{"version":3,"file":"ml-gsd.umd.min.js","sources":["../node_modules/ml-savitzky-golay-generalized/node_modules/is-any-array/lib/index.js","../node_modules/ml-savitzky-golay-generalized/lib/index.js","../node_modules/ml-spectra-processing/node_modules/is-any-array/lib/index.js","../node_modules/ml-spectra-processing/lib/x/xMedian.js","../node_modules/ml-spectra-processing/lib/x/xCheck.js","../node_modules/ml-spectra-processing/lib/x/xFindClosestIndex.js","../node_modules/ml-spectra-processing/lib/x/xGetFromToIndex.js","../node_modules/ml-matrix/matrix.js","../node_modules/ml-matrix/matrix.mjs","../node_modules/ml-spectra-processing/lib/x/xMean.js","../node_modules/ml-spectra-processing/lib/x/xMaxValue.js","../node_modules/ml-spectra-processing/lib/x/xMinValue.js","../node_modules/ml-spectra-processing/lib/x/xMaxAbsoluteValue.js","../node_modules/ml-spectra-processing/lib/x/xNoiseStandardDeviation.js","../node_modules/ml-spectra-processing/lib/x/xMedianAbsoluteDeviation.js","../node_modules/ml-spectra-processing/lib/x/xNorm.js","../lib/algorithms/getMinMaxIntervals.js","../lib/algorithms/tryMatchOneIntervalWithMinData.js","../lib/algorithms/getPeaksFromIntervals.js","../lib/algorithms/xGetCrossZeroPoints.js","../lib/algorithms/firstDerivative.js","../node_modules/ml-peak-shape-generator/lib/util/constants.js","../node_modules/ml-peak-shape-generator/lib/shapes/1d/gaussian/Gaussian.js","../node_modules/ml-peak-shape-generator/lib/util/erfinv.js","../node_modules/ml-peak-shape-generator/lib/shapes/1d/lorentzian/Lorentzian.js","../node_modules/ml-peak-shape-generator/lib/shapes/1d/lorentzianDispersive/LorentzianDispersive.js","../node_modules/ml-peak-shape-generator/lib/shapes/1d/pseudoVoigt/computeFactor.js","../node_modules/ml-peak-shape-generator/lib/shapes/1d/pseudoVoigt/PseudoVoigt.js","../node_modules/ml-peak-shape-generator/lib/shapes/1d/pseudoVoigtTCH/PseudoVoigtTCH.js","../node_modules/ml-peak-shape-generator/lib/shapes/1d/generalizedLorentzian/GeneralizedLorentzian.js","../node_modules/ml-peak-shape-generator/lib/shapes/1d/splitGaussian/SplitGaussian.js","../node_modules/ml-peak-shape-generator/lib/shapes/1d/getShape1D.js","../node_modules/ml-spectra-fitting/lib/util/assert.js","../node_modules/ml-spectra-fitting/lib/util/buildOptimizationLayout.js","../node_modules/ml-spectra-fitting/lib/util/reconstructPeaks.js","../node_modules/ml-spectra-fitting/lib/util/internalPeaks/DefaultParameters.js","../node_modules/ml-spectra-fitting/lib/util/internalPeaks/getInternalPeaks.js","../node_modules/ml-levenberg-marquardt/node_modules/is-any-array/lib/index.js","../node_modules/ml-levenberg-marquardt/lib/check_options.js","../node_modules/ml-levenberg-marquardt/lib/error_calculation.js","../node_modules/ml-levenberg-marquardt/lib/step.js","../node_modules/ml-levenberg-marquardt/lib/gradient_function.js","../node_modules/ml-levenberg-marquardt/lib/levenberg_marquardt.js","../node_modules/ml-direct/src/util/antiLowerConvexHull.js","../node_modules/ml-direct/src/index.js","../node_modules/ml-spectra-fitting/lib/util/wrappers/directOptimization.js","../node_modules/ml-spectra-fitting/lib/index.js","../node_modules/ml-spectra-fitting/lib/util/selectMethod.js","../node_modules/ml-spectra-fitting/lib/shapes/getSumOfShapes.js","../node_modules/ml-spectra-fitting/lib/util/getFixedParametersResult.js","../lib/utils/addMissingShape.js","../lib/utils/splitGroup.js","../lib/utils/groupPeaks.js","../lib/post/optimizePeaksWithLogs.js","../lib/utils/addMissingIDs.js","../lib/post/joinBroadPeaks.js","../lib/post/broadenPeaks.js","../lib/gsd.js","../node_modules/ml-spectra-processing/lib/x/xIsMonotonic.js","../node_modules/ml-spectra-processing/lib/x/xIsEquallySpaced.js","../node_modules/ml-spectra-processing/lib/x/xMinMaxValues.js","../lib/algorithms/secondDerivative.js","../lib/algorithms/autoAlgorithm.js","../lib/utils/optimizeTop.js","../lib/post/optimizePeaks.js","../lib/utils/setShape.js"],"sourcesContent":["// eslint-disable-next-line @typescript-eslint/unbound-method\nconst toString = Object.prototype.toString;\n/**\n * Checks if an object is an instance of an Array (array or typed array, except those that contain bigint values).\n * @param value - Object to check.\n * @returns True if the object is an array or a typed array.\n */\nexport function isAnyArray(value) {\n    const tag = toString.call(value);\n    return tag.endsWith('Array]') && !tag.includes('Big');\n}\n//# sourceMappingURL=index.js.map","import { isAnyArray } from 'is-any-array';\n/**\n * Apply Savitzky Golay algorithm.\n * @param ys - Array of y values.\n * @param xs - Array of X or deltaX.\n * @param options - Options controlling window size, derivative and polynomial order.\n * @returns Array containing the new ys (same length).\n */\nexport function sgg(ys, xs, options = {}) {\n    const { windowSize = 9, derivative = 0, polynomial = 3 } = options;\n    if (windowSize % 2 === 0 || windowSize < 5 || !Number.isInteger(windowSize)) {\n        throw new RangeError('Invalid window size (should be odd and at least 5 integer number)');\n    }\n    if (!isAnyArray(ys)) {\n        throw new TypeError('Y values must be an array');\n    }\n    if (xs === undefined) {\n        throw new TypeError('X must be defined');\n    }\n    if (windowSize > ys.length) {\n        throw new RangeError(`Window size is higher than the data length ${windowSize}>${ys.length}`);\n    }\n    if (derivative < 0 || !Number.isInteger(derivative)) {\n        throw new RangeError('Derivative should be a positive integer');\n    }\n    if (polynomial < 1 || !Number.isInteger(polynomial)) {\n        throw new RangeError('Polynomial should be a positive integer');\n    }\n    if (polynomial >= 6) {\n        // eslint-disable-next-line no-console\n        console.warn('You should not use polynomial grade higher than 5 if you are' +\n            ' not sure that your data arises from such a model. Possible polynomial oscillation problems');\n    }\n    const half = Math.floor(windowSize / 2);\n    const np = ys.length;\n    const ans = new Float64Array(np);\n    const weights = fullWeights(windowSize, polynomial, derivative);\n    let hs = 0;\n    let constantH = true;\n    if (isAnyArray(xs)) {\n        constantH = false;\n    }\n    else {\n        hs = xs ** derivative;\n    }\n    //For the borders\n    for (let i = 0; i < half; i++) {\n        const wg1 = weights[half - i - 1];\n        const wg2 = weights[half + i + 1];\n        let d1 = 0;\n        let d2 = 0;\n        for (let l = 0; l < windowSize; l++) {\n            d1 += wg1[l] * ys[l];\n            d2 += wg2[l] * ys[np - windowSize + l];\n        }\n        if (constantH) {\n            ans[half - i - 1] = d1 / hs;\n            ans[np - half + i] = d2 / hs;\n        }\n        else {\n            hs = getHs(xs, half - i - 1, half, derivative);\n            ans[half - i - 1] = d1 / hs;\n            hs = getHs(xs, np - half + i, half, derivative);\n            ans[np - half + i] = d2 / hs;\n        }\n    }\n    //For the internal points\n    const wg = weights[half];\n    for (let i = windowSize; i <= np; i++) {\n        let d = 0;\n        for (let l = 0; l < windowSize; l++)\n            d += wg[l] * ys[l + i - windowSize];\n        if (!constantH) {\n            hs = getHs(xs, i - half - 1, half, derivative);\n        }\n        ans[i - half - 1] = d / hs;\n    }\n    return ans;\n}\nfunction getHs(h, center, half, derivative) {\n    let hs = 0;\n    let count = 0;\n    for (let i = center - half; i < center + half; i++) {\n        if (i >= 0 && i < h.length - 1) {\n            hs += h[i + 1] - h[i];\n            count++;\n        }\n    }\n    return (hs / count) ** derivative;\n}\nfunction gramPoly(i, m, k, s) {\n    let Grampoly = 0;\n    if (k > 0) {\n        Grampoly =\n            ((4 * k - 2) / (k * (2 * m - k + 1))) *\n                (i * gramPoly(i, m, k - 1, s) + s * gramPoly(i, m, k - 1, s - 1)) -\n                (((k - 1) * (2 * m + k)) / (k * (2 * m - k + 1))) *\n                    gramPoly(i, m, k - 2, s);\n    }\n    else if (k === 0 && s === 0) {\n        Grampoly = 1;\n    }\n    else {\n        Grampoly = 0;\n    }\n    return Grampoly;\n}\nfunction genFact(a, b) {\n    let gf = 1;\n    if (a >= b) {\n        for (let j = a - b + 1; j <= a; j++) {\n            gf *= j;\n        }\n    }\n    return gf;\n}\nfunction weight(i, t, m, n, s) {\n    let sum = 0;\n    for (let k = 0; k <= n; k++) {\n        sum +=\n            (2 * k + 1) *\n                (genFact(2 * m, k) / genFact(2 * m + k + 1, k + 1)) *\n                gramPoly(i, m, k, 0) *\n                gramPoly(t, m, k, s);\n    }\n    return sum;\n}\n/**\n * Compute the full weights matrix for every position inside the window.\n * @param m - Number of points.\n * @param n - Polynomial grade.\n * @param s - Derivative.\n * @returns Array of Float64Array weight vectors, one per position in the window.\n */\nfunction fullWeights(m, n, s) {\n    const weights = new Array(m);\n    const np = Math.floor(m / 2);\n    for (let t = -np; t <= np; t++) {\n        weights[t + np] = new Float64Array(m);\n        for (let j = -np; j <= np; j++) {\n            weights[t + np][j + np] = weight(j, t, np, n, s);\n        }\n    }\n    return weights;\n}\n//# sourceMappingURL=index.js.map","// eslint-disable-next-line @typescript-eslint/unbound-method\nconst toString = Object.prototype.toString;\n/**\n * Checks if an object is an instance of an Array (array or typed array, except those that contain bigint values).\n * @param value - Object to check.\n * @returns True if the object is an array or a typed array.\n */\nexport function isAnyArray(value) {\n    const tag = toString.call(value);\n    return tag.endsWith('Array]') && !tag.includes('Big');\n}\n//# sourceMappingURL=index.js.map","import { isAnyArray } from 'is-any-array';\n/**\n * Calculates the median of an array.\n * @param input - array containing values.\n * @param options - options.\n * @returns median.\n */\nexport function xMedian(input, options = {}) {\n    if (!isAnyArray(input)) {\n        throw new TypeError('input must be an array');\n    }\n    const { exact = false, fromIndex = 0, toIndex = input.length, } = options || {};\n    const array = input.slice(fromIndex, toIndex);\n    if (array.length === 0) {\n        throw new TypeError('input must not be empty');\n    }\n    const middleIndex = calcMiddle(0, array.length - 1);\n    const median = quickSelect(array, middleIndex);\n    if (array.length % 2 === 1 || !exact) {\n        return median;\n    }\n    const medianNext = quickSelect(array, middleIndex + 1);\n    return (median + medianNext) / 2;\n}\nfunction quickSelect(array, middleIndex) {\n    let low = 0;\n    let high = array.length - 1;\n    let middle = 0;\n    let currentLow = 0;\n    let currentHigh = 0;\n    while (true) {\n        if (high <= low) {\n            return array[middleIndex];\n        }\n        if (high === low + 1) {\n            if (array[low] > array[high]) {\n                swap(array, low, high);\n            }\n            return array[middleIndex];\n        }\n        // Find median of low, middle and high items; swap into position low\n        middle = calcMiddle(low, high);\n        if (array[middle] > array[high])\n            swap(array, middle, high);\n        if (array[low] > array[high])\n            swap(array, low, high);\n        if (array[middle] > array[low])\n            swap(array, middle, low);\n        // Swap low item (now in position middle) into position (low+1)\n        swap(array, middle, low + 1);\n        // Nibble from each end towards middle, swapping items when stuck\n        currentLow = low + 1;\n        currentHigh = high;\n        while (true) {\n            do\n                currentLow++;\n            while (array[low] > array[currentLow]);\n            do\n                currentHigh--;\n            while (array[currentHigh] > array[low]);\n            if (currentHigh < currentLow) {\n                break;\n            }\n            swap(array, currentLow, currentHigh);\n        }\n        // Swap middle item (in position low) back into correct position\n        swap(array, low, currentHigh);\n        // Re-set active partition\n        if (currentHigh <= middleIndex) {\n            low = currentLow;\n        }\n        if (currentHigh >= middleIndex) {\n            high = currentHigh - 1;\n        }\n    }\n}\nfunction swap(array, i, j) {\n    const temp = array[j];\n    array[j] = array[i];\n    array[i] = temp;\n}\nfunction calcMiddle(i, j) {\n    return Math.floor((i + j) / 2);\n}\n//# sourceMappingURL=xMedian.js.map","import { isAnyArray } from 'is-any-array';\n/**\n * Checks if the input is a non-empty array of numbers.\n * Only checks the first element.\n * @param input - array to check.\n * @param options - additional checks.\n */\nexport function xCheck(input, options = {}) {\n    const { minLength = 1 } = options;\n    if (!isAnyArray(input)) {\n        throw new TypeError('input must be an array');\n    }\n    if (input.length === 0) {\n        throw new TypeError('input must not be empty');\n    }\n    if (typeof input[0] !== 'number') {\n        throw new TypeError('input must contain numbers');\n    }\n    if (input.length < minLength) {\n        throw new Error(`input must have a length of at least ${minLength}`);\n    }\n}\n//# sourceMappingURL=xCheck.js.map","/**\n * Returns the closest index of a `target`\n * @param array - array of numbers.\n * @param target - target value.\n * @param options - options.\n * @returns closest index.\n */\nexport function xFindClosestIndex(array, target, options = {}) {\n    const { sorted = true } = options;\n    if (sorted) {\n        let low = 0;\n        let high = array.length - 1;\n        let middle = 0;\n        while (high - low > 1) {\n            middle = low + ((high - low) >> 1);\n            if (array[middle] < target) {\n                low = middle;\n            }\n            else if (array[middle] > target) {\n                high = middle;\n            }\n            else {\n                return middle;\n            }\n        }\n        if (low < array.length - 1) {\n            if (Math.abs(target - array[low]) < Math.abs(array[low + 1] - target)) {\n                return low;\n            }\n            else {\n                return low + 1;\n            }\n        }\n        else {\n            return low;\n        }\n    }\n    else {\n        let index = 0;\n        let diff = Number.POSITIVE_INFINITY;\n        for (let i = 0; i < array.length; i++) {\n            const currentDiff = Math.abs(array[i] - target);\n            if (currentDiff < diff) {\n                diff = currentDiff;\n                index = i;\n            }\n        }\n        return index;\n    }\n}\n//# sourceMappingURL=xFindClosestIndex.js.map","import { xFindClosestIndex } from \"./xFindClosestIndex.js\";\n/**\n * Returns an object with {fromIndex, toIndex} for a specific from / to\n * @param x - array of numbers\n * @param options - options.\n */\nexport function xGetFromToIndex(x, options = {}) {\n    let { fromIndex, toIndex } = options;\n    const { from, to } = options;\n    if (fromIndex === undefined) {\n        if (from !== undefined) {\n            fromIndex = xFindClosestIndex(x, from);\n        }\n        else {\n            fromIndex = 0;\n        }\n    }\n    if (toIndex === undefined) {\n        if (to !== undefined) {\n            toIndex = xFindClosestIndex(x, to);\n        }\n        else {\n            toIndex = x.length - 1;\n        }\n    }\n    if (fromIndex < 0)\n        fromIndex = 0;\n    if (toIndex < 0)\n        toIndex = 0;\n    if (fromIndex >= x.length)\n        fromIndex = x.length - 1;\n    if (toIndex >= x.length)\n        toIndex = x.length - 1;\n    if (fromIndex > toIndex)\n        [fromIndex, toIndex] = [toIndex, fromIndex];\n    return { fromIndex, toIndex };\n}\n//# sourceMappingURL=xGetFromToIndex.js.map","'use strict';\n\nObject.defineProperty(exports, '__esModule', { value: true });\n\n// eslint-disable-next-line @typescript-eslint/unbound-method\nconst toString = Object.prototype.toString;\n/**\n * Checks if an object is an instance of an Array (array or typed array, except those that contain bigint values).