功率谱密度图示例
在Matplotlib中绘制功率谱密度(PSD)。
PSD是信号处理领域中常见的图形。NumPy有许多用于计算PSD的有用库。下面，我们演示一些如何使用Matplotlib实现和可视化这一点的示例。
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.gridspec as gridspec
# Fixing random state for reproducibility
np.random.seed(19680801)
dt = 0.01
t = np.arange(0, 10, dt)
nse = np.random.randn(len(t))
r = np.exp(-t / 0.05)
cnse = np.convolve(nse, r) * dt
cnse = cnse[:len(t)]
s = 0.1 * np.sin(2 * np.pi * t) + cnse
plt.subplot(211)
plt.plot(t, s)
plt.subplot(212)
plt.psd(s, 512, 1 / dt)
plt.show()
将其与等效的Matlab代码进行比较，以完成相同的任务：
dt = 0.01;
t = [0:dt:10];
nse = randn(size(t));
r = exp(-t/0.05);
cnse = conv(nse, r)*dt;
cnse = cnse(1:length(t));
s = 0.1*sin(2*pi*t) + cnse;
subplot(211)
plot(t,s)
subplot(212)
psd(s, 512, 1/dt)
下面，我们将展示一个稍微复杂一些的示例，演示填充如何影响产生的PSD。
dt = np.pi / 100.
fs = 1. / dt
t = np.arange(0, 8, dt)
y = 10. * np.sin(2 * np.pi * 4 * t) + 5. * np.sin(2 * np.pi * 4.25 * t)
y = y + np.random.randn(*t.shape)
# Plot the raw time series
fig = plt.figure(constrained_layout=True)
gs = gridspec.GridSpec(2, 3, figure=fig)
ax = fig.add_subplot(gs[0, :])
ax.plot(t, y)
ax.set_xlabel('time [s]')
ax.set_ylabel('signal')
# Plot the PSD with different amounts of zero padding. This uses the entire
# time series at once
ax2 = fig.add_subplot(gs[1, 0])
ax2.psd(y, NFFT=len(t), pad_to=len(t), Fs=fs)
ax2.psd(y, NFFT=len(t), pad_to=len(t) * 2, Fs=fs)
ax2.psd(y, NFFT=len(t), pad_to=len(t) * 4, Fs=fs)
plt.title('zero padding')
# Plot the PSD with different block sizes, Zero pad to the length of the
# original data sequence.
ax3 = fig.add_subplot(gs[1, 1], sharex=ax2, sharey=ax2)
ax3.psd(y, NFFT=len(t), pad_to=len(t), Fs=fs)
ax3.psd(y, NFFT=len(t) // 2, pad_to=len(t), Fs=fs)
ax3.psd(y, NFFT=len(t) // 4, pad_to=len(t), Fs=fs)
ax3.set_ylabel('')
plt.title('block size')
# Plot the PSD with different amounts of overlap between blocks
ax4 = fig.add_subplot(gs[1, 2], sharex=ax2, sharey=ax2)
ax4.psd(y, NFFT=len(t) // 2, pad_to=len(t), noverlap=0, Fs=fs)
ax4.psd(y, NFFT=len(t) // 2, pad_to=len(t),
noverlap=int(0.05 * len(t) / 2.), Fs=fs)
ax4.psd(y, NFFT=len(t) // 2, pad_to=len(t),
noverlap=int(0.2 * len(t) / 2.), Fs=fs)
ax4.set_ylabel('')
plt.title('overlap')
plt.show()
这是一个来自信号处理工具箱的MATLAB示例的移植版本，它显示了Matplotlib和MATLAB对PSD的缩放之间的一些差异。
fs = 1000
t = np.linspace(0, 0.3, 301)
A = np.array([2, 8]).reshape(-1, 1)
f = np.array([150, 140]).reshape(-1, 1)
xn = (A * np.sin(2 * np.pi * f * t)).sum(axis=0)
xn += 5 * np.random.randn(*t.shape)
fig, (ax0, ax1) = plt.subplots(ncols=2, constrained_layout=True)
yticks = np.arange(-50, 30, 10)
yrange = (yticks[0], yticks[-1])
xticks = np.arange(0, 550, 100)
ax0.psd(xn, NFFT=301, Fs=fs, window=mlab.window_none, pad_to=1024,
scale_by_freq=True)
ax0.set_title('Periodogram')
ax0.set_yticks(yticks)
ax0.set_xticks(xticks)
ax0.grid(True)
ax0.set_ylim(yrange)
ax1.psd(xn, NFFT=150, Fs=fs, window=mlab.window_none, pad_to=512, noverlap=75,
scale_by_freq=True)
ax1.set_title('Welch')
ax1.set_xticks(xticks)
ax1.set_yticks(yticks)
ax1.set_ylabel('') # overwrite the y-label added by `psd`
ax1.grid(True)
ax1.set_ylim(yrange)
plt.show()
这是一个来自信号处理工具箱的MATLAB示例的移植版本，它显示了Matplotlib和MATLAB对PSD的缩放之间的一些差异。
它使用了一个复杂的信号，所以我们可以看到，复杂的PSD的工作正常。
prng = np.random.RandomState(19680801) # to ensure reproducibility
fs = 1000
t = np.linspace(0, 0.3, 301)
A = np.array([2, 8]).reshape(-1, 1)
f = np.array([150, 140]).reshape(-1, 1)
xn = (A * np.exp(2j * np.pi * f * t)).sum(axis=0) + 5 * prng.randn(*t.shape)
fig, (ax0, ax1) = plt.subplots(ncols=2, constrained_layout=True)
yticks = np.arange(-50, 30, 10)
yrange = (yticks[0], yticks[-1])
xticks = np.arange(-500, 550, 200)
ax0.psd(xn, NFFT=301, Fs=fs, window=mlab.window_none, pad_to=1024,
scale_by_freq=True)
ax0.set_title('Periodogram')
ax0.set_yticks(yticks)
ax0.set_xticks(xticks)
ax0.grid(True)
ax0.set_ylim(yrange)
ax1.psd(xn, NFFT=150, Fs=fs, window=mlab.window_none, pad_to=512, noverlap=75,
scale_by_freq=True)
ax1.set_title('Welch')
ax1.set_xticks(xticks)
ax1.set_yticks(yticks)
ax1.set_ylabel('') # overwrite the y-label added by `psd`
ax1.grid(True)
ax1.set_ylim(yrange)
plt.show()
