FFT Utilities

function fftfreq(n: number, d?: number): NDArray

import * as np from 'numpy-ts';

const freq = np.fft.fftfreq(5, 0.1);
// array([ 0.,  2.,  4., -4., -2.])

const freq8 = np.fft.fftfreq(8);
// array([ 0.   ,  0.125,  0.25 ,  0.375, -0.5  , -0.375, -0.25 , -0.125])

function rfftfreq(n: number, d?: number): NDArray

import * as np from 'numpy-ts';

const freq = np.fft.rfftfreq(8);
// array([0.   , 0.125, 0.25 , 0.375, 0.5  ])

const freq_hz = np.fft.rfftfreq(1000, 1 / 44100);
// Frequency bins in Hz for a 1000-sample window at 44.1 kHz

function fftshift(a: ArrayLike, axes?: number | number[]): NDArray

import * as np from 'numpy-ts';

const freq = np.fft.fftfreq(5, 0.1);
// array([ 0.,  2.,  4., -4., -2.])

const shifted = np.fft.fftshift(freq);
// array([-4., -2.,  0.,  2.,  4.])

function ifftshift(a: ArrayLike, axes?: number | number[]): NDArray

import * as np from 'numpy-ts';

const freq = np.fft.fftfreq(5, 0.1);
const shifted = np.fft.fftshift(freq);
const restored = np.fft.ifftshift(shifted);
// restored matches freq exactly

import * as np from 'numpy-ts';

// Generate a signal: 50 Hz sine wave sampled at 1000 Hz
const fs = 1000;           // Sampling frequency
const t = np.arange(0, 1, 1 / fs);  // 1 second of samples
const signal = np.sin(np.multiply(2 * Math.PI * 50, t));

// Compute FFT and corresponding frequencies
const spectrum = np.fft.fft(signal);
const freq = np.fft.fftfreq(signal.size, 1 / fs);

// Center the spectrum for visualization
const centered_spectrum = np.fft.fftshift(spectrum);
const centered_freq = np.fft.fftshift(freq);

// Or use rfft for real signals (more efficient)
const half_spectrum = np.fft.rfft(signal);
const half_freq = np.fft.rfftfreq(signal.size, 1 / fs);