\n * @param value - Object to check.\n * @returns True if the object is an array or a typed array.\n */\nfunction isAnyArray(value) {\n    const tag = toString.call(value);\n    return tag.endsWith('Array]') && !tag.includes('Big');\n}\n\n/**\n * Computes the maximum of the given values.\n *\n * @param input\n * @param options\n */\nfunction max(input, options = {}) {\n    if (!isAnyArray(input)) {\n        throw new TypeError('input must be an array');\n    }\n    if (input.length === 0) {\n        throw new TypeError('input must not be empty');\n    }\n    const { fromIndex = 0, toIndex = input.length } = options;\n    if (fromIndex < 0 ||\n        fromIndex >= input.length ||\n        !Number.isInteger(fromIndex)) {\n        throw new Error('fromIndex must be a positive integer smaller than length');\n    }\n    if (toIndex <= fromIndex ||\n        toIndex > input.length ||\n        !Number.isInteger(toIndex)) {\n        throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n    }\n    let maxValue = input[fromIndex];\n    for (let i = fromIndex + 1; i < toIndex; i++) {\n        if (input[i] > maxValue)\n            maxValue = input[i];\n    }\n    return maxValue;\n}\n\n/**\n * Computes the minimum of the given values.\n */\nfunction min(input, options = {}) {\n    if (!isAnyArray(input)) {\n        throw new TypeError('input must be an array');\n    }\n    if (input.length === 0) {\n        throw new TypeError('input must not be empty');\n    }\n    const { fromIndex = 0, toIndex = input.length } = options;\n    if (fromIndex < 0 ||\n        fromIndex >= input.length ||\n        !Number.isInteger(fromIndex)) {\n        throw new Error('fromIndex must be a positive integer smaller than length');\n    }\n    if (toIndex <= fromIndex ||\n        toIndex > input.length ||\n        !Number.isInteger(toIndex)) {\n        throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n    }\n    let minValue = input[fromIndex];\n    for (let i = fromIndex + 1; i < toIndex; i++) {\n        if (input[i] < minValue)\n            minValue = input[i];\n    }\n    return minValue;\n}\n\n/**\n * Rescale an array into a range.\n */\nfunction rescale(input, options = {}) {\n    if (!isAnyArray(input)) {\n        throw new TypeError('input must be an array');\n    }\n    else if (input.length === 0) {\n        throw new TypeError('input must not be empty');\n    }\n    let output;\n    if (options.output !== undefined) {\n        if (!isAnyArray(options.output)) {\n            throw new TypeError('output option must be an array if specified');\n        }\n        output = options.output;\n    }\n    else {\n        output = new Array(input.length);\n    }\n    const currentMin = min(input);\n    const currentMax = max(input);\n    if (currentMin === currentMax) {\n        throw new RangeError('minimum and maximum input values are equal. Cannot rescale a constant array');\n    }\n    const { min: minValue = options.autoMinMax ? currentMin : 0, max: maxValue = options.autoMinMax ? currentMax : 1, } = options;\n    if (minValue >= maxValue) {\n        throw new RangeError('min option must be smaller than max option');\n    }\n    const factor = (maxValue - minValue) / (currentMax - currentMin);\n    for (let i = 0; i < input.length; i++) {\n        output[i] = (input[i] - currentMin) * factor + minValue;\n    }\n    return output;\n}\n\nconst indent = ' '.repeat(2);\nconst indentData = ' '.repeat(4);\n\n/**\n * @this {Matrix}\n * @returns {string}\n */\nfunction inspectMatrix() {\n  return inspectMatrixWithOptions(this);\n}\n\nfunction inspectMatrixWithOptions(matrix, options = {}) {\n  const {\n    maxRows = 15,\n    maxColumns = 10,\n    maxNumSize = 8,\n    padMinus = 'auto',\n  } = options;\n  return `${matrix.constructor.name} {\n${indent}[\n${indentData}${inspectData(matrix, maxRows, maxColumns, maxNumSize, padMinus)}\n${indent}]\n${indent}rows: ${matrix.rows}\n${indent}columns: ${matrix.columns}\n}`;\n}\n\nfunction inspectData(matrix, maxRows, maxColumns, maxNumSize, padMinus) {\n  const { rows, columns } = matrix;\n  const maxI = Math.min(rows, maxRows);\n  const maxJ = Math.min(columns, maxColumns);\n  const result = [];\n\n  if (padMinus === 'auto') {\n    padMinus = false;\n    loop: for (let i = 0; i < maxI; i++) {\n      for (let j = 0; j < maxJ; j++) {\n        if (matrix.get(i, j) < 0) {\n          padMinus = true;\n          break loop;\n        }\n      }\n    }\n  }\n\n  for (let i = 0; i < maxI; i++) {\n    let line = [];\n    for (let j = 0; j < maxJ; j++) {\n      line.push(formatNumber(matrix.get(i, j), maxNumSize, padMinus));\n    }\n    result.push(`${line.join(' ')}`);\n  }\n  if (maxJ !== columns) {\n    result[result.length - 1] += ` ... ${columns - maxColumns} more columns`;\n  }\n  if (maxI !== rows) {\n    result.push(`... ${rows - maxRows} more rows`);\n  }\n  return result.join(`\\n${indentData}`);\n}\n\nfunction formatNumber(num, maxNumSize, padMinus) {\n  return (\n    num >= 0 && padMinus\n      ? ` ${formatNumber2(num, maxNumSize - 1)}`\n      : formatNumber2(num, maxNumSize)\n  ).padEnd(maxNumSize);\n}\n\nfunction formatNumber2(num, len) {\n  // small.length numbers should be as is\n  let str = num.toString();\n  if (str.length <= len) return str;\n\n  // (7)'0.00123' is better then (7)'1.23e-2'\n  // (8)'0.000123' is worse then (7)'1.23e-3',\n  let fix = num.toFixed(len);\n  if (fix.length > len) {\n    fix = num.toFixed(Math.max(0, len - (fix.length - len)));\n  }\n  if (\n    fix.length <= len &&\n    !fix.startsWith('0.000') &&\n    !fix.startsWith('-0.000')\n  ) {\n    return fix;\n  }\n\n  // well, if it's still too long the user should've used longer numbers\n  let exp = num.toExponential(len);\n  if (exp.length > len) {\n    exp = num.toExponential(Math.max(0, len - (exp.length - len)));\n  }\n  return exp.slice(0);\n}\n\nfunction installMathOperations(AbstractMatrix, Matrix) {\n  AbstractMatrix.prototype.add = function add(value) {\n    if (typeof value === 'number') return this.addS(value);\n    return this.addM(value);\n  };\n\n  AbstractMatrix.prototype.addS = function addS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.addM = function addM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.add = function add(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.add(value);\n  };\n\n  AbstractMatrix.prototype.sub = function sub(value) {\n    if (typeof value === 'number') return this.subS(value);\n    return this.subM(value);\n  };\n\n  AbstractMatrix.prototype.subS = function subS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.subM = function subM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sub = function sub(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sub(value);\n  };\n  AbstractMatrix.prototype.subtract = AbstractMatrix.prototype.sub;\n  AbstractMatrix.prototype.subtractS = AbstractMatrix.prototype.subS;\n  AbstractMatrix.prototype.subtractM = AbstractMatrix.prototype.subM;\n  AbstractMatrix.subtract = AbstractMatrix.sub;\n\n  AbstractMatrix.prototype.mul = function mul(value) {\n    if (typeof value === 'number') return this.mulS(value);\n    return this.mulM(value);\n  };\n\n  AbstractMatrix.prototype.mulS = function mulS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.mulM = function mulM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.mul = function mul(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.mul(value);\n  };\n  AbstractMatrix.prototype.multiply = AbstractMatrix.prototype.mul;\n  AbstractMatrix.prototype.multiplyS = AbstractMatrix.prototype.mulS;\n  AbstractMatrix.prototype.multiplyM = AbstractMatrix.prototype.mulM;\n  AbstractMatrix.multiply = AbstractMatrix.mul;\n\n  AbstractMatrix.prototype.div = function div(value) {\n    if (typeof value === 'number') return this.divS(value);\n    return this.divM(value);\n  };\n\n  AbstractMatrix.prototype.divS = function divS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.divM = function divM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.div = function div(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.div(value);\n  };\n  AbstractMatrix.prototype.divide = AbstractMatrix.prototype.div;\n  AbstractMatrix.prototype.divideS = AbstractMatrix.prototype.divS;\n  AbstractMatrix.prototype.divideM = AbstractMatrix.prototype.divM;\n  AbstractMatrix.divide = AbstractMatrix.div;\n\n  AbstractMatrix.prototype.mod = function mod(value) {\n    if (typeof value === 'number') return this.modS(value);\n    return this.modM(value);\n  };\n\n  AbstractMatrix.prototype.modS = function modS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) % value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.modM = function modM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) % matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.mod = function mod(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.mod(value);\n  };\n  AbstractMatrix.prototype.modulus = AbstractMatrix.prototype.mod;\n  AbstractMatrix.prototype.modulusS = AbstractMatrix.prototype.modS;\n  AbstractMatrix.prototype.modulusM = AbstractMatrix.prototype.modM;\n  AbstractMatrix.modulus = AbstractMatrix.mod;\n\n  AbstractMatrix.prototype.and = function and(value) {\n    if (typeof value === 'number') return this.andS(value);\n    return this.andM(value);\n  };\n\n  AbstractMatrix.prototype.andS = function andS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) & value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.andM = function andM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) & matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.and = function and(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.and(value);\n  };\n\n  AbstractMatrix.prototype.or = function or(value) {\n    if (typeof value === 'number') return this.orS(value);\n    return this.orM(value);\n  };\n\n  AbstractMatrix.prototype.orS = function orS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) | value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.orM = function orM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) | matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.or = function or(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.or(value);\n  };\n\n  AbstractMatrix.prototype.xor = function xor(value) {\n    if (typeof value === 'number') return this.xorS(value);\n    return this.xorM(value);\n  };\n\n  AbstractMatrix.prototype.xorS = function xorS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) ^ value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.xorM = function xorM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) ^ matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.xor = function xor(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.xor(value);\n  };\n\n  AbstractMatrix.prototype.leftShift = function leftShift(value) {\n    if (typeof value === 'number') return this.leftShiftS(value);\n    return this.leftShiftM(value);\n  };\n\n  AbstractMatrix.prototype.leftShiftS = function leftShiftS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) << value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.leftShiftM = function leftShiftM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) << matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.leftShift = function leftShift(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.leftShift(value);\n  };\n\n  AbstractMatrix.prototype.signPropagatingRightShift = function signPropagatingRightShift(value) {\n    if (typeof value === 'number') return this.signPropagatingRightShiftS(value);\n    return this.signPropagatingRightShiftM(value);\n  };\n\n  AbstractMatrix.prototype.signPropagatingRightShiftS = function signPropagatingRightShiftS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >> value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.signPropagatingRightShiftM = function signPropagatingRightShiftM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >> matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.signPropagatingRightShift = function signPropagatingRightShift(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.signPropagatingRightShift(value);\n  };\n\n  AbstractMatrix.prototype.rightShift = function rightShift(value) {\n    if (typeof value === 'number') return this.rightShiftS(value);\n    return this.rightShiftM(value);\n  };\n\n  AbstractMatrix.prototype.rightShiftS = function rightShiftS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >>> value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.rightShiftM = function rightShiftM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >>> matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.rightShift = function rightShift(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.rightShift(value);\n  };\n  AbstractMatrix.prototype.zeroFillRightShift = AbstractMatrix.prototype.rightShift;\n  AbstractMatrix.prototype.zeroFillRightShiftS = AbstractMatrix.prototype.rightShiftS;\n  AbstractMatrix.prototype.zeroFillRightShiftM = AbstractMatrix.prototype.rightShiftM;\n  AbstractMatrix.zeroFillRightShift = AbstractMatrix.rightShift;\n\n  AbstractMatrix.prototype.not = function not() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, ~(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.not = function not(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.not();\n  };\n\n  AbstractMatrix.prototype.abs = function abs() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.abs(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.abs = function abs(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.abs();\n  };\n\n  AbstractMatrix.prototype.acos = function acos() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.acos(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.acos = function acos(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.acos();\n  };\n\n  AbstractMatrix.prototype.acosh = function acosh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.acosh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.acosh = function acosh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.acosh();\n  };\n\n  AbstractMatrix.prototype.asin = function asin() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.asin(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.asin = function asin(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.asin();\n  };\n\n  AbstractMatrix.prototype.asinh = function asinh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.asinh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.asinh = function asinh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.asinh();\n  };\n\n  AbstractMatrix.prototype.atan = function atan() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.atan(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.atan = function atan(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.atan();\n  };\n\n  AbstractMatrix.prototype.atanh = function atanh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.atanh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.atanh = function atanh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.atanh();\n  };\n\n  AbstractMatrix.prototype.cbrt = function cbrt() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.cbrt(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.cbrt = function cbrt(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.cbrt();\n  };\n\n  AbstractMatrix.prototype.ceil = function ceil() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.ceil(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.ceil = function ceil(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.ceil();\n  };\n\n  AbstractMatrix.prototype.clz32 = function clz32() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.clz32(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.clz32 = function