import Foundation

/// allowed values are 0, 1, 2 or 3. It's the index in coefficients
private var coefficientsRowToUse = 3

/// Savitzky Golay coefficients
private let coefficients = [
    [-3.0, 12.0, 17.0, 12.0, -3.0],
    [-2.0, 3.0, 6.0, 7.0, 6.0, 3.0, -2.0],
    [-21.0, 14.0, 39.0, 54.0, 59.0, 54.0, 39.0, 14.0, -21.0],
    [-36.0, 9.0, 44.0, 69.0, 84.0, 89.0, 84.0, 69.0, 44.0, 9.0, -36.0]
]

/// an array with elements of a type that conforms to Smoothable, can be filtered using  the Savitzky Golay algorithm
protocol SavitzkyGolaySmoothable {
    /// value to be smoothed
    var value: Double { get set }
}

/// local help class
private class IsSmoothable: SavitzkyGolaySmoothable {
    var value: Double = 0.0

    init(withValue value: Double = 0.0) {
        self.value = value
    }
}

extension Array where Element: SavitzkyGolaySmoothable {
    /// - apply Savitzky Golay filter
    /// - before applying the filter, the array will be prepended and append with a number of elements equal to the filterwidth, filterWidth default 5. Allowed values are 5, 4, 3, 2. If any other value is assigned, then 5 will be used
    /// - ...continue with 5 here in the explanation ...
    /// - for the 5 last elements and 5 first elements, a regression is done. This regression is done used to give values to the 5 prepended and appended values. Which means it's as if we draw a line through the first 5 and 5 last original values, and use this line to give values to the 5 prepended and appended values
    /// - the 5 prepended and appended values are then used in the filter algorithm, which means we can also filter the original 5 first and last elements
    /// see also example https://github.com/JohanDegraeve/xdripswift/wiki/Libre-value-smoothing
    mutating func smoothSavitzkyGolayQuaDratic(withFilterWidth filterWidth: Int = 5) {
        // filterWidthToUse is the value of filterWidth to use in the algorithm. By default filterWidthToUse = parameter value filterWidth
        var filterWidthToUse = filterWidth

        // calculate coefficientsRowToUse based on filterWdith
        switch filterWidth {
        case 5:
            coefficientsRowToUse = 3

        case 4:
            coefficientsRowToUse = 2

        case 3:
            coefficientsRowToUse = 1

        case 2:
            coefficientsRowToUse = 0

        default:
            // invalid filterWidth was given in parameterList, use default value
            coefficientsRowToUse = 3

            filterWidthToUse = 5
        }

        // using 5 here in the comments as value for filterWidthToUse

        // the amount of elements must be at least 5. If that's not the case then don't apply any smoothing
        guard count >= filterWidthToUse else { return }

        // create a new array, to which we will prepend and append 5 elements so that we can do also smoothing for the 5 last and 5 first values of the input array (which is self)
        // the 5 elements will be estimated by doing linear regression of the first 5 and last 5 elements of the original input array respectively
        // this is only a temporary array, but it will hold the elements of the original array, those elements will get a new value when doing the smoothing
        var tempArray = [SavitzkyGolaySmoothable]()
        for element in self {
            tempArray.append(element)
        }

        // now prepend and append with 5 elements, each with a default value 0.0
        for _ in 0 ..< filterWidthToUse {
            tempArray.insert(IsSmoothable(), at: 0)
            tempArray.append(IsSmoothable())
        }

        // so now we have tempArray, of length size of original array + 2 * 5
        // the first 5 and the last 5 elements are of type IsSmoothable with value 0

        // - indicesArray is a help array needed for the function linearRegressionCreator
        // - this will be the first parameter in the call to the linearRegression function, in fact it's an array of IsSmoothable with length = length of tempArray
        // - we give each IsSmoothable the value of the index, meaning from 0 up to (length of tempArray) - 1
        // - in fact it's not really smoothable, it's just because we use isSmoothable in function linearRegressionCreator
        var indicesArray = [SavitzkyGolaySmoothable]()
        for index in 0 ..< (count + (filterWidthToUse * 2)) {
            indicesArray.append(IsSmoothable(withValue: Double(index)))
        }

