package convolve

import (
	"fmt"
	"log"
	"math"
	"math/cmplx"
	"os"

	"image/png"

	"image/color"

	"path/filepath"

	"github.com/mjibson/go-dsp/fft"
	"gonum.org/v1/gonum/dsp/fourier"
	"gonum.org/v1/plot"
	"gonum.org/v1/plot/font"
	"gonum.org/v1/plot/plotter"
	"gonum.org/v1/plot/vg"
	"gonum.org/v1/plot/vg/draw"
	"gonum.org/v1/plot/vg/vgimg"
)

// nextPowerOfTwo returns the next power of two >= n
func nextPowerOfTwo(n int) int {
	p := 1
	for p < n {
		p <<= 1
	}
	return p
}

// Convolve performs FFT-based convolution of two audio signals
// Deprecated: Use Deconvolve for IR extraction from sweep and recorded signals
func Convolve(signal1, signal2 []float64) []float64 {
	resultLen := len(signal1) + len(signal2) - 1
	fftLen := nextPowerOfTwo(resultLen)

	log.Printf("[convolve] signal1: %d, signal2: %d, resultLen: %d, fftLen: %d", len(signal1), len(signal2), resultLen, fftLen)

	// Zero-pad both signals to fftLen as float64
	x := make([]float64, fftLen)
	copy(x, signal1)
	y := make([]float64, fftLen)
	copy(y, signal2)

	// FFT
	fft := fourier.NewFFT(fftLen)
	xFreq := fft.Coefficients(nil, x) // []complex128
	yFreq := fft.Coefficients(nil, y) // []complex128

	log.Printf("[convolve] xFreq length: %d, yFreq length: %d", len(xFreq), len(yFreq))

	// Multiply in frequency domain
	outFreq := make([]complex128, len(xFreq))
	for i := 0; i < len(xFreq) && i < len(yFreq); i++ {
		outFreq[i] = xFreq[i] * yFreq[i]
	}

	// Inverse FFT (returns []float64)
	outTime := fft.Sequence(nil, outFreq)
	log.Printf("[convolve] outTime length: %d", len(outTime))

	// Defensive: avoid index out of range
	copyLen := resultLen
	if len(outTime) < resultLen {
		log.Printf("[convolve] Warning: outTime length (%d) < resultLen (%d), truncating resultLen", len(outTime), resultLen)
		copyLen = len(outTime)
	}

	result := make([]float64, copyLen)
	copy(result, outTime[:copyLen])

	return result
}

// Deconvolve extracts the impulse response (IR) from a sweep and its recorded version
// by dividing the FFT of the recorded by the FFT of the sweep, with regularization.
func Deconvolve(sweep, recorded []float64) []float64 {
	resultLen := len(recorded)
	fftLen := nextPowerOfTwo(resultLen)

	log.Printf("[deconvolve] sweep: %d, recorded: %d, resultLen: %d, fftLen: %d", len(sweep), len(recorded), resultLen, fftLen)

	// Zero-pad both signals to fftLen
	sweepPadded := make([]float64, fftLen)
	recordedPadded := make([]float64, fftLen)
	copy(sweepPadded, sweep)
	copy(recordedPadded, recorded)

	fft := fourier.NewFFT(fftLen)
	sweepFFT := fft.Coefficients(nil, sweepPadded)
	recordedFFT := fft.Coefficients(nil, recordedPadded)

	log.Printf("[deconvolve] sweepFFT length: %d, recordedFFT length: %d", len(sweepFFT), len(recordedFFT))

	// Regularization epsilon to avoid division by zero
	const epsilon = 1e-10
	minLen := len(sweepFFT)
	if len(recordedFFT) < minLen {
		minLen = len(recordedFFT)
	}
	irFFT := make([]complex128, minLen)
	for i := 0; i < minLen; i++ {
		denom := sweepFFT[i]
		if cmplx.Abs(denom) < epsilon {
			denom = complex(epsilon, 0)
		}
		irFFT[i] = recordedFFT[i] / denom
	}

	irTime := fft.Sequence(nil, irFFT)
	log.Printf("[deconvolve] irTime length: %d", len(irTime))