clz32(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.clz32();\n  };\n\n  AbstractMatrix.prototype.cos = function cos() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.cos(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.cos = function cos(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.cos();\n  };\n\n  AbstractMatrix.prototype.cosh = function cosh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.cosh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.cosh = function cosh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.cosh();\n  };\n\n  AbstractMatrix.prototype.exp = function exp() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.exp(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.exp = function exp(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.exp();\n  };\n\n  AbstractMatrix.prototype.expm1 = function expm1() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.expm1(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.expm1 = function expm1(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.expm1();\n  };\n\n  AbstractMatrix.prototype.floor = function floor() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.floor(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.floor = function floor(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.floor();\n  };\n\n  AbstractMatrix.prototype.fround = function fround() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.fround(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.fround = function fround(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.fround();\n  };\n\n  AbstractMatrix.prototype.log = function log() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log = function log(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log();\n  };\n\n  AbstractMatrix.prototype.log1p = function log1p() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log1p(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log1p = function log1p(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log1p();\n  };\n\n  AbstractMatrix.prototype.log10 = function log10() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log10(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log10 = function log10(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log10();\n  };\n\n  AbstractMatrix.prototype.log2 = function log2() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log2(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log2 = function log2(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log2();\n  };\n\n  AbstractMatrix.prototype.round = function round() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.round(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.round = function round(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.round();\n  };\n\n  AbstractMatrix.prototype.sign = function sign() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sign(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sign = function sign(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sign();\n  };\n\n  AbstractMatrix.prototype.sin = function sin() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sin(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sin = function sin(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sin();\n  };\n\n  AbstractMatrix.prototype.sinh = function sinh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sinh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sinh = function sinh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sinh();\n  };\n\n  AbstractMatrix.prototype.sqrt = function sqrt() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sqrt(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sqrt = function sqrt(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sqrt();\n  };\n\n  AbstractMatrix.prototype.tan = function tan() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.tan(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.tan = function tan(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.tan();\n  };\n\n  AbstractMatrix.prototype.tanh = function tanh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.tanh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.tanh = function tanh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.tanh();\n  };\n\n  AbstractMatrix.prototype.trunc = function trunc() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.trunc(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.trunc = function trunc(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.trunc();\n  };\n\n  AbstractMatrix.pow = function pow(matrix, arg0) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.pow(arg0);\n  };\n\n  AbstractMatrix.prototype.pow = function pow(value) {\n    if (typeof value === 'number') return this.powS(value);\n    return this.powM(value);\n  };\n\n  AbstractMatrix.prototype.powS = function powS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) ** value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.powM = function powM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) ** matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n}\n\n/**\n * @private\n * Check that a row index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nfunction checkRowIndex(matrix, index, outer) {\n  let max = outer ? matrix.rows : matrix.rows - 1;\n  if (index < 0 || index > max) {\n    throw new RangeError('Row index out of range');\n  }\n}\n\n/**\n * @private\n * Check that a column index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nfunction checkColumnIndex(matrix, index, outer) {\n  let max = outer ? matrix.columns : matrix.columns - 1;\n  if (index < 0 || index > max) {\n    throw new RangeError('Column index out of range');\n  }\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nfunction checkRowVector(matrix, vector) {\n  if (vector.to1DArray) {\n    vector = vector.to1DArray();\n  }\n  if (vector.length !== matrix.columns) {\n    throw new RangeError(\n      'vector size must be the same as the number of columns',\n    );\n  }\n  return vector;\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nfunction checkColumnVector(matrix, vector) {\n  if (vector.to1DArray) {\n    vector = vector.to1DArray();\n  }\n  if (vector.length !== matrix.rows) {\n    throw new RangeError('vector size must be the same as the number of rows');\n  }\n  return vector;\n}\n\nfunction checkRowIndices(matrix, rowIndices) {\n  if (!isAnyArray(rowIndices)) {\n    throw new TypeError('row indices must be an array');\n  }\n\n  for (let i = 0; i < rowIndices.length; i++) {\n    if (rowIndices[i] < 0 || rowIndices[i] >= matrix.rows) {\n      throw new RangeError('row indices are out of range');\n    }\n  }\n}\n\nfunction checkColumnIndices(matrix, columnIndices) {\n  if (!isAnyArray(columnIndices)) {\n    throw new TypeError('column indices must be an array');\n  }\n\n  for (let i = 0; i < columnIndices.length; i++) {\n    if (columnIndices[i] < 0 || columnIndices[i] >= matrix.columns) {\n      throw new RangeError('column indices are out of range');\n    }\n  }\n}\n\nfunction checkRange(matrix, startRow, endRow, startColumn, endColumn) {\n  if (arguments.length !== 5) {\n    throw new RangeError('expected 4 arguments');\n  }\n  checkNumber('startRow', startRow);\n  checkNumber('endRow', endRow);\n  checkNumber('startColumn', startColumn);\n  checkNumber('endColumn', endColumn);\n  if (\n    startRow > endRow ||\n    startColumn > endColumn ||\n    startRow < 0 ||\n    startRow >= matrix.rows ||\n    endRow < 0 ||\n    endRow >= matrix.rows ||\n    startColumn < 0 ||\n    startColumn >= matrix.columns ||\n    endColumn < 0 ||\n    endColumn >= matrix.columns\n  ) {\n    throw new RangeError('Submatrix indices are out of range');\n  }\n}\n\nfunction newArray(length, value = 0) {\n  let array = [];\n  for (let i = 0; i < length; i++) {\n    array.push(value);\n  }\n  return array;\n}\n\nfunction checkNumber(name, value) {\n  if (typeof value !== 'number') {\n    throw new TypeError(`${name} must be a number`);\n  }\n}\n\nfunction checkNonEmpty(matrix) {\n  if (matrix.isEmpty()) {\n    throw new Error('Empty matrix has no elements to index');\n  }\n}\n\nfunction sumByRow(matrix) {\n  let sum = newArray(matrix.rows);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[i] += matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nfunction sumByColumn(matrix) {\n  let sum = newArray(matrix.columns);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[j] += matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nfunction sumAll(matrix) {\n  let v = 0;\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      v += matrix.get(i, j);\n    }\n  }\n  return v;\n}\n\nfunction productByRow(matrix) {\n  let sum = newArray(matrix.rows, 1);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[i] *= matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nfunction productByColumn(matrix) {\n  let sum = newArray(matrix.columns, 1);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[j] *= matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nfunction productAll(matrix) {\n  let v = 1;\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      v *= matrix.get(i, j);\n    }\n  }\n  return v;\n}\n\nfunction varianceByRow(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const variance = [];\n\n  for (let i = 0; i < rows; i++) {\n    let sum1 = 0;\n    let sum2 = 0;\n    let x = 0;\n    for (let j = 0; j < cols; j++) {\n      x = matrix.get(i, j) - mean[i];\n      sum1 += x;\n      sum2 += x * x;\n    }\n    if (unbiased) {\n      variance.push((sum2 - (sum1 * sum1) / cols) / (cols - 1));\n    } else {\n      variance.push((sum2 - (sum1 * sum1) / cols) / cols);\n    }\n  }\n  return variance;\n}\n\nfunction varianceByColumn(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const variance = [];\n\n  for (let j = 0; j < cols; j++) {\n    let sum1 = 0;\n    let sum2 = 0;\n    let x = 0;\n    for (let i = 0; i < rows; i++) {\n      x = matrix.get(i, j) - mean[j];\n      sum1 += x;\n      sum2 += x * x;\n    }\n    if (unbiased) {\n      variance.push((sum2 - (sum1 * sum1) / rows) / (rows - 1));\n    } else {\n      variance.push((sum2 - (sum1 * sum1) / rows) / rows);\n    }\n  }\n  return variance;\n}\n\nfunction varianceAll(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const size = rows * cols;\n\n  let sum1 = 0;\n  let sum2 = 0;\n  let x = 0;\n  for (let i = 0; i < rows; i++) {\n    for (let j = 0; j < cols; j++) {\n      x = matrix.get(i, j) - mean;\n      sum1 += x;\n      sum2 += x * x;\n    }\n  }\n  if (unbiased) {\n    return (sum2 - (sum1 * sum1) / size) / (size - 1);\n  } else {\n    return (sum2 - (sum1 * sum1) / size) / size;\n  }\n}\n\nfunction centerByRow(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean[i]);\n    }\n  }\n}\n\nfunction centerByColumn(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean[j]);\n    }\n  }\n}\n\nfunction centerAll(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean);\n    }\n  }\n}\n\nfunction getScaleByRow(matrix) {\n  const scale = [];\n  for (let i = 0; i < matrix.rows; i++) {\n    let sum = 0;\n    for (let j = 0; j < matrix.columns; j++) {\n      sum += matrix.get(i, j) ** 2 / (matrix.columns - 1);\n    }\n    scale.push(Math.sqrt(sum));\n  }\n  return scale;\n}\n\nfunction scaleByRow(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale[i]);\n    }\n  }\n}\n\nfunction getScaleByColumn(matrix) {\n  const scale = [];\n  for (let j = 0; j < matrix.columns; j++) {\n    let sum = 0;\n    for (let i = 0; i < matrix.rows; i++) {\n      sum += matrix.get(i, j) ** 2 / (matrix.rows - 1);\n    }\n    scale.push(Math.sqrt(sum));\n  }\n  return scale;\n}\n\nfunction scaleByColumn(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale[j]);\n    }\n  }\n}\n\nfunction getScaleAll(matrix) {\n  const divider = matrix.size - 1;\n  let sum = 0;\n  for (let j = 0; j < matrix.columns; j++) {\n    for (let i = 0; i < matrix.rows; i++) {\n      sum += matrix.get(i, j) ** 2 / divider;\n    }\n  }\n  return Math.sqrt(sum);\n}\n\nfunction scaleAll(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale);\n    }\n  }\n}\n\nclass AbstractMatrix {\n  static from1DArray(newRows, newColumns, newData) {\n    let length = newRows * newColumns;\n    if (length !== newData.length) {\n      throw new RangeError('data length does not match given dimensions');\n    }\n    let newMatrix = new Matrix(newRows, newColumns);\n    for (let row = 0; row < newRows; row++) {\n      for (let column = 0; column < newColumns; column++) {\n        newMatrix.set(row, column, newData[row * newColumns + column]);\n      }\n    }\n    return newMatrix;\n  }\n\n  static rowVector(newData) {\n    let vector = new Matrix(1, newData.length);\n    for (let i = 0; i < newData.length; i++) {\n      vector.set(0, i, newData[i]);\n    }\n    return vector;\n  }\n\n  static columnVector(newData) {\n    let vector = new Matrix(newData.length, 1);\n    for (let i = 0; i < newData.length; i++) {\n      vector.set(i, 0, newData[i]);\n    }\n    return vector;\n  }\n\n  static zeros(rows, columns) {\n    return new Matrix(rows, columns);\n  }\n\n  static ones(rows, columns) {\n    return new Matrix(rows, columns).fill(1);\n  }\n\n  static rand(rows, columns, options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { random = Math.random } = options;\n    let matrix = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        matrix.set(i, j, random());\n      }\n    }\n    return matrix;\n  }\n\n  static randInt(rows, columns, options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1000, random = Math.random } = options;\n    if (!Number.isInteger(min)) throw new TypeError('min must be an integer');\n    if (!Number.isInteger(max)) throw new TypeError('max must be an integer');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let interval = max - min;\n    let matrix = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        let value = min + Math.round(random() * interval);\n        matrix.set(i, j, value);\n      }\n    }\n    return matrix;\n  }\n\n  static eye(rows, columns, value) {\n    if (columns === undefined) columns = rows;\n    if (value === undefined) value = 1;\n    let min = Math.min(rows, columns);\n    let matrix = this.zeros(rows, columns);\n    for (let i = 0; i < min; i++) {\n      matrix.set(i, i, value);\n    }\n    return matrix;\n  }\n\n  static diag(data, rows, columns) {\n    let l = data.length;\n    if (rows === undefined) rows = l;\n    if (columns === undefined) columns = rows;\n    let min = Math.min(l, rows, columns);\n    let matrix = this.zeros(rows, columns);\n    for (let i = 0; i < min; i++) {\n      matrix.set(i, i, data[i]);\n    }\n    return matrix;\n  }\n\n  static min(matrix1, matrix2) {\n    matrix1 = this.checkMatrix(matrix1);\n    matrix2 = this.checkMatrix(matrix2);\n    let rows = matrix1.rows;\n    let columns = matrix1.columns;\n    let result = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        result.set(i, j, Math.min(matrix1.get(i, j), matrix2.get(i, j)));\n      }\n    }\n    return result;\n  }\n\n  static max(matrix1, matrix2) {\n    matrix1 = this.checkMatrix(matrix1);\n    matrix2 = this.checkMatrix(matrix2);\n    let rows = matrix1.rows;\n    let columns = matrix1.columns;\n    let result = new this(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        result.set(i, j, Math.max(matrix1.get(i, j), matrix2.get(i, j)));\n      }\n    }\n    return result;\n  }\n\n  static checkMatrix(value) {\n    return AbstractMatrix.isMatrix(value) ? value : new Matrix(value);\n  }\n\n  static isMatrix(value) {\n    return value != null && value.klass === 'Matrix';\n  }\n\n  get size() {\n    return this.rows * this.columns;\n  }\n\n  apply(callback) {\n    if (typeof callback !