package detector

import (
	"math"
	"sort"
	"time"

	"sdr-visual-suite/internal/cfar"
	"sdr-visual-suite/internal/classifier"
	"sdr-visual-suite/internal/config"
)

type Event struct {
	ID        int64                      `json:"id"`
	Start     time.Time                  `json:"start"`
	End       time.Time                  `json:"end"`
	CenterHz  float64                    `json:"center_hz"`
	Bandwidth float64                    `json:"bandwidth_hz"`
	PeakDb    float64                    `json:"peak_db"`
	SNRDb     float64                    `json:"snr_db"`
	FirstBin  int                        `json:"first_bin"`
	LastBin   int                        `json:"last_bin"`
	Class     *classifier.Classification `json:"class,omitempty"`
}

type Detector struct {
	ThresholdDb     float64
	MinDuration     time.Duration
	Hold            time.Duration
	EmaAlpha        float64
	HysteresisDb    float64
	MinStableFrames int
	GapTolerance    time.Duration
	CFARScaleDb     float64
	EdgeMarginDb    float64
	MaxSignalBwHz   float64
	MergeGapHz      float64
	classHistorySize int
	classSwitchRatio float64
	binWidth        float64
	nbins           int
	sampleRate      int

	ema            []float64
	active         map[int64]*activeEvent
	nextID         int64
	cfarEngine     cfar.CFAR
	lastThresholds []float64
	lastNoiseFloor float64
}

type activeEvent struct {
	id         int64
	start      time.Time
	lastSeen   time.Time
	centerHz   float64
	bwHz       float64
	peakDb     float64
	snrDb      float64
	firstBin   int
	lastBin    int
	class        *classifier.Classification
	stableHits   int
	classHistory []classifier.SignalClass
	classIdx     int
}

type Signal struct {
	FirstBin int                        `json:"first_bin"`
	LastBin  int                        `json:"last_bin"`
	CenterHz float64                    `json:"center_hz"`
	BWHz     float64                    `json:"bw_hz"`
	PeakDb   float64                    `json:"peak_db"`
	SNRDb    float64                    `json:"snr_db"`
	NoiseDb  float64                    `json:"noise_db,omitempty"`
	Class    *classifier.Classification `json:"class,omitempty"`
}

func New(detCfg config.DetectorConfig, sampleRate int, fftSize int) *Detector {
	minDur := time.Duration(detCfg.MinDurationMs) * time.Millisecond
	hold := time.Duration(detCfg.HoldMs) * time.Millisecond
	gapTolerance := time.Duration(detCfg.GapToleranceMs) * time.Millisecond
	emaAlpha := detCfg.EmaAlpha
	hysteresis := detCfg.HysteresisDb
	minStable := detCfg.MinStableFrames
	cfarMode := detCfg.CFARMode
	binWidth := float64(sampleRate) / float64(fftSize)
	cfarGuard := int(math.Ceil(detCfg.CFARGuardHz / binWidth))
	if cfarGuard < 0 {
		cfarGuard = 0
	}
	cfarTrain := int(math.Ceil(detCfg.CFARTrainHz / binWidth))
	if cfarTrain < 1 {
		cfarTrain = 1
	}
	cfarRank := detCfg.CFARRank
	cfarScaleDb := detCfg.CFARScaleDb
	cfarWrap := detCfg.CFARWrapAround
	thresholdDb := detCfg.ThresholdDb
	edgeMarginDb := detCfg.EdgeMarginDb
	maxSignalBwHz := detCfg.MaxSignalBwHz
	mergeGapHz := detCfg.MergeGapHz
	classHistorySize := detCfg.ClassHistorySize
	classSwitchRatio := detCfg.ClassSwitchRatio

