def main():
filename = 'mystery0.dat'
data = read_file(filename)
pmf = thinkstats2.MakePmfFromList(data)
cdf = thinkstats2.MakeCdfFromList(data)
pdf = thinkstats2.EstimatedPdf(data)
low, high = min(data), max(data)
xs = numpy.linspace(low, high, 101)
kde_pmf = pdf.MakePmf(xs)
bin_data = BinData(data, low, high, 51)
bin_pmf = thinkstats2.MakePmfFromList(bin_data)
thinkplot.SubPlot(2, 2, 1)
thinkplot.Hist(pmf, width=0.1)
thinkplot.Config(title='Naive Pmf')
thinkplot.SubPlot(2, 2, 2)
thinkplot.Hist(bin_pmf)
thinkplot.Config(title='Binned Hist')
thinkplot.SubPlot(2, 2, 3)
thinkplot.Pmf(kde_pmf)
thinkplot.Config(title='KDE PDF')
thinkplot.SubPlot(2, 2, 4)
thinkplot.Cdf(cdf)
thinkplot.Config(title='CDF')
thinkplot.Show()
def EstimateHazardFuncion(past, current):
"""Estimates the hazard function by Kaplan-Meier.
http://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator
past: list of durations for complete pregnancies
current: list of durations for current pregnancies
"""
# pmf of pregnancies known to have ended at each timestep
pmf = thinkstats2.MakePmfFromList(past)
# survival curve for the known pregnancy lengths
n = len(past)
cdf_dur = thinkstats2.MakeCdfFromList(past)
ts, ss = SurvivalFunction(cdf_dur)
# CDF of duration for current pregnancies
m = len(current)
cdf_cur = thinkstats2.MakeCdfFromList(current)
hazard_func = []
for t, s in zip(ts, ss):
ended = n * pmf.Prob(t)
ongoing = n * s + m * (1 - cdf_cur.Prob(t))
at_risk = ended + ongoing
hazard = ended / at_risk
hazard_func.append((t, hazard))
return zip(*hazard_func)
def main():
results = ReadResults()
speeds = GetSpeeds(results)
# speeds = BinData(speeds, 3, 12, 100)
pmf = thinkstats2.MakePmfFromList(speeds, 'speeds')
thinkplot.Hist(pmf)
thinkplot.Show(title='PMF of running speed',
xlabel='speed (mph)',
ylabel='probability')
def main():
results = ReadResults()
speeds = GetSpeeds(results)
#speeds = BinData(speeds, 3, 12, 25)
pmf = thinkstats2.MakePmfFromList(speeds, 'speeds')
mean = pmf.Mean()
var = pmf.Var()
print 'mean=',mean
print 'var=',var
thinkplot.Hist(pmf)
thinkplot.Show(title='PMF of running speed',
xlabel='speed (mph)',
ylabel='probability')
def main():
results = ReadResults()
speeds = GetSpeeds(results)
speeds = BinData(speeds, 3, 12, 50)
pmf = thinkstats2.MakePmfFromList(speeds, 'speeds')
speed_observer = 7.5
bimodalpmf = BiasPmf(pmf, speed_observer)
thinkplot.Hist(bimodalpmf)
thinkplot.Show(
title='PMF of running speed (relative to observer speed = 7.5 MPH)',
xlabel='speed (mph)',
ylabel='probability')
def PlotConditionalSurvival(durations):
"""Plots conditional survival curves for a range of t0.
durations: list of durations
"""
pmf = thinkstats2.MakePmfFromList(durations)
times = [8, 16, 24, 32]
thinkplot.PrePlot(len(times))
for t0 in times:
ts, ss = ConditionalSurvival(pmf, t0)
label = 't0=%d' % t0
thinkplot.Plot(ts, ss, label=label)
duration_cdf = DurationCdf(ts, ss)
print t0, duration_cdf.Mean()
thinkplot.Show()
def Process(table, name):
"""Runs various analyses on this table.
Creates instance variables:
ages: sequence of int ages in years
age_pmf: Pmf object
age_cdf: Cdf object
weights: sequence of total weight in ounces
weight_cdf: Cdf object
"""
cumulative.Process(table, name)
table.ages = [p.agepreg for p in table.records
if p.agepreg != 'NA']
table.age_pmf = thinkstats2.MakePmfFromList(table.ages, table.name)
table.age_cdf = thinkstats2.MakeCdfFromList(table.ages, table.name)
table.weights = [p.totalwgt_oz for p in table.records
if p.totalwgt_oz != 'NA']
table.weight_cdf = thinkstats2.MakeCdfFromList(table.weights, table.name)
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')
Inputs:
pmf: Probability Mass Function Object of lifetimes
age: age (a integer in years)
Outputs:
"""
copy = pmf.Copy()
# leave remaining lifetime years (remove ages younger than current)
for val in copy.Values():
if val <= age:
pmf.Remove(val)
pmf.Normalize()
return pmf
if __name__ == '__main__':
pmf = thinkstats2.MakePmfFromList([1, 2, 2, 3, 4, 5])
rl_pmf = RemainingLifetimes(pmf, 2)
pyplot.bar
import thinkstats2
def PmfMean(pmf):
"""
pmf: prob mass function object
"""
mean = 0
for val, freq in pmf.Items():
mean += freq * val
return mean
v = [1, 1, 2, 3, 4]
pmf = thinkstats2.MakePmfFromList(v)
print "Mean = ", PmfMean(pmf)