def ConditionalSurvival(pmf, t0):
"""Computes conditional survival function.
Probability that duration exceeds t0+t, given that
duration >= t0.
pmf: Pmf of durations
t0: minimum time
returns: tuple of (ts, conditional survivals)
"""
cond = thinkstats2.Pmf()
for t, p in pmf.Items():
if t >= t0:
cond.Set(t - t0, p)
return SurvivalFunction(thinkstats2.MakeCdfFromPmf(cond))
def main():
results = relay.ReadResults()
speeds = relay.GetSpeeds(results)
# plot the distribution of actual speeds
pmf = thinkstats2.MakePmfFromList(speeds, 'actual speeds')
# plot the biased distribution seen by the observer
biased = BiasPmf(pmf, 7.5, name='observed speeds')
thinkplot.Hist(biased)
thinkplot.Save(root='observed_speeds',
title='PMF of running speed',
xlabel='speed (mph)',
ylabel='probability')
cdf = thinkstats2.MakeCdfFromPmf(biased)
thinkplot.Clf()
thinkplot.Cdf(cdf)
thinkplot.Save(root='observed_speeds_cdf',
title='CDF of running speed',
xlabel='speed (mph)',
ylabel='cumulative probability')
def testCdf(self):
t = [1, 2, 2, 3, 5]
pmf = thinkstats2.Pmf(t)
hist = thinkstats2.Hist(t)
cdf = thinkstats2.Cdf(pmf)
self.assertEqual(len(str(cdf)), 37)
self.assertEqual(cdf[0], 0)
self.assertAlmostEqual(cdf[1], 0.2)
self.assertAlmostEqual(cdf[2], 0.6)
self.assertAlmostEqual(cdf[3], 0.8)
self.assertAlmostEqual(cdf[4], 0.8)
self.assertAlmostEqual(cdf[5], 1)
self.assertAlmostEqual(cdf[6], 1)
xs = range(7)
ps = cdf.Probs(xs)
for p1, p2 in zip(ps, [0, 0.2, 0.6, 0.8, 0.8, 1, 1]):
self.assertAlmostEqual(p1, p2)
self.assertEqual(cdf.Value(0), 1)
self.assertEqual(cdf.Value(0.1), 1)
self.assertEqual(cdf.Value(0.2), 1)
self.assertEqual(cdf.Value(0.3), 2)
self.assertEqual(cdf.Value(0.4), 2)
self.assertEqual(cdf.Value(0.5), 2)
self.assertEqual(cdf.Value(0.6), 2)
self.assertEqual(cdf.Value(0.7), 3)
self.assertEqual(cdf.Value(0.8), 3)
self.assertEqual(cdf.Value(0.9), 5)
self.assertEqual(cdf.Value(1), 5)
ps = np.linspace(0, 1, 11)
xs = cdf.ValueArray(ps)
self.assertTrue((xs == [1, 1, 1, 2, 2, 2, 2, 3, 3, 5, 5]).all())
np.random.seed(17)
xs = cdf.Sample(7)
self.assertListEqual(xs.tolist(), [2, 2, 1, 1, 3, 3, 3])
# when you make a Cdf from a Pdf, you might get some floating
# point representation error
self.assertEqual(len(cdf), 4)
self.assertAlmostEqual(cdf.Prob(2), 0.6)
self.assertAlmostEqual(cdf[2], 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromPmf(pmf)
self.assertEqual(len(cdf), 4)
self.assertAlmostEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromItems(pmf.Items())
self.assertEqual(len(cdf), 4)
self.assertAlmostEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(pmf.d)
self.assertEqual(len(cdf), 4)
self.assertAlmostEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromDict(pmf.d)
self.assertEqual(len(cdf), 4)
self.assertAlmostEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(hist)
self.assertEqual(len(cdf), 4)
self.assertEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromHist(hist)
self.assertEqual(len(cdf), 4)
self.assertEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(t)
self.assertEqual(len(cdf), 4)
