使用Python作FFT（快速傅里叶转换）

使用 Python 作FFT 时频转换

引入

1 2	import numpy as np import matplotlib.pyplot as plt

设定函数

Fs = 100                         # sampling rate
Ts = 1.0/Fs                      # sampling interval
t = np.arange(0,1,Ts)            # time vector
ff = 5                           # frequency of the signal
y = np.sin(2 * np.pi * ff * t)

### 图表一设定

``` bach
plt.subplot(2,1,1)
plt.plot(t,y,'b-')
plt.xlabel('time')
plt.ylabel('amplitude')

设定图表二横纵坐标

plt.subplot(2,1,2)
n = len(y)                       # length of the signal
k = np.arange(n)
T = n/Fs
frq = k/T # two sides frequency range
freq = frq[range(int(n/2))]           # one side frequency range

FFT

1 2	Y = np.fft.fft(y)/n # fft computing and normalization Y = Y[range(int(n/2))]

图表二设定

1
2
3

plt.plot(freq, abs(Y), 'r-')
plt.xlabel('freq (Hz)')
plt.ylabel('|Y(freq)|')

显示图表

1	plt.show()

完整代码：


import numpy as np
import matplotlib.pyplot as plt

Fs = 100                         # sampling rate
Ts = 1.0/Fs                      # sampling interval
t = np.arange(0,1,Ts)            # time vector
ff = 5                           # frequency of the signal
y = np.sin(2 * np.pi * ff * t)

plt.subplot(2,1,1)
plt.plot(t,y,'b-')
plt.xlabel('time')
plt.ylabel('amplitude')

plt.subplot(2,1,2)
n = len(y)                       # length of the signal
k = np.arange(n)
T = n/Fs
frq = k/T # two sides frequency range
freq = frq[range(int(n/2))]           # one side frequency range

Y = np.fft.fft(y)/n              # fft computing and normalization
Y = Y[range(int(n/2))]

plt.plot(freq, abs(Y), 'r-')
plt.xlabel('freq (Hz)')
plt.ylabel('|Y(freq)|')

plt.show()