# spectral-density

问题 I would like to use pwelch on a set of signals and I have some questions. First, let's say that we have 32 (EEG) signals of 30 seconds duration. The sampling frequency is fs=256 samples/sec, and thus each signal has length 7680. I would like to use pwelch in order to estimate the power spectral density (PSD) of those signals. Question 1: Based on the pwelch 's documentation, pxx = pwelch(x) returns the power spectral density (PSD) estimate, pxx, of the input signal, x, found using Welch's

## Using pwelch to a set of signals: some questions (Matlab)

## Using IFFT to get original signal and Parseval's Theorem

问题 I have a current signal (extracted in csv) which I obtained from cadence simulation over 30ns time. I have removed DC offset and applied windowing function before FFT. And normalized FFT by sqrt(N) . I have shift zero-frequency component to center of my desired spectrum with fftshift(X) . I got my desired FFT. I also want to get back to my original windowed signal by ifft but it is not showing my windowed signal instead it is showing only a version of the window function that I used. My

## Using IFFT to get original signal and Parseval's Theorem

## Interpreting jTransform FFT results

问题 Merged with Power Spectral Density from jTransforms DoubleFFT_1D. I'm using Jtransforms java library to perform analysis on a give dataset. An example of the data is as follows: 980,988,1160,1080,928,1068,1156,1152,1176,1264 I'm using the DoubleFFT_1D funtion in jTransforms. The data output is as follows: 10952, -152, 80.052, 379.936, -307.691, 12.734, -224.052, 427.607, -48.308, 81.472 I'm having trouble interpreting the output. I understand that the first element in the output array is the

## FFT and PSD of current signal and analysis

问题 I have a current signal which I obtained from cadence simulation over 30ns time but my current actually varies (which I call active region) over certain period. I extracted the data of current signal over 1ps fixed step and plotted the current. Then to analyze frequency spectrum and powers spectral density I used FFT and PSD in MATLAB. But I am confused about what this analyse mean and how can realize it. The operation frequency of my circuit is 2GHz. So my goal is to see the frequency

## Fast Fourier Transform in R

问题 I have a dataset with the number of hourly visits an animal made during a period of 12 months. I want to use the Fast Fourier Transform to examine cyclical patterns and periodicity. In the past, I have used Statistica for this this; however, I would like to use R to get a plot of the spectral density vs. period. Is there an easy way to do this in R? I would like to identify 12 and 24 hr peak in activity if possible. 回答1: You may consider the following functions. periodogram from TSA package

## How to use the cross-spectral density to calculate the phase shift of two related signals

问题 I've two signals, from which I expect that one is responding on the other, but with a certain phase shift. Now I would like to calculate the coherence or the normalized cross spectral density to estimate if there is any causality between the input and output to find out on which frequencies this coherence appear. See for example this image (from here) which seems to have high coherence at the frequency 10: Now I know that I can calculate the phase shift of two signals using the cross

## Computing a power spectrum

I would like to compute a power spectrum using Python3. From another thread about this topic I got the basic ingredients. I think it should be something like: ps = np.abs(np.fft.fft(x))**2 timeres = t[1]-t[0] freqs = np.fft.fftfreq(x.size, timeres) idx = np.argsort(freqs) plt.plot(freqs[idx], ps[idx]) plt.show() Here t are the times and x is the photon count. I have also tried: W = fftfreq(x.size, timeres=t[1]-t[0]) f_x = rfft(x) plt.plot(W,f_x) plt.show() But both mostly just give me a peak around zero (though they are not the same). I am trying to compute the power spectrum from this: Which

## Computing a power spectrum

问题 I would like to compute a power spectrum using Python3. From another thread about this topic I got the basic ingredients. I think it should be something like: ps = np.abs(np.fft.fft(x))**2 timeres = t[1]-t[0] freqs = np.fft.fftfreq(x.size, timeres) idx = np.argsort(freqs) plt.plot(freqs[idx], ps[idx]) plt.show() Here t are the times and x is the photon count. I have also tried: W = fftfreq(x.size, timeres=t[1]-t[0]) f_x = rfft(x) plt.plot(W,f_x) plt.show() But both mostly just give me a peak