def main():
data = 20, 15, 3
probs = numpy.linspace(0, 1, 31)
hypos = []
for n in range(32, 350):
for p1 in probs:
for p2 in probs:
hypos.append((n, p1, p2))
suite = Lincoln(hypos)
suite.Update(data)
n_marginal = suite.Marginal(0)
thinkplot.Pmf(n_marginal, label='n')
thinkplot.Save(root='lincoln1',
xlabel='number of bugs',
ylabel='PMF',
formats=['pdf', 'png'])
print('post mean n', n_marginal.Mean())
print('MAP n', n_marginal.MaximumLikelihood())
pmf1 = suite.Marginal(1, label='tester1')
pmf2 = suite.Marginal(2, label='tester2')
thinkplot.Pdfs([pmf1,pmf2])
thinkplot.show(xlabel = 'testing effectiveness', ylabel = 'PMF')
print('prob greater', pmf1 > pmf2)
print('prob greater', pmf1 < pmf2)
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 ex3():
def VertLine(x, y=1):
thinkplot.Plot([x, x], [0, y], color='0.8', linewidth=3)
lam = 4
goal_totals = [SimulateGame(lam=lam) for _ in range(1000)]
print('RMSE', RMSE(goal_totals, lam))
hist = thinkstats2.Hist(goal_totals)
cdf = thinkstats2.Cdf(goal_totals)
thinkplot.PrePlot(rows=2, cols=2)
thinkplot.SubPlot(1)
thinkplot.Hist(hist)
thinkplot.SubPlot(2)
thinkplot.Cdf(cdf)
VertLine(cdf.Percentile(5))
VertLine(cdf.Percentile(95))
thinkplot.SubPlot(3)
# lambda vs. rmse
# rmse goes up as lambda goes up
lams = range(1, 15)
rmses = [RMSE([SimulateGame(lam=l) for _ in range(1000)], l) for l in lams]
thinkplot.Plot(lams, rmses)
thinkplot.SubPlot(4)
# m vs. rmse
# maybe rmse very slowly goes down as m goes up?
# not at all clear that's really the case...
ms = np.arange(10, 1000, 10)
rmses = [RMSE([SimulateGame() for _ in range(m)], 4) for m in ms]
thinkplot.Plot(ms, rmses)
thinkplot.show()
def main():
"""main."""
suite = version3()
print(suite.mean())
thinkplot.pmf(suite)
thinkplot.show()
def view_harmonics(freq, framerate):
signal = thinkdsp.SawtoothSignal(freq)
wave = signal.make_wave(duration=0.5, framerate=framerate)
spectrum = wave.make_spectrum()
spectrum.plot(color='blue')
thinkplot.show(xlabel='frequency', ylabel='amplitude')
display(wave.make_audio())
def sawtooth_chirp():
signal = thinkdsp.SawtoothChirp(start=220, end=880)
wave = signal.make_wave(duration=2, framerate=44100)
wave.apodize()
wave.play()
sp = wave.make_spectrogram(1024)
sp.plot()
thinkplot.show()
def sawtooth_chirp():
signal = thinkdsp.SawtoothChirp(start=220, end=880)
wave = signal.make_wave(duration=2, framerate=44100)
wave.apodize()
wave.play()
sp = wave.make_spectrogram(1024)
sp.plot()
thinkplot.show()
def main():
hypos = range(0, 101)
dataset = 'H' * 140 + 'T' * 110
euro = Euro(hypos)
for data in dataset:
euro.Update(data)
thinkplot.Clf()
thinkplot.Pmf(euro)
thinkplot.show()
def Scatter(d, var1, var2, **kwargs):
"""Assuming numerical for now (will call isNumerical()"""
xs = list(d[var1])
ys = list(d[var2])
RemoveNone(xs,ys)
#print 'Spearman corr', thinkstats2.SpearmanCorr(xs, ys)
thinkplot.Scatter(xs, ys, **kwargs)
thinkplot.show()
def main():
thinkdsp.random_seed(19)