	// Defensive: avoid index out of range
	copyLen := resultLen
	if len(irTime) < resultLen {
		log.Printf("[deconvolve] Warning: irTime length (%d) < resultLen (%d), truncating resultLen", len(irTime), resultLen)
		copyLen = len(irTime)
	}

	result := make([]float64, copyLen)
	copy(result, irTime[:copyLen])

	return result
}

// Normalize normalizes the audio data to prevent clipping
// targetPeak is the maximum peak value (e.g., 0.95 for 95% of full scale)
func Normalize(data []float64, targetPeak float64) []float64 {
	if len(data) == 0 {
		return data
	}
	// Find the maximum absolute value
	maxVal := 0.0
	for _, sample := range data {
		absVal := math.Abs(sample)
		if absVal > maxVal {
			maxVal = absVal
		}
	}
	if maxVal == 0 {
		return data
	}
	// Calculate normalization factor
	normFactor := targetPeak / maxVal
	// Apply normalization
	normalized := make([]float64, len(data))
	for i, sample := range data {
		normalized[i] = sample * normFactor
	}
	return normalized
}

// TrimSilence removes leading and trailing silence from the audio data
// threshold is the amplitude threshold below which samples are considered silence
func TrimSilence(data []float64, threshold float64) []float64 {
	if len(data) == 0 {
		return data
	}
	// Find start (first non-silent sample)
	start := 0
	for i, sample := range data {
		if math.Abs(sample) > threshold {
			start = i
			break
		}
	}
	// Find end (last non-silent sample)
	end := len(data) - 1
	for i := len(data) - 1; i >= 0; i-- {
		if math.Abs(data[i]) > threshold {
			end = i
			break
		}
	}
	if start >= end {
		return []float64{}
	}
	return data[start : end+1]
}

// TrimOrPad trims or zero-pads the data to the specified number of samples
func TrimOrPad(data []float64, targetSamples int) []float64 {
	if len(data) == targetSamples {
		return data
	} else if len(data) > targetSamples {
		return data[:targetSamples]
	} else {
		out := make([]float64, targetSamples)
		copy(out, data)
		// zero-padding is default
		return out
	}
}

// padOrTruncate ensures a slice is exactly n elements long
func padOrTruncate[T any](in []T, n int) []T {
	if len(in) == n {
		return in
	} else if len(in) > n {
		return in[:n]
	}
	out := make([]T, n)
	copy(out, in)
	return out
}

// Helper to reconstruct full Hermitian spectrum from N/2+1 real FFT
func hermitianSymmetric(fullLen int, halfSpec []complex128) []complex128 {
	full := make([]complex128, fullLen)
	N := fullLen
	// DC
	full[0] = halfSpec[0]
	// Positive freqs
	for k := 1; k < N/2; k++ {
		full[k] = halfSpec[k]
		full[N-k] = cmplx.Conj(halfSpec[k])
	}
	// Nyquist (if even)
	if N%2 == 0 {
		full[N/2] = halfSpec[N/2]
	}
	return full
}

// MinimumPhaseTransform using go-dsp/fft for full complex FFT/IFFT
func MinimumPhaseTransform(ir []float64) []float64 {
	if len(ir) == 0 {
		return ir
	}

	origLen := len(ir)
	fftLen := nextPowerOfTwo(origLen)
	padded := padOrTruncate(ir, fftLen)
	log.Printf("[MPT] fftLen: %d, padded len: %d", fftLen, len(padded))

	// Convert to complex
	signal := make([]complex128, fftLen)
	for i, v := range padded {
		signal[i] = complex(v, 0)
	}

	// FFT
	X := fft.FFT(signal)

	// Log-magnitude spectrum (complex)
	logMag := make([]complex128, fftLen)
	for i, v := range X {
		mag := cmplx.Abs(v)
		if mag < 1e-12 {
			mag = 1e-12
		}
		logMag[i] = complex(math.Log(mag), 0)
	}

	// IFFT to get real cepstrum
	cepstrumC := fft.IFFT(logMag)

	// Minimum phase cepstrum
	minPhaseCep := make([]complex128, fftLen)
	minPhaseCep[0] = cepstrumC[0] // DC
	for i := 1; i < fftLen/2; i++ {
		minPhaseCep[i] = 2 * cepstrumC[i]
	}
	if fftLen%2 == 0 {
		minPhaseCep[fftLen/2] = cepstrumC[fftLen/2] // Nyquist (if even)
	}
	// Negative quefrency: zero (already zero by default)

	// FFT of minimum phase cepstrum
	minPhaseSpec := fft.FFT(minPhaseCep)

	// Exponentiate to get minimum phase spectrum
	for i := range minPhaseSpec {
		minPhaseSpec[i] = cmplx.Exp(minPhaseSpec[i])
	}

