// Copyright 2017 Tendermint. All rights reserved.
// Use of this source code is governed by Apache 2 LICENSE that can be found in the LICENSE file.

package trust

import (
	"math"
	"sync"
	"time"

	cmn "github.com/tendermint/tendermint/libs/common"
)

//---------------------------------------------------------------------------------------

const (
	// The weight applied to the derivative when current behavior is >= previous behavior
	defaultDerivativeGamma1 = 0

	// The weight applied to the derivative when current behavior is less than previous behavior
	defaultDerivativeGamma2 = 1.0

	// The weight applied to history data values when calculating the history value
	defaultHistoryDataWeight = 0.8
)

// MetricHistoryJSON - history data necessary to save the trust metric
type MetricHistoryJSON struct {
	NumIntervals int       `json:"intervals"`
	History      []float64 `json:"history"`
}

// Metric - keeps track of peer reliability
// See tendermint/docs/architecture/adr-006-trust-metric.md for details
type Metric struct {
	cmn.BaseService

	// Mutex that protects the metric from concurrent access
	mtx sync.Mutex

	// Determines the percentage given to current behavior
	proportionalWeight float64

	// Determines the percentage given to prior behavior
	integralWeight float64

	// Count of how many time intervals this metric has been tracking
	numIntervals int

	// Size of the time interval window for this trust metric
	maxIntervals int

	// The time duration for a single time interval
	intervalLen time.Duration

	// Stores the trust history data for this metric
	history []float64

	// Weights applied to the history data when calculating the history value
	historyWeights []float64

	// The sum of the history weights used when calculating the history value
	historyWeightSum float64

	// The current number of history data elements
	historySize int

	// The maximum number of history data elements
	historyMaxSize int

	// The calculated history value for the current time interval
	historyValue float64

	// The number of recorded good and bad events for the current time interval
	bad, good float64

	// While true, history data is not modified
	paused bool

	// Used during testing in order to control the passing of time intervals
	testTicker MetricTicker
}

// NewMetric returns a trust metric with the default configuration.
// Use Start to begin tracking the quality of peer behavior over time
func NewMetric() *Metric {
	return NewMetricWithConfig(DefaultConfig())
}

// NewMetricWithConfig returns a trust metric with a custom configuration.
// Use Start to begin tracking the quality of peer behavior over time
func NewMetricWithConfig(tmc MetricConfig) *Metric {
	tm := new(Metric)
	config := customConfig(tmc)

	// Setup using the configuration values
	tm.proportionalWeight = config.ProportionalWeight
	tm.integralWeight = config.IntegralWeight
	tm.intervalLen = config.IntervalLength
	// The maximum number of time intervals is the tracking window / interval length
	tm.maxIntervals = int(config.TrackingWindow / tm.intervalLen)
	// The history size will be determined by the maximum number of time intervals
	tm.historyMaxSize = intervalToHistoryOffset(tm.maxIntervals) + 1
	// This metric has a perfect history so far
	tm.historyValue = 1.0

	tm.BaseService = *cmn.NewBaseService(nil, "Metric", tm)
	return tm
}

// OnStart implements Service
func (tm *Metric) OnStart() error {
	if err := tm.BaseService.OnStart(); err != nil {
		return err
	}
	go tm.processRequests()
	return nil
}

// OnStop implements Service
// Nothing to do since the goroutine shuts down by itself via BaseService.Quit()
func (tm *Metric) OnStop() {}

// Returns a snapshot of the trust metric history data
func (tm *Metric) HistoryJSON() MetricHistoryJSON {
	tm.mtx.Lock()
	defer tm.mtx.Unlock()

	return MetricHistoryJSON{
		NumIntervals: tm.numIntervals,
		History:      tm.history,
	}
}