signal = thinkdsp.BrownianNoise()
make_periodogram(signal)
return
signal = thinkdsp.UncorrelatedUniformNoise()
wave1 = signal.make_wave(duration=1.0, framerate=11025)
wave2 = signal.make_wave(duration=1.0, framerate=11025)
print wave1.cov(wave2)
print wave1.cov(wave1)
print wave2.cov(wave2)
print wave1.cov_mat(wave2)
print wave1.corr(wave2)
print wave1.corr(wave1)
print wave2.corr(wave2)
return
signal = thinkdsp.BrownianNoise()
process_noise(signal, root='red')
return
signal = thinkdsp.UncorrelatedUniformNoise()
process_noise(signal, root='white')
return
signal = thinkdsp.WhiteNoise()
white, integ_white = test_noise(signal, 'white-noise')
print white.estimate_slope()
signal = thinkdsp.BrownianNoise()
red, integ_red = test_noise(signal, 'red-noise')
print red.estimate_slope()
return
signal = thinkdsp.PinkNoise(beta=1.0)
pink, integ_pink = test_noise(signal, 'pink-noise')
print pink.estimate_slope()
thinkplot.preplot(num=3)
white.plot(low=1, exponent=2, label='white', linewidth=2)
pink.plot(low=1, exponent=2, label='pink', linewidth=2)
red.plot(low=1, exponent=2, label='red', linewidth=2)
thinkplot.show(xlabel='frequency (Hz)',
ylabel='power',
xscale='log',
yscale='log',
axis=[1, 18000, 1e-5, 1e8])
return
def main():
thinkdsp.random_seed(19)
signal = thinkdsp.BrownianNoise()
make_periodogram(signal)
return
signal = thinkdsp.UncorrelatedUniformNoise()
wave1 = signal.make_wave(duration=1.0, framerate=11025)
wave2 = signal.make_wave(duration=1.0, framerate=11025)
print wave1.cov(wave2)
print wave1.cov(wave1)
print wave2.cov(wave2)
print wave1.cov_mat(wave2)
print wave1.corr(wave2)
print wave1.corr(wave1)
print wave2.corr(wave2)
return
signal = thinkdsp.BrownianNoise()
process_noise(signal, root='red')
return
signal = thinkdsp.UncorrelatedUniformNoise()
process_noise(signal, root='white')
return
signal = thinkdsp.WhiteNoise()
white, integ_white = test_noise(signal, 'white-noise')
print white.estimate_slope()
signal = thinkdsp.BrownianNoise()
red, integ_red = test_noise(signal, 'red-noise')
print red.estimate_slope()
return
signal = thinkdsp.PinkNoise(beta=1.0)
pink, integ_pink = test_noise(signal, 'pink-noise')
print pink.estimate_slope()
thinkplot.preplot(num=3)
white.plot(low=1, exponent=2, label='white', linewidth=2)
pink.plot(low=1, exponent=2, label='pink', linewidth=2)
red.plot(low=1, exponent=2, label='red', linewidth=2)
thinkplot.show(xlabel='frequency (Hz)',
ylabel='power',
xscale='log',
yscale='log',
axis=[1, 18000, 1e-5, 1e8])
return
def readNplotNUMKDHH(dctfile='2002FemResp.dct',
datfile='2002FemResp.dat.gz',
compression='gzip'):
""" read data to fram and plot NUMKDHH PMF"""
dct = thinkstats2.ReadStataDct(dctfile)
df = dct.ReadFixedWidth(datfile, compression=compression)
pmf = thinkstats2.Pmf(df.numkdhh)
thinkplot.Pmf(pmf, label='numkdhh')
pmf2 = BiasedPmf(pmf)
thinkplot.Pmf(pmf2, label='biased')
thinkplot.show()
def step_4():
Sin1 = thinkdsp.CosSignal(freq=261.63, amp=0.5, offset=0)
