def bootstrap_extradata(self, nBoot, extradataA, nbins = 20):
pops =[]
meanpop = [[] for i in data.cat]
pylab.figure(figsize = (14,14))
for i in xrange(min(4, len(extradataA))):
#pylab.subplot(2,2,i+1)
if i ==0:
pylab.title("Bootstrap on means", fontsize = 20.)
pop = extradataA[i]# & (self.GFP > 2000)]#
for index in xrange(nBoot):
newpop = np.random.choice(pop, size=len(pop), replace=True)
#meanpop[i].append(np.mean(newpop))
pops.append(newpop)
pylab.legend()
#pylab.title(cat[i])
pylab.xlabel("Angle(degree)", fontsize = 15)
pylab.xlim([0., 90.])
for i in xrange(len(extradataA)):
for j in xrange(i+1, len(extradataA)):
statT, pvalue = scipy.stats.ttest_ind(pops[i], pops[j], equal_var=False)
print "cat{0} & cat{1} get {2} ({3})".format(i,j, pvalue,statT)
pylab.savefig("/users/biocomp/frose/frose/Graphics/FINALRESULTS-diff-f3/mean_nBootstrap{0}_bins{1}_GFPsup{2}_FLO_{3}.png".format(nBoot, nbins, 'all', randint(0,999)))
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