// Instantiates a trust metric by loading the history data for a single peer.
// This is called only once and only right after creation, which is why the
// lock is not held while accessing the trust metric struct members
func (tm *Metric) Init(hist MetricHistoryJSON) {
	// Restore the number of time intervals we have previously tracked
	if hist.NumIntervals > tm.maxIntervals {
		hist.NumIntervals = tm.maxIntervals
	}
	tm.numIntervals = hist.NumIntervals
	// Restore the history and its current size
	if len(hist.History) > tm.historyMaxSize {
		// Keep the history no larger than historyMaxSize
		last := len(hist.History) - tm.historyMaxSize
		hist.History = hist.History[last:]
	}
	tm.history = hist.History
	tm.historySize = len(tm.history)
	// Create the history weight values and weight sum
	for i := 1; i <= tm.numIntervals; i++ {
		x := math.Pow(defaultHistoryDataWeight, float64(i)) // Optimistic weight
		tm.historyWeights = append(tm.historyWeights, x)
	}

	for _, v := range tm.historyWeights {
		tm.historyWeightSum += v
	}
	// Calculate the history value based on the loaded history data
	tm.historyValue = tm.calcHistoryValue()
}

// Pause tells the metric to pause recording data over time intervals.
// All method calls that indicate events will unpause the metric
func (tm *Metric) Pause() {
	tm.mtx.Lock()
	defer tm.mtx.Unlock()

	// Pause the metric for now
	tm.paused = true
}

// BadEvents indicates that an undesirable event(s) took place
func (tm *Metric) BadEvents(num int) {
	tm.mtx.Lock()
	defer tm.mtx.Unlock()

	tm.unpause()
	tm.bad += float64(num)
}

// GoodEvents indicates that a desirable event(s) took place
func (tm *Metric) GoodEvents(num int) {
	tm.mtx.Lock()
	defer tm.mtx.Unlock()

	tm.unpause()
	tm.good += float64(num)
}

// TrustValue gets the dependable trust value; always between 0 and 1
func (tm *Metric) TrustValue() float64 {
	tm.mtx.Lock()
	defer tm.mtx.Unlock()

	return tm.calcTrustValue()
}

// TrustScore gets a score based on the trust value always between 0 and 100
func (tm *Metric) TrustScore() int {
	score := tm.TrustValue() * 100

	return int(math.Floor(score))
}

// NextTimeInterval saves current time interval data and prepares for the following interval
func (tm *Metric) NextTimeInterval() {
	tm.mtx.Lock()
	defer tm.mtx.Unlock()

	if tm.paused {
		// Do not prepare for the next time interval while paused
		return
	}

	// Add the current trust value to the history data
	newHist := tm.calcTrustValue()
	tm.history = append(tm.history, newHist)

	// Update history and interval counters
	if tm.historySize < tm.historyMaxSize {
		tm.historySize++
	} else {
		// Keep the history no larger than historyMaxSize
		last := len(tm.history) - tm.historyMaxSize
		tm.history = tm.history[last:]
	}

	if tm.numIntervals < tm.maxIntervals {
		tm.numIntervals++
		// Add the optimistic weight for the new time interval
		wk := math.Pow(defaultHistoryDataWeight, float64(tm.numIntervals))
		tm.historyWeights = append(tm.historyWeights, wk)
		tm.historyWeightSum += wk
	}

	// Update the history data using Faded Memories
	tm.updateFadedMemory()
	// Calculate the history value for the upcoming time interval
	tm.historyValue = tm.calcHistoryValue()
	tm.good = 0
	tm.bad = 0
}

// SetTicker allows a TestTicker to be provided that will manually control
// the passing of time from the perspective of the Metric.
// The ticker must be set before Start is called on the metric
func (tm *Metric) SetTicker(ticker MetricTicker) {
	tm.mtx.Lock()
	defer tm.mtx.Unlock()

	tm.testTicker = ticker
}

// Copy returns a new trust metric with members containing the same values
func (tm *Metric) Copy() *Metric {
	if tm == nil {
		return nil
	}

	tm.mtx.Lock()
	defer tm.mtx.Unlock()

	return &Metric{
		proportionalWeight: tm.proportionalWeight,
		integralWeight:     tm.integralWeight,
		numIntervals:       tm.numIntervals,
		maxIntervals:       tm.maxIntervals,
		intervalLen:        tm.intervalLen,
		history:            tm.history,
		historyWeights:     tm.historyWeights,
		historyWeightSum:   tm.historyWeightSum,
		historySize:        tm.historySize,
		historyMaxSize:     tm.historyMaxSize,
		historyValue:       tm.historyValue,
		good:               tm.good,
		bad:                tm.bad,
		paused:             tm.paused,
	}