Sin2 = thinkdsp.CosSignal(freq=329.63, amp=0.5, offset=0)
Sin3 = thinkdsp.CosSignal(freq=392.0, amp=0.5, offset=0)
SumSignal = Sin1 + Sin2 + Sin3
SumSignal_wave = SumSignal.make_wave(duration=3, start=0, framerate=8000)
period = SumSignal.period
SumSignal_seg = SumSignal_wave.segment(start=0, duration=period * 5)
SumSignal_seg.plot()
tp.show()
SumSignal_wave.write(filename='output.wav')
def main():
N = 64
#test_dct(N, freq=N/4)
#test_dct(N, freq=N/4 + 0.25)
test_fft(N, freq=N/4 + 0.25, window=True)
test_dct(N, freq=N/4 + 0.25, window=True)
thinkplot.show()
return
return
def main():
locomotiveNumbers = range(1, 1001)
locomotive = Locomotive(locomotiveNumbers)
locomotive.Update(60)
locomotive.Update(160)
locomotive.Update(80)
locomotive.Update(570)
locomotive.Update(640)
print('Mean: {}'.format(locomotive.Mean()))
thinkplot.preplot(1)
thinkplot.Pmf(locomotive)
thinkplot.show(xlabel='Number of train', ylabel='Probability')
def preprocessA(sample):
signal = thinkDSP.read_wave("samples"+sample+".wav")
spectrum = signal.make_spectrum()
spectrum.high_pass(cutoff=100, factor=0.1)
signal = spectrum.make_wave()
#signal.write("outcome.wav")
spectrogram = signal.make_spectrogram(seg_length=4096)
specMatrix = spectrogram.plot(returnArray=1,length=None,high=8000, cmap="gray")
thinkplot.show()
maxi = np.amax(specMatrix,axis = 0)
indi = np.argmax(specMatrix,axis = 0)
final = np.column_stack([maxi,indi])
np.save("sample"+sample,final)
def autocorr(wave):
n = len(wave.ys)
corrs = []
lags = range(n//2)
for lag in lags:
y1 = wave.ys[lag:]
y2 = wave.ys[:n-lag]
corr = numpy.corrcoef(y1, y2)[0, 1]
corrs.append(corr)
thinkplot.plot(lags, corrs)
thinkplot.show()
def make_periodogram(signal):
specs = []
for i in range(10000):
wave = signal.make_wave(duration=1.0, framerate=1024)
spec = wave.make_spectrum()
specs.append(spec)
spectrum = sum(specs)
print spectrum.estimate_slope()
spectrum.plot_power(low=1)
thinkplot.show(xlabel='frequency (Hz)',
ylabel='power density',
xscale='log',
yscale='log')
def main(): maleRates = [0.52, 0.38, 0.39, 1.01, 2.63] femaleRates = [50, 20.5] pdfMale = thinkbayes2.EstimatedPdf(maleRates) pdfFemale = thinkbayes2.EstimatedPdf(femaleRates) low, high = 0, 100 n = 1001 xs = numpy.linspace(low, high, n) pmfMale = pdfMale.MakePmf(steps=xs) pmfFemale = pdfFemale.MakePmf(steps=xs) thinkplot.Pdf(pdfMale, label='Male Prior') thinkplot.Pdf(pdfFemale, label='Female Prior') thinkplot.show()
def make_periodogram(signal):
specs = []
for i in range(10000):
wave = signal.make_wave(duration=1.0, framerate=1024)
spec = wave.make_spectrum()
specs.append(spec)
spectrum = sum(specs)
print spectrum.estimate_slope()
spectrum.plot_power(low=1)
thinkplot.show(xlabel='frequency (Hz)',
ylabel='power density',
xscale='log',
yscale='log')
def signal(offset):
cos_sig = thinkdsp.CosSignal(freq=1.0, amp=1.0, offset=offset)
sin_sig = thinkdsp.SinSignal(freq=1.0, amp=1.0, offset=offset)
