def plotOutliers(samples):
"""Make CDFs showing the distribution of outliers."""
cdfs = []
for label, sample in samples.iteritems():
outliers = [x for x in sample if x < 150]
cdf = thinkbayes.makeCdfFromList(outliers, label)
cdfs.append(cdf)
thinkplot.clf()
thinkplot.cdfs(cdfs)
thinkplot.save(root='variability_cdfs',
title='CDF of height',
xlabel='Reported height (cm)',
ylabel='CDF')
def plotPosteriors(self, other):
"""Plots posterior distributions of efficacy.
self, other: Sat objects.
"""
thinkplot.clf()
thinkplot.prePlot(num=2)
cdf1 = thinkbayes.makeCdfFromPmf(self, 'posterior %d' % self.score)
cdf2 = thinkbayes.makeCdfFromPmf(other, 'posterior %d' % other.score)
thinkplot.cdfs([cdf1, cdf2])
thinkplot.save(xlabel='efficacy',
ylabel='CDF',
axis=[0, 4.6, 0.0, 1.0],
root='sat_5_posteriors_eff',
formats=['pdf'])
def makePlot(self, root='redline1'):
"""Plot the prior and posterior CDF of passengers arrival rate.
root: string
"""
thinkplot.clf()
thinkplot.prePlot(2)
# convert units to passengers per minute
prior = self.priorLambda.makeCdf().scale(60)
post = self.posteriorLambda.makeCdf().scale(60)
thinkplot.cdfs([prior, post])
thinkplot.save(root=root,
xlabel='Arrival rate (passengers / min)',
ylabel='CDF',
formats=FORMATS)
def makePlot(self, root='redline3'):
"""Plot the CDFs.
root: string
"""
# observed gaps
cdf_prior_x = self.prior_x.makeCdf()
cdf_post_x = self.post_x.makeCdf()
cdf_y = self.pmf_y.makeCdf()
cdfs = scaleDists([cdf_prior_x, cdf_post_x, cdf_y], 1.0 / 60)
thinkplot.clf()
thinkplot.prePlot(3)
thinkplot.cdfs(cdfs)
thinkplot.save(root=root,
xlabel='Time (min)',
ylabel='CDF',
formats=FORMATS)
def makePlot(self, root='redline2'):
"""Plots the computed CDFs.
root: string
"""
print('Mean z', self.pmf_z.mean() / 60)
print('Mean zb', self.pmf_zb.mean() / 60)
print('Mean y', self.pmf_y.mean() / 60)
cdf_z = self.pmf_z.makeCdf()
cdf_zb = self.pmf_zb.makeCdf()
cdf_y = self.pmf_y.makeCdf()
cdfs = scaleDists([cdf_z, cdf_zb, cdf_y], 1.0 / 60)
thinkplot.clf()
thinkplot.prePlot(3)
thinkplot.cdfs(cdfs)
thinkplot.save(root=root,
xlabel='Time (min)',
ylabel='CDF',
formats=FORMATS)
def makePlots(player1, player2):
"""Generates two plots.
price1 shows the priors for the two players
price2 shows the distribution of diff for the two players
"""
# plot the prior distribution of price for both players
thinkplot.clf()
thinkplot.prePlot(num=2)
pmf1 = player1.pmfPrice()
pmf1.name = 'showcase 1'
pmf2 = player2.pmfPrice()
pmf2.name = 'showcase 2'
thinkplot.pmfs([pmf1, pmf2])
thinkplot.save(root='price1_showcase1,2_priorPmfs',
xlabel='price ($)',
ylabel='PDF',
formats=FORMATS)
# plot the historical distribution of underness for both players
thinkplot.clf()
thinkplot.prePlot(num=2)
cdf1 = player1.cdfDiff()
cdf1.name = 'player 1'
cdf2 = player2.cdfDiff()
cdf2.name = 'player 2'
print('\n\nPlayer median', cdf1.percentile(50))
print('Player median', cdf2.percentile(50))
print('\nPlayer 1 overbids', player1.probOverbid())
print('Player 2 overbids', player2.probOverbid())
thinkplot.cdfs([cdf1, cdf2])
thinkplot.save(root='price2_diffs_cdf',
xlabel='diff ($)',
ylabel='CDF',
formats=FORMATS)