Fourier-Transform | An implementation of the Fourier Transform using Python | Video Utils library

by fotisk07 Python Version: Current License: MIT

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kandi X-RAY | Fourier-Transform Summary

Fourier-Transform is a Python library typically used in Video, Video Utils, Numpy applications. Fourier-Transform has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Fourier-Transform build file is not available. You can download it from GitHub.

The Fourier transform (FT) decomposes a function of time (a signal) into the frequencies that make it up, in a way similar to how a musical chord can be expressed as the frequencies (or pitches) of its constituent notes. In this sort repository I will be implementing a general Fourier Transform algorithm capable of decomposing a function f(x) = sin(2apix) + sin(2bpix) ... for constants a,b,.. > 0. Here is an image of the man who came up with this idea.

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Fourier-Transform has a low active ecosystem.

It has 41 star(s) with 7 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Fourier-Transform is current.

Quality

Fourier-Transform has 0 bugs and 0 code smells.

Security

Fourier-Transform has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Fourier-Transform code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Fourier-Transform is licensed under the MIT License. This license is Permissive.

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

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Fourier-Transform releases are not available. You will need to build from source code and install.

Fourier-Transform has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Fourier-Transform saves you 19 person hours of effort in developing the same functionality from scratch.

It has 54 lines of code, 3 functions and 2 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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

Computes the Fourier transform of a signal
FFT
Generates a signal

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Fourier-Transform Key Features

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Fourier-Transform Examples and Code Snippets

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Community Discussions

Trending Discussions on Fourier-Transform

Plot a fourier transform of a sin wav with matplotlib

Issue with displaying DFT output with openCV's imshow

Efficient Kullback–Leibler calculation

Spectrogram generation in java using FFT on a .wav file not producing expected output

Intel integrated performance primitives Fourier Transform magnitudes

ValueError: x and y can be no greater than 2-D, but have shapes (2, 1, 1) and (2,)

Want to detect blur from image, but couldn't get it right

FFT using Python

Using Intel MKL to convert real time domain data to complex frequency and phase data and the inverse as well

Manipulate lists in a pandas data frame column (e.g. divide by another column)

QUESTION

Plot a fourier transform of a sin wav with matplotlib

Asked 2021-Jan-15 at 00:30

I am trying to plot a fourier transform of a sign wave based on the scipy documentation

...

ANSWER

Answered 2021-Jan-11 at 04:24

import numpy as np
import matplotlib.pyplot as plt
import scipy.fft

def sinWav(amp, freq, time, phase=0):
    return amp * np.sin(2 * np.pi * (freq * time - phase))

def plotFFT(f, speriod, time):
    """Plots a fast fourier transform

    Args:
        f (np.arr): A signal wave
        speriod (int): Number of samples per second
        time ([type]): total seconds in wave
    """

    N = speriod * time
    # sample spacing
    T = 1.0 / 800.0
    x = np.linspace(0.0, N*T, N, endpoint=False)

    yf = scipy.fft.fft(f)
    xf = scipy.fft.fftfreq(N, T)[:N//2]

    amplitudes = 1/speriod* np.abs(yf[:N//2])
  
    plt.plot(xf, amplitudes)
    plt.grid()
    plt.xlim([1,3])
    plt.show()


speriod = 800
time  = {
    0: np.arange(0, 4, 1/speriod),
    1: np.arange(4, 8, 1/speriod),
    2: np.arange(8, 12, 1/speriod)
}

signal = np.concatenate([
    sinWav(amp=0.25, freq=2, time=time[0]),
    sinWav(amp=1, freq=2, time=time[1]),
    sinWav(amp=0.5, freq=2, time=time[2])
])   # generate signal

plotFFT(signal, speriod, 12)

Source https://stackoverflow.com/questions/65660991

QUESTION

Issue with displaying DFT output with openCV's imshow

Asked 2020-Dec-21 at 21:28

I was following the tutorial on this website to create a low-pass filter using NumPy and OpenCV in python. Instead of displaying the transformed image using pyplot as shown in the tutorial, I tried using OpenCV's imshow function but I am not getting the desired output.

...

ANSWER

Answered 2020-Dec-19 at 12:20

If you want to display data in type np.float32 you have to normalize it to 1.0

Source https://stackoverflow.com/questions/65369482

QUESTION

Efficient Kullback–Leibler calculation

Asked 2020-Oct-10 at 21:40

I am looking to implement KL Divergence in C++ efficiently. (CPU Only for now).

Much like AES or FTT (Fast Fourier transform) whereby use of a common function has lead to hardware level optimizations (Intel AES and Intel FTT). Is there anything similar for natural log, or slightly higher level efficiencies (ASM/C) that prevent bottlenecks of executing many Natural log functions in success (If they exist)?

Same use case examples:

...

ANSWER

Answered 2020-Oct-10 at 21:28

You can use SSE instructions to calculate the logarithm of many values in parallel. But whether you can actually make use of those instructions depends heavily on how the rest of the calculations you are going to do depend on the logarithms you calculate, so it is not possible to give a more specific answer.

Source https://stackoverflow.com/questions/64298405

QUESTION

Spectrogram generation in java using FFT on a .wav file not producing expected output

Asked 2020-Jul-25 at 11:07

So I am making an AI project that classifies speech into either "up", "down", "left", right or background noise, and from this, a character in a videogame is moved.

I have made an FFT algorithm deriving it from the mathematical explanation, which I believe is correct as I have tested its output against that from this site (https://engineering.icalculator.info/discrete-fourier-transform-calculator.html)

I then have tried to generate a spectrogram and have used code based on the code from the main function of the App class from this site (Creating spectrogram from .wav using FFT in java)

I tested my code on a .wav file of me saying hello and the spectrogram generated is not what I was expecting out, see below the difference between my java made spectrogram and my python made spectrogram (ignore the colour difference).