cos_wave = cos_sig.make_wave(duration=0.5, start=0, framerate=8000)
sin_wave = sin_sig.make_wave(duration=0.5, start=0, framerate=8000)
period = sin_sig.period
cos_seg = cos_wave.segment(start=0, duration=period * 5)
sin_seg = sin_wave.segment(start=0, duration=period * 5)
sin_seg.plot()
tp.show()
cos_seg.plot()
tp.show()
def main():
gss = read_gss()
sample = utils.ResampleByYear(gss)
grouped_year = sample.groupby(['year'])
plot_relig(grouped_year)
thinkplot.config(xlabel='Year of survey', ylabel='Percent')
thinkplot.show()
return
# run the resamples
dfs = [run(gss) for _ in range(101)]
# make the plots
single_plot(dfs)
multi_plot(dfs)
def main():
df = ReadData()
cdf = thinkstats2.Cdf(df['ps'])
thinkplot.PrePlot(rows=1, cols=2)
thinkplot.SubPlot(1)
scale = thinkplot.Cdf(cdf, xscale='log')
thinkplot.Config(title='logx', **scale)
thinkplot.SubPlot(2)
scale = thinkplot.Cdf(cdf, transform='pareto')
thinkplot.Config(title='pareto', **scale)
thinkplot.show(legend=False)
print(df)
def PMFDiff(scale1, scale2):
"""
create (and plot) the PMF of the differences in corrolations between the scales
based on gender.
"""
# more the merrier (and slower)
n = 1000
# create the diff
diff = randomDiffs(scale1, scale2, n)
diff = [round(num, 3) for num in diff]
pmf = MakePmfFromList(diff)
thinkplot.Pmf(pmf)
thinkplot.show()
def plot_diff_filters(wave):
window1 = np.array([1, -1])
window2 = np.array([-1, 4, -3]) / 2.0
window3 = np.array([2, -9, 18, -11]) / 6.0
window4 = np.array([-3, 16, -36, 48, -25]) / 12.0
window5 = np.array([12, -75, 200, -300, 300, -137]) / 60.0
thinkplot.preplot(5)
for i, window in enumerate([window1, window2, window3, window4, window5]):
padded = thinkdsp.zero_pad(window, len(wave))
fft_window = np.fft.rfft(padded)
n = len(fft_window)
thinkplot.plot(abs(fft_window)[:], label=i+1)
thinkplot.show()
def plot_diff_filters(wave):
window1 = np.array([1, -1])
window2 = np.array([-1, 4, -3]) / 2.0
window3 = np.array([2, -9, 18, -11]) / 6.0
window4 = np.array([-3, 16, -36, 48, -25]) / 12.0
window5 = np.array([12, -75, 200, -300, 300, -137]) / 60.0
thinkplot.preplot(5)
for i, window in enumerate([window1, window2, window3, window4, window5]):
padded = thinkdsp.zero_pad(window, len(wave))
fft_window = np.fft.rfft(padded)
n = len(fft_window)
thinkplot.plot(abs(fft_window)[:], label=i + 1)
thinkplot.show()
def plot_amen():
wave = thinkdsp.read_wave('263868__kevcio__amen-break-a-160-bpm.wav')
wave.normalize()
ax1 = thinkplot.preplot(2, rows=2)
wave.make_spectrum(full=True).plot()
xlim = [-22050, 22050]
thinkplot.config(xlim=xlim, xticklabels='invisible')
ax2 = thinkplot.subplot(2, sharey=ax1)
sampled = sample(wave, 4)
sampled.make_spectrum(full=True).plot()
thinkplot.config(xlim=xlim, xlabel='Frequency (Hz)')
thinkplot.show()
return
def SurvivalHaz(introductions, lifetimes, plot=False):
"""Given lists of ages and lifespans, this function calculates the
Hazard and Survial curves. If plot is set True, it will plot them."""