	if minDur <= 0 {
		minDur = 250 * time.Millisecond
	}
	if hold <= 0 {
		hold = 500 * time.Millisecond
	}
	if emaAlpha <= 0 || emaAlpha > 1 {
		emaAlpha = 0.2
	}
	if hysteresis <= 0 {
		hysteresis = 3
	}
	if minStable <= 0 {
		minStable = 3
	}
	if gapTolerance <= 0 {
		gapTolerance = hold
	}
	if cfarScaleDb <= 0 {
		cfarScaleDb = 6
	}
	if edgeMarginDb <= 0 {
		edgeMarginDb = 3.0
	}
	if maxSignalBwHz <= 0 {
		maxSignalBwHz = 150000
	}
	if mergeGapHz <= 0 {
		mergeGapHz = 5000
	}
	if classHistorySize <= 0 {
		classHistorySize = 10
	}
	if classSwitchRatio <= 0 || classSwitchRatio > 1 {
		classSwitchRatio = 0.6
	}
	if cfarRank <= 0 || cfarRank > 2*cfarTrain {
		cfarRank = int(math.Round(0.75 * float64(2*cfarTrain)))
		if cfarRank <= 0 {
			cfarRank = 1
		}
	}
	var cfarEngine cfar.CFAR
	if cfarMode != "" && cfarMode != "OFF" {
		cfarEngine = cfar.New(cfar.Config{
			Mode:       cfar.Mode(cfarMode),
			GuardCells: cfarGuard,
			TrainCells: cfarTrain,
			Rank:       cfarRank,
			ScaleDb:    cfarScaleDb,
			WrapAround: cfarWrap,
		})
	}
	return &Detector{
		ThresholdDb:     thresholdDb,
		MinDuration:     minDur,
		Hold:            hold,
		EmaAlpha:        emaAlpha,
		HysteresisDb:    hysteresis,
		MinStableFrames: minStable,
		GapTolerance:    gapTolerance,
		CFARScaleDb:     cfarScaleDb,
		EdgeMarginDb:    edgeMarginDb,
		MaxSignalBwHz:   maxSignalBwHz,
		MergeGapHz:      mergeGapHz,
		classHistorySize: classHistorySize,
		classSwitchRatio: classSwitchRatio,
		binWidth:        float64(sampleRate) / float64(fftSize),
		nbins:           fftSize,
		sampleRate:      sampleRate,
		ema:             make([]float64, fftSize),
		active:          map[int64]*activeEvent{},
		nextID:          1,
		cfarEngine:      cfarEngine,
	}
}

func (d *Detector) Process(now time.Time, spectrum []float64, centerHz float64) ([]Event, []Signal) {
	signals := d.detectSignals(spectrum, centerHz)
	finished := d.matchSignals(now, signals)
	return finished, signals
}

func (d *Detector) LastThresholds() []float64 {
	if len(d.lastThresholds) == 0 {
		return nil
	}
	return append([]float64(nil), d.lastThresholds...)
}

func (d *Detector) LastNoiseFloor() float64 {
	return d.lastNoiseFloor
}

func (ev *activeEvent) updateClass(newCls *classifier.Classification, historySize int, switchRatio float64) {
	if newCls == nil {
		return
	}
	if historySize <= 0 {
		historySize = 10
	}
	if switchRatio <= 0 || switchRatio > 1 {
		switchRatio = 0.6
	}
	if len(ev.classHistory) != historySize {
		ev.classHistory = make([]classifier.SignalClass, historySize)
		ev.classIdx = 0
	}
	ev.classHistory[ev.classIdx%len(ev.classHistory)] = newCls.ModType
	ev.classIdx++
	if ev.class == nil {
		clone := *newCls
		ev.class = &clone
		return
	}
	counts := map[classifier.SignalClass]int{}
	filled := ev.classIdx
	if filled > len(ev.classHistory) {
		filled = len(ev.classHistory)
	}
	for i := 0; i < filled; i++ {
		c := ev.classHistory[i]
		if c != "" {
			counts[c]++
		}
	}
	var majority classifier.SignalClass
	majorityCount := 0
	for c, n := range counts {
		if n > majorityCount {
			majority = c
			majorityCount = n
		}
	}
	threshold := int(math.Ceil(float64(filled) * switchRatio))
	if threshold < 1 {
		threshold = 1
	}
	if majorityCount >= threshold && majority != ev.class.ModType {
		clone := *newCls
		clone.ModType = majority
		ev.class = &clone
	} else if majority == ev.class.ModType && newCls.Confidence > ev.class.Confidence {
		ev.class.Confidence = newCls.Confidence
		ev.class.Features = newCls.Features
		ev.class.SecondBest = newCls.SecondBest
		ev.class.Scores = newCls.Scores
	}
}