== 'function') {\n      throw new TypeError('callback must be a function');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        callback.call(this, i, j);\n      }\n    }\n    return this;\n  }\n\n  to1DArray() {\n    let array = [];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        array.push(this.get(i, j));\n      }\n    }\n    return array;\n  }\n\n  to2DArray() {\n    let copy = [];\n    for (let i = 0; i < this.rows; i++) {\n      copy.push([]);\n      for (let j = 0; j < this.columns; j++) {\n        copy[i].push(this.get(i, j));\n      }\n    }\n    return copy;\n  }\n\n  toJSON() {\n    return this.to2DArray();\n  }\n\n  isRowVector() {\n    return this.rows === 1;\n  }\n\n  isColumnVector() {\n    return this.columns === 1;\n  }\n\n  isVector() {\n    return this.rows === 1 || this.columns === 1;\n  }\n\n  isSquare() {\n    return this.rows === this.columns;\n  }\n\n  isEmpty() {\n    return this.rows === 0 || this.columns === 0;\n  }\n\n  isSymmetric() {\n    if (this.isSquare()) {\n      for (let i = 0; i < this.rows; i++) {\n        for (let j = 0; j <= i; j++) {\n          if (this.get(i, j) !== this.get(j, i)) {\n            return false;\n          }\n        }\n      }\n      return true;\n    }\n    return false;\n  }\n\n  isDistance() {\n    if (!this.isSymmetric()) return false;\n\n    for (let i = 0; i < this.rows; i++) {\n      if (this.get(i, i) !== 0) return false;\n    }\n\n    return true;\n  }\n\n  isEchelonForm() {\n    let i = 0;\n    let j = 0;\n    let previousColumn = -1;\n    let isEchelonForm = true;\n    let checked = false;\n    while (i < this.rows && isEchelonForm) {\n      j = 0;\n      checked = false;\n      while (j < this.columns && checked === false) {\n        if (this.get(i, j) === 0) {\n          j++;\n        } else if (this.get(i, j) === 1 && j > previousColumn) {\n          checked = true;\n          previousColumn = j;\n        } else {\n          isEchelonForm = false;\n          checked = true;\n        }\n      }\n      i++;\n    }\n    return isEchelonForm;\n  }\n\n  isReducedEchelonForm() {\n    let i = 0;\n    let j = 0;\n    let previousColumn = -1;\n    let isReducedEchelonForm = true;\n    let checked = false;\n    while (i < this.rows && isReducedEchelonForm) {\n      j = 0;\n      checked = false;\n      while (j < this.columns && checked === false) {\n        if (this.get(i, j) === 0) {\n          j++;\n        } else if (this.get(i, j) === 1 && j > previousColumn) {\n          checked = true;\n          previousColumn = j;\n        } else {\n          isReducedEchelonForm = false;\n          checked = true;\n        }\n      }\n      for (let k = j + 1; k < this.rows; k++) {\n        if (this.get(i, k) !== 0) {\n          isReducedEchelonForm = false;\n        }\n      }\n      i++;\n    }\n    return isReducedEchelonForm;\n  }\n\n  echelonForm() {\n    let result = this.clone();\n    let h = 0;\n    let k = 0;\n    while (h < result.rows && k < result.columns) {\n      let iMax = h;\n      for (let i = h; i < result.rows; i++) {\n        if (result.get(i, k) > result.get(iMax, k)) {\n          iMax = i;\n        }\n      }\n      if (result.get(iMax, k) === 0) {\n        k++;\n      } else {\n        result.swapRows(h, iMax);\n        let tmp = result.get(h, k);\n        for (let j = k; j < result.columns; j++) {\n          result.set(h, j, result.get(h, j) / tmp);\n        }\n        for (let i = h + 1; i < result.rows; i++) {\n          let factor = result.get(i, k) / result.get(h, k);\n          result.set(i, k, 0);\n          for (let j = k + 1; j < result.columns; j++) {\n            result.set(i, j, result.get(i, j) - result.get(h, j) * factor);\n          }\n        }\n        h++;\n        k++;\n      }\n    }\n    return result;\n  }\n\n  reducedEchelonForm() {\n    let result = this.echelonForm();\n    let m = result.columns;\n    let n = result.rows;\n    let h = n - 1;\n    while (h >= 0) {\n      if (result.maxRow(h) === 0) {\n        h--;\n      } else {\n        let p = 0;\n        let pivot = false;\n        while (p < n && pivot === false) {\n          if (result.get(h, p) === 1) {\n            pivot = true;\n          } else {\n            p++;\n          }\n        }\n        for (let i = 0; i < h; i++) {\n          let factor = result.get(i, p);\n          for (let j = p; j < m; j++) {\n            let tmp = result.get(i, j) - factor * result.get(h, j);\n            result.set(i, j, tmp);\n          }\n        }\n        h--;\n      }\n    }\n    return result;\n  }\n\n  set() {\n    throw new Error('set method is unimplemented');\n  }\n\n  get() {\n    throw new Error('get method is unimplemented');\n  }\n\n  repeat(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { rows = 1, columns = 1 } = options;\n    if (!Number.isInteger(rows) || rows <= 0) {\n      throw new TypeError('rows must be a positive integer');\n    }\n    if (!Number.isInteger(columns) || columns <= 0) {\n      throw new TypeError('columns must be a positive integer');\n    }\n    let matrix = new Matrix(this.rows * rows, this.columns * columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        matrix.setSubMatrix(this, this.rows * i, this.columns * j);\n      }\n    }\n    return matrix;\n  }\n\n  fill(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, value);\n      }\n    }\n    return this;\n  }\n\n  neg() {\n    return this.mulS(-1);\n  }\n\n  getRow(index) {\n    checkRowIndex(this, index);\n    let row = [];\n    for (let i = 0; i < this.columns; i++) {\n      row.push(this.get(index, i));\n    }\n    return row;\n  }\n\n  getRowVector(index) {\n    return Matrix.rowVector(this.getRow(index));\n  }\n\n  setRow(index, array) {\n    checkRowIndex(this, index);\n    array = checkRowVector(this, array);\n    for (let i = 0; i < this.columns; i++) {\n      this.set(index, i, array[i]);\n    }\n    return this;\n  }\n\n  swapRows(row1, row2) {\n    checkRowIndex(this, row1);\n    checkRowIndex(this, row2);\n    for (let i = 0; i < this.columns; i++) {\n      let temp = this.get(row1, i);\n      this.set(row1, i, this.get(row2, i));\n      this.set(row2, i, temp);\n    }\n    return this;\n  }\n\n  getColumn(index) {\n    checkColumnIndex(this, index);\n    let column = [];\n    for (let i = 0; i < this.rows; i++) {\n      column.push(this.get(i, index));\n    }\n    return column;\n  }\n\n  getColumnVector(index) {\n    return Matrix.columnVector(this.getColumn(index));\n  }\n\n  setColumn(index, array) {\n    checkColumnIndex(this, index);\n    array = checkColumnVector(this, array);\n    for (let i = 0; i < this.rows; i++) {\n      this.set(i, index, array[i]);\n    }\n    return this;\n  }\n\n  swapColumns(column1, column2) {\n    checkColumnIndex(this, column1);\n    checkColumnIndex(this, column2);\n    for (let i = 0; i < this.rows; i++) {\n      let temp = this.get(i, column1);\n      this.set(i, column1, this.get(i, column2));\n      this.set(i, column2, temp);\n    }\n    return this;\n  }\n\n  addRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + vector[j]);\n      }\n    }\n    return this;\n  }\n\n  subRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - vector[j]);\n      }\n    }\n    return this;\n  }\n\n  mulRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * vector[j]);\n      }\n    }\n    return this;\n  }\n\n  divRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / vector[j]);\n      }\n    }\n    return this;\n  }\n\n  addColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + vector[i]);\n      }\n    }\n    return this;\n  }\n\n  subColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - vector[i]);\n      }\n    }\n    return this;\n  }\n\n  mulColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * vector[i]);\n      }\n    }\n    return this;\n  }\n\n  divColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / vector[i]);\n      }\n    }\n    return this;\n  }\n\n  mulRow(index, value) {\n    checkRowIndex(this, index);\n    for (let i = 0; i < this.columns; i++) {\n      this.set(index, i, this.get(index, i) * value);\n    }\n    return this;\n  }\n\n  mulColumn(index, value) {\n    checkColumnIndex(this, index);\n    for (let i = 0; i < this.rows; i++) {\n      this.set(i, index, this.get(i, index) * value);\n    }\n    return this;\n  }\n\n  max(by) {\n    if (this.isEmpty()) {\n      return NaN;\n    }\n    switch (by) {\n      case 'row': {\n        const max = new Array(this.rows).fill(Number.NEGATIVE_INFINITY);\n        for (let row = 0; row < this.rows; row++) {\n          for (let column = 0; column < this.columns; column++) {\n            if (this.get(row, column) > max[row]) {\n              max[row] = this.get(row, column);\n            }\n          }\n        }\n        return max;\n      }\n      case 'column': {\n        const max = new Array(this.columns).fill(Number.NEGATIVE_INFINITY);\n        for (let row = 0; row < this.rows; row++) {\n          for (let column = 0; column < this.columns; column++) {\n            if (this.get(row, column) > max[column]) {\n              max[column] = this.get(row, column);\n            }\n          }\n        }\n        return max;\n      }\n      case undefined: {\n        let max = this.get(0, 0);\n        for (let row = 0; row < this.rows; row++) {\n          for (let column = 0; column < this.columns; column++) {\n            if (this.get(row, column) > max) {\n              max = this.get(row, column);\n            }\n          }\n        }\n        return max;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  maxIndex() {\n    checkNonEmpty(this);\n    let v = this.get(0, 0);\n    let idx = [0, 0];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) > v) {\n          v = this.get(i, j);\n          idx[0] = i;\n          idx[1] = j;\n        }\n      }\n    }\n    return idx;\n  }\n\n  min(by) {\n    if (this.isEmpty()) {\n      return NaN;\n    }\n\n    switch (by) {\n      case 'row': {\n        const min = new Array(this.rows).fill(Number.POSITIVE_INFINITY);\n        for (let row = 0; row < this.rows; row++) {\n          for (let column = 0; column < this.columns; column++) {\n            if (this.get(row, column) < min[row]) {\n              min[row] = this.get(row, column);\n            }\n          }\n        }\n        return min;\n      }\n      case 'column': {\n        const min = new Array(this.columns).fill(Number.POSITIVE_INFINITY);\n        for (let row = 0; row < this.rows; row++) {\n          for (let column = 0; column < this.columns; column++) {\n            if (this.get(row, column) < min[column]) {\n              min[column] = this.get(row, column);\n            }\n          }\n        }\n        return min;\n      }\n      case undefined: {\n        let min = this.get(0, 0);\n        for (let row = 0; row < this.rows; row++) {\n          for (let column = 0; column < this.columns; column++) {\n            if (this.get(row, column) < min) {\n              min = this.get(row, column);\n            }\n          }\n        }\n        return min;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  minIndex() {\n    checkNonEmpty(this);\n    let v = this.get(0, 0);\n    let idx = [0, 0];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) < v) {\n          v = this.get(i, j);\n          idx[0] = i;\n          idx[1] = j;\n        }\n      }\n    }\n    return idx;\n  }\n\n  maxRow(row) {\n    checkRowIndex(this, row);\n    if (this.isEmpty()) {\n      return NaN;\n    }\n    let v = this.get(row, 0);\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) > v) {\n        v = this.get(row, i);\n      }\n    }\n    return v;\n  }\n\n  maxRowIndex(row) {\n    checkRowIndex(this, row);\n    checkNonEmpty(this);\n    let v = this.get(row, 0);\n    let idx = [row, 0];\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) > v) {\n        v = this.get(row, i);\n        idx[1] = i;\n      }\n    }\n    return idx;\n  }\n\n  minRow(row) {\n    checkRowIndex(this, row);\n    if (this.isEmpty()) {\n      return NaN;\n    }\n    let v = this.get(row, 0);\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) < v) {\n        v = this.get(row, i);\n      }\n    }\n    return v;\n  }\n\n  minRowIndex(row) {\n    checkRowIndex(this, row);\n    checkNonEmpty(this);\n    let v = this.get(row, 0);\n    let idx = [row, 0];\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) < v) {\n        v = this.get(row, i);\n        idx[1] = i;\n      }\n    }\n    return idx;\n  }\n\n  maxColumn(column) {\n    checkColumnIndex(this, column);\n    if (this.isEmpty()) {\n      return NaN;\n    }\n    let v = this.get(0, column);\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) > v) {\n        v = this.get(i, column);\n      }\n    }\n    return v;\n  }\n\n  maxColumnIndex(column) {\n    checkColumnIndex(this, column);\n    checkNonEmpty(this);\n    let v = this.get(0, column);\n    let idx = [0, column];\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) > v) {\n        v = this.get(i, column);\n        idx[0] = i;\n      }\n    }\n    return idx;\n  }\n\n  minColumn(column) {\n    checkColumnIndex(this, column);\n    if (this.isEmpty()) {\n      return NaN;\n    }\n    let v = this.get(0, column);\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) < v) {\n        v = this.get(i, column);\n      }\n    }\n    return v;\n  }\n\n  minColumnIndex(column) {\n    checkColumnIndex(this, column);\n    checkNonEmpty(this);\n    let v = this.get(0, column);\n    let idx = [0, column];\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) < v) {\n        v = this.get(i, column);\n        idx[0] = i;\n      }\n    }\n    return idx;\n  }\n\n  diag() {\n    let min = Math.min(this.rows, this.columns);\n    let diag = [];\n    for (let i = 0; i < min; i++) {\n      diag.push(this.get(i, i));\n    }\n    return diag;\n  }\n\n  norm(type = 'frobenius') {\n    switch (type) {\n      case 'max':\n        return this.max();\n      case 'frobenius':\n        return Math.sqrt(this.dot(this));\n      default:\n        throw new RangeError(`unknown norm type: ${type}`);\n    }\n  }\n\n  cumulativeSum() {\n    let sum = 0;\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        sum += this.get(i, j);\n        this.set(i, j, sum);\n      }\n    }\n    return this;\n  }\n\n  dot(vector2) {\n    if (AbstractMatrix.isMatrix(vector2)) vector2 = vector2.to1DArray();\n    let vector1 = this.to1DArray();\n    if (vector1.length !== vector2.length) {\n      throw new RangeError('vectors do not have the same size');\n    }\n    let dot = 0;\n    for (let i = 0; i < vector1.length; i++) {\n      dot += vector1[i] * vector2[i];\n    }\n    return dot;\n  }\n\n  mmul(other) {\n    other = Matrix.checkMatrix(other);\n\n    let m = this.rows;\n    let n = this.columns;\n    let p = other.columns;\n\n    let result = new Matrix(m, p);\n\n    let Bcolj = new Float64Array(n);\n    for (let j = 0; j < p; j++) {\n      for (let k = 0; k < n; k++) {\n        Bcolj[k] = other.get(k, j);\n      }\n\n      for (let i = 0; i < m; i++) {\n        let s = 0;\n        for (let k = 0; k < n; k++) {\n          s += this.get(i, k) * Bcolj[k];\n        }\n\n        result.set(i, j, s);\n      }\n    }\n    return result;\n  }\n\n  gram() {\n    const rows = this.rows;\n    const n = this.columns;\n\n    // The Gram matrix `thisᵀ · this` is symmetric, so only its upper triangle is\n    // accumulated (then mirrored) and the transpose is never materialized.\n    // Row-streaming rank-1 updates read each row of `this` contiguously and skip\n    // zero entries, so the cost scales with the number of non-zeros: it is as\n    // fast as the dense version on dense matrices (the skip never fires) and far\n    // faster on sparse ones.\n    const gramData = new Float64Array(n * n);\n    for (let r = 0; r < rows; r++) {\n      for (let i = 0; i < n; i++) {\n        const value = this.get(r, i);\n        if (value === 0) continue;\n        const offset = i * n;\n        for (let j = i; j < n; j++) {\n          gramData[offset + j] += value * this.get(r, j);\n        }\n      }\n    }\n\n    const result = new Matrix(n, n);\n    for (let i = 0; i < n; i++) {\n      const offset = i * n;\n      for (let j = i; j < n; j++) {\n        const value = gramData[offset + j];\n        result.set(i, j, value);\n        result.set(j, i, value);\n      }\n    }\n    return result;\n  }\n\n  transposeMultiply(other) {\n    other = Matrix.checkMatrix(other);\n    if (this.rows !