haz = survival.EstimateHazardFunction(lifetimes, introductions)
sf = haz.MakeSurvival()
if plot:
thinkplot.plot(sf, color='Grey')
plt.xlabel("Age (books)")
plt.ylabel("Probability of Surviving")
plt.title('Survial Function')
thinkplot.show()
thinkplot.plot(haz, color='Grey')
plt.title('Hazard Function')
plt.xlabel("Age (books)")
plt.ylabel("Percent of Lives That End")
thinkplot.show()
return sf, haz
def plot_amen():
wave = thinkdsp.read_wave('263868__kevcio__amen-break-a-160-bpm.wav')
wave.normalize()
ax1 = thinkplot.preplot(2, rows=2)
wave.make_spectrum(full=True).plot()
xlim = [-22050, 22050]
thinkplot.config(xlim=xlim, xticklabels='invisible')
ax2 = thinkplot.subplot(2, sharey=ax1)
sampled = sample(wave, 4)
sampled.make_spectrum(full=True).plot()
thinkplot.config(xlim=xlim, xlabel='Frequency (Hz)')
thinkplot.show()
return
def main(): maleRates = [0.52, 0.38, 0.39, 1.01, 2.63, 30] femaleRates = [50, 20.5, 40, 30, 45] pdfMale = thinkbayes2.EstimatedPdf(maleRates) pdfFemale = thinkbayes2.EstimatedPdf(femaleRates) low, high = 0, 100 n = 1001 xs = numpy.linspace(low, high, n) pmfMale = MakePmfTest(pdfMale,steps=xs) pmfFemale = MakePmfTest(pdfFemale,steps=xs) pmfMale.Normalize() pmfFemale.Normalize() thinkplot.Pdf(pmfMale, label='Male Prior') thinkplot.Pdf(pmfFemale, label='Female Prior') thinkplot.show()
def SurvivalHaz(introductions,lifetimes,plot=False):
"""Given lists of ages and lifespans, this function calculates the
Hazard and Survial curves. If plot is set True, it will plot them."""
haz=survival.EstimateHazardFunction(lifetimes, introductions)
sf=haz.MakeSurvival()
if plot:
thinkplot.plot(sf,color='Grey')
plt.xlabel("Age (books)")
plt.ylabel("Probability of Surviving")
plt.title('Survial Function')
thinkplot.show()
thinkplot.plot(haz,color='Grey')
plt.title('Hazard Function')
plt.xlabel("Age (books)")
plt.ylabel("Percent of Lives That End")
thinkplot.show()
return sf,haz
def CDFCabinLoadFactor(self, network): """ Plots a Cumulative Distrubtion Function for Cabin Load Factor. Cabin Load Factor is defined as the ratio between the total number of passengers booked for a flight and the total capacity of the plane. """ clf_list = network.countFinalCabinLoadFactor().values() # remove any invalid floats 'nan' or 'inf' clf_list[:] = [e for e in clf_list if not isnan(e) and not isinf(e)] clf_cdf = thinkstats2.MakeCdfFromList(clf_list) thinkplot.Cdf(clf_cdf) thinkplot.show(title='Fraction of the cabin filled at departure', xlabel='Cabin Load Factor', ylabel='CDF')
def test_make_dct():
N = 32
framerate = N
amps = numpy.zeros(N)
amps[2] = 1.0
dct = thinkdsp.Dct(amps, framerate)
wave = dct.make_wave()
print wave.ys
cos_sig = thinkdsp.CosSignal(freq=1, offset=math.pi/N)
wave = cos_sig.make_wave(duration=1, start=0, framerate=framerate)
print wave.ys
dct = wave.make_dct()
dct.plot()
print dct.fs
iv = dct_iv(wave.ys)
thinkplot.plot(dct.fs, iv)
thinkplot.show()
def filter_wave(wave, start, duration, cutoff):
"""Selects a segment from the wave and filters it.