}

/* Private methods */

// This method is for a goroutine that handles all requests on the metric
func (tm *Metric) processRequests() {
	t := tm.testTicker
	if t == nil {
		// No test ticker was provided, so we create a normal ticker
		t = NewTicker(tm.intervalLen)
	}
	defer t.Stop()
	// Obtain the raw channel
	tick := t.GetChannel()
loop:
	for {
		select {
		case <-tick:
			tm.NextTimeInterval()
		case <-tm.Quit():
			// Stop all further tracking for this metric
			break loop
		}
	}
}

// Wakes the trust metric up if it is currently paused
// This method needs to be called with the mutex locked
func (tm *Metric) unpause() {
	// Check if this is the first experience with
	// what we are tracking since being paused
	if tm.paused {
		tm.good = 0
		tm.bad = 0
		// New events cause us to unpause the metric
		tm.paused = false
	}
}

// Calculates the trust value for the request processing
func (tm *Metric) calcTrustValue() float64 {
	weightedP := tm.proportionalWeight * tm.proportionalValue()
	weightedI := tm.integralWeight * tm.historyValue
	weightedD := tm.weightedDerivative()

	tv := weightedP + weightedI + weightedD
	// Do not return a negative value.
	if tv < 0 {
		tv = 0
	}
	return tv
}

// Calculates the current score for good/bad experiences
func (tm *Metric) proportionalValue() float64 {
	value := 1.0

	total := tm.good + tm.bad
	if total > 0 {
		value = tm.good / total
	}
	return value
}

// Strengthens the derivative component when the change is negative
func (tm *Metric) weightedDerivative() float64 {
	var weight float64 = defaultDerivativeGamma1

	d := tm.derivativeValue()
	if d < 0 {
		weight = defaultDerivativeGamma2
	}
	return weight * d
}

// Calculates the derivative component
func (tm *Metric) derivativeValue() float64 {
	return tm.proportionalValue() - tm.historyValue
}

// Calculates the integral (history) component of the trust value
func (tm *Metric) calcHistoryValue() float64 {
	var hv float64

	for i := 0; i < tm.numIntervals; i++ {
		hv += tm.fadedMemoryValue(i) * tm.historyWeights[i]
	}

	return hv / tm.historyWeightSum
}

// Retrieves the actual history data value that represents the requested time interval
func (tm *Metric) fadedMemoryValue(interval int) float64 {
	first := tm.historySize - 1

	if interval == 0 {
		// Base case
		return tm.history[first]
	}

	offset := intervalToHistoryOffset(interval)
	return tm.history[first-offset]
}

// Performs the update for our Faded Memories process, which allows the
// trust metric tracking window to be large while maintaining a small
// number of history data values
func (tm *Metric) updateFadedMemory() {
	if tm.historySize < 2 {
		return
	}

	end := tm.historySize - 1
	// Keep the most recent history element
	for count := 1; count < tm.historySize; count++ {
		i := end - count
		// The older the data is, the more we spread it out
		x := math.Pow(2, float64(count))
		// Two history data values are merged into a single value
		tm.history[i] = ((tm.history[i] * (x - 1)) + tm.history[i+1]) / x
	}
}

// Map the interval value down to an offset from the beginning of history
func intervalToHistoryOffset(interval int) int {
	// The system maintains 2^m interval values in the form of m history
	// data values. Therefore, we access the ith interval by obtaining
	// the history data index = the floor of log2(i)
	return int(math.Floor(math.Log2(float64(interval))))
}