        /// - this is a piece of code that we will execute two times, once for the firs 5 elements, then for the last 5, so we put it in a closure variable
        /// - it calculates the regression function (which is nothing else but doing y = intercept + slope*x) for range defined by predictorRange in tempArray. It will be used for the 5 first and 5 last real values, ie the 5 first and 5 last real glucose values
        /// - then executes the regression for every element in the range defined by targetRange, again in tempArray
        let doRegression = { (predictorRange: Range<Int>, targetRange: Range<Int>) in

            // calculate the linearRegression function
            let linearRegression = linearRegressionCreator(indicesArray[predictorRange], tempArray[predictorRange])

            // ready to do the linear regression for the targetRange in tempArray
            for index in targetRange {
                tempArray[index].value = linearRegression(indicesArray[index].value)
            }
        }

        // now do the regression for the 5 first elements
        doRegression(filterWidthToUse ..< (filterWidthToUse * 2), 0 ..< filterWidthToUse)

        // now do the regression for the 5 last elements
        doRegression(
            (tempArray.count - filterWidthToUse * 2) ..< (tempArray.count - filterWidthToUse),
            (tempArray.count - filterWidthToUse) ..< tempArray.count
        )

        // now start filtering

        // initialize array that will hold the resulting filtered values
        var filteredValues = [Double]()

        // calculate divider
        let divider = coefficients[coefficientsRowToUse].reduce(0, { x, y in
            x + y
        })

        // filter each original value
        for _ in 0 ..< count {
            // add a new element to filteredValues, start value is 0.0
            // this new value will be the last element, so we access it with index filteredValues.count - 1
            filteredValues.append(0.0)

            // iterate through the coefficients
            for (index, coefficient) in coefficients[coefficientsRowToUse].enumerated() {
                filteredValues[filteredValues.count - 1] = filteredValues[filteredValues.count - 1] + coefficient *
                    tempArray[index + filteredValues.count - 1].value
            }

            filteredValues[filteredValues.count - 1] = filteredValues[filteredValues.count - 1] / divider
        }

        // now assign the new values to the original objects
        for (index, _) in enumerated() {
            self[index].value = filteredValues[index]
        }
    }
}

/// source https://github.com/raywenderlich/swift-algorithm-club/tree/master/Linear%20Regression
private func multiply(
    _ a: ArraySlice<SavitzkyGolaySmoothable>,
    _ b: ArraySlice<SavitzkyGolaySmoothable>
) -> ArraySlice<SavitzkyGolaySmoothable> {
    zip(a, b).map({ IsSmoothable(withValue: $0.value * $1.value) })[0 ..< a.count]
}

/// source https://github.com/raywenderlich/swift-algorithm-club/tree/master/Linear%20Regression
private func average(_ input: ArraySlice<SavitzkyGolaySmoothable>) -> Double {
    (input.reduce(IsSmoothable(), { (x: SavitzkyGolaySmoothable, y: SavitzkyGolaySmoothable) in
        IsSmoothable(withValue: x.value + y.value) })).value / Double(input.count)
}

/// source https://github.com/raywenderlich/swift-algorithm-club/tree/master/Linear%20Regression
private func linearRegressionCreator(
    _ xs: ArraySlice<SavitzkyGolaySmoothable>,
    _ ys: ArraySlice<SavitzkyGolaySmoothable>
) -> (Double) -> Double {
    let sum1 = average(multiply(ys, xs)) - average(xs) * average(ys)
    let sum2 = average(multiply(xs, xs)) - pow(average(xs), 2)
    let slope = sum1 / sum2
    let intercept = average(ys) - slope * average(xs)

    return { x in intercept + slope * x }
}