Plots the spectrum and displays an Audio widget.
wave: Wave object
start: time in s
duration: time in s
cutoff: frequency in Hz
"""
segment = wave.segment(start, duration)
spectrum = segment.make_spectrum()
spectrum.plot(color='0.7')
spectrum.low_pass(cutoff)
spectrum.plot(color='#045a8d')
thinkplot.show(xlabel='Frequency (Hz)')
audio = spectrum.make_wave().make_audio()
display(audio)
def voice_analysis_test(filepath):
f = thinkdsp.read_wave(filepath)
# spectrum = f.make_spectrum()
# spectrum.plot() # the squiggly blue line
# sp = spectrum.peaks()
fig, ax = plt.subplots()
# ok, i am currently not recording x=frequency, y=occurrences like ithought i was
f.make_spectrum().plot(high=2000)
thinkplot.config(title='Spectrum',
xlabel='frequency (Hz)',
ylabel='number occurrences')
thinkplot.show()
f_spectrogram = f.make_spectrogram(seg_length=1024)
f_spectrogram.plot(high=2000)
thinkplot.config(title='Spectrogram',
xlabel='frequency (Hz)',
ylabel='number occurrences')
thinkplot.show()
def ex2(n=10, m=1000):
def VertLine(x, y=1):
thinkplot.Plot([x, x], [0, y], color='0.8', linewidth=3)
print("Exericse 8.2")
lam = 2
estimates = ex2_helper()
cdf = thinkstats2.Cdf(estimates)
print('90% confidence interval is', cdf.Percentile(5), cdf.Percentile(95))
print('RMSE', RMSE(estimates, lam))
thinkplot.Cdf(cdf)
VertLine(cdf.Percentile(5))
VertLine(cdf.Percentile(95))
thinkplot.show(xlabel='lambda', ylabel='probability')
errors = []
for n in np.arange(5, 55, 5):
estimates = ex2_helper(n=n)
errors.append(RMSE(estimates, lam))
thinkplot.Plot(np.arange(5, 55, 5), errors)
thinkplot.show(xlabel='n', ylabel='RMSE')
def trombone_gliss():
"""Tests TromboneGliss.
"""
low = 262
high = 340
signal = TromboneGliss(high, low)
wave1 = signal.make_wave(duration=1)
wave1.apodize()
signal = TromboneGliss(low, high)
wave2 = signal.make_wave(duration=1)
wave2.apodize()
wave = wave1 | wave2
filename = 'gliss.wav'
wave.write(filename)
thinkdsp.play_wave(filename)
sp = wave.make_spectrogram(1024)
sp.plot(high=40)
thinkplot.show()
def trombone_gliss():
"""Tests TromboneGliss.