== other.rows) {\n      throw new RangeError(\n        'the number of rows of the two matrices must be equal',\n      );\n    }\n    const n = this.columns;\n    const p = other.columns;\n\n    const result = new Matrix(n, p);\n    const otherRow = new Float64Array(p);\n    for (let r = 0; r < this.rows; r++) {\n      for (let j = 0; j < p; j++) {\n        otherRow[j] = other.get(r, j);\n      }\n      for (let i = 0; i < n; i++) {\n        const value = this.get(r, i);\n        if (value === 0) continue;\n        const resultRow = result.data[i];\n        for (let j = 0; j < p; j++) {\n          resultRow[j] += value * otherRow[j];\n        }\n      }\n    }\n    return result;\n  }\n\n  mmulByTranspose(scale) {\n    let m = this.rows;\n    let n = this.columns;\n\n    if (scale !== undefined && scale.length !== n) {\n      throw new RangeError('scale must have one value per column');\n    }\n\n    let result = new Matrix(m, m);\n\n    // result = this · diag(scale) · thisᵀ is symmetric, so only the upper\n    // triangle is computed and mirrored, and the transpose is never\n    // materialized. `scale` (one factor per column) is folded into one operand.\n    let rowj = new Float64Array(n);\n    for (let j = 0; j < m; j++) {\n      if (scale === undefined) {\n        for (let k = 0; k < n; k++) {\n          rowj[k] = this.get(j, k);\n        }\n      } else {\n        for (let k = 0; k < n; k++) {\n          rowj[k] = scale[k] * this.get(j, k);\n        }\n      }\n\n      for (let i = j; i < m; i++) {\n        let s = 0;\n        for (let k = 0; k < n; k++) {\n          s += this.get(i, k) * rowj[k];\n        }\n\n        result.set(i, j, s);\n        result.set(j, i, s);\n      }\n    }\n    return result;\n  }\n\n  mpow(scalar) {\n    if (!this.isSquare()) {\n      throw new RangeError('Matrix must be square');\n    }\n    if (!Number.isInteger(scalar) || scalar < 0) {\n      throw new RangeError('Exponent must be a non-negative integer');\n    }\n    // Russian Peasant exponentiation, i.e. exponentiation by squaring\n    let result = Matrix.eye(this.rows);\n    let bb = this;\n    // Note: Don't bit shift. In JS, that would truncate at 32 bits\n    for (let e = scalar; e >= 1; e /= 2) {\n      if ((e & 1) !== 0) {\n        result = result.mmul(bb);\n      }\n      bb = bb.mmul(bb);\n    }\n    return result;\n  }\n\n  strassen2x2(other) {\n    other = Matrix.checkMatrix(other);\n    let result = new Matrix(2, 2);\n    const a11 = this.get(0, 0);\n    const b11 = other.get(0, 0);\n    const a12 = this.get(0, 1);\n    const b12 = other.get(0, 1);\n    const a21 = this.get(1, 0);\n    const b21 = other.get(1, 0);\n    const a22 = this.get(1, 1);\n    const b22 = other.get(1, 1);\n\n    // Compute intermediate values.\n    const m1 = (a11 + a22) * (b11 + b22);\n    const m2 = (a21 + a22) * b11;\n    const m3 = a11 * (b12 - b22);\n    const m4 = a22 * (b21 - b11);\n    const m5 = (a11 + a12) * b22;\n    const m6 = (a21 - a11) * (b11 + b12);\n    const m7 = (a12 - a22) * (b21 + b22);\n\n    // Combine intermediate values into the output.\n    const c00 = m1 + m4 - m5 + m7;\n    const c01 = m3 + m5;\n    const c10 = m2 + m4;\n    const c11 = m1 - m2 + m3 + m6;\n\n    result.set(0, 0, c00);\n    result.set(0, 1, c01);\n    result.set(1, 0, c10);\n    result.set(1, 1, c11);\n    return result;\n  }\n\n  strassen3x3(other) {\n    other = Matrix.checkMatrix(other);\n    let result = new Matrix(3, 3);\n\n    const a00 = this.get(0, 0);\n    const a01 = this.get(0, 1);\n    const a02 = this.get(0, 2);\n    const a10 = this.get(1, 0);\n    const a11 = this.get(1, 1);\n    const a12 = this.get(1, 2);\n    const a20 = this.get(2, 0);\n    const a21 = this.get(2, 1);\n    const a22 = this.get(2, 2);\n\n    const b00 = other.get(0, 0);\n    const b01 = other.get(0, 1);\n    const b02 = other.get(0, 2);\n    const b10 = other.get(1, 0);\n    const b11 = other.get(1, 1);\n    const b12 = other.get(1, 2);\n    const b20 = other.get(2, 0);\n    const b21 = other.get(2, 1);\n    const b22 = other.get(2, 2);\n\n    const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;\n    const m2 = (a00 - a10) * (-b01 + b11);\n    const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);\n    const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);\n    const m5 = (a10 + a11) * (-b00 + b01);\n    const m6 = a00 * b00;\n    const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);\n    const m8 = (-a00 + a20) * (b02 - b12);\n    const m9 = (a20 + a21) * (-b00 + b02);\n    const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;\n    const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);\n    const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);\n    const m13 = (a02 - a22) * (b11 - b21);\n    const m14 = a02 * b20;\n    const m15 = (a21 + a22) * (-b20 + b21);\n    const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);\n    const m17 = (a02 - a12) * (b12 - b22);\n    const m18 = (a11 + a12) * (-b20 + b22);\n    const m19 = a01 * b10;\n    const m20 = a12 * b21;\n    const m21 = a10 * b02;\n    const m22 = a20 * b01;\n    const m23 = a22 * b22;\n\n    const c00 = m6 + m14 + m19;\n    const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;\n    const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;\n    const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;\n    const c11 = m2 + m4 + m5 + m6 + m20;\n    const c12 = m14 + m16 + m17 + m18 + m21;\n    const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;\n    const c21 = m12 + m13 + m14 + m15 + m22;\n    const c22 = m6 + m7 + m8 + m9 + m23;\n\n    result.set(0, 0, c00);\n    result.set(0, 1, c01);\n    result.set(0, 2, c02);\n    result.set(1, 0, c10);\n    result.set(1, 1, c11);\n    result.set(1, 2, c12);\n    result.set(2, 0, c20);\n    result.set(2, 1, c21);\n    result.set(2, 2, c22);\n    return result;\n  }\n\n  mmulStrassen(y) {\n    y = Matrix.checkMatrix(y);\n    let x = this.clone();\n    let r1 = x.rows;\n    let c1 = x.columns;\n    let r2 = y.rows;\n    let c2 = y.columns;\n    if (c1 !== r2) {\n      // eslint-disable-next-line no-console\n      console.warn(\n        `Multiplying ${r1} x ${c1} and ${r2} x ${c2} matrix: dimensions do not match.`,\n      );\n    }\n\n    // Put a matrix into the top left of a matrix of zeros.\n    // `rows` and `cols` are the dimensions of the output matrix.\n    function embed(mat, rows, cols) {\n      let r = mat.rows;\n      let c = mat.columns;\n      if (r === rows && c === cols) {\n        return mat;\n      } else {\n        let resultat = AbstractMatrix.zeros(rows, cols);\n        resultat = resultat.setSubMatrix(mat, 0, 0);\n        return resultat;\n      }\n    }\n\n    // Make sure both matrices are the same size.\n    // This is exclusively for simplicity:\n    // this algorithm can be implemented with matrices of different sizes.\n\n    let r = Math.max(r1, r2);\n    let c = Math.max(c1, c2);\n    x = embed(x, r, c);\n    y = embed(y, r, c);\n\n    // Our recursive multiplication function.\n    function blockMult(a, b, rows, cols) {\n      // For small matrices, resort to naive multiplication.\n      if (rows <= 512 || cols <= 512) {\n        return a.mmul(b); // a is equivalent to this\n      }\n\n      // Apply dynamic padding.\n      if (rows % 2 === 1 && cols % 2 === 1) {\n        a = embed(a, rows + 1, cols + 1);\n        b = embed(b, rows + 1, cols + 1);\n      } else if (rows % 2 === 1) {\n        a = embed(a, rows + 1, cols);\n        b = embed(b, rows + 1, cols);\n      } else if (cols % 2 === 1) {\n        a = embed(a, rows, cols + 1);\n        b = embed(b, rows, cols + 1);\n      }\n\n      let halfRows = parseInt(a.rows / 2, 10);\n      let halfCols = parseInt(a.columns / 2, 10);\n      // Subdivide input matrices.\n      let a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n      let b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n\n      let a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);\n      let b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);\n\n      let a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);\n      let b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);\n\n      let a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);\n      let b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1);\n\n      // Compute intermediate values.\n      let m1 = blockMult(\n        AbstractMatrix.add(a11, a22),\n        AbstractMatrix.add(b11, b22),\n        halfRows,\n        halfCols,\n      );\n      let m2 = blockMult(AbstractMatrix.add(a21, a22), b11, halfRows, halfCols);\n      let m3 = blockMult(a11, AbstractMatrix.sub(b12, b22), halfRows, halfCols);\n      let m4 = blockMult(a22, AbstractMatrix.sub(b21, b11), halfRows, halfCols);\n      let m5 = blockMult(AbstractMatrix.add(a11, a12), b22, halfRows, halfCols);\n      let m6 = blockMult(\n        AbstractMatrix.sub(a21, a11),\n        AbstractMatrix.add(b11, b12),\n        halfRows,\n        halfCols,\n      );\n      let m7 = blockMult(\n        AbstractMatrix.sub(a12, a22),\n        AbstractMatrix.add(b21, b22),\n        halfRows,\n        halfCols,\n      );\n\n      // Combine intermediate values into the output.\n      let c11 = AbstractMatrix.add(m1, m4);\n      c11.sub(m5);\n      c11.add(m7);\n      let c12 = AbstractMatrix.add(m3, m5);\n      let c21 = AbstractMatrix.add(m2, m4);\n      let c22 = AbstractMatrix.sub(m1, m2);\n      c22.add(m3);\n      c22.add(m6);\n\n      // Crop output to the desired size (undo dynamic padding).\n      let result = AbstractMatrix.zeros(2 * c11.rows, 2 * c11.columns);\n      result = result.setSubMatrix(c11, 0, 0);\n      result = result.setSubMatrix(c12, c11.rows, 0);\n      result = result.setSubMatrix(c21, 0, c11.columns);\n      result = result.setSubMatrix(c22, c11.rows, c11.columns);\n      return result.subMatrix(0, rows - 1, 0, cols - 1);\n    }\n\n    return blockMult(x, y, r, c);\n  }\n\n  scaleRows(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1 } = options;\n    if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n    if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let i = 0; i < this.rows; i++) {\n      const row = this.getRow(i);\n      if (row.length > 0) {\n        rescale(row, { min, max, output: row });\n      }\n      newMatrix.setRow(i, row);\n    }\n    return newMatrix;\n  }\n\n  scaleColumns(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1 } = options;\n    if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n    if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let i = 0; i < this.columns; i++) {\n      const column = this.getColumn(i);\n      if (column.length) {\n        rescale(column, {\n          min,\n          max,\n          output: column,\n        });\n      }\n      newMatrix.setColumn(i, column);\n    }\n    return newMatrix;\n  }\n\n  flipRows() {\n    const middle = Math.ceil(this.columns / 2);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < middle; j++) {\n        let first = this.get(i, j);\n        let last = this.get(i, this.columns - 1 - j);\n        this.set(i, j, last);\n        this.set(i, this.columns - 1 - j, first);\n      }\n    }\n    return this;\n  }\n\n  flipColumns() {\n    const middle = Math.ceil(this.rows / 2);\n    for (let j = 0; j < this.columns; j++) {\n      for (let i = 0; i < middle; i++) {\n        let first = this.get(i, j);\n        let last = this.get(this.rows - 1 - i, j);\n        this.set(i, j, last);\n        this.set(this.rows - 1 - i, j, first);\n      }\n    }\n    return this;\n  }\n\n  kroneckerProduct(other) {\n    other = Matrix.checkMatrix(other);\n\n    let m = this.rows;\n    let n = this.columns;\n    let p = other.rows;\n    let q = other.columns;\n\n    let result = new Matrix(m * p, n * q);\n    for (let i = 0; i < m; i++) {\n      for (let j = 0; j < n; j++) {\n        for (let k = 0; k < p; k++) {\n          for (let l = 0; l < q; l++) {\n            result.set(p * i + k, q * j + l, this.get(i, j) * other.get(k, l));\n          }\n        }\n      }\n    }\n    return result;\n  }\n\n  kroneckerSum(other) {\n    other = Matrix.checkMatrix(other);\n    if (!this.isSquare() || !other.isSquare()) {\n      throw new Error('Kronecker Sum needs two Square Matrices');\n    }\n    let m = this.rows;\n    let n = other.rows;\n    let AxI = this.kroneckerProduct(Matrix.eye(n, n));\n    let IxB = Matrix.eye(m, m).kroneckerProduct(other);\n    return AxI.add(IxB);\n  }\n\n  transpose() {\n    let result = new Matrix(this.columns, this.rows);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        result.set(j, i, this.get(i, j));\n      }\n    }\n    return result;\n  }\n\n  sortRows(compareFunction = compareNumbers) {\n    for (let i = 0; i < this.rows; i++) {\n      this.setRow(i, this.getRow(i).sort(compareFunction));\n    }\n    return this;\n  }\n\n  sortColumns(compareFunction = compareNumbers) {\n    for (let i = 0; i < this.columns; i++) {\n      this.setColumn(i, this.getColumn(i).sort(compareFunction));\n    }\n    return this;\n  }\n\n  subMatrix(startRow, endRow, startColumn, endColumn) {\n    checkRange(this, startRow, endRow, startColumn, endColumn);\n    let newMatrix = new Matrix(\n      endRow - startRow + 1,\n      endColumn - startColumn + 1,\n    );\n    for (let i = startRow; i <= endRow; i++) {\n      for (let j = startColumn; j <= endColumn; j++) {\n        newMatrix.set(i - startRow, j - startColumn, this.get(i, j));\n      }\n    }\n    return newMatrix;\n  }\n\n  subMatrixRow(indices, startColumn, endColumn) {\n    if (startColumn === undefined) startColumn = 0;\n    if (endColumn === undefined) endColumn = this.columns - 1;\n    if (\n      startColumn > endColumn ||\n      startColumn < 0 ||\n      startColumn >= this.columns ||\n      endColumn < 0 ||\n      endColumn >= this.columns\n    ) {\n      throw new RangeError('Argument out of range');\n    }\n\n    let newMatrix = new Matrix(indices.length, endColumn - startColumn + 1);\n    for (let i = 0; i < indices.length; i++) {\n      for (let j = startColumn; j <= endColumn; j++) {\n        if (indices[i] < 0 || indices[i] >= this.rows) {\n          throw new RangeError(`Row index out of range: ${indices[i]}`);\n        }\n        newMatrix.set(i, j - startColumn, this.get(indices[i], j));\n      }\n    }\n    return newMatrix;\n  }\n\n  subMatrixColumn(indices, startRow, endRow) {\n    if (startRow === undefined) startRow = 0;\n    if (endRow === undefined) endRow = this.rows - 1;\n    if (\n      startRow > endRow ||\n      startRow < 0 ||\n      startRow >= this.rows ||\n      endRow < 0 ||\n      endRow >= this.rows\n    ) {\n      throw new RangeError('Argument out of range');\n    }\n\n    let newMatrix = new Matrix(endRow - startRow + 1, indices.length);\n    for (let i = 0; i < indices.length; i++) {\n      for (let j = startRow; j <= endRow; j++) {\n        if (indices[i] < 0 || indices[i] >= this.columns) {\n          throw new RangeError(`Column index out of range: ${indices[i]}`);\n        }\n        newMatrix.set(j - startRow, i, this.get(j, indices[i]));\n      }\n    }\n    return newMatrix;\n  }\n\n  setSubMatrix(matrix, startRow, startColumn) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (matrix.isEmpty()) {\n      return this;\n    }\n    let endRow = startRow + matrix.rows - 1;\n    let endColumn = startColumn + matrix.columns - 1;\n    checkRange(this, startRow, endRow, startColumn, endColumn);\n    for (let i = 0; i < matrix.rows; i++) {\n      for (let j = 0; j < matrix.columns; j++) {\n        this.set(startRow + i, startColumn + j, matrix.get(i, j));\n      }\n    }\n    return this;\n  }\n\n  selection(rowIndices, columnIndices) {\n    checkRowIndices(this, rowIndices);\n    checkColumnIndices(this, columnIndices);\n    let newMatrix = new Matrix(rowIndices.length, columnIndices.length);\n    for (let i = 0; i < rowIndices.length; i++) {\n      let rowIndex = rowIndices[i];\n      for (let j = 0; j < columnIndices.length; j++) {\n        let columnIndex = columnIndices[j];\n        newMatrix.set(i, j, this.get(rowIndex, columnIndex));\n      }\n    }\n    return newMatrix;\n  }\n\n  trace() {\n    let min = Math.min(this.rows, this.columns);\n    let trace = 0;\n    for (let i = 0; i < min; i++) {\n      trace += this.get(i, i);\n    }\n    return trace;\n  }\n\n  clone() {\n    return this.constructor.copy(this, new Matrix(this.rows, this.columns));\n  }\n\n  /**\n   * @template {AbstractMatrix} M\n   * @param {AbstractMatrix} from\n   * @param {M} to\n   * @return {M}\n   */\n  static copy(from, to) {\n    for (const [row, column, value] of from.entries()) {\n      to.set(row, column, value);\n    }\n\n    return to;\n  }\n\n  sum(by) {\n    switch (by) {\n      case 'row':\n        return sumByRow(this);\n      case 'column':\n        return sumByColumn(this);\n      case undefined:\n        return sumAll(this);\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  product(by) {\n    switch (by) {\n      case 'row':\n        return productByRow(this);\n      case 'column':\n        return productByColumn(this);\n      case undefined:\n        return productAll(this);\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  mean(by) {\n    const sum = this.sum(by);\n    switch (by) {\n      case 'row': {\n        for (let i = 0; i < this.rows; i++) {\n          sum[i] /= this.columns;\n        }\n        return sum;\n      }\n      case 'column': {\n        for (let i = 0; i < this.columns; i++) {\n          sum[i] /= this.rows;\n        }\n        return sum;\n      }\n      case undefined:\n        return sum / this.size;\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  variance(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { unbiased = true, mean = this.mean(by) } = options;\n    if (typeof unbiased !