func (d *Detector) UpdateClasses(signals []Signal) {
	for _, s := range signals {
		for _, ev := range d.active {
			if overlapHz(s.CenterHz, s.BWHz, ev.centerHz, ev.bwHz) && math.Abs(s.CenterHz-ev.centerHz) < (s.BWHz+ev.bwHz)/2.0 {
				if s.Class != nil {
					ev.updateClass(s.Class, d.classHistorySize, d.classSwitchRatio)
				}
			}
		}
	}
}

func (d *Detector) detectSignals(spectrum []float64, centerHz float64) []Signal {
	n := len(spectrum)
	if n == 0 {
		return nil
	}
	smooth := d.smoothSpectrum(spectrum)
	var thresholds []float64
	if d.cfarEngine != nil {
		thresholds = d.cfarEngine.Thresholds(smooth)
	}
	d.lastThresholds = append(d.lastThresholds[:0], thresholds...)
	noiseGlobal := median(smooth)
	d.lastNoiseFloor = noiseGlobal
	var signals []Signal
	in := false
	start := 0
	peak := -1e9
	peakBin := 0
	for i := 0; i < n; i++ {
		v := smooth[i]
		thresholdOn := d.ThresholdDb
		if thresholds != nil && !math.IsNaN(thresholds[i]) {
			thresholdOn = thresholds[i]
		}
		thresholdOff := thresholdOn - d.HysteresisDb
		if v >= thresholdOn {
			if !in {
				in = true
				start = i
				peak = v
				peakBin = i
			} else if v > peak {
				peak = v
				peakBin = i
			}
		} else if in && v < thresholdOff {
			noise := noiseGlobal
			if thresholds != nil && peakBin >= 0 && peakBin < len(thresholds) && !math.IsNaN(thresholds[peakBin]) {
				noise = thresholds[peakBin] - d.CFARScaleDb
			}
			signals = append(signals, d.makeSignal(start, i-1, peak, peakBin, noise, centerHz, smooth))
			in = false
		}
	}
	if in {
		noise := noiseGlobal
		if thresholds != nil && peakBin >= 0 && peakBin < len(thresholds) && !math.IsNaN(thresholds[peakBin]) {
			noise = thresholds[peakBin] - d.CFARScaleDb
		}
		signals = append(signals, d.makeSignal(start, n-1, peak, peakBin, noise, centerHz, smooth))
	}
	signals = d.expandSignalEdges(signals, smooth, noiseGlobal, centerHz)
	for i := range signals {
		centerBin := float64(signals[i].FirstBin+signals[i].LastBin) / 2.0
		signals[i].CenterHz = d.centerFreqForBin(centerBin, centerHz)
		signals[i].BWHz = float64(signals[i].LastBin-signals[i].FirstBin+1) * d.binWidth
	}
	return signals
}

