legend(bars, sys.argv[1:])
pyplot.show()