== 'boolean') {\n      throw new TypeError('unbiased must be a boolean');\n    }\n    switch (by) {\n      case 'row': {\n        if (!isAnyArray(mean)) {\n          throw new TypeError('mean must be an array');\n        }\n        return varianceByRow(this, unbiased, mean);\n      }\n      case 'column': {\n        if (!isAnyArray(mean)) {\n          throw new TypeError('mean must be an array');\n        }\n        return varianceByColumn(this, unbiased, mean);\n      }\n      case undefined: {\n        if (typeof mean !== 'number') {\n          throw new TypeError('mean must be a number');\n        }\n        return varianceAll(this, unbiased, mean);\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  standardDeviation(by, options) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    const variance = this.variance(by, options);\n    if (by === undefined) {\n      return Math.sqrt(variance);\n    } else {\n      for (let i = 0; i < variance.length; i++) {\n        variance[i] = Math.sqrt(variance[i]);\n      }\n      return variance;\n    }\n  }\n\n  center(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { center = this.mean(by) } = options;\n    switch (by) {\n      case 'row': {\n        if (!isAnyArray(center)) {\n          throw new TypeError('center must be an array');\n        }\n        centerByRow(this, center);\n        return this;\n      }\n      case 'column': {\n        if (!isAnyArray(center)) {\n          throw new TypeError('center must be an array');\n        }\n        centerByColumn(this, center);\n        return this;\n      }\n      case undefined: {\n        if (typeof center !== 'number') {\n          throw new TypeError('center must be a number');\n        }\n        centerAll(this, center);\n        return this;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  scale(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    let scale = options.scale;\n    switch (by) {\n      case 'row': {\n        if (scale === undefined) {\n          scale = getScaleByRow(this);\n        } else if (!isAnyArray(scale)) {\n          throw new TypeError('scale must be an array');\n        }\n        scaleByRow(this, scale);\n        return this;\n      }\n      case 'column': {\n        if (scale === undefined) {\n          scale = getScaleByColumn(this);\n        } else if (!isAnyArray(scale)) {\n          throw new TypeError('scale must be an array');\n        }\n        scaleByColumn(this, scale);\n        return this;\n      }\n      case undefined: {\n        if (scale === undefined) {\n          scale = getScaleAll(this);\n        } else if (typeof scale !== 'number') {\n          throw new TypeError('scale must be a number');\n        }\n        scaleAll(this, scale);\n        return this;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  toString(options) {\n    return inspectMatrixWithOptions(this, options);\n  }\n\n  [Symbol.iterator]() {\n    return this.entries();\n  }\n\n  /**\n   * iterator from left to right, from top to bottom\n   * yield [row, column, value]\n   * @returns {Generator<[number, number, number], void, void>}\n   */\n  *entries() {\n    for (let row = 0; row < this.rows; row++) {\n      for (let col = 0; col < this.columns; col++) {\n        yield [row, col, this.get(row, col)];\n      }\n    }\n  }\n\n  /**\n   * iterator from left to right, from top to bottom\n   * yield value\n   * @returns {Generator<number, void, void>}\n   */\n  *values() {\n    for (let row = 0; row < this.rows; row++) {\n      for (let col = 0; col < this.columns; col++) {\n        yield this.get(row, col);\n      }\n    }\n  }\n}\n\nAbstractMatrix.prototype.klass = 'Matrix';\nif (typeof Symbol !== 'undefined') {\n  AbstractMatrix.prototype[Symbol.for('nodejs.util.inspect.custom')] =\n    inspectMatrix;\n}\n\nfunction compareNumbers(a, b) {\n  return a - b;\n}\n\nfunction isArrayOfNumbers(array) {\n  return array.every((element) => {\n    return typeof element === 'number';\n  });\n}\n\n// Synonyms\nAbstractMatrix.random = AbstractMatrix.rand;\nAbstractMatrix.randomInt = AbstractMatrix.randInt;\nAbstractMatrix.diagonal = AbstractMatrix.diag;\nAbstractMatrix.prototype.diagonal = AbstractMatrix.prototype.diag;\nAbstractMatrix.identity = AbstractMatrix.eye;\nAbstractMatrix.prototype.negate = AbstractMatrix.prototype.neg;\nAbstractMatrix.prototype.tensorProduct =\n  AbstractMatrix.prototype.kroneckerProduct;\n\nclass Matrix extends AbstractMatrix {\n  /**\n   * @type {Float64Array[]}\n   */\n  data;\n\n  /**\n   * Init an empty matrix\n   * @param {number} nRows\n   * @param {number} nColumns\n   */\n  #initData(nRows, nColumns) {\n    this.data = [];\n\n    if (Number.isInteger(nColumns) && nColumns >= 0) {\n      for (let i = 0; i < nRows; i++) {\n        this.data.push(new Float64Array(nColumns));\n      }\n    } else {\n      throw new TypeError('nColumns must be a positive integer');\n    }\n\n    this.rows = nRows;\n    this.columns = nColumns;\n  }\n\n  constructor(nRows, nColumns) {\n    super();\n    if (Matrix.isMatrix(nRows)) {\n      this.#initData(nRows.rows, nRows.columns);\n      Matrix.copy(nRows, this);\n    } else if (Number.isInteger(nRows) && nRows >= 0) {\n      this.#initData(nRows, nColumns);\n    } else if (isAnyArray(nRows)) {\n      // Copy the values from the 2D array\n      const arrayData = nRows;\n      nRows = arrayData.length;\n      nColumns = nRows ? arrayData[0].length : 0;\n      if (typeof nColumns !== 'number') {\n        throw new TypeError(\n          'Data must be a 2D array with at least one element',\n        );\n      }\n      this.data = [];\n\n      for (let i = 0; i < nRows; i++) {\n        if (arrayData[i].length !== nColumns) {\n          throw new RangeError('Inconsistent array dimensions');\n        }\n        if (!isArrayOfNumbers(arrayData[i])) {\n          throw new TypeError('Input data contains non-numeric values');\n        }\n        this.data.push(Float64Array.from(arrayData[i]));\n      }\n\n      this.rows = nRows;\n      this.columns = nColumns;\n    } else {\n      throw new TypeError(\n        'First argument must be a positive number or an array',\n      );\n    }\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.data[rowIndex][columnIndex] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.data[rowIndex][columnIndex];\n  }\n\n  removeRow(index) {\n    checkRowIndex(this, index);\n    this.data.splice(index, 1);\n    this.rows -= 1;\n    return this;\n  }\n\n  addRow(index, array) {\n    if (array === undefined) {\n      array = index;\n      index = this.rows;\n    }\n    checkRowIndex(this, index, true);\n    array = Float64Array.from(checkRowVector(this, array));\n    this.data.splice(index, 0, array);\n    this.rows += 1;\n    return this;\n  }\n\n  removeColumn(index) {\n    checkColumnIndex(this, index);\n    for (let i = 0; i < this.rows; i++) {\n      const newRow = new Float64Array(this.columns - 1);\n      for (let j = 0; j < index; j++) {\n        newRow[j] = this.data[i][j];\n      }\n      for (let j = index + 1; j < this.columns; j++) {\n        newRow[j - 1] = this.data[i][j];\n      }\n      this.data[i] = newRow;\n    }\n    this.columns -= 1;\n    return this;\n  }\n\n  addColumn(index, array) {\n    if (typeof array === 'undefined') {\n      array = index;\n      index = this.columns;\n    }\n    checkColumnIndex(this, index, true);\n    array = checkColumnVector(this, array);\n    for (let i = 0; i < this.rows; i++) {\n      const newRow = new Float64Array(this.columns + 1);\n      let j = 0;\n      for (; j < index; j++) {\n        newRow[j] = this.data[i][j];\n      }\n      newRow[j++] = array[i];\n      for (; j < this.columns + 1; j++) {\n        newRow[j] = this.data[i][j - 1];\n      }\n      this.data[i] = newRow;\n    }\n    this.columns += 1;\n    return this;\n  }\n}\n\ninstallMathOperations(AbstractMatrix, Matrix);\n\n/**\n * @typedef {0 | 1 | number | boolean} Mask\n */\n\nclass SymmetricMatrix extends AbstractMatrix {\n  /** @type {Matrix} */\n  #matrix;\n\n  get size() {\n    return this.#matrix.size;\n  }\n\n  get rows() {\n    return this.#matrix.rows;\n  }\n\n  get columns() {\n    return this.#matrix.columns;\n  }\n\n  get diagonalSize() {\n    return this.rows;\n  }\n\n  /**\n   * not the same as matrix.isSymmetric()\n   * Here is to check if it's instanceof SymmetricMatrix without bundling issues\n   *\n   * @param value\n   * @returns {boolean}\n   */\n  static isSymmetricMatrix(value) {\n    return Matrix.isMatrix(value) && value.klassType === 'SymmetricMatrix';\n  }\n\n  /**\n   * @param diagonalSize\n   * @return {SymmetricMatrix}\n   */\n  static zeros(diagonalSize) {\n    return new this(diagonalSize);\n  }\n\n  /**\n   * @param diagonalSize\n   * @return {SymmetricMatrix}\n   */\n  static ones(diagonalSize) {\n    return new this(diagonalSize).fill(1);\n  }\n\n  /**\n   * @param {number | AbstractMatrix | ArrayLike<ArrayLike<number>>} diagonalSize\n   * @return {this}\n   */\n  constructor(diagonalSize) {\n    super();\n\n    if (Matrix.isMatrix(diagonalSize)) {\n      if (!diagonalSize.isSymmetric()) {\n        throw new TypeError('not symmetric data');\n      }\n\n      this.#matrix = Matrix.copy(\n        diagonalSize,\n        new Matrix(diagonalSize.rows, diagonalSize.rows),\n      );\n    } else if (Number.isInteger(diagonalSize) && diagonalSize >= 0) {\n      this.#matrix = new Matrix(diagonalSize, diagonalSize);\n    } else {\n      this.#matrix = new Matrix(diagonalSize);\n\n      if (!this.isSymmetric()) {\n        throw new TypeError('not symmetric data');\n      }\n    }\n  }\n\n  clone() {\n    const matrix = new SymmetricMatrix(this.diagonalSize);\n\n    for (const [row, col, value] of this.upperRightEntries()) {\n      matrix.set(row, col, value);\n    }\n\n    return matrix;\n  }\n\n  toMatrix() {\n    return new Matrix(this);\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.#matrix.get(rowIndex, columnIndex);\n  }\n  set(rowIndex, columnIndex, value) {\n    // symmetric set\n    this.#matrix.set(rowIndex, columnIndex, value);\n    this.#matrix.set(columnIndex, rowIndex, value);\n\n    return this;\n  }\n\n  removeCross(index) {\n    // symmetric remove side\n    this.#matrix.removeRow(index);\n    this.#matrix.removeColumn(index);\n\n    return this;\n  }\n\n  addCross(index, array) {\n    if (array === undefined) {\n      array = index;\n      index = this.diagonalSize;\n    }\n\n    const row = array.slice();\n    row.splice(index, 1);\n\n    this.#matrix.addRow(index, row);\n    this.#matrix.addColumn(index, array);\n\n    return this;\n  }\n\n  /**\n   * @param {Mask[]} mask\n   */\n  applyMask(mask) {\n    if (mask.length !== this.diagonalSize) {\n      throw new RangeError('Mask size do not match with matrix size');\n    }\n\n    // prepare sides to remove from matrix from mask\n    /** @type {number[]} */\n    const sidesToRemove = [];\n    for (const [index, passthroughs] of mask.entries()) {\n      if (passthroughs) continue;\n      sidesToRemove.push(index);\n    }\n    // to remove from highest to lowest for no mutation shifting\n    sidesToRemove.reverse();\n\n    // remove sides\n    for (const sideIndex of sidesToRemove) {\n      this.removeCross(sideIndex);\n    }\n\n    return this;\n  }\n\n  /**\n   * Compact format upper-right corner of matrix\n   * iterate from left to right, from top to bottom.\n   *\n   * ```\n   *   A B C D\n   * A 1 2 3 4\n   * B 2 5 6 7\n   * C 3 6 8 9\n   * D 4 7 9 10\n   * ```\n   *\n   * will return compact 1D array `[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]`\n   *\n   * length is S(i=0, n=sideSize) => 10 for a 4 sideSized matrix\n   *\n   * @returns {number[]}\n   */\n  toCompact() {\n    const { diagonalSize } = this;\n\n    /** @type {number[]} */\n    const compact = new Array((diagonalSize * (diagonalSize + 1)) / 2);\n    for (let col = 0, row = 0, index = 0; index < compact.length; index++) {\n      compact[index] = this.get(row, col);\n\n      if (++col >= diagonalSize) col = ++row;\n    }\n\n    return compact;\n  }\n\n  /**\n   * @param {number[]} compact\n   * @return {SymmetricMatrix}\n   */\n  static fromCompact(compact) {\n    const compactSize = compact.length;\n    // compactSize = (sideSize * (sideSize + 1)) / 2\n    // https://mathsolver.microsoft.com/fr/solve-problem/y%20%3D%20%20x%20%60cdot%20%20%20%60frac%7B%20%20%60left(%20x%2B1%20%20%60right)%20%20%20%20%7D%7B%202%20%20%7D\n    // sideSize = (Sqrt(8 × compactSize + 1) - 1) / 2\n    const diagonalSize = (Math.sqrt(8 * compactSize + 1) - 1) / 2;\n\n    if (!Number.isInteger(diagonalSize)) {\n      throw new TypeError(\n        `This array is not a compact representation of a Symmetric Matrix, ${JSON.stringify(\n          compact,\n        )}`,\n      );\n    }\n\n    const matrix = new SymmetricMatrix(diagonalSize);\n    for (let col = 0, row = 0, index = 0; index < compactSize; index++) {\n      matrix.set(col, row, compact[index]);\n      if (++col >= diagonalSize) col = ++row;\n    }\n\n    return matrix;\n  }\n\n  /**\n   * half iterator upper-right-corner from left to right, from top to bottom\n   * yield [row, column, value]\n   *\n   * @returns {Generator<[number, number, number], void, void>}\n   */\n  *upperRightEntries() {\n    for (let row = 0, col = 0; row < this.diagonalSize; void 0) {\n      const value = this.get(row, col);\n\n      yield [row, col, value];\n\n      // at the end of row, move cursor to next row at diagonal position\n      if (++col >= this.diagonalSize) col = ++row;\n    }\n  }\n\n  /**\n   * half iterator upper-right-corner from left to right, from top to bottom\n   * yield value\n   *\n   * @returns {Generator<[number, number, number], void, void>}\n   */\n  *upperRightValues() {\n    for (let row = 0, col = 0; row < this.diagonalSize; void 0) {\n      const value = this.get(row, col);\n\n      yield value;\n\n      // at the end of row, move cursor to next row at diagonal position\n      if (++col >= this.diagonalSize) col = ++row;\n    }\n  }\n}\nSymmetricMatrix.prototype.klassType = 'SymmetricMatrix';\n\nclass DistanceMatrix extends SymmetricMatrix {\n  /**\n   * not the same as matrix.isSymmetric()\n   * Here is to check if it's instanceof SymmetricMatrix without bundling issues\n   *\n   * @param value\n   * @returns {boolean}\n   */\n  static isDistanceMatrix(value) {\n    return (\n      SymmetricMatrix.isSymmetricMatrix(value) &&\n      value.klassSubType === 'DistanceMatrix'\n    );\n  }\n\n  constructor(sideSize) {\n    super(sideSize);\n\n    if (!this.isDistance()) {\n      throw new TypeError('Provided arguments do no produce a distance matrix');\n    }\n  }\n\n  set(rowIndex, columnIndex, value) {\n    // distance matrix diagonal is 0\n    if (rowIndex === columnIndex) value = 0;\n\n    return super.set(rowIndex, columnIndex, value);\n  }\n\n  addCross(index, array) {\n    if (array === undefined) {\n      array = index;\n      index = this.diagonalSize;\n    }\n\n    // ensure distance\n    array = array.slice();\n    array[index] = 0;\n\n    return super.addCross(index, array);\n  }\n\n  toSymmetricMatrix() {\n    return new SymmetricMatrix(this);\n  }\n\n  clone() {\n    const matrix = new DistanceMatrix(this.diagonalSize);\n\n    for (const [row, col, value] of this.upperRightEntries()) {\n      if (row === col) continue;\n      matrix.set(row, col, value);\n    }\n\n    return matrix;\n  }\n\n  /**\n   * Compact format upper-right corner of matrix\n   * no diagonal (only zeros)\n   * iterable from left to right, from top to bottom.