self.assertEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromList(t)
self.assertEqual(len(cdf), 4)
self.assertEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(Counter(t))
self.assertEqual(len(cdf), 4)
self.assertEqual(cdf.Prob(2), 0.6)
self.assertEqual(cdf.Value(0.6), 2)
cdf2 = cdf.Copy()
self.assertEqual(cdf2.Prob(2), 0.6)
self.assertEqual(cdf2.Value(0.6), 2)
def testCdf(self):
t = [1, 2, 2, 3, 5]
pmf = thinkstats2.Pmf(t)
hist = thinkstats2.Hist(t)
cdf = thinkstats2.Cdf(pmf)
self.assertEquals(len(str(cdf)), 40)
# when you make a Cdf from a Pdf, you might get some floating
# point representation error
self.assertEquals(len(cdf), 4)
self.assertAlmostEquals(cdf.Prob(2), 0.6)
self.assertAlmostEquals(cdf[2], 0.6)
self.assertEquals(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromPmf(pmf)
self.assertEquals(len(cdf), 4)
self.assertAlmostEquals(cdf.Prob(2), 0.6)
self.assertEquals(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(pmf.Items())
self.assertEquals(len(cdf), 4)
self.assertAlmostEquals(cdf.Prob(2), 0.6)
self.assertEquals(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromItems(pmf.Items())
self.assertEquals(len(cdf), 4)
self.assertAlmostEquals(cdf.Prob(2), 0.6)
self.assertEquals(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(pmf.d)
self.assertEquals(len(cdf), 4)
self.assertAlmostEquals(cdf.Prob(2), 0.6)
self.assertEquals(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromDict(pmf.d)
self.assertEquals(len(cdf), 4)
self.assertAlmostEquals(cdf.Prob(2), 0.6)
self.assertEquals(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(hist)
self.assertEquals(len(cdf), 4)
self.assertEquals(cdf.Prob(2), 0.6)
self.assertEquals(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromHist(hist)
self.assertEquals(len(cdf), 4)
self.assertEquals(cdf.Prob(2), 0.6)
self.assertEquals(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(t)
self.assertEquals(len(cdf), 4)
self.assertEquals(cdf.Prob(2), 0.6)
self.assertEquals(cdf.Value(0.6), 2)
cdf = thinkstats2.MakeCdfFromList(t)
self.assertEquals(len(cdf), 4)
self.assertEquals(cdf.Prob(2), 0.6)
self.assertEquals(cdf.Value(0.6), 2)
cdf = thinkstats2.Cdf(Counter(t))
self.assertEquals(len(cdf), 4)
self.assertEquals(cdf.Prob(2), 0.6)
self.assertEquals(cdf.Value(0.6), 2)
cdf2 = cdf.Copy()
self.assertEquals(cdf2.Prob(2), 0.6)
self.assertEquals(cdf2.Value(0.6), 2)
"""Convert height 'm' to (ft, in)
return: tuple of (ft, in)
"""
return CMToHeight(100 * m)
## main scripts
if __name__ == '__main__':
"""main scripts"""
## make exponential cdfs
lambdas = [0.5, 1, 2]
thinkplot.PrePlot(len(lambdas))
for lam in lambdas:
pmf = thinkstats2.MakeExponentialPmf(lam, 3)
label = r'$\lambda = %g$' % lam
cdf = thinkstats2.MakeCdfFromPmf(pmf, label=label)
thinkplot.Cdf(cdf)
thinkplot.show(xlabel='x', ylabel='CDF')
## ReadBabyBoom
df = analytic.ReadBabyBoom()
## plot time differences
diffs = df.minutes.diff()
cdf = thinkstats2.Cdf(diffs, label='actual')
thinkplot.PrePlot(2)
thinkplot.Cdf(cdf, complement=True)
pmf = thinkstats2.MakeExponentialPmf(0.0306, 160)
cdf_model = thinkstats2.MakeCdfFromPmf(pmf)
