// Copyright (c) 2026 Petr Balvín <opensource@petrbalvin.org> (https://petrbalvin.org)
// SPDX-License-Identifier: MIT

// Command fft demonstrates the Fourier transform: it synthesises a
// signal from two sinusoids, transforms it, and prints the dominant
// frequency components.
//
// Usage: go run ./examples/fft
package main

import (
	"fmt"
	"log"
	"math"

	"sourcedock.dev/petrbalvin/tensor"
	sig "sourcedock.dev/petrbalvin/tensor/signal"
)

func main() {
	// Two sinusoids: 5 Hz and 13 Hz, sampled at 100 Hz for 2 seconds.
	const (
		fs      = 100.0
		seconds = 2.0
	)
	n := int(fs * seconds)
	vals := make([]float64, n)
	for i := range n {
		t := float64(i) / fs
		vals[i] = math.Sin(2*math.Pi*5*t) + 0.5*math.Sin(2*math.Pi*13*t)
	}
	signal, err := tensor.FromFloats(vals, n)
	if err != nil {
		log.Fatal(err)
	}

	spec, err := sig.FFT(signal)
	if err != nil {
		log.Fatal(err)
	}
	freqs := sig.FFTFreq(n, 1/fs)

	// Find the two strongest bins (excluding DC).
	var peaks [2]struct {
		freq float64
		mag  float64
	}
	for i := 1; i < n/2; i++ {
		m, _ := tensor.ComplexAt(spec, i)
		mag := math.Hypot(real(m), imag(m))
		for p := range peaks {
			if mag > peaks[p].mag {
				peaks[p].freq, _ = tensor.FloatAt(freqs, i)
				peaks[p].mag = mag
				break
			}
		}
	}
	for _, p := range peaks {
		fmt.Printf("peak at %.1f Hz (magnitude %.1f)\n", p.freq, p.mag)
	}
}