一维数据
def show(ori_func, ft, sampling_period = 5):
n = len(ori_func)
interval = sampling_period / np.float(n)
# 绘制原始函数
plt.subplot(2, 1, 1)
plt.plot(np.arange(0, sampling_period, interval), ori_func, 'black')
plt.xlabel('Time'), plt.ylabel('Amplitude')
# 绘制变换后的函数
plt.subplot(2,1,2)
frequency = np.arange(n / 2) / (n * interval)
nfft = abs(ft[range(int(n / 2))] / n )
plt.plot(frequency, nfft, 'red')
plt.xlabel('Freq (Hz)'), plt.ylabel('Amp. Spectrum')
plt.show()
qs = q # (1,1000)
fs = fft(q) # np.fft.fft
show(qs,fs)
二维数据
def show(ori_func, ft, sampling_period = 5): # 0.21s or 5.
n = len(ori_func)
interval = sampling_period / np.float(n)
# 绘制原始函数
plt.subplot(2, 1, 1)
plt.plot(np.arange(0, sampling_period, interval), ori_func)
plt.xlabel('Time'), plt.ylabel('Amplitude')
# 绘制变换后的函数
plt.subplot(2,1,2)
frequency = [np.arange(n / 2.) / (n * interval)]*ft.shape[0]
nfft = abs(ft[:,range(int(n / 2.))] / n )
plt.plot(frequency, nfft)
plt.xlabel('Freq (Hz)'), plt.ylabel('Amp. Spectrum')
plt.show()
qs = q # (200,1000)
fs = fft(q)
show(qs,fs)
参考