"""
low = 262
high = 340
signal = TromboneGliss(high, low)
wave1 = signal.make_wave(duration=1)
wave1.apodize()
signal = TromboneGliss(low, high)
wave2 = signal.make_wave(duration=1)
wave2.apodize()
wave = wave1 | wave2
filename = 'gliss.wav'
wave.write(filename)
thinkdsp.play_wave(filename)
sp = wave.make_spectrogram(1024)
sp.plot(high=40)
thinkplot.show()
def plot_filter(M=11, std=2):
signal = thinkdsp.SquareSignal(freq=440)
wave = signal.make_wave(duration=1, framerate=44100)
spectrum = wave.make_spectrum()
gaussian = scipy.signal.gaussian(M=M, std=std)
gaussian /= sum(gaussian)
high = gaussian.max()
thinkplot.preplot(cols=2)
thinkplot.plot(gaussian)
thinkplot.config(xlabel='Index',
ylabel='Window',
xlim=[0, len(gaussian) - 1],
ylim=[0, 1.1 * high])
ys = np.convolve(wave.ys, gaussian, mode='same')
smooth = thinkdsp.Wave(ys, framerate=wave.framerate)
spectrum2 = smooth.make_spectrum()
# plot the ratio of the original and smoothed spectrum
amps = spectrum.amps
amps2 = spectrum2.amps
ratio = amps2 / amps
ratio[amps < 560] = 0
# plot the same ratio along with the FFT of the window
padded = thinkdsp.zero_pad(gaussian, len(wave))
dft_gaussian = np.fft.rfft(padded)
thinkplot.subplot(2)
thinkplot.plot(abs(dft_gaussian), color=GRAY, label='Gaussian filter')
thinkplot.plot(ratio, label='amplitude ratio')
thinkplot.show(xlabel='Frequency (Hz)',
ylabel='Amplitude ratio',
xlim=[0, 22050],
ylim=[0, 1.05])
import sys
sys.path.insert(1, 'dsp-modulo')
import numpy as np
import thinkplot as plt
from thinkdsp import decorate
from thinkdsp import UncorrelatedUniformNoise
senal = UncorrelatedUniformNoise()
wave = senal.make_wave(duration=0.5, framerate=22050)
wave.write("ruido_no_correlacional_uniforme.wav")
segmento = wave.segment(duration=0.05)
segmento.plot()
decorate(xlabel="Tiempo(s)", ylabel="Amplitud")
plt.show()
espectro = wave.make_spectrum()
espectro.plot_power()
decorate(xlabel="Frecuencia (Hz)", ylabel="Poder")
plt.show()
espectro_integrado = espectro.make_integrated_spectrum()
espectro_integrado.plot_power()
decorate(xlabel="Frecuencia (Hz)", ylabel="Poder acumulado")
plt.show()
pendiente = espectro.estimate_slope()
print("Pendiente: " + str(pendiente.slope))
# -*- coding: utf-8 -*- """ Created on Fri Apr 1 22:08:35 2016 @author: azg """ import random import thinkstats2 import thinkplot random_list = [random.random() for _ in range(1000)] random_list_pmf = thinkstats2.Pmf(random_list) thinkplot.Pmf(random_list_pmf, linewidth=0.08, label="Random List PMF") thinkplot.show() random_list_cdf = thinkstats2.Cdf(random_list) thinkplot.Cdf(random_list_cdf, label="Random List CDF") thinkplot.show()
import thinkstats2
import chap01soln
import thinkplot
resp = chap01soln.ReadFemResp()
numkdhh_hist = thinkstats2.Hist(resp.numkdhh)
thinkplot.Hist(numkdhh_hist)
thinkplot.show(xlabel='# children', ylabel ='frequency')
numkdhh_pmf = thinkstats2.Pmf(resp.numkdhh)
thinkplot.Pmf(numkdhh_pmf)
thinkplot.show(xlabel='# children', ylabel ='probability')
def BiasPmf(pmf, label):
new_pmf = pmf.Copy(label=label)
for x, p in pmf.Items():
new_pmf.Mult(x, x)
new_pmf.Normalize()
return new_pmf
numkdhh_biased = BiasPmf(numkdhh_pmf, label='biased')
thinkplot.Pmf(numkdhh_biased)
