def main():
results = _10_relay._read_results()
speeds = _10_relay._get_speeds(results)
# plot the distribution of actual speeds
pmf = _04_Pmf._make_pmf_from_list(speeds, 'actual speeds')
# myplot.Clf()
# myplot.Hist(pmf)
# myplot.Save(root='observed_speeds',
# title='PMF of running speed',
# xlabel='speed (mph)',
# ylabel='probability')
# plot the biased distribution seen by the observer
biased = _bias_pmf(pmf, 7.5, name='observed speeds')
_05_myplot._clf()
_05_myplot._hist(biased)
_05_myplot._save(root='observed_speeds',
title='PMF of running speed',
xlabel='speed (mph)',
ylabel='probability')
cdf = _13_Cdf._make_cdf_from_pmf(biased)
_05_myplot._clf()
_05_myplot._cdf(cdf)
_05_myplot._save(root='observed_speeds_cdf',
title='CDF of running speed',
xlabel='speed (mph)',
ylabel='cumulative probability')
def testRender(self):
t = [1, 2, 2, 3, 5]
pmf = _04_Pmf._make_pmf_from_list(t)
xs, ps = pmf._render()
d = dict(zip(xs, ps))
new_pmf = _04_Pmf._make_pmf_from_dict(d)
self.checkPmf(new_pmf)
def testMakePmfFromCdf(self):
t = [1, 2, 2, 3, 5]
pmf = _04_Pmf._make_pmf_from_list(t)
self.checkPmf(pmf)
cdf = _13_Cdf._make_cdf_from_pmf(pmf)
pmf2 = _04_Pmf._make_pmf_from_cdf(cdf)
self.checkPmf(pmf2)
def main():
results = _read_results()
speeds = _get_speeds(results)
pmf = _04_Pmf._make_pmf_from_list(speeds, 'speeds')
_05_myplot._pmf(pmf)
_05_myplot._show(title='PMF of running speed',
xlabel='speed (mph)',
ylabel='probability')
def _make_pmfs(lens):
"""Computes the PMF of the given list and the biased PMF."""
pmf = _04_Pmf._make_pmf_from_list(lens, 'slashdot')
print('unbiased mean', pmf._mean())
biased_pmf = _bias_pmf(pmf, 'biased')
print('biased mean', biased_pmf._mean())
return pmf, biased_pmf
def _log_cdf_time_interval():
timeInterval = _calc_time_interval()
pmf = _04_Pmf._make_pmf_from_list(timeInterval, "baby birth interval")
cdf = _13_Cdf._make_cdf_from_pmf(pmf, "baby birth interval")
_05_myplot._clf()
_05_myplot._cdf(cdf, complement=True, xscale="linear", yscale="log")
_05_myplot._save(
root="baby_birth_interval_logccdf",
title="LogCCDF of baby birth interval",
xlabel="interval(minutes)",
ylabel="LogCCdf",
)
def testMeanAndVar(self):
t = [1, 2, 2, 3, 5]
mu = _03_thinkstats._mean(t)
var = _03_thinkstats._var(t, mu)
pmf = _04_Pmf._make_pmf_from_list(t)
mu2 = pmf._mean()
var2 = pmf._var()
var3 = pmf._var(mu2)
self.assertAlmostEquals(mu, mu2)
self.assertAlmostEquals(var, var2)
self.assertAlmostEquals(var, var3)
def _make_uniform_suite(low, high, steps):
"""
Makes a PMF that represents a suite of hypotheses with equal p.
Args:
low: low end of range
high: high end of range
steps: number of values
Returns:
Pmf object
"""
hypos = [low + (high - low) * i / (steps - 1.0) for i in range(steps)]
pmf = _04_Pmf._make_pmf_from_list(hypos)
return pmf
def _process(table, name):
"""
Runs various analyses on this table.
Creates instance variables:
weights: sequence of int total weights in ounces
weight_pmf: Pmf object
weight_cdf: Cdf object
oz_pmf: Pmf of just the ounce field
"""
_06_descriptive._process(table, name)
table.weights = [p.totalwgt_oz for p in table.records if p.totalwgt_oz != 'NA']
table.weight_pmf = _04_Pmf._make_pmf_from_list(table.weights, table.name)
table.weight_cdf = _13_Cdf._make_cdf_from_list(table.weights, table.name)
def testMakePmf(self):
t = [1, 2, 2, 3, 5]
pmf = _04_Pmf._make_pmf_from_list(t)
self.checkPmf(pmf)
d = pmf._get_dict()
self.assertAlmostEquals(d[2], 0.4)
vals = pmf._values()
self.assertEquals(sorted(vals), [1, 2, 3, 5])
items = pmf._items()
d = dict(items)
new_pmf = _04_Pmf._make_pmf_from_dict(d)
self.checkPmf(new_pmf)
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 = _04_Pmf._make_pmf_from_list(table.ages, table.name)
table.age_cdf = _13_Cdf._make_cdf_from_list(table.ages, table.name)
table.weights = [p.totalwgt_oz for p in table.records if p.totalwgt_oz != 'NA']
table.weight_cdf = _13_Cdf._make_cdf_from_list(table.weights, table.name)
def _cdf_time_interval():
timeInterval = _calc_time_interval()
pmf = _04_Pmf._make_pmf_from_list(timeInterval, "baby birth interval")
_05_myplot._clf()
_05_myplot._hist(pmf)
_05_myplot._save(
root="baby_birth_interval_pmf",
title="PMF of baby birth interval",
xlabel="interval(minutes)",
ylabel="probability",
)
cdf = _13_Cdf._make_cdf_from_pmf(pmf)
_05_myplot._clf()
_05_myplot._cdf(cdf)
_05_myplot._save(
root="baby_birth_interval_cdf",
title="CDF of baby birth interval",
xlabel="interval(minutes)",
ylabel="cumulative probability",
)
def testMultAndNormalize(self):
t = [1, 2, 3, 5]
pmf = _04_Pmf._make_pmf_from_list(t)
pmf._mult(2, 2)
pmf._normalize()
self.checkPmf(pmf)