	// IFFT to get minimum phase IR
	minPhaseIR := fft.IFFT(minPhaseSpec)

	// Return the real part, original length
	result := make([]float64, origLen)
	for i := range result {
		result[i] = real(minPhaseIR[i])
	}
	return result
}

// realSlice extracts the real part of a []complex128 as []float64
func realSlice(in []complex128) []float64 {
	out := make([]float64, len(in))
	for i, v := range in {
		out[i] = real(v)
	}
	return out
}

// Resample resamples audio data from one sample rate to another using linear interpolation
func Resample(data []float64, fromSampleRate, toSampleRate int) []float64 {
	if fromSampleRate == toSampleRate {
		return data
	}

	// Calculate the resampling ratio
	ratio := float64(toSampleRate) / float64(fromSampleRate)
	newLength := int(float64(len(data)) * ratio)

	if newLength == 0 {
		return []float64{}
	}

	result := make([]float64, newLength)

	for i := 0; i < newLength; i++ {
		// Calculate the position in the original data
		pos := float64(i) / ratio

		// Get the integer and fractional parts
		posInt := int(pos)
		posFrac := pos - float64(posInt)

		// Linear interpolation
		if posInt >= len(data)-1 {
			// Beyond the end of the data, use the last sample
			result[i] = data[len(data)-1]
		} else {
			// Interpolate between two samples
			sample1 := data[posInt]
			sample2 := data[posInt+1]
			result[i] = sample1 + posFrac*(sample2-sample1)
		}
	}

	return result
}

// FadeOutLinear applies a linear fade-out to the last fadeSamples of the data.
// fadeSamples is the number of samples over which to fade to zero.
func FadeOutLinear(data []float64, fadeSamples int) []float64 {
	if fadeSamples <= 0 || len(data) == 0 {
		return data
	}
	if fadeSamples > len(data) {
		fadeSamples = len(data)
	}
	out := make([]float64, len(data))
	copy(out, data)
	start := len(data) - fadeSamples
	for i := start; i < len(data); i++ {
		fade := float64(len(data)-i) / float64(fadeSamples)
		out[i] *= fade
	}
	return out
}

// ApplyLowpassButterworth applies a 2nd-order Butterworth low-pass filter to the data.
// cutoffHz: cutoff frequency in Hz, sampleRate: sample rate in Hz.
func ApplyLowpassButterworth(data []float64, sampleRate int, cutoffHz float64) []float64 {
	if cutoffHz <= 0 || cutoffHz >= float64(sampleRate)/2 {
		return data
	}
	// Biquad coefficients
	w0 := 2 * math.Pi * cutoffHz / float64(sampleRate)
	cosw0 := math.Cos(w0)
	sinw0 := math.Sin(w0)
	Q := 1.0 / math.Sqrt(2) // Butterworth Q
	alpha := sinw0 / (2 * Q)

	b0 := (1 - cosw0) / 2
	b1 := 1 - cosw0
	b2 := (1 - cosw0) / 2
	a0 := 1 + alpha
	a1 := -2 * cosw0
	a2 := 1 - alpha

	// Normalize
	b0 /= a0
	b1 /= a0
	b2 /= a0
	a1 /= a0
	a2 /= a0

	// Apply filter (Direct Form I)
	out := make([]float64, len(data))
	var x1, x2, y1, y2 float64
	for i := 0; i < len(data); i++ {
		x0 := data[i]
		y0 := b0*x0 + b1*x1 + b2*x2 - a1*y1 - a2*y2
		out[i] = y0
		x2 = x1
		x1 = x0
		y2 = y1
		y1 = y0
	}
	return out
}

// ApplyHighpassButterworth applies a 2nd-order Butterworth high-pass filter to the data.
// cutoffHz: cutoff frequency in Hz, sampleRate: sample rate in Hz.
func ApplyHighpassButterworth(data []float64, sampleRate int, cutoffHz float64) []float64 {
	if cutoffHz <= 0 || cutoffHz >= float64(sampleRate)/2 {
		return data
	}
	// Biquad coefficients
	w0 := 2 * math.Pi * cutoffHz / float64(sampleRate)
	cosw0 := math.Cos(w0)
	sinw0 := math.Sin(w0)
	Q := 1.0 / math.Sqrt(2) // Butterworth Q
	alpha := sinw0 / (2 * Q)

	b0 := (1 + cosw0) / 2
	b1 := -(1 + cosw0)
	b2 := (1 + cosw0) / 2
	a0 := 1 + alpha
	a1 := -2 * cosw0
	a2 := 1 - alpha