\n   *\n   * ```\n   *   A B C D\n   * A 0 1 2 3\n   * B 1 0 4 5\n   * C 2 4 0 6\n   * D 3 5 6 0\n   * ```\n   *\n   * will return compact 1D array `[1, 2, 3, 4, 5, 6]`\n   *\n   * length is S(i=0, n=sideSize-1) => 6 for a 4 side sized matrix\n   *\n   * @returns {number[]}\n   */\n  toCompact() {\n    const { diagonalSize } = this;\n    const compactLength = ((diagonalSize - 1) * diagonalSize) / 2;\n\n    /** @type {number[]} */\n    const compact = new Array(compactLength);\n    for (let col = 1, row = 0, index = 0; index < compact.length; index++) {\n      compact[index] = this.get(row, col);\n\n      if (++col >= diagonalSize) col = ++row + 1;\n    }\n\n    return compact;\n  }\n\n  /**\n   * @param {number[]} compact\n   */\n  static fromCompact(compact) {\n    const compactSize = compact.length;\n\n    if (compactSize === 0) {\n      return new this(0);\n    }\n\n    // compactSize in Natural integer range ]0;∞]\n    // compactSize = (sideSize * (sideSize - 1)) / 2\n    // sideSize = (Sqrt(8 × compactSize + 1) + 1) / 2\n    const diagonalSize = (Math.sqrt(8 * compactSize + 1) + 1) / 2;\n\n    if (!Number.isInteger(diagonalSize)) {\n      throw new TypeError(\n        `This array is not a compact representation of a DistanceMatrix, ${JSON.stringify(\n          compact,\n        )}`,\n      );\n    }\n\n    const matrix = new this(diagonalSize);\n    for (let col = 1, row = 0, index = 0; index < compactSize; index++) {\n      matrix.set(col, row, compact[index]);\n      if (++col >= diagonalSize) col = ++row + 1;\n    }\n\n    return matrix;\n  }\n}\nDistanceMatrix.prototype.klassSubType = 'DistanceMatrix';\n\nclass BaseView extends AbstractMatrix {\n  constructor(matrix, rows, columns) {\n    super();\n    this.matrix = matrix;\n    this.rows = rows;\n    this.columns = columns;\n  }\n}\n\nclass MatrixColumnView extends BaseView {\n  constructor(matrix, column) {\n    checkColumnIndex(matrix, column);\n    super(matrix, matrix.rows, 1);\n    this.column = column;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(rowIndex, this.column, value);\n    return this;\n  }\n\n  get(rowIndex) {\n    return this.matrix.get(rowIndex, this.column);\n  }\n}\n\nclass MatrixColumnSelectionView extends BaseView {\n  constructor(matrix, columnIndices) {\n    checkColumnIndices(matrix, columnIndices);\n    super(matrix, matrix.rows, columnIndices.length);\n    this.columnIndices = columnIndices;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(rowIndex, this.columnIndices[columnIndex], value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(rowIndex, this.columnIndices[columnIndex]);\n  }\n}\n\nclass MatrixFlipColumnView extends BaseView {\n  constructor(matrix) {\n    super(matrix, matrix.rows, matrix.columns);\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(rowIndex, this.columns - columnIndex - 1, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(rowIndex, this.columns - columnIndex - 1);\n  }\n}\n\nclass MatrixFlipRowView extends BaseView {\n  constructor(matrix) {\n    super(matrix, matrix.rows, matrix.columns);\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(this.rows - rowIndex - 1, columnIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(this.rows - rowIndex - 1, columnIndex);\n  }\n}\n\nclass MatrixRowView extends BaseView {\n  constructor(matrix, row) {\n    checkRowIndex(matrix, row);\n    super(matrix, 1, matrix.columns);\n    this.row = row;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(this.row, columnIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(this.row, columnIndex);\n  }\n}\n\nclass MatrixRowSelectionView extends BaseView {\n  constructor(matrix, rowIndices) {\n    checkRowIndices(matrix, rowIndices);\n    super(matrix, rowIndices.length, matrix.columns);\n    this.rowIndices = rowIndices;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(this.rowIndices[rowIndex], columnIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(this.rowIndices[rowIndex], columnIndex);\n  }\n}\n\nclass MatrixSelectionView extends BaseView {\n  constructor(matrix, rowIndices, columnIndices) {\n    checkRowIndices(matrix, rowIndices);\n    checkColumnIndices(matrix, columnIndices);\n    super(matrix, rowIndices.length, columnIndices.length);\n    this.rowIndices = rowIndices;\n    this.columnIndices = columnIndices;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(\n      this.rowIndices[rowIndex],\n      this.columnIndices[columnIndex],\n      value,\n    );\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(\n      this.rowIndices[rowIndex],\n      this.columnIndices[columnIndex],\n    );\n  }\n}\n\nclass MatrixSubView extends BaseView {\n  constructor(matrix, startRow, endRow, startColumn, endColumn) {\n    checkRange(matrix, startRow, endRow, startColumn, endColumn);\n    super(matrix, endRow - startRow + 1, endColumn - startColumn + 1);\n    this.startRow = startRow;\n    this.startColumn = startColumn;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(\n      this.startRow + rowIndex,\n      this.startColumn + columnIndex,\n      value,\n    );\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(\n      this.startRow + rowIndex,\n      this.startColumn + columnIndex,\n    );\n  }\n}\n\nclass MatrixTransposeView extends BaseView {\n  constructor(matrix) {\n    super(matrix, matrix.columns, matrix.rows);\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(columnIndex, rowIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(columnIndex, rowIndex);\n  }\n}\n\nclass WrapperMatrix1D extends AbstractMatrix {\n  constructor(data, options = {}) {\n    const { rows = 1 } = options;\n\n    if (data.length % rows !== 0) {\n      throw new Error('the data length is not divisible by the number of rows');\n    }\n    super();\n    this.rows = rows;\n    this.columns = data.length / rows;\n    this.data = data;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    let index = this._calculateIndex(rowIndex, columnIndex);\n    this.data[index] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    let index = this._calculateIndex(rowIndex, columnIndex);\n    return this.data[index];\n  }\n\n  _calculateIndex(row, column) {\n    return row * this.columns + column;\n  }\n}\n\nclass WrapperMatrix2D extends AbstractMatrix {\n  constructor(data) {\n    super();\n    this.data = data;\n    this.rows = data.length;\n    this.columns = data[0].length;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.data[rowIndex][columnIndex] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.data[rowIndex][columnIndex];\n  }\n}\n\nfunction wrap(array, options) {\n  if (isAnyArray(array)) {\n    if (array[0] && isAnyArray(array[0])) {\n      return new WrapperMatrix2D(array);\n    } else {\n      return new WrapperMatrix1D(array, options);\n    }\n  } else {\n    throw new Error('the argument is not an array');\n  }\n}\n\nclass LuDecomposition {\n  constructor(matrix) {\n    matrix = WrapperMatrix2D.checkMatrix(matrix);\n\n    let lu = matrix.clone();\n    let rows = lu.rows;\n    let columns = lu.columns;\n    let pivotVector = new Float64Array(rows);\n    let pivotSign = 1;\n    let i, j, k, p, s, t, v;\n    let LUcolj, kmax;\n\n    for (i = 0; i < rows; i++) {\n      pivotVector[i] = i;\n    }\n\n    LUcolj = new Float64Array(rows);\n\n    for (j = 0; j < columns; j++) {\n      for (i = 0; i < rows; i++) {\n        LUcolj[i] = lu.get(i, j);\n      }\n\n      for (i = 0; i < rows; i++) {\n        kmax = Math.min(i, j);\n        s = 0;\n        for (k = 0; k < kmax; k++) {\n          s += lu.get(i, k) * LUcolj[k];\n        }\n        LUcolj[i] -= s;\n        lu.set(i, j, LUcolj[i]);\n      }\n\n      p = j;\n      for (i = j + 1; i < rows; i++) {\n        if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {\n          p = i;\n        }\n      }\n\n      if (p !== j) {\n        for (k = 0; k < columns; k++) {\n          t = lu.get(p, k);\n          lu.set(p, k, lu.get(j, k));\n          lu.set(j, k, t);\n        }\n\n        v = pivotVector[p];\n        pivotVector[p] = pivotVector[j];\n        pivotVector[j] = v;\n\n        pivotSign = -pivotSign;\n      }\n\n      if (j < rows && lu.get(j, j) !== 0) {\n        for (i = j + 1; i < rows; i++) {\n          lu.set(i, j, lu.get(i, j) / lu.get(j, j));\n        }\n      }\n    }\n\n    this.LU = lu;\n    this.pivotVector = pivotVector;\n    this.pivotSign = pivotSign;\n  }\n\n  isSingular() {\n    let data = this.LU;\n    let col = data.columns;\n    for (let j = 0; j < col; j++) {\n      if (data.get(j, j) === 0) {\n        return true;\n      }\n    }\n    return false;\n  }\n\n  solve(value) {\n    value = Matrix.checkMatrix(value);\n\n    let lu = this.LU;\n    let rows = lu.rows;\n\n    if (rows !== value.rows) {\n      throw new Error('Invalid matrix dimensions');\n    }\n    if (this.isSingular()) {\n      throw new Error('LU matrix is singular');\n    }\n\n    let count = value.columns;\n    let X = value.subMatrixRow(this.pivotVector, 0, count - 1);\n    let columns = lu.columns;\n    let i, j, k;\n\n    for (k = 0; k < columns; k++) {\n      for (i = k + 1; i < columns; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n        }\n      }\n    }\n    for (k = columns - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        X.set(k, j, X.get(k, j) / lu.get(k, k));\n      }\n      for (i = 0; i < k; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n        }\n      }\n    }\n    return X;\n  }\n\n  get determinant() {\n    let data = this.LU;\n    if (!data.isSquare()) {\n      throw new Error('Matrix must be square');\n    }\n    let determinant = this.pivotSign;\n    let col = data.columns;\n    for (let j = 0; j < col; j++) {\n      determinant *= data.get(j, j);\n    }\n    return determinant;\n  }\n\n  get lowerTriangularMatrix() {\n    let data = this.LU;\n    let rows = data.rows;\n    let columns = data.columns;\n    let X = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        if (i > j) {\n          X.set(i, j, data.get(i, j));\n        } else if (i === j) {\n          X.set(i, j, 1);\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get upperTriangularMatrix() {\n    let data = this.LU;\n    let rows = data.rows;\n    let columns = data.columns;\n    let X = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        if (i <= j) {\n          X.set(i, j, data.get(i, j));\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get pivotPermutationVector() {\n    return Array.from(this.pivotVector);\n  }\n}\n\n/**\n * Transpose a square matrix in place, without allocating a copy.\n * Used to restore the logical layout of decomposition outputs that were\n * accumulated in transposed storage for cache-sequential inner loops.\n * @param {import('../matrix').default} matrix - square matrix, mutated in place\n * @returns {import('../matrix').default} the same matrix\n */\nfunction transposeSquareInPlace(matrix) {\n  const data = matrix.data;\n  const n = matrix.rows;\n  for (let i = 0; i < n; i++) {\n    const rowI = data[i];\n    for (let j = i + 1; j < n; j++) {\n      const tmp = rowI[j];\n      rowI[j] = data[j][i];\n      data[j][i] = tmp;\n    }\n  }\n  return matrix;\n}\n\nfunction hypotenuse(a, b) {\n  let r = 0;\n  if (Math.abs(a) > Math.abs(b)) {\n    r = b / a;\n    return Math.abs(a) * Math.sqrt(1 + r * r);\n  }\n  if (b !== 0) {\n    r = a / b;\n    return Math.abs(b) * Math.sqrt(1 + r * r);\n  }\n  return 0;\n}\n\nclass QrDecomposition {\n  constructor(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let qr = value.clone();\n    let m = value.rows;\n    let n = value.columns;\n    let rdiag = new Float64Array(n);\n    let i, j, k, s;\n\n    for (k = 0; k < n; k++) {\n      let nrm = 0;\n      for (i = k; i < m; i++) {\n        nrm = hypotenuse(nrm, qr.get(i, k));\n      }\n      if (nrm !== 0) {\n        if (qr.get(k, k) < 0) {\n          nrm = -nrm;\n        }\n        for (i = k; i < m; i++) {\n          qr.set(i, k, qr.get(i, k) / nrm);\n        }\n        qr.set(k, k, qr.get(k, k) + 1);\n        for (j = k + 1; j < n; j++) {\n          s = 0;\n          for (i = k; i < m; i++) {\n            s += qr.get(i, k) * qr.get(i, j);\n          }\n          s = -s / qr.get(k, k);\n          for (i = k; i < m; i++) {\n            qr.set(i, j, qr.get(i, j) + s * qr.get(i, k));\n          }\n        }\n      }\n      rdiag[k] = -nrm;\n    }\n\n    this.QR = qr;\n    this.Rdiag = rdiag;\n  }\n\n  solve(value) {\n    value = Matrix.checkMatrix(value);\n\n    let qr = this.QR;\n    let m = qr.rows;\n\n    if (value.rows !== m) {\n      throw new Error('Matrix row dimensions must agree');\n    }\n    if (!this.isFullRank()) {\n      throw new Error('Matrix is rank deficient');\n    }\n\n    let count = value.columns;\n    let X = value.clone();\n    let n = qr.columns;\n    let i, j, k, s;\n\n    for (k = 0; k < n; k++) {\n      for (j = 0; j < count; j++) {\n        s = 0;\n        for (i = k; i < m; i++) {\n          s += qr.get(i, k) * X.get(i, j);\n        }\n        s = -s / qr.get(k, k);\n        for (i = k; i < m; i++) {\n          X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n        }\n      }\n    }\n    for (k = n - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        X.set(k, j, X.get(k, j) / this.Rdiag[k]);\n      }\n      for (i = 0; i < k; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * qr.get(i, k));\n        }\n      }\n    }\n\n    return X.subMatrix(0, n - 1, 0, count - 1);\n  }\n\n  isFullRank() {\n    let columns = this.QR.columns;\n    for (let i = 0; i < columns; i++) {\n      if (this.Rdiag[i] === 0) {\n        return false;\n      }\n    }\n    return true;\n  }\n\n  get upperTriangularMatrix() {\n    let qr = this.QR;\n    let n = qr.columns;\n    let X = new Matrix(n, n);\n    let i, j;\n    for (i = 0; i < n; i++) {\n      for (j = 0; j < n; j++) {\n        if (i < j) {\n          X.set(i, j, qr.get(i, j));\n        } else if (i === j) {\n          X.set(i, j, this.Rdiag[i]);\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get orthogonalMatrix() {\n    let qr = this.QR;\n    let rows = qr.rows;\n    let columns = qr.columns;\n    let X = new Matrix(rows, columns);\n    let i, j, k, s;\n\n    for (k = columns - 1; k >= 0; k--) {\n      for (i = 0; i < rows; i++) {\n        X.set(i, k, 0);\n      }\n      X.set(k, k, 1);\n      for (j = k; j < columns; j++) {\n        if (qr.get(k, k) !