def get_square_wave_data(freq,
offset,
duration=0.1,
framerate=40000,
noise_ratio=0):
signal = thinkdsp.SquareSignal(freq=freq, amp=1.0, offset=offset)
wave = signal.make_wave(duration=duration, start=0, framerate=framerate)
if noise_ratio != 0:
wave.ys += numpy.random.standard_normal(wave.ys.shape) * noise_ratio
return wave.ys
def waveform(freq, btn_input):
if btn_input == 1:
signal = thinkdsp.SinSignal(freq)
if btn_input == 2:
signal = thinkdsp.SquareSignal(freq)
if btn_input == 3:
signal = thinkdsp.TriangleSignal(freq)
wave = signal.make_wave(framerate=samplingFreq)
return wave
def get_sawtooth_square_wave(freq, offset, duration=0.1, framerate=40000):
signal_sawtooth = thinkdsp.SawtoothSignal(freq=freq,
amp=1.0,
offset=offset)
signal_square = thinkdsp.SquareSignal(freq=freq,
amp=1.0,
offset=offset + 2 * numpy.pi * 0.2)
components = [signal_sawtooth, signal_square]
signal = thinkdsp.SumSignal(*components)
wave = signal.make_wave(duration=duration, start=0, framerate=40000)
wave.ys = wave.ys / len(components) # normalization
return wave.ys
def square_example(freq):
framerate = 10000
signal = thinkdsp.SquareSignal(freq)
duration = signal.period*3
segment = signal.make_wave(duration, framerate=framerate)
segment.plot()
thinkplot.save(root='square-%d-1' % freq,
xlabel='time (s)',
axis=[0, duration, -1.05, 1.05])
wave = signal.make_wave(duration=0.5, framerate=framerate)
spectrum = wave.make_spectrum()
spectrum.plot()
thinkplot.save(root='square-%d-2' % freq,
xlabel='frequency (Hz)',
ylabel='amplitude')
def plot_gaussian():
"""Makes a plot showing the effect of convolution with a boxcar window.
"""
# start with a square signal
signal = thinkdsp.SquareSignal(freq=440)
wave = signal.make_wave(duration=1, framerate=44100)
spectrum = wave.make_spectrum()
# and a boxcar window
boxcar = numpy.ones(11)
boxcar /= sum(boxcar)
# and a gaussian window
gaussian = scipy.signal.gaussian(M=11, std=2)
gaussian /= sum(gaussian)
thinkplot.preplot(2)
thinkplot.plot(boxcar, label='boxcar')
thinkplot.plot(gaussian, label='Gaussian')
thinkplot.config(xlabel='index', ylabel='amplitude')
thinkplot.save(root='convolution7')
ys = numpy.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 = zero_pad(gaussian, len(wave))
dft_gaussian = numpy.fft.rfft(padded)
thinkplot.plot(abs(dft_gaussian), color='0.7', label='Gaussian filter')
thinkplot.plot(ratio, label='amplitude ratio')
thinkplot.config(xlabel='frequency (Hz)',
ylabel='amplitude ratio',
xlim=[0, 22050],
legend=False)
thinkplot.save(root='convolution8')
def square_example(freq):
"""Makes a figure showing a square wave.
