fft | radix-2 fast Fourier transform | Video Utils library

by ktye Go Version: Current License: Unlicense

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kandi X-RAY | fft Summary

fft is a Go library typically used in Video, Video Utils, Numpy applications. fft has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

radix-2 fast Fourier transform. Package fft provides a fast discrete Fourier transformation algorithm. Implemented is the 1-dimensional DFT of complex input data for with input lengths which are powers of 2. The algorithm is non-recursive and works in-place overwriting the input array. Before doing the transform on acutal data, allocate an FFT object with t := fft.New(N) where N is the length of the input array. Then multiple calls to t.Transform(x) can be done with different input vectors having the same length.

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fft has a low active ecosystem.

It has 28 star(s) with 3 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

fft has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of fft is current.

Quality

fft has no bugs reported.

Security

fft has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

fft is licensed under the Unlicense License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

fft releases are not available. You will need to build from source code and install.

Top functions reviewed by kandi - BETA

kandi has reviewed fft and discovered the below as its top functions. This is intended to give you an instant insight into fft implemented functionality, and help decide if they suit your requirements.

permutationIndex returns the index of the sequence .
lastPow2 returns the last power of 2 .
New returns a FFT .
roots returns a list of roots .
Input permutation
newSlowPre returns a new slowPre .

Get all kandi verified functions for this library.

fft Key Features

No Key Features are available at this moment for fft.

fft Examples and Code Snippets

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Community Discussions

Trending Discussions on fft

What is wrong in my code using DHT to compute convolution?

Numpy exp() function gives me wrong value for no reason

How can I get exactly the same results in OpenCV as with ImageMagick's "convert -fft"?

Load an audio file and find the frequency

Efficient pythonic way for this operation

Why does NumPy's irfft2 of rfft2 lead to a matrix with one less column when the original matrix has an odd second index?

Matplotlib plot's title is missing for unknown reason in Python

Why do my FFT plots have these horizontal lines?

Why does FFT size equals the numbers of samples in the time domain?

Continuous Convolution Using 3 Different Methods in MATLAB Gives Different Results

QUESTION

What is wrong in my code using DHT to compute convolution?

Asked 2021-Jun-12 at 04:10

The Discrete Hartley Transform can be computed as

...

ANSWER

Answered 2021-Jun-12 at 04:10

The issue is about the flipping part. To show with an example:

Source https://stackoverflow.com/questions/67940011

QUESTION

Numpy exp() function gives me wrong value for no reason

Asked 2021-Jun-10 at 13:48

import matplotlib.pyplot as plt

#from numpy.fft import fft as numpyfft

#from scipy.fftpack import fft as scipyfft

import numpy as np

    
    print(np.exp([-2j*np.pi]))

    print(np.exp([-2.j*np.pi]))

    print(np.exp(-2j*np.pi))

    print(np.exp(-2.j*np.pi))

    [1.+2.4492936e-16j]
    [1.+2.4492936e-16j]
    (1+2.4492935982947064e-16j)
    (1+2.4492935982947064e-16j)

...

ANSWER

Answered 2021-Jun-10 at 13:41

This is the correct answer. Numpy like all computers calculating in binary has a small margin of error. You can circumvent this by rounding to 14 digits (for 32-bit floating point numbers):

Source https://stackoverflow.com/questions/67922268

QUESTION

How can I get exactly the same results in OpenCV as with ImageMagick's "convert -fft"?

Asked 2021-Jun-10 at 11:50

When I used ImageMagick-Q16, everything was solved. I was completely wrong.

To analyze images from anime shows., I would like to display the magnitude values obtained by the FFT.; ImageMagick's convert INPUT -fft OUTPUTworked well, but is unwieldy on Windows because it requires FFTW. Therefore, I would like to use OpenCV to get the exact same FFT in Python as ImageMagick. Thank you very much for your help.

...

ANSWER

Answered 2021-Jun-10 at 03:02

Here is how to get the spectrum (log of mangitude of the dft) of an image in Python/OpenCV.

Input:

Source https://stackoverflow.com/questions/67905509

QUESTION

Load an audio file and find the frequency

Asked 2021-Jun-10 at 10:36

I have a following code:

...

ANSWER

Answered 2021-Jun-10 at 10:36

np.argmax gives you the index of the maximum element in the Fourier frequency, not the actual frequency. The relation to obtain the frequency from the index is frequency = index*rate/len(Fourier). So, applying this in your case should give you the desired frequency:

Source https://stackoverflow.com/questions/67919182

QUESTION

Efficient pythonic way for this operation

Asked 2021-Jun-09 at 14:28

I am looking for an efficient way to perform the following operation; here is a minimal working code:

...

ANSWER

Answered 2021-Jun-09 at 14:28

Reference implementation

I will start wrapping the operation in a function so that I can easily compare implementations

Source https://stackoverflow.com/questions/67904490

QUESTION

Why does NumPy's irfft2 of rfft2 lead to a matrix with one less column when the original matrix has an odd second index?

Asked 2021-Jun-07 at 22:23

I am confused by the following behavior of rfft2 and irfft2 in NumPy. If I start with a real matrix that is m x n where n is odd, then if I take rfft2 followed by irfft2, I end up with an m x (n-1) matrix. Since irfft2 is the inverse of rfft2, I would have expected to get back a matrix of size m x n. In addition, the values in the matrix are not what I started with -- see output below.

...

ANSWER

Answered 2021-Jun-07 at 18:57

FFTs in general do not handle odd-length inputs. They actually want powers of 2. When you FFT an odd-length vector of reals, you lose some information. If you try your experiment with a (4,4), you'll see that the output exactly matches the input.

Source https://stackoverflow.com/questions/67876671

QUESTION

Matplotlib plot's title is missing for unknown reason in Python

Asked 2021-Jun-04 at 18:23

Can anyone tell me what is wrong with this code? It is from https://jakevdp.github.io/blog/2012/09/05/quantum-python/ . Everything in it worked out except the title of the plot.I can't figure it out.

It should look like this

but when the code is run, it polts this

Here is the code given:-

...

ANSWER

Answered 2021-Jun-04 at 18:23

The problem is resolved when blit=False, though it may slow down your animation.

Just quoting from a previous answer:

"Possible solutions are:

Put the title inside the axes.

Don't use blitting"

See: How to update plot title with matplotlib using animation?

You also need ffmpeg installed. There are other answers on stackoverflow that help you through that installation. But for this script, here are my recommended new lines you need to add, assuming you're using Windows:

Source https://stackoverflow.com/questions/67841265

QUESTION

Why do my FFT plots have these horizontal lines?

Asked 2021-Jun-03 at 17:41

I'm new to Python and signal processing, and I'm having a problem with FFT.

I'm supposed to analyze a set of data and find the modulation frequencies from it. I wrote a basic FFT script to do this, and the output looked kinda weird. It does show the peaks like a normal FFT graph. However, for each line it has a horizontal line that connects the two ends, instead of the ends spreading out.

I would like to ask what might be the problem here.

This is my script:

...

ANSWER

Answered 2021-Jun-03 at 17:41

Answer: The graph look like that because of the order of the fft calculation output: it starts with 0 Hz (more details presented here: https://numpy.org/doc/stable/reference/generated/numpy.fft.fftfreq.html)

I fixed this by using numpy.fft.fftshift() function (more details presented here: https://numpy.org/doc/stable/reference/generated/numpy.fft.fftshift.html).

Solution has been suggested by @gavinb and @yanziselman.

Source https://stackoverflow.com/questions/67802878

QUESTION

Why does FFT size equals the numbers of samples in the time domain?

Asked 2021-Jun-03 at 14:02

My question is simple. I think I kind of understand FFT and DFT. what I dont understand is why, in Python or matlab, do we use FFT size as the number of samples? why does every sample taken in the time domain corresponds to a frequency bin in the frequency domain.

For example the Scipy's fft pack, in order to plot the spectrum of a .wav file signal we use:

...

ANSWER

Answered 2021-Jun-03 at 14:02

Mathematically a key property of the fourier transform is that it is linear and invertible. The latter means that if two signals have the same fourier transform they are equal, and that for any spectrum there is a signal with that spectrum.

For implementations with a finite collection of samples of a signal the first property means that the fourier tramsform can be represented by a N x M matrix where N is the number of time samples and M the number of frequency samples. The second property means that the matrix must be invertible, and so square, ie we must have M == N.

You say that time bins and frequency correspond, and that is true in the sense that there are the same number of them. However the value in each frequency bin will depend on all time values.

Source https://stackoverflow.com/questions/67821397

QUESTION

Continuous Convolution Using 3 Different Methods in MATLAB Gives Different Results

Asked 2021-May-29 at 14:27

I'm trying to understand why the results for the convolution of two functions in MATLAB is different when I'm trying different methods. As an example suppose that my functions are sin(x) and cos(x). The first approach is using the conv() command in MATLAB. The second approach is to calculate it directly using the definition of convolution. The third method is using the convolution theorem and calculating it using fft() and ifft(). The code is attached here. I have used sin(x) and exp(-x) just as an example. I want to do the same for any two arbitrary functions.

...

ANSWER

Answered 2021-May-29 at 14:27

Let’s look at the three methods implemented in the OP:

Source https://stackoverflow.com/questions/67744060

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