def main(): results = relay.ReadResults() speeds = relay.GetSpeeds(results) speeds = relay.BinData(speeds, 3, 12, 100) # plot the distribution of actual speeds pmf = thinkstats2.Pmf(speeds, 'actual speeds') # plot the biased distribution seen by the observer biased = ObservedPmf(pmf, 7.5, label='observed speeds') thinkplot.Pmf(biased) thinkplot.Save(root='observed_speeds', title='PMF of running speed', xlabel='speed (mph)', ylabel='PMF') cdf = thinkstats2.Cdf(pmf) cdf_biased = thinkstats2.Cdf(biased) thinkplot.PrePlot(2) thinkplot.Cdfs([cdf, cdf_biased]) thinkplot.Save(root='observed_speeds_cdf', title='CDF of running speed', xlabel='speed (mph)', ylabel='CDF')
def main(): results = relay.ReadResults() speeds = relay.GetSpeeds(results) # plot the distribution of actual speeds pmf = Pmf.MakePmfFromList(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 = BiasPmf(pmf, 7.5, name='observed speeds') myplot.Clf() myplot.Hist(biased) myplot.Save(root='observed_speeds', title='PMF of running speed', xlabel='speed (mph)', ylabel='probability') cdf = Cdf.MakeCdfFromPmf(biased) myplot.Clf() myplot.Cdf(cdf) myplot.show(root='observed_speeds_cdf', title='CDF of running speed', xlabel='speed (mph)', ylabel='cumulative probability')
def main(): #Exercise 3.5 results = relay.ReadResults() speeds = relay.GetSpeeds(results) speedsCdf = Cdf.MakeCdfFromList(speeds, "race speeds") mplt.Cdf(speedsCdf) mplt.show(title="Race Speed CDF", xlabel="speed in mph", ylabel="probability")
def main(): results = relay.ReadResults() speeds = relay.GetSpeeds(results) # plot the distribution of actual speeds cdf = Cdf.MakeCdfFromList(speeds, 'speeds') myplot.Cdf(cdf, title='CDF of running speed', xlabel='speed (mph)', ylabel='probability', show=True)
def main(): results = relay.ReadResults() speeds = relay.GetSpeeds(results) pmf = Pmf.MakePmfFromList(speeds, 'actual speeds') observed = BiasPmf(pmf, 7.5, 'observed speeds') myplot.Clf() myplot.Hist(observed) myplot.Show(title='observed speeds', xlabel='speed (mph)', ylabel='probability')
def main(): results = relay.ReadResults() speeds = relay.GetSpeeds(results) pmf = Pmf.MakePmfFromList(speeds, 'speeds') pmf = BiasPmf(7,pmf) myplot.Hist(pmf) #myplot.Show(title='PMF of observed speed', # xlabel='speed (mph)', # ylabel='probability') myplot.Save( formats=['png'], root='runner', title='PMF of observed speed', xlabel='speed (mph)', ylabel='probability')
def main(): # Exercise 3.1 d = { 7: 8, 12: 8, 17: 14, 22: 4, 27: 6, 32: 12, 37: 8, 42: 3, 47: 2 } classSizeDean = Pmf.MakePmfFromDict(d, name='Actual') print(classSizeDean.Mean()) classSizeStudent = classSizeDean.Copy(name='Student Perspective') for x, _ in classSizeStudent.Items(): classSizeStudent.Mult(x, x) classSizeStudent.Normalize() print(classSizeStudent.Mean()) classSizeUnbaised = UnbiasPmf(classSizeStudent, 'Student Unbiased') print(classSizeUnbaised.Mean()) getValue = itemgetter(0) deanPlot = sorted(classSizeDean.Items(), key=getValue) studentPlot = sorted(classSizeStudent.Items(), key=getValue) plt.plot(zip(*deanPlot)[0], zip(*deanPlot)[1], 'g-', label='Actual') plt.plot(zip(*studentPlot)[0], zip(*studentPlot)[1], 'r-', label='Student Perspective') plt.legend(loc=4) plt.xlabel('Class Size') plt.ylabel('Probability') plt.show() #Exercise 3.2 results = relay.ReadResults() speeds = relay.GetSpeeds(results) unbaisedSpeedsPmf = Pmf.MakePmfFromList(speeds, 'speeds') biasedSpeedsPmf = BiasPmf(unbaisedSpeedsPmf, 7.5, '7.5 mph biased speeds') biasedPlot = sorted(biasedSpeedsPmf.Items(), key=getValue) myplot.Pmf(biasedSpeedsPmf) myplot.Show(title='7.5mph biased speeds', xlabel='speeds (mph)', ylabel='probability')
def main(): results = relay.ReadResults() speeds = relay.GetSpeeds(results) rankit.MakeNormalPlot(speeds, root='relay_normal', ylabel='Speed (MPH)')
def __init__(self): results = relay.ReadResults() speeds = relay.GetSpeeds(results) self.speeds = speeds self.pmf = Pmf.MakePmfFromList(speeds)
import relay import Cdf import myplot results = "http://coolrunning.com/results/10/ma/Apr25_27thAn_set1.shtml" results = relay.ReadResults(url=results) def convert_speeds_to_time(speed): khm = speed * 1.609344 time = (10/khm) * 60 minute = int(time) second = int((time - minute)*60) return '{m}:{s}'.format( m=minute, s=second ) def get_place(results, column=0): return [int(result[column]) for result in results] def total_percentile_rank(results, place=97): places = get_place(results) places.sort(reverse=True) print(places) cdf = Cdf.MakeCdfFromList(places,is_sorted=True) return cdf, cdf.Prob(place) def GetSpeeds_F2039(results): return GetSpeeds_with_div(results, 'F2039')
import relay import Cdf import Pmf import myplot speeds = relay.GetSpeeds(relay.ReadResults()) pmf = Pmf.MakePmfFromList(speeds) cdf = Cdf.MakeCdfFromList(speeds) myplot.Pmf(pmf) myplot.Cdf(cdf) myplot.Show(title='PMF vs CDF of running speeds', xlabel='speed (mph)', ylabel='probability')
import math import myplot import numpy import relay results = relay.ReadResults() speeds = relay.GetSpeeds(results) xs = sorted(numpy.random.normal(0, 1, len(speeds))) ys = sorted(speeds) myplot.Plot(xs, ys) myplot.Show(title="Normal Plot for Running Speeds")