thinkplot.Show(xlabel='# children', ylabel ='probability')
sys.path.insert(1, 'dsp-modulo')
import numpy as np
import thinkplot as pl
from thinkdsp import decorate
from thinkdsp import UncorrelatedUniformNoise
senal = UncorrelatedUniformNoise()
wave = senal.make_wave(duration=0.5, framerate=22050)
wave.write("ruidoNoCorrelacionadoUniforme.wav")
segmento = wave.segment(duration=0.05)
segmento.plot()
decorate(xlabel="tiempo", ylabel="amplitud")
pl.show()
espectro = wave.make_spectrum()
espectro.plot_power()
decorate(xlabel="frecuencia", ylabel="power")
pl.show()
espectro_integrado = espectro.make_integrated_spectrum()
espectro_integrado.plot_power()
decorate(xlabel="frecuencia", ylabel="poder acumulado")
pl.show()
pendiente = espectro.estimate_slope()
print("pendiente: " + str(pendiente.slope))
# 第1子と第2子以降に分ける
tmp = live.copy()
tmp.loc[tmp.prglngth <= 27] = np.nan
tmp.loc[tmp.prglngth > 47] = np.nan
firsts = tmp[tmp.birthord == 1]
others = tmp[tmp.birthord != 1]
first_pmf = thinkstats2.Pmf(firsts.prglngth)
others_pmf = thinkstats2.Pmf(others.prglngth)
# %%
# 棒グラフ表示
width = 0.45
thinkplot.PrePlot(2, cols=2)
thinkplot.Hist(first_pmf, align='right', width=width)
thinkplot.Hist(others_pmf, align='left', width=width)
thinkplot.Config(xlabl='week', ylabel='probability', axis=[27, 46, 0, 0.6])
thinkplot.show()
# %%
# ステップ関数表示
thinkplot.PrePlot(2)
thinkplot.Pmfs([first_pmf, others_pmf])
thinkplot.show(xlabl='week', ylabel='probability', axis=[27, 46, 0, 0.6])
# %% [markdown]
# ## 3.3 その他の可視化
# %%
# 差を棒グラフで表示
weeks = range(35, 46)
diffs = []
for week in weeks:
p1 = first_pmf.Prob(week)
p2 = others_pmf.Prob(week)
diff = 100 * (p1 - p2)
#%%
t = [1, 2, 2, 3, 5]
for x in range(6):
print("CDF({0}) = {1}".format(x, EvalCdf(t, x)))
#%% [markdown]
# ## 4.4 CDF の表現
#%%
import thinkstats2
import first
import thinkplot
live , firsts,others = first.MakeFrames()
cdf = thinkstats2.Cdf(live.prglngth, label='prglngth')
thinkplot.Cdf(cdf)
thinkplot.show(xlabel='weeks', ylabel='CDF')
#%%
print("10% {0} weeks".format(cdf.Value(0.1)))
print("90% {0} weeks".format(cdf.Value(0.9)))
#%% [markdown]
# ## 4.5 CDFを比較する
#%%
first_cdf = thinkstats2.Cdf(firsts.totalwgt_lb, label='first')
other_cdf = thinkstats2.Cdf(others.totalwgt_lb, label='other')
thinkplot.PrePlot(2)
thinkplot.Cdfs([first_cdf, other_cdf])
import sys sys.path.insert(1, 'dsp-modulo') from thinkdsp import SinSignal from thinkdsp import decorate import thinkplot frecuencia1 = SinSignal(freq=1500, amp=0.3, offset=0) frecuencia2 = SinSignal(freq=380, amp=0.6, offset=0) frecuencia3 = SinSignal(freq=5300, amp=1.0, offset=0) frecuencia4 = SinSignal(freq=12300, amp=0.1, offset=0) resultante1 = frecuencia1 + frecuencia2 resultante2 = frecuencia3 + frecuencia4 waveResultante1 = resultante1.make_wave(duration=1,start=0,framerate=44100) waveResultante2 = resultante2.make_wave(duration=6,start=1.3,framerate=44100) waveFinal = waveResultante1 + waveResultante2 waveFinal.plot() decorate(xLabel="Tiempo (s)") thinkplot.show() espectro = waveFinal.make_spectrum() espectro.plot() decorate(xLabel="Frecuencia (Hz)") thinkplot.show()
def correlate(wave):
corrs = numpy.correlate(wave.ys, wave.ys, mode='valid')
print(len(corrs))
thinkplot.plot(corrs)
thinkplot.show()
