def MakeNormalPlot(weights):
"""Generates a normal probability plot of birth weights."""
rankit.MakeNormalPlot(
weights,
root='nsfg_birthwgt_normal',
ylabel='Birth weights (oz)',
)
def MakeFigures():
pops = populations.Process()
print len(pops)
cdf = Cdf.MakeCdfFromList(pops, 'populations')
myplot.Cdf(cdf,
root='populations',
title='City/Town Populations',
xlabel='population',
ylabel='CDF',
legend=False)
myplot.Cdf(cdf,
root='populations_logx',
title='City/Town Populations',
xlabel='population',
ylabel='CDF',
xscale='log',
legend=False)
myplot.Cdf(cdf,
root='populations_loglog',
complement=True,
title='City/Town Populations',
xlabel='population',
ylabel='Complementary CDF',
yscale='log',
xscale='log',
legend=False)
t = [math.log(x) for x in pops]
t.sort()
rankit.MakeNormalPlot(t, 'populations_rankit')
def MakeFigures(self):
"""Generates CDFs and normal prob plots for weights and log weights."""
weights = [record.wtkg2 for record in self.records
if record.wtkg2 != 'NA']
self.MakeNormalModel(weights, root='brfss_weight_model')
rankit.MakeNormalPlot(weights,
root='brfss_weight_normal',
title='Adult weight',
ylabel='Weight (kg)')
log_weights = [math.log(weight) for weight in weights]
xmax = math.log(175.0)
axis = [3.5, 5.2, 0, 1]
self.MakeNormalModel(log_weights,
root='brfss_weight_log',
xmax=xmax,
xlabel='adult weight (log kg)',
axis=axis)
rankit.MakeNormalPlot(log_weights,
root='brfss_weight_lognormal',
title='Adult weight',
ylabel='Weight (log kg)')
def main():
results = relay.ReadResults()
speeds = relay.GetSpeeds(results)
rankit.MakeNormalPlot(speeds, root='relay_normal', ylabel='Speed (MPH)')
#生成10个不同随机变量的随机数之和，重复多次，计算其分布 import numpy as np import random import rankit def once(): binomial = np.random.binomial(2, 0.4) poisson = np.random.poisson(2) uniform = random.uniform(1, 10) chisquare = np.random.chisquare(2) normal = np.random.normal(3, 3) lognormal = np.random.lognormal(2, 3) standard_t = np.random.standard_t(2) exponential = np.random.exponential(2) triangular = np.random.triangular(1, 3, 10) beta = np.random.beta(3, 2) gamma = np.random.gamma(3) weibull = np.random.weibull(3) gumbel = np.random.gumbel(3, 2) return ( binomial + poisson + uniform + chisquare + normal + lognormal + standard_t + exponential + triangular + beta + gamma + weibull + gumbel) if __name__ == "__main__": rankit.MakeNormalPlot([once() for i in range(10000)])