	// Normalize
	b0 /= a0
	b1 /= a0
	b2 /= a0
	a1 /= a0
	a2 /= a0

	// Apply filter (Direct Form I)
	out := make([]float64, len(data))
	var x1, x2, y1, y2 float64
	for i := 0; i < len(data); i++ {
		x0 := data[i]
		y0 := b0*x0 + b1*x1 + b2*x2 - a1*y1 - a2*y2
		out[i] = y0
		x2 = x1
		x1 = x0
		y2 = y1
		y1 = y0
	}
	return out
}

// CascadeLowcut applies the low-cut (high-pass) filter multiple times for steeper slopes.
// slopeDb: 12, 24, 36, ... (dB/octave)
func CascadeLowcut(data []float64, sampleRate int, cutoffHz float64, slopeDb int) []float64 {
	if slopeDb < 12 {
		slopeDb = 12
	}
	n := slopeDb / 12
	out := data
	for i := 0; i < n; i++ {
		out = ApplyHighpassButterworth(out, sampleRate, cutoffHz)
	}
	return out
}

// CascadeHighcut applies the high-cut (low-pass) filter multiple times for steeper slopes.
// slopeDb: 12, 24, 36, ... (dB/octave)
func CascadeHighcut(data []float64, sampleRate int, cutoffHz float64, slopeDb int) []float64 {
	if slopeDb < 12 {
		slopeDb = 12
	}
	n := slopeDb / 12
	out := data
	for i := 0; i < n; i++ {
		out = ApplyLowpassButterworth(out, sampleRate, cutoffHz)
	}
	return out
}

// PlotIR plots the frequency response (magnitude in dB vs. frequency in Hz) of the IR to ir_plot.png
func PlotIR(ir []float64, sampleRate int, irFileName string) error {
	if len(ir) == 0 {
		return nil
	}
	// Use only the first 8192 samples of the IR for plotting
	windowLen := 8192
	if len(ir) < windowLen {
		windowLen = len(ir)
	}
	irWin := ir[:windowLen]
	X := fft.FFTReal(irWin)
	// Plot from 20 Hz up to 20kHz, include every bin
	var plotPts plotter.XYs
	var minDb float64 = 1e9
	var maxDb float64 = -1e9
	var minDbFreq float64
	freqBins := windowLen / 2
	for i := 1; i < freqBins; i++ {
		freq := float64(i) * float64(sampleRate) / float64(windowLen)
		if freq < 20.0 {
			continue
		}
		if freq > 20000.0 {
			break
		}
		mag := cmplx.Abs(X[i])
		if mag < 1e-12 {
			mag = 1e-12
		}
		db := 20 * math.Log10(mag)
		plotPts = append(plotPts, plotter.XY{X: freq, Y: db})
		if db < minDb {
			minDb = db
			minDbFreq = freq
		}
		if db > maxDb {
			maxDb = db
		}
	}
	fmt.Printf("[PlotIR] minDb in plotted range: %.2f dB at %.2f Hz\n", minDb, minDbFreq)
	p := plot.New()
	p.Title.Text = "IR Frequency Response (dB, 2048-sample window)"
	p.X.Label.Text = "Frequency (Hz)"
	p.Y.Label.Text = "Magnitude (dB)"
	p.X.Scale = plot.LogScale{}
	p.X.Tick.Marker = plot.TickerFunc(func(min, max float64) []plot.Tick {
		ticks := []float64{20, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000}
		labels := []string{"20", "50", "100", "200", "500", "1k", "2k", "5k", "10k", "20k"}
		var result []plot.Tick
		for i, v := range ticks {
			if v >= min && v <= max {
				result = append(result, plot.Tick{Value: v, Label: labels[i]})
			}
		}
		return result
	})
	line, err := plotter.NewLine(plotPts)
	if err != nil {
		return err
	}
	// Set line color to blue
	line.Color = color.RGBA{R: 30, G: 100, B: 220, A: 255}
	p.Add(line)
	// Find minimum dB value between 20 Hz and 50 Hz for y-axis anchor
	minDb2050 := 1e9
	for i := 1; i < freqBins; i++ {
		freq := float64(i) * float64(sampleRate) / float64(windowLen)
		if freq < 20.0 {
			continue
		}
		if freq > 50.0 {
			break
		}
		mag := cmplx.Abs(X[i])
		if mag < 1e-12 {
			mag = 1e-12
		}
		db := 20 * math.Log10(mag)
		if db < minDb2050 {
			minDb2050 = db
		}
	}
	p.Y.Min = minDb2050
	p.Y.Max = math.Ceil(maxDb)
	p.X.Min = 20.0
	p.X.Max = 20000.0

