Denoising Data with FFT [Python]
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- čas přidán 6. 04. 2020
- This video describes how to clean data with the Fast Fourier Transform (FFT) in Python.
Book Website: databookuw.com
Book PDF: databookuw.com/databook.pdf
These lectures follow Chapter 2 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: www.amazon.com/Data-Driven-Sc...
Brunton Website: eigensteve.com
This video was produced at the University of Washington
![Computing Derivatives with FFT [Matlab]](http://i.ytimg.com/vi/BCexJ-yfdi8/mqdefault.jpg)
![Computing Derivatives with FFT [Matlab]](/img/tr.png)
![Denoising Data with FFT [Matlab]](http://i.ytimg.com/vi/c249W6uc7ho/mqdefault.jpg)
Mathematicians make awful variable names.
Dude this guy is crazy, I still cant believe these videos are free.Thank You for making it free means a lot.❤️
that one guy who disliked is terrible
Are you writing all this backwards, or is there some kind of trick lol
Every body's a gangsta until a man with glasses enters the room and explains fft
You forgot to multiply your np.fft.fft(...) output by dt. np.fft module assumes the sampling spacing is 1. So we have to fix for this if we have a different sampling spacing. Also, you don't have to give n as the second argument of your np.fft.fft because n is also the length of your signal, so it's effectless.
the way this video is setup, overlaying him, the code and a whiteboard, is really slick
The magnitudes of the signals with 50 and 120 Hz depends on the random numbers that you generate at the beginning of the code. I had this problem that my magnitudes where different from those presented in this video.
very helpfull
THANK YOU SO MUCH
Pure gold. Fourier analysis still feels a bit like voodoo to me as I'm just learning the basics, and your videos have been very helpful. The python examples are really handy. Thanks for taking the time to do these things in both MatLab and Python.
I've spent many hours trying to apply FFT to my data and I've finally done it with your amazing explanation. For sure, the best Fourier Transform video.
Steve Brunton, I may never meet you in person but you helped me a lot with these videos. I wish you a good health and a prosper life.
Brilliant. Just brilliant. The quality of this lecture is off the charts.
People like you make the world a better place. Free education helps everybody in the end. Thank you.
Professor Brunton, I love all your videos.
This has been very useful for me. I am a Mechanical Engineer and I am working in dynamic studies of steel structures. This method is very practical to apply to the acquisition of accelerometer data in dynamic tests. Thank you so much Steve!!!
I found it very refreshing to watch in MATLAB, try to code it in Python myself, then actually watching the explanation in Python to correct my mistakes! Thank you very much for all you did!
Your videos are crystal clear! I cant thank enough for sharing this high quality content. Loved the approach you took of writing parallel to the code!
Thanks a lot again Prof! Just wanted to mention that, when calculating the power spectrum (PSD), the data type of the production result of complex number with its conjugate is actually: complex with 0j (Python3)