freq: frequency in Hz
"""
framerate = 10000
signal = thinkdsp.SquareSignal(freq)
duration = signal.period * 3
segment = signal.make_wave(duration, framerate=framerate)
segment.plot()
thinkplot.save(root='square-%d-1' % freq,
xlabel='Time (s)',
axis=[0, duration, -1.05, 1.05])
wave = signal.make_wave(duration=0.5, framerate=framerate)
spectrum = wave.make_spectrum()
spectrum.plot()
thinkplot.save(root='square-%d-2' % freq, xlabel='Frequency (Hz)')
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 thinkstats2 import numpy as np import pandas as pd import scipy.signal np.set_printoptions(precision=3, suppress=True) # In[2]: PI2 = 2 * np.pi GRAY = '0.7' # In[3]: signal = thinkdsp.SquareSignal(freq=440) wave = signal.make_wave(duration=1, framerate=4400) segment = wave.segment(duration=0.01) # In[4]: window = np.ones(11) window /= sum(window) # In[5]: ys = segment.ys N = len(ys) padded = thinkdsp.zero_pad(window, N) # In[6]:
def get_square_wave(freq, offset, duration=0.1, framerate=40000):
signal = thinkdsp.SquareSignal(freq=freq, amp=1.0, offset=offset)
wave = signal.make_wave(duration=duration, start=0, framerate=framerate)
return wave.ys
def plot_boxcar():
"""Makes a plot showing the effect of convolution with a boxcar window.
"""
# start with a square signal
signal = thinkdsp.SquareSignal(freq=440)
wave = signal.make_wave(duration=1, framerate=44100)
# and a boxcar window
window = numpy.ones(11)
window /= sum(window)
# select a short segment of the wave
segment = wave.segment(duration=0.01)
# and pad with window out to the length of the array
padded = zero_pad(window, len(segment))
# compute the first element of the smoothed signal
prod = padded * segment.ys
print(sum(prod))
# compute the rest of the smoothed signal
smoothed = numpy.zeros_like(segment.ys)
rolled = padded
for i in range(len(segment.ys)):
smoothed[i] = sum(rolled * segment.ys)
rolled = numpy.roll(rolled, 1)
# plot the results
segment.plot(color='0.7')
smooth = thinkdsp.Wave(smoothed, framerate=wave.framerate)
smooth.plot()
thinkplot.config(ylim=[-1.05, 1.05], legend=False)
thinkplot.save(root='convolution2')
# compute the same thing using numpy.convolve
segment.plot(color='0.7')
ys = numpy.convolve(segment.ys, window, mode='valid')
smooth2 = thinkdsp.Wave(ys, framerate=wave.framerate)
smooth2.plot()
thinkplot.config(ylim=[-1.05, 1.05], legend=False)
thinkplot.save(root='convolution3')
# plot the spectrum before and after smoothing
spectrum = wave.make_spectrum()
spectrum.plot(color='0.7')
ys = numpy.convolve(wave.ys, window, mode='same')
smooth = thinkdsp.Wave(ys, framerate=wave.framerate)
spectrum2 = smooth.make_spectrum()
spectrum2.plot()
thinkplot.config(xlabel='frequency (Hz)',
ylabel='amplitude',
xlim=[0, 22050],
legend=False)
thinkplot.save(root='convolution4')
# plot the ratio of the original and smoothed spectrum
amps = spectrum.amps
amps2 = spectrum2.amps
ratio = amps2 / amps
ratio[amps < 560] = 0
thinkplot.plot(ratio)
thinkplot.config(xlabel='frequency (Hz)',
ylabel='amplitude ratio',
xlim=[0, 22050],
legend=False)
thinkplot.save(root='convolution5')
# plot the same ratio along with the FFT of the window
padded = zero_pad(window, len(wave))
dft_window = numpy.fft.rfft(padded)
thinkplot.plot(abs(dft_window), color='0.7', label='boxcar filter')
thinkplot.plot(ratio, label='amplitude ratio')
thinkplot.config(xlabel='frequency (Hz)',
ylabel='amplitude ratio',
xlim=[0, 22050],