== 0) {\n          s = 0;\n          for (i = k; i < rows; i++) {\n            s += qr.get(i, k) * X.get(i, j);\n          }\n\n          s = -s / qr.get(k, k);\n\n          for (i = k; i < rows; i++) {\n            X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n          }\n        }\n      }\n    }\n    return X;\n  }\n}\n\nclass SingularValueDecomposition {\n  constructor(value, options = {}) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    if (value.isEmpty()) {\n      throw new Error('Matrix must be non-empty');\n    }\n\n    let m = value.rows;\n    let n = value.columns;\n\n    const {\n      computeLeftSingularVectors = true,\n      computeRightSingularVectors = true,\n      autoTranspose = false,\n    } = options;\n\n    let wantu = Boolean(computeLeftSingularVectors);\n    let wantv = Boolean(computeRightSingularVectors);\n\n    // Work on the transpose of the input so the hot inner loops (which iterate\n    // over rows for a fixed column) scan memory sequentially in the row-major\n    // backing store. `at` holds the transpose: at.get(j, i) === a.get(i, j)\n    // where `a` is the logical m x n working matrix.\n    let swapped = false;\n    let at;\n    if (m < n) {\n      if (!autoTranspose) {\n        // eslint-disable-next-line no-console\n        console.warn(\n          'Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose',\n        );\n        at = value.transpose();\n      } else {\n        at = value.clone();\n        m = value.columns;\n        n = value.rows;\n        swapped = true;\n        let aux = wantu;\n        wantu = wantv;\n        wantv = aux;\n      }\n    } else {\n      at = value.transpose();\n    }\n\n    let nu = Math.min(m, n);\n    let ni = Math.min(m + 1, n);\n    let s = new Float64Array(ni);\n    // U and V are stored transposed during the computation so the inner loops\n    // (which always vary the row index) scan memory sequentially. They are\n    // transposed back to their logical layout before being returned.\n    // Ut.get(j, i) === U.get(i, j) and Vt.get(j, i) === V.get(i, j).\n    let U = new Matrix(nu, m);\n    let V = new Matrix(n, n);\n\n    let e = new Float64Array(n);\n    let work = new Float64Array(m);\n\n    let si = new Float64Array(ni);\n    for (let i = 0; i < ni; i++) si[i] = i;\n\n    let nct = Math.min(m - 1, n);\n    let nrt = Math.max(0, Math.min(n - 2, m));\n    let mrc = Math.max(nct, nrt);\n\n    for (let k = 0; k < mrc; k++) {\n      if (k < nct) {\n        s[k] = 0;\n        for (let i = k; i < m; i++) {\n          s[k] = hypotenuse(s[k], at.get(k, i));\n        }\n        if (s[k] !== 0) {\n          if (at.get(k, k) < 0) {\n            s[k] = -s[k];\n          }\n          for (let i = k; i < m; i++) {\n            at.set(k, i, at.get(k, i) / s[k]);\n          }\n          at.set(k, k, at.get(k, k) + 1);\n        }\n        s[k] = -s[k];\n      }\n\n      for (let j = k + 1; j < n; j++) {\n        if (k < nct && s[k] !== 0) {\n          let t = 0;\n          for (let i = k; i < m; i++) {\n            t += at.get(k, i) * at.get(j, i);\n          }\n          t = -t / at.get(k, k);\n          for (let i = k; i < m; i++) {\n            at.set(j, i, at.get(j, i) + t * at.get(k, i));\n          }\n        }\n        e[j] = at.get(j, k);\n      }\n\n      if (wantu && k < nct) {\n        for (let i = k; i < m; i++) {\n          U.set(k, i, at.get(k, i));\n        }\n      }\n\n      if (k < nrt) {\n        e[k] = 0;\n        for (let i = k + 1; i < n; i++) {\n          e[k] = hypotenuse(e[k], e[i]);\n        }\n        if (e[k] !== 0) {\n          if (e[k + 1] < 0) {\n            e[k] = 0 - e[k];\n          }\n          for (let i = k + 1; i < n; i++) {\n            e[i] /= e[k];\n          }\n          e[k + 1] += 1;\n        }\n        e[k] = -e[k];\n        if (k + 1 < m && e[k] !== 0) {\n          for (let i = k + 1; i < m; i++) {\n            work[i] = 0;\n          }\n          for (let i = k + 1; i < m; i++) {\n            for (let j = k + 1; j < n; j++) {\n              work[i] += e[j] * at.get(j, i);\n            }\n          }\n          for (let j = k + 1; j < n; j++) {\n            let t = -e[j] / e[k + 1];\n            for (let i = k + 1; i < m; i++) {\n              at.set(j, i, at.get(j, i) + t * work[i]);\n            }\n          }\n        }\n        if (wantv) {\n          for (let i = k + 1; i < n; i++) {\n            V.set(k, i, e[i]);\n          }\n        }\n      }\n    }\n\n    let p = Math.min(n, m + 1);\n    if (nct < n) {\n      s[nct] = at.get(nct, nct);\n    }\n    if (m < p) {\n      s[p - 1] = 0;\n    }\n    if (nrt + 1 < p) {\n      e[nrt] = at.get(p - 1, nrt);\n    }\n    e[p - 1] = 0;\n\n    if (wantu) {\n      for (let j = nct; j < nu; j++) {\n        for (let i = 0; i < m; i++) {\n          U.set(j, i, 0);\n        }\n        U.set(j, j, 1);\n      }\n      for (let k = nct - 1; k >= 0; k--) {\n        if (s[k] !== 0) {\n          for (let j = k + 1; j < nu; j++) {\n            let t = 0;\n            for (let i = k; i < m; i++) {\n              t += U.get(k, i) * U.get(j, i);\n            }\n            t = -t / U.get(k, k);\n            for (let i = k; i < m; i++) {\n              U.set(j, i, U.get(j, i) + t * U.get(k, i));\n            }\n          }\n          for (let i = k; i < m; i++) {\n            U.set(k, i, -U.get(k, i));\n          }\n          U.set(k, k, 1 + U.get(k, k));\n          for (let i = 0; i < k - 1; i++) {\n            U.set(k, i, 0);\n          }\n        } else {\n          for (let i = 0; i < m; i++) {\n            U.set(k, i, 0);\n          }\n          U.set(k, k, 1);\n        }\n      }\n    }\n\n    if (wantv) {\n      for (let k = n - 1; k >= 0; k--) {\n        if (k < nrt && e[k] !== 0) {\n          for (let j = k + 1; j < n; j++) {\n            let t = 0;\n            for (let i = k + 1; i < n; i++) {\n              t += V.get(k, i) * V.get(j, i);\n            }\n            t = -t / V.get(k, k + 1);\n            for (let i = k + 1; i < n; i++) {\n              V.set(j, i, V.get(j, i) + t * V.get(k, i));\n            }\n          }\n        }\n        for (let i = 0; i < n; i++) {\n          V.set(k, i, 0);\n        }\n        V.set(k, k, 1);\n      }\n    }\n\n    let pp = p - 1;\n    let eps = Number.EPSILON;\n    while (p > 0) {\n      let k, kase;\n      for (k = p - 2; k >= -1; k--) {\n        if (k === -1) {\n          break;\n        }\n        const alpha =\n          Number.MIN_VALUE + eps * Math.abs(s[k] + Math.abs(s[k + 1]));\n        if (Math.abs(e[k]) <= alpha || Number.isNaN(e[k])) {\n          e[k] = 0;\n          break;\n        }\n      }\n      if (k === p - 2) {\n        kase = 4;\n      } else {\n        let ks;\n        for (ks = p - 1; ks >= k; ks--) {\n          if (ks === k) {\n            break;\n          }\n          let t =\n            (ks !== p ? Math.abs(e[ks]) : 0) +\n            (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);\n          if (Math.abs(s[ks]) <= eps * t) {\n            s[ks] = 0;\n            break;\n          }\n        }\n        if (ks === k) {\n          kase = 3;\n        } else if (ks === p - 1) {\n          kase = 1;\n        } else {\n          kase = 2;\n          k = ks;\n        }\n      }\n\n      k++;\n\n      switch (kase) {\n        case 1: {\n          let f = e[p - 2];\n          e[p - 2] = 0;\n          for (let j = p - 2; j >= k; j--) {\n            let t = hypotenuse(s[j], f);\n            let cs = s[j] / t;\n            let sn = f / t;\n            s[j] = t;\n            if (j !== k) {\n              f = -sn * e[j - 1];\n              e[j - 1] = cs * e[j - 1];\n            }\n            if (wantv) {\n              for (let i = 0; i < n; i++) {\n                t = cs * V.get(j, i) + sn * V.get(p - 1, i);\n                V.set(p - 1, i, -sn * V.get(j, i) + cs * V.get(p - 1, i));\n                V.set(j, i, t);\n              }\n            }\n          }\n          break;\n        }\n        case 2: {\n          let f = e[k - 1];\n          e[k - 1] = 0;\n          for (let j = k; j < p; j++) {\n            let t = hypotenuse(s[j], f);\n            let cs = s[j] / t;\n            let sn = f / t;\n            s[j] = t;\n            f = -sn * e[j];\n            e[j] = cs * e[j];\n            if (wantu) {\n              for (let i = 0; i < m; i++) {\n                t = cs * U.get(j, i) + sn * U.get(k - 1, i);\n                U.set(k - 1, i, -sn * U.get(j, i) + cs * U.get(k - 1, i));\n                U.set(j, i, t);\n              }\n            }\n          }\n          break;\n        }\n        case 3: {\n          const scale = Math.max(\n            Math.abs(s[p - 1]),\n            Math.abs(s[p - 2]),\n            Math.abs(e[p - 2]),\n            Math.abs(s[k]),\n            Math.abs(e[k]),\n          );\n          const sp = s[p - 1] / scale;\n          const spm1 = s[p - 2] / scale;\n          const epm1 = e[p - 2] / scale;\n          const sk = s[k] / scale;\n          const ek = e[k] / scale;\n          const b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;\n          const c = sp * epm1 * (sp * epm1);\n          let shift = 0;\n          if (b !== 0 || c !== 0) {\n            if (b < 0) {\n              shift = 0 - Math.sqrt(b * b + c);\n            } else {\n              shift = Math.sqrt(b * b + c);\n            }\n            shift = c / (b + shift);\n          }\n          let f = (sk + sp) * (sk - sp) + shift;\n          let g = sk * ek;\n          for (let j = k; j < p - 1; j++) {\n            let t = hypotenuse(f, g);\n            if (t === 0) t = Number.MIN_VALUE;\n            let cs = f / t;\n            let sn = g / t;\n            if (j !== k) {\n              e[j - 1] = t;\n            }\n            f = cs * s[j] + sn * e[j];\n            e[j] = cs * e[j] - sn * s[j];\n            g = sn * s[j + 1];\n            s[j + 1] = cs * s[j + 1];\n            if (wantv) {\n              for (let i = 0; i < n; i++) {\n                t = cs * V.get(j, i) + sn * V.get(j + 1, i);\n                V.set(j + 1, i, -sn * V.get(j, i) + cs * V.get(j + 1, i));\n                V.set(j, i, t);\n              }\n            }\n            t = hypotenuse(f, g);\n            if (t === 0) t = Number.MIN_VALUE;\n            cs = f / t;\n            sn = g / t;\n            s[j] = t;\n            f = cs * e[j] + sn * s[j + 1];\n            s[j + 1] = -sn * e[j] + cs * s[j + 1];\n            g = sn * e[j + 1];\n            e[j + 1] = cs * e[j + 1];\n            if (wantu && j < m - 1) {\n              for (let i = 0; i < m; i++) {\n                t = cs * U.get(j, i) + sn * U.get(j + 1, i);\n                U.set(j + 1, i, -sn * U.get(j, i) + cs * U.get(j + 1, i));\n                U.set(j, i, t);\n              }\n            }\n          }\n          e[p - 2] = f;\n          break;\n        }\n        case 4: {\n          if (s[k] <= 0) {\n            s[k] = s[k] < 0 ? -s[k] : 0;\n            if (wantv) {\n              for (let i = 0; i <= pp; i++) {\n                V.set(k, i, -V.get(k, i));\n              }\n            }\n          }\n          while (k < pp) {\n            if (s[k] >= s[k + 1]) {\n              break;\n            }\n            let t = s[k];\n            s[k] = s[k + 1];\n            s[k + 1] = t;\n            if (wantv && k < n - 1) {\n              for (let i = 0; i < n; i++) {\n                t = V.get(k + 1, i);\n                V.set(k + 1, i, V.get(k, i));\n                V.set(k, i, t);\n              }\n            }\n            if (wantu && k < m - 1) {\n              for (let i = 0; i < m; i++) {\n                t = U.get(k + 1, i);\n                U.set(k + 1, i, U.get(k, i));\n                U.set(k, i, t);\n              }\n            }\n            k++;\n          }\n          p--;\n          break;\n        }\n        // no default\n      }\n    }\n\n    // Restore the logical (row-major) layout of the singular vectors, which were\n    // accumulated in transposed storage for cache-sequential inner loops. V is\n    // always square and U is square whenever the input is, so this is done in\n    // place (no allocation) in the common case.\n    U = U.isSquare() ? transposeSquareInPlace(U) : U.transpose();\n    V = transposeSquareInPlace(V);\n\n    if (swapped) {\n      let tmp = V;\n      V = U;\n      U = tmp;\n    }\n\n    this.m = m;\n    this.n = n;\n    this.s = s;\n    this.U = U;\n    this.V = V;\n  }\n\n  solve(value) {\n    let Y = value;\n    let e = this.threshold;\n    let scols = this.s.length;\n    let Ls = Matrix.zeros(scols, scols);\n\n    for (let i = 0; i < scols; i++) {\n      if (Math.abs(this.s[i]) <= e) {\n        Ls.set(i, i, 0);\n      } else {\n        Ls.set(i, i, 1 / this.s[i]);\n      }\n    }\n\n    let U = this.U;\n    let V = this.rightSingularVectors;\n\n    let VL = V.mmul(Ls);\n    let vrows = V.rows;\n    let urows = U.rows;\n    let VLU = Matrix.zeros(vrows, urows);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < urows; j++) {\n        let sum = 0;\n        for (let k = 0; k < scols; k++) {\n          sum += VL.get(i, k) * U.get(j, k);\n        }\n        VLU.set(i, j, sum);\n      }\n    }\n\n    return VLU.mmul(Y);\n  }\n\n  solveForDiagonal(value) {\n    return this.solve(Matrix.diag(value));\n  }\n\n  inverse() {\n    let V = this.V;\n    let e = this.threshold;\n    let vrows = V.rows;\n    let vcols = V.columns;\n    let X = new Matrix(vrows, this.s.length);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < vcols; j++) {\n        if (Math.abs(this.s[j]) > e) {\n          X.set(i, j, V.get(i, j) / this.s[j]);\n        }\n      }\n    }\n\n    let U = this.U;\n\n    let urows = U.rows;\n    let ucols = U.columns;\n    let Y = new Matrix(vrows, urows);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < urows; j++) {\n        let sum = 0;\n        for (let k = 0; k < ucols; k++) {\n          sum += X.get(i, k) * U.get(j, k);\n        }\n        Y.set(i, j, sum);\n      }\n    }\n\n    return Y;\n  }\n\n  get condition() {\n    return this.s[0] / this.s[Math.min(this.m, this.n) - 1];\n  }\n\n  get norm2() {\n    return this.s[0];\n  }\n\n  get rank() {\n    let tol = Math.max(this.m, this.n) * this.s[0] * Number.EPSILON;\n    let r = 0;\n    let s = this.s;\n    for (let i = 0, ii = s.length; i < ii; i++) {\n      if (s[i] > tol) {\n        r++;\n      }\n    }\n    return r;\n  }\n\n  get diagonal() {\n    return Array.from(this.s);\n  }\n\n  get threshold() {\n    return (Number.EPSILON / 2) * Math.max(this.m, this.n) * this.s[0];\n  }\n\n  get leftSingularVectors() {\n    return this.U;\n  }\n\n  get rightSingularVectors() {\n    return this.V;\n  }\n\n  get diagonalMatrix() {\n    return Matrix.diag(this.s);\n  }\n}\n\nfunction inverse(matrix, useSVD = false) {\n  matrix = WrapperMatrix2D.checkMatrix(matrix);\n  if (useSVD) {\n    return new SingularValueDecomposition(matrix).inverse();\n  } else {\n    return solve(matrix, Matrix.eye(matrix.rows));\n  }\n}\n\nfunction solve(leftHandSide, rightHandSide, useSVD = false) {\n  leftHandSide = WrapperMatrix2D.checkMatrix(leftHandSide);\n  rightHandSide = WrapperMatrix2D.checkMatrix(rightHandSide);\n  if (useSVD) {\n    return new SingularValueDecomposition(leftHandSide).solve(rightHandSide);\n  } else {\n    return leftHandSide.isSquare()\n      ? new LuDecomposition(leftHandSide).solve(rightHandSide)\n      : new QrDecomposition(leftHandSide).solve(rightHandSide);\n  }\n}\n\nfunction determinant(matrix) {\n  matrix = Matrix.checkMatrix(matrix);\n  if (matrix.isSquare()) {\n    if (matrix.columns === 0) {\n      return 1;\n    }\n\n    let a, b, c, d;\n    if (matrix.columns === 2) {\n      // 2 x 2 matrix\n      a = matrix.get(0, 0);\n      b = matrix.get(0, 1);\n      c = matrix.get(1, 0);\n      d = matrix.get(1, 1);\n\n      return a * d - b * c;\n    } else if (matrix.columns === 3) {\n      // 3 x 3 matrix\n      let subMatrix0, subMatrix1, subMatrix2;\n      subMatrix0 = new MatrixSelectionView(matrix, [1, 2], [1, 2]);\n      subMatrix1 = new MatrixSelectionView(matrix, [1, 2], [0, 2]);\n      subMatrix2 = new MatrixSelectionView(matrix, [1, 2], [0, 1]);\n      a = matrix.get(0, 0);\n      b = matrix.get(0, 1);\n      c = matrix.get(0, 2);\n\n      return (\n        a * determinant(subMatrix0) -\n        b * determinant(