func (d *Detector) expandSignalEdges(signals []Signal, smooth []float64, noiseFloor float64, centerHz float64) []Signal {
	n := len(smooth)
	if n == 0 || len(signals) == 0 {
		return signals
	}
	margin := d.EdgeMarginDb
	if margin <= 0 {
		margin = 3.0
	}
	maxExpansionBins := int(d.MaxSignalBwHz / d.binWidth)
	if maxExpansionBins < 10 {
		maxExpansionBins = 10
	}
	for i := range signals {
		seed := signals[i]
		peakDb := seed.PeakDb
		localNoise := noiseFloor
		leftProbe := seed.FirstBin - 50
		rightProbe := seed.LastBin + 50
		if leftProbe >= 0 && rightProbe < n {
			leftNoise := minInRange(smooth, maxInt(0, leftProbe), maxInt(0, seed.FirstBin-5))
			rightNoise := minInRange(smooth, minInt(n-1, seed.LastBin+5), minInt(n-1, rightProbe))
			localNoise = math.Min(leftNoise, rightNoise)
		}
		edgeThreshold := localNoise + margin
		newFirst := seed.FirstBin
		prevVal := smooth[newFirst]
		for j := 0; j < maxExpansionBins; j++ {
			next := newFirst - 1
			if next < 0 {
				break
			}
			val := smooth[next]
			if val <= edgeThreshold {
				break
			}
			if val > prevVal+1.0 && val < peakDb-6.0 {
				break
			}
			prevVal = val
			newFirst = next
		}
		newLast := seed.LastBin
		prevVal = smooth[newLast]
		for j := 0; j < maxExpansionBins; j++ {
			next := newLast + 1
			if next >= n {
				break
			}
			val := smooth[next]
			if val <= edgeThreshold {
				break
			}
			if val > prevVal+1.0 && val < peakDb-6.0 {
				break
			}
			prevVal = val
			newLast = next
		}
		signals[i].FirstBin = newFirst
		signals[i].LastBin = newLast
		centerBin := float64(newFirst+newLast) / 2.0
		signals[i].CenterHz = d.centerFreqForBin(centerBin, centerHz)
		signals[i].BWHz = float64(newLast-newFirst+1) * d.binWidth
	}
	signals = d.mergeOverlapping(signals, centerHz)
	return signals
}

func (d *Detector) mergeOverlapping(signals []Signal, centerHz float64) []Signal {
	if len(signals) <= 1 {
		return signals
	}
	gapBins := 0
	if d.MergeGapHz > 0 && d.binWidth > 0 {
		gapBins = int(math.Ceil(d.MergeGapHz / d.binWidth))
	}
	sort.Slice(signals, func(i, j int) bool {
		return signals[i].FirstBin < signals[j].FirstBin
	})
	merged := []Signal{signals[0]}
	for i := 1; i < len(signals); i++ {
		last := &merged[len(merged)-1]
		cur := signals[i]
		gap := cur.FirstBin - last.LastBin - 1
		if gap <= gapBins {
			if cur.LastBin > last.LastBin {
				last.LastBin = cur.LastBin
			}
			if cur.PeakDb > last.PeakDb {
				last.PeakDb = cur.PeakDb
			}
			if cur.SNRDb > last.SNRDb {
				last.SNRDb = cur.SNRDb
			}
			centerBin := float64(last.FirstBin+last.LastBin) / 2.0
			last.BWHz = float64(last.LastBin-last.FirstBin+1) * d.binWidth
			last.CenterHz = d.centerFreqForBin(centerBin, centerHz)
			if cur.NoiseDb < last.NoiseDb || last.NoiseDb == 0 {
				last.NoiseDb = cur.NoiseDb
			}
		} else {
			merged = append(merged, cur)
		}
	}
	return merged
}

func (d *Detector) centerFreqForBin(bin float64, centerHz float64) float64 {
	return centerHz + (bin-float64(d.nbins)/2.0)*d.binWidth
}

func minInRange(s []float64, from, to int) float64 {
	if len(s) == 0 {
		return 0
	}
	if from < 0 {
		from = 0
	}
	if to >= len(s) {
		to = len(s) - 1
	}
	if from > to {
		return s[minInt(maxInt(from, 0), len(s)-1)]
	}
	m := s[from]
	for i := from + 1; i <= to; i++ {
		if s[i] < m {
			m = s[i]
		}
	}
	return m
}

func maxInt(a, b int) int {
	if a > b {
		return a
	}
	return b
}

func minInt(a, b int) int {
	if a < b {
		return a
	}
	return b
}

func overlapHz(center1, bw1, center2, bw2 float64) bool {
	left1 := center1 - bw1/2
	right1 := center1 + bw1/2
	left2 := center2 - bw2/2
	right2 := center2 + bw2/2
	return left1 <= right2 && left2 <= right1
}

func median(vals []float64) float64 {
	if len(vals) == 0 {
		return 0
	}
	cp := append([]float64(nil), vals...)
	sort.Float64s(cp)
	mid := len(cp) / 2
	if len(cp)%2 == 0 {
		return (cp[mid-1] + cp[mid]) / 2
	}
	return cp[mid]
}