legend=False)
thinkplot.save(root='convolution6')
from thinkdsp import TriangleSignal
from thinkdsp import decorate
import matplotlib.pyplot as plt
import numpy as np
#方波
class SquareSignal(Sinusoid):
def evaliate(self,ts):
cycles = self.freq*ts+self.offset/PI2
frac,_ = np.modf(cycles)
ys =self.amp*np.sign(unbias(frac))
return ys
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
square = thinkdsp.SquareSignal(1100)
plt.subplot(2,1,1)
plt.ylabel('1100hz方波')
square.plot()
wave = square.make_wave(duration=0.5,framerate = 10000)
spectrum = wave.make_spectrum()
plt.subplot(2,1,2)
plt.ylabel('频谱')
spectrum.plot()
plt.show()
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Aug 2 16:59:32 2018 @author: MikeNagler """ import thinkdsp from thinkdsp import Signal, Wave, Spectrum signal1 = thinkdsp.SquareSignal(freq=1100, amp=1.0, offset=0) wave1 = signal1.make_wave(duration=1, start=0, framerate=10000) spectrum1 = wave1.make_spectrum() spectrum1.plot() wave1.write(filename='temp.wav') thinkdsp.play_wave(filename='temp.wav', player='afplay')
import thinkdsp
import matplotlib.pyplot as plt
from winsound import PlaySound
signal_tri = thinkdsp.SquareSignal(1100)
plt.subplot(211)
signal_tri.plot()
wave = signal_tri.make_wave(duration=5, framerate=10000)
spectrum = wave.make_spectrum()
wave.write("1.wav")
PlaySound("1.wav", flags=8)
plt.subplot(212)
spectrum.plot()
plt.show()
signal1.plot()
sawtooth = SawtoothSignal().make_wave(duration=0.5, framerate=10000)
plt.subplot(3, 2, 2)
plt.title('频谱')
sawtooth.make_spectrum().plot()
signal2 = thinkdsp.TriangleSignal(200)
plt.subplot(3, 2, 3)
plt.ylabel('200Hz三角波')
signal2.plot()
wave2 = signal2.make_wave(duration=0.5, framerate=10000)
spectrum = wave2.make_spectrum()
plt.subplot(3, 2, 4)
spectrum.plot()
signal3 = thinkdsp.SquareSignal(200)
plt.subplot(3, 2, 5)
plt.ylabel('200Hz方波')
signal3.plot()
wave3 = signal3.make_wave(duration=0.5, framerate=10000)
spectrum = wave3.make_spectrum()
plt.subplot(3, 2, 6)
spectrum.plot()
plt.show()
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 2 18:06:16 2018
@author: MikeNagler
"""
import thinkdsp
from thinkdsp import Signal, Wave, Spectrum
def modify_spectrum(spectrum):
spectrum.hs[0] = 0
for i in range(1, len(spectrum.fs)):
spectrum.hs[i] = spectrum.hs[i] / spectrum.fs[i]
signal = thinkdsp.SquareSignal()
signal.plot()
wave = signal.make_wave(duration=1)
wave.plot()
wave.write(filename='temp.wav')
spectrum = wave.make_spectrum()
print(spectrum.hs[1000])
spectrum.plot()
# print(spectrum.fs[10] == 10) ### ok, so fs is just an array of all the frequencies
modify_spectrum(spectrum)
print(spectrum.hs[1000])
# print(spectrum.fs[i])
# print(spectrum.hs[i])
return spectrum
signal_tri = thinkdsp.TriangleSignal(200) #生成一个200Hz的方波
plt.subplot(331)
signal_tri.plot()
wave = signal_tri.make_wave(duration=0.01, framerate=10000)
spectrum = wave.make_spectrum()
plt.subplot(334)
spectrum.plot()
s = af(spectrum)
plt.subplot(337)
s.plot()
signal_squ = thinkdsp.SquareSignal(200) #生成一个200Hz的三角波
plt.subplot(332)
signal_squ.plot()
wave = signal_squ.make_wave(duration=0.01, framerate=10000)
spectrum = wave.make_spectrum()
plt.subplot(335)
spectrum.plot()
s = af(spectrum)
plt.subplot(338)
s.plot()
signal_saw = thinkdsp.SawtoothSignal(200) #生成一个200Hz的锯齿波
plt.subplot(333)
signal_saw.plot()
wave = signal_saw.make_wave(duration=0.01, framerate=10000)
spectrum = wave.make_spectrum()
plt.subplot(336)
