I have a data that has two columns time and corresponding amplitude of signal. I need to compute the frequency of the signal. How can do this. I read about scipy and numpy fft. But I really do not understand how to use them in my case.
Lift Coefficient vs Time
I need to Calculate the frequency for the signal that is after time say 4s. which seems periodic.
Related
I want to compare the power spectra of the time traces of two random processes but the frequency range returned is different.
How is that frequency range chosen and how can I modify it ?
More specifically, what I do is the following:
from scipy import signal as sgn
spectrum1=sgn.periodogram(signal1,fs=fs1)
spectrum2=sgn.periodogram(signal2,fs=fs2)
and my problem is that spectrum1[0] has a significantly different range with respect to spectrum2[0].
The periodogram is computed using FFT (Fast Fourier Transform), which implements the DFT (Discrete Fourier Transform). The DFT of a periodic signal features discrete frequencies, all multiple of a fundamental frequency consistent with the duration of the frame T : f_0=1/T.
As a consequence, to get the same frequencies, the durations of the frame must be similar, of at least a multiple of one another:
len(signal1)/fs1 = k*len(signal2)/fs2
It may require to truncate one of the arrays. The argument nfft of scipy.signal.periodgram() may also be tried, the requirement becomes:
nfft1/fs1 = k*nfft2/fs2
If the duration of the frame is not consistent with the actual period of the signal, or if the signal is not periodic, windowing may limit the effects of spectral leakage. It is so useful that it is integrated to scipy.signal.periodgram() as an argument. You may try values 'hann' or 'parzen' as listed here.
If the sampling rates are not similar, resampling the signal may be required. To this end scipy.signal.resample() can be applied. It also features the argument window and makes use of FFT for resampling, thus avoiding some errors that linear interpolation would trigger.
I am doing a task which involves taking two signals from a phase doppler anemometry system and calculating the phase shift and the frequency which will further help in finding the velocity and diameter of the droplet. Before getting into the actual task, I am now taking two sine signals from a function generator and producing a phase shift and then calculating the phase and frequency via a program in python with FFT to verify if both are the same. In that process, I am now getting the frequency value same as what I have set in the function generator. So frequency problem is solved. I am currently stuck up in a state where I need to find the bin number where my frequency belongs and using that I can calculate the exact phase shift.
Also, I would like to know how to find the number of bins used in the FFT.
My signal is 40MHz, my sampling frequency is 125MHz.
Thank you!
It might be slightly overkilling but you can use numpy.where to find the index of a specific value in an array
>>> import numpy as np
>>> np.where(np.linspace(1,10,10)==4)
(array([3]),)
I have a data with unevenly spaced (time) samples. How can I find the FFT of the signal and plot it.
Apart from the suggested answers, if your goal is find the frequencies (and not have to use FFT for some reason - which I can't infer from your question), you can consider using periodograms; more specifically, the Lomb-Scargle Periodogram - which can yield frequencies corresponding to unevenly spaced data.
Here is a great answer illustrating this suggestion.
You can't do an FFT of an unevenly sampled signal. That invalidates the assumptions of the math the FFT is based upon.
You'll have to resample the signal so you have evenly spaced samples.
This is slightly out of scope of this forum, but you can start in the dsp stackexchange
If you want a quick and dirty solution use the following approach :
choose a time delay less than or equal your smallest time between points --> dt or alternatively 20% of the inverse of the maximum frequency you are interested in.
make a buffer with N points with N a power of 2 and N*dt > Tmax - Tmin, or whatever the time window you are interested in.
distribute your points over the 2 closest points, or if you do not mind a bit more 'fuzz' just put it at the nearest point.
You'll end up with a buffer with spikes and zeroes in it, but with the same energy as your original signal.
Now FFT and only use the lowest 20% or so of the frequency lines.
This is an incredibly 'raw' and 'approximative' way of doing things, but it will give some approximation of wiggly power bars over frequency. You can clean the signal up by applying windows.
Note that digital signal processing is a field unto itself. I recommend to explore that rabbithole, but do expect to spent quite some time down there.
To use an FFT, you will need to created a vector of samples evenly spaced in time.
If the signal was bandlimited to below a sample rate implied by the widest sample spacings, you can try polynomial interpolation between your unevenly spaced samples to create a grid of about the same number of equally spaced samples in time. But, depending on polynomial degree, this might be highly sensitive to any noise in the bandlimiting or sampling process.
I spent couple days trying to solve this problem, but no luck so I turn to you. I have file for a photometry of a star with time and amplitude data. I'm supposed to use this data to find period changes. I used Lomb-Scargle from pysca library, but I have to use Fourier analysis. I tried fft (dft) from scipy and numpy but I couldn't get anything that would resemble frequency spectrum or Fourier coefficients. I even tried to use nfft from pynfft library because my data are not evenly sampled, but I did not get anywhere with this. So if any of you know how to get from Fourier analysis main frequency in periodical data, please let me know.
Before doing the FFT, you will need to resample or interpolate the data until you get a set of amplitude values equally spaced in time.
Does anyone know if it is possible to find a power spectral density of a signal with gaps in it. For example (in matlab syntax cause that is what I'm familiar with)
ta=1:1000;
tb=1200:3000;
t=[ta tb]; % this is the timebase
signal=randn(size(t)); this is a signal
figure(101)
plot(t,signal,'.')
I'd like to be able to determine frequencies on a longer time base that just the individual sections of data. Obviously I could just take the PSD of individual sections but that will limit the lowest frequency. I could interpolate the data, but this would colour the PSD.
Any thoughts would be much appreciated.
The Lomb-Scargle periodogram algorithm is usually used to perform analysis on unevenly spaced data (sampled at arbitrary time points) or when a proportion of the data is missing.
Here's a couple of MATLAB implementations:
lombscargle.m (FEX)
Lomb (Lomb-Scargle) Periodogram (FEX)
lomb.m - ECG tools by Gari Clifford
I found this Non Uniform FFT but I'm not sure that its exactly what I need as it might really be for data that is mostly sampled on an uneven time base, rather than evenly spaced data with significant gaps. I'll give it a go!
Leaving out segments of the Fourier basis vectors results in exactly the same FT, thus PSD, as using the complete basis, but multiplying by zeros within a zero padding in any signal "gaps".