	// --- Time-aligned waveform plot ---
	p2 := plot.New()
	p2.Title.Text = "IR Waveform (Time Aligned)"
	p2.X.Label.Text = "Time (ms)"
	p2.Y.Label.Text = "Amplitude"
	// Prepare waveform data (only first 10ms)
	var pts plotter.XYs
	maxTimeMs := 10.0
	for i := 0; i < windowLen; i++ {
		t := float64(i) * 1000.0 / float64(sampleRate) // ms
		if t > maxTimeMs {
			break
		}
		pts = append(pts, plotter.XY{X: t, Y: irWin[i]})
	}
	wline, err := plotter.NewLine(pts)
	if err != nil {
		return err
	}
	wline.Color = color.RGBA{R: 30, G: 100, B: 220, A: 255}
	p2.Add(wline)
	p2.X.Min = 0
	p2.X.Max = maxTimeMs
	// Y range auto

	// --- Compose both plots vertically ---
	const width = 6 * vg.Inch
	const height = 8 * vg.Inch                // increased height for frequency diagram
	img := vgimg.New(width, height+1*vg.Inch) // extra space for logo/headline
	dc := draw.New(img)

	// Draw logo at the top left, headline to the right, IR filename below
	logoPath := "assets/logo.png"
	logoW := 2.4 * vg.Inch  // doubled size
	logoH := 0.68 * vg.Inch // doubled size
	logoX := 0.3 * vg.Inch
	logoY := height + 0.2*vg.Inch // move logo down by an additional ~10px
	logoDrawn := false
	f, err := os.Open(logoPath)
	if err == nil {
		defer f.Close()
		logoImg, err := png.Decode(f)
		if err == nil {
			rect := vg.Rectangle{
				Min: vg.Point{X: logoX, Y: logoY},
				Max: vg.Point{X: logoX + logoW, Y: logoY + logoH},
			}
			dc.DrawImage(rect, logoImg)
			logoDrawn = true
		}
	}
	// Draw headline (bold, larger) to the right of the logo
	headline := "Valhallir Deconvolver IR Analysis"
	fntSize := vg.Points(14) // Same as IR filename
	if logoDrawn {
		headlineX := logoX + logoW + 0.3*vg.Inch
		headlineY := logoY + logoH - vg.Points(16) - vg.Points(5) // move headline up by ~10px
		boldFont := plot.DefaultFont
		boldFont.Weight = 3 // font.WeightBold is 3 in gonum/plot/font
		boldFace := font.DefaultCache.Lookup(boldFont, fntSize)
		dc.SetColor(color.Black)
		dc.FillString(boldFace, vg.Point{X: headlineX, Y: headlineY}, headline)
		// Draw IR filename below headline, left-aligned, standard font
		fileLabel := "IR-File: " + filepath.Base(irFileName)
		fileY := headlineY - fntSize - vg.Points(6)
		fileFace := font.DefaultCache.Lookup(plot.DefaultFont, vg.Points(10))
		dc.FillString(fileFace, vg.Point{X: headlineX, Y: fileY}, fileLabel)
	}

	// Custom tile arrangement: frequency diagram gets more height, waveform gets less
	tiles := draw.Tiles{
		Rows:   2,
		Cols:   1,
		PadX:   vg.Millimeter,
		PadY:   20 * vg.Millimeter, // more space between plots to emphasize frequency diagram
		PadTop: vg.Points(15),      // move diagrams down by ~20px
	}

	// Offset the plots down by 1 inch to make space for logo/headline
	imgPlots := vgimg.New(width, height)
	dcPlots := draw.New(imgPlots)
	canvases := plot.Align([][]*plot.Plot{{p}, {p2}}, tiles, dcPlots)
	p.Draw(canvases[0][0])
	p2.Draw(canvases[1][0])
	dc.DrawImage(vg.Rectangle{Min: vg.Point{X: 0, Y: 0}, Max: vg.Point{X: width, Y: height}}, imgPlots.Image())

	// Save as PNG
	f, err = os.Create("ir_plot.png")
	if err != nil {
		return err
	}
	defer f.Close()
	_, err = vgimg.PngCanvas{Canvas: img}.WriteTo(f)
	return err
}