func (d *Detector) makeSignal(first, last int, peak float64, peakBin int, noise float64, centerHz float64, spectrum []float64) Signal {
	centerBin := float64(first+last) / 2.0
	centerFreq := centerHz + (centerBin-float64(d.nbins)/2.0)*d.binWidth
	bw := float64(last-first+1) * d.binWidth
	snr := peak - noise
	return Signal{
		FirstBin: first,
		LastBin:  last,
		CenterHz: centerFreq,
		BWHz:     bw,
		PeakDb:   peak,
		SNRDb:    snr,
		NoiseDb:  noise,
	}
}

func (d *Detector) smoothSpectrum(spectrum []float64) []float64 {
	if d.ema == nil || len(d.ema) != len(spectrum) {
		d.ema = make([]float64, len(spectrum))
		copy(d.ema, spectrum)
		return d.ema
	}
	alpha := d.EmaAlpha
	for i := range spectrum {
		v := spectrum[i]
		d.ema[i] = alpha*v + (1-alpha)*d.ema[i]
	}
	return d.ema
}

func (d *Detector) matchSignals(now time.Time, signals []Signal) []Event {
	used := make(map[int64]bool, len(d.active))
	for _, s := range signals {
		var best *activeEvent
		var candidates []struct {
			ev   *activeEvent
			dist float64
		}
		for _, ev := range d.active {
			if overlapHz(s.CenterHz, s.BWHz, ev.centerHz, ev.bwHz) && math.Abs(s.CenterHz-ev.centerHz) < (s.BWHz+ev.bwHz)/2.0 {
				candidates = append(candidates, struct {
					ev   *activeEvent
					dist float64
				}{ev: ev, dist: math.Abs(s.CenterHz - ev.centerHz)})
			}
		}
		if len(candidates) > 0 {
			sort.Slice(candidates, func(i, j int) bool { return candidates[i].dist < candidates[j].dist })
			best = candidates[0].ev
		}
		if best == nil {
			id := d.nextID
			d.nextID++
			d.active[id] = &activeEvent{
				id:         id,
				start:      now,
				lastSeen:   now,
				centerHz:   s.CenterHz,
				bwHz:       s.BWHz,
				peakDb:     s.PeakDb,
				snrDb:      s.SNRDb,
				firstBin:   s.FirstBin,
				lastBin:    s.LastBin,
				class:      s.Class,
				stableHits: 1,
			}
			continue
		}
		used[best.id] = true
		best.lastSeen = now
		best.stableHits++
		best.centerHz = (best.centerHz + s.CenterHz) / 2.0
		if best.bwHz <= 0 {
			best.bwHz = s.BWHz
		} else {
			const alpha = 0.15
			best.bwHz = alpha*s.BWHz + (1-alpha)*best.bwHz
		}
		if s.PeakDb > best.peakDb {
			best.peakDb = s.PeakDb
		}
		if s.SNRDb > best.snrDb {
			best.snrDb = s.SNRDb
		}
		if s.FirstBin < best.firstBin {
			best.firstBin = s.FirstBin
		}
		if s.LastBin > best.lastBin {
			best.lastBin = s.LastBin
		}
		if s.Class != nil {
			best.updateClass(s.Class, d.classHistorySize, d.classSwitchRatio)
		}
	}

	var finished []Event
	for id, ev := range d.active {
		if used[id] {
			continue
		}
		if now.Sub(ev.lastSeen) < d.GapTolerance {
			continue
		}
		duration := ev.lastSeen.Sub(ev.start)
		if duration < d.MinDuration || ev.stableHits < d.MinStableFrames {
			delete(d.active, id)
			continue
		}
		finished = append(finished, Event{
			ID:        ev.id,
			Start:     ev.start,
			End:       ev.lastSeen,
			CenterHz:  ev.centerHz,
			Bandwidth: ev.bwHz,
			PeakDb:    ev.peakDb,
			SNRDb:     ev.snrDb,
			FirstBin:  ev.firstBin,
			LastBin:   ev.lastBin,
			Class:     ev.class,
		})
		delete(d.active, id)
	}
	return finished
}