# Discrete Fourier Transform (fft)¶

The Discrete Fourier Transform (DFT) converts the observation sequence into a real-valued, same-length sequence of equally-spaced samples of the discrete-time Fourier transform.

The popular Fast Fourier Transform (FFT) implementation is used to efficiently compute the DFT.

For further information, please see the preprocessing tutorial notebook.

## Example¶

 1 2 3 4 5 6 7 8 import numpy as np from sequentia.preprocessing import fft # Create some sample data X = [np.random.random((10 * i, 3)) for i in range(1, 4)] # Transform the data X = fft(X) 

## API reference¶

sequentia.preprocessing.fft(X)[source]

Applies a Discrete Fourier Transform to the input observation sequence(s).

Parameters: X: numpy.ndarray or List[numpy.ndarray] An individual observation sequence or a list of multiple observation sequences. transformed: numpy.ndarray or List[numpy.ndarray] The transformed input observation sequence(s).