def processScoresTeamwise(pairs):
"""Average number of goals for each team.
pairs: map from (team1, team2) to (score1, score2)
"""
# map from team to list of goals scored
goals_scored = {}
for key, entries in pairs.iteritems():
t1, t2 = key
for entry in entries:
g1, g2 = entry
goals_scored.setdefault(t1, []).append(g1)
goals_scored.setdefault(t2, []).append(g2)
# make a list of average goals scored
lams = []
for key, goals in iter(goals_scored):
lam = thinkstats.mean(goals)
lams.append(lam)
# make the distribution of average goals scored
cdf = thinkbayes.makeCdfFromList(lams)
thinkplot.cdf(cdf)
thinkplot.show()
mu, var = thinkstats.meanAndVariance(lams)
print('mu, sig', mu, math.sqrt(var))
def process_noise(signal, root='red'):
wave = signal.make_wave(duration=0.5, framerate=11025)
# 0: waveform
segment = wave.segment(duration=0.1)
segment.plot(linewidth=1, alpha=0.5)
thinkplot.save(root=root+'noise0',
xlabel='time (s)',
ylabel='amplitude')
spectrum = wave.make_spectrum()
# 1: spectrum
spectrum.plot_power(linewidth=1, alpha=0.5)
thinkplot.save(root=root+'noise1',
xlabel='frequency (Hz)',
ylabel='power density')
slope, _, _, _, _ = spectrum.estimate_slope()
print 'estimated slope', slope
# 2: integrated spectrum
integ = spectrum.make_integrated_spectrum()
integ.plot_power()
thinkplot.save(root=root+'noise2',
xlabel='frequency (Hz)',
ylabel='normalized power')
# 3: log-log spectral density
spectrum.plot_power(low=1, linewidth=1, alpha=0.5)
thinkplot.save(root=root+'noise3',
xlabel='frequency (Hz)',
ylabel='power density',
xscale='log',
yscale='log')
# 4: CDF of power density
cdf = thinkstats2.MakeCdfFromList(spectrum.power)
thinkplot.cdf(cdf)
thinkplot.save(root=root+'noise4',
xlabel='power density',
ylabel='CDF')
# 5: CCDF of power density, log-y
thinkplot.cdf(cdf, complement=True)
thinkplot.save(root=root+'noise5',
xlabel='power density',
ylabel='log(CCDF)',
yscale='log')
thinkstats2.NormalProbabilityPlot(spectrum.real, label='real',
data_color='#253494')
thinkstats2.NormalProbabilityPlot(spectrum.imag-50, label='imag-50',
data_color='#1D91C0')
thinkplot.save(root=root+'noise6',
xlabel='normal sample',
ylabel='power density')
def process_noise(signal, root='red'):
wave = signal.make_wave(duration=0.5, framerate=11025)
# 0: waveform
segment = wave.segment(duration=0.1)
segment.plot(linewidth=1, alpha=0.5)
thinkplot.save(root=root + 'noise0', xlabel='time (s)', ylabel='amplitude')
spectrum = wave.make_spectrum()
# 1: spectrum
spectrum.plot_power(linewidth=1, alpha=0.5)
thinkplot.save(root=root + 'noise1',
xlabel='frequency (Hz)',
ylabel='power density')
slope, _, _, _, _ = spectrum.estimate_slope()
print 'estimated slope', slope
# 2: integrated spectrum
integ = spectrum.make_integrated_spectrum()
integ.plot_power()
thinkplot.save(root=root + 'noise2',
xlabel='frequency (Hz)',
ylabel='normalized power')
# 3: log-log spectral density
spectrum.plot_power(low=1, linewidth=1, alpha=0.5)
thinkplot.save(root=root + 'noise3',
xlabel='frequency (Hz)',
ylabel='power density',
xscale='log',
yscale='log')
# 4: CDF of power density
cdf = thinkstats2.MakeCdfFromList(spectrum.power)
thinkplot.cdf(cdf)
thinkplot.save(root=root + 'noise4', xlabel='power density', ylabel='CDF')
# 5: CCDF of power density, log-y
thinkplot.cdf(cdf, complement=True)
thinkplot.save(root=root + 'noise5',
xlabel='power density',
ylabel='log(CCDF)',
yscale='log')
thinkstats2.NormalProbabilityPlot(spectrum.real,
label='real',
data_color='#253494')
thinkstats2.NormalProbabilityPlot(spectrum.imag - 50,
label='imag-50',
data_color='#1D91C0')
thinkplot.save(root=root + 'noise6',
xlabel='normal sample',
ylabel='power density')
def plot_power_density(root, spectrum):
"""
"""
# 4: CDF of power density
cdf = thinkstats2.MakeCdfFromList(spectrum.power)
thinkplot.cdf(cdf)
thinkplot.save(root=root + 'noise4', xlabel='power density', ylabel='CDF')
# 5: CCDF of power density, log-y
thinkplot.cdf(cdf, complement=True)
thinkplot.save(root=root + 'noise5',
xlabel='power density',
ylabel='log(CCDF)',
yscale='log')
def plot_power_density(root, spectrum):
"""
"""
# 4: CDF of power density
cdf = thinkstats2.MakeCdfFromList(spectrum.power)
thinkplot.cdf(cdf)
thinkplot.save(root=root+'noise4',
xlabel='power density',
ylabel='CDF')
# 5: CCDF of power density, log-y
thinkplot.cdf(cdf, complement=True)
thinkplot.save(root=root+'noise5',
xlabel='power density',
ylabel='log(CCDF)',
yscale='log')
