fftshift的作用正是让正半轴部分和负半轴部分的图像分别关于各自的中心对称。因为直接用fft得出的数据与频率不是对应的,fftshift可以纠正过来 以下是Matlab的帮助文件中对fftshift的说明: Y = fftshift(X) rearranges the outputs of fft, fft2, and fftn by moving the zero-frequency component...
y=fft(x,N);%对原始信号做FFT变换 Mag=abs(y);%求FFT转换结果的模值subplot(2,1,1);plot(f,Mag);%绘制幅频相应曲线title('幅频相应');xlabel('频率/Hz');ylabel('幅度');subplot(2,1,2);plot(f,angle(y)*180/pi);%绘制相频响应曲线,注意这将弧度转换成了角度title('相频响应方式一');xlab...
Y = fftshift(X) rearranges the outputs of fft, fft2, and fftn by moving the zero-frequency component to the center of the array. It is useful for visualizing a Fourier transform with the zero-frequency component in the middle of the spectrum. For vectors, fftshift(X) swaps the left ...
import matplotlib.pyplot as pltFs=1# HzN=100# number of points to simulate, and our FFT sizet=np.arange(N)# because our sample rate is 1 Hzs=np.sin(0.15*2*np.pi*t)S=np.fft.fftshift(np.fft.fft(s))S_mag=np.abs(S)S_phase=np.angle(S)f=np.arange(Fs/-2, Fs/2, Fs/N)pl...
For vectors, fftshift(X) swaps the left and right halves of X。 下面我们在Matlab上面实现一个如下的代码来说明fftshift的使用: Fs = 256; % 采样率 N = 256; % 采样点数 n = 0:N-1; % 采样序列 t = 0:1/Fs:1-1/Fs; % 时间序列 f = (-N/2:N/2-1) * Fs / N; %真实的频率 ...
for i = 1:1000 x = sum(sine(),2)+1e-1*randn(L,1); z = zfft(x); z = fftshift(z); ap(z.*conj(z)); end Compute Zoom FFT of Variable-Size Inputs Copy Code Copy Command The dsp.ZoomFFT object accepts variable-size inputs as long as the input is a multiple of the dec...
y5=abs(fftshift(fft(x5))); figure(9) subplot(3,1,1),plot(fre,y3),xlabel('Hz'),title('X3频谱'),xlabel('频率Hz'),axis([-100,100,1.2*min(y3),1.2*max(y3)]); subplot(3,1,2),plot(fre,y4),xlabel('Hz'),title('X4频谱'),xlabel('频率Hz'),axis([-200,200,1.2*min(y4),...
因为直接用fft得出的数据与频率不是对应的，fftshift可以纠正过来以下是Matlab的帮助文件中对fftshift的...
subplot(223),plot(freq*1e-6,fftshift(abs(fft(St))),'k'); title('零中频线性调频信号的频谱'); xlabel('Frequency in MHz'),ylabel('S(f)'); grid on;axis tight; fi=K*t; subplot(224),plot(t*1e6,fi*1e-6,'k'); title('零中频线性调频信号的瞬时频率'); ...
H = np.abs(np.fft.fft(h_band_pass, 1024)) # take the 1024-point FFT and magnitude H = np.fft.fftshift(H) # make 0 Hz in the center w = np.linspace(-sample_rate/2, sample_rate/2, len(H)) # x axis plt.figure('freq') ...