Java Spectrogram

Python Spectrogram

New Java Spectrogram with SleuthEyes help

Here is the original code I have used/written:

...

ANSWER

Answered 2020-Jul-24 at 10:47

Reading the .wav file

The .wav file decoding included in that other question you linked is hardly a full blown decoder. It accounts for the OP's specific stereo 2bytes-per-sample use-case.

It looks like you stumbled upon other decoding issues while trying to adapt it to a different use case. As a general piece of advice, I'd suggest to use a more complete .wav decoder which would take into account the number of channels, the number of bytes-per-sample, etc.

If on the other hand you want to craft your own decoder (for example as a learning exercise), then a slightly more robust implementation may look like the following:

Source https://stackoverflow.com/questions/63015469

QUESTION

Intel integrated performance primitives Fourier Transform magnitudes

Asked 2020-Jul-23 at 02:17

When I am using Intel IPP's ippsFFTFwd_RToCCS_64f and then ippsMagnitude_64fc I get a massive peak at zero index in magnitudes array.

My sine wave is long and main component I am interested is somewhere between 0.15 Hz and 0.25 Hz. I take the sample with 500Hz sampling frequency. If I reduce mean from the signal before FFT I get really small zero component not that peak anymore. Below is a pic of magnitudes array head:

Also the magnitude scaling seems to be 10 times the magnitude I see in the time series of the signal e.g. if amplitude is 29 in magnitudes it is 290.

I Am not sure why this is so and my question is 1. Do I really need to address the zero index peak with mean reduction and 2. Where does this scale of 10 come?

...

ANSWER

Answered 2020-Jul-23 at 02:17

Do I really need to address the zero index peak with mean reduction?

For low frequency signal analysis a small bias can really interfere (especially due to spectral leakage). For sake of illustration, consider the following low-frequency signal tone and another one with a constant bias tone_with_bias:

Source https://stackoverflow.com/questions/62896442

QUESTION

ValueError: x and y can be no greater than 2-D, but have shapes (2, 1, 1) and (2,)

Asked 2020-Jul-07 at 16:56

Source code

...

ANSWER

Answered 2020-Jul-07 at 16:09

You need to use reshape:

Source https://stackoverflow.com/questions/62779125

QUESTION

Want to detect blur from image, but couldn't get it right

Asked 2020-Jun-29 at 12:43

I am actually want to convert this blur detection into C++. As a beginner in OpenCV, I am actually following this for conversion, But maybe I am getting it wrong. Here is my approach. I have to use DFT instead of FFT in C++.

...

ANSWER

Answered 2020-Jun-29 at 12:43

I think this comes close to the original Python code

Source https://stackoverflow.com/questions/62611751

QUESTION

FFT using Python

Asked 2020-Jun-19 at 16:47

I am receiving data from a vibration sensor in the form of two numpy arrays. The first array represents the actual values for the vibration measurement and the second array is the corresponding time information (timestamp). For example:

...

ANSWER

Answered 2020-Jun-19 at 16:45

This is really a signal processing question, not a Python one. You have several options here:

if your data is uniformly sampled - you can ignore the timestamps altogether. All the information you need is in the data, and the (constant) sampling frequency: f_s = 1.0 / (timestamps[1] - timestamps[0])
if not, you can either:
- use Non-uniform DFT (here is one implementation, haven't tried)
- interpolate the data between non-uniform timestamps so it becomes uniform. Note that effectively, this applies a low-pass filter to your data, which may be not what you want (more on the effects of interpolation here).

In all cases, when you perform FFT, time information is not required anymore, as you are in the frequency domain.

Source https://stackoverflow.com/questions/62474562

QUESTION

Using Intel MKL to convert real time domain data to complex frequency and phase data and the inverse as well

Asked 2020-Jun-07 at 15:50

I'm writing an audio plugin and I would like to convert a series of real numbers representing sample values into a complex array representing frequency and phase. Then I want to be able to do the opposite, turning a complex array of frequency and phases to a series of real numbers, reconstructing the original data.

I'm using Intel MKL and I see only the possibility to perform real->real conversions or complex->complex conversions. Here's the reference I'm using: Intel MKL FFT Functions.

In that reference, there are two overloaded functions: DftiComputeForward and DftiComputeBackward. So, I would like to use these to do the conversions. In the reference for DftiCreateDescriptor, the only options available for DFTI_FORWARD_DOMAIN are DFTI_COMPLEX and DFTI_REAL, but no option for mixed conversions.

Edit I found that the phase is actually equal to atan(imaginary/real). I don't want to mislead anyone getting information from questions.

Edit I just learned that it's best to use atan2(imaginary,real). More information is in the comments.

...

ANSWER

Answered 2020-Jun-07 at 15:50

Every real number is a complex number: ℝ ⊂ ℤ. So going forward from float or double in time domain to complex is trivial. The language does that for you!

Source https://stackoverflow.com/questions/62238240

QUESTION

Manipulate lists in a pandas data frame column (e.g. divide by another column)

Asked 2020-May-28 at 09:24

I have a pandas data frame with one column containing lists. I wish to divide each list element in each row by a scalar value in another column. In the following example, I wish to divide each element in a by b:

...

ANSWER

Answered 2020-May-28 at 09:03

zip the two columns, divide each entry in col a with its corresponding entry in col b, through a combination of product and starmap, and convert the iterator back into a list.

Source https://stackoverflow.com/questions/62060584

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Fourier-Transform

You can download it from GitHub.
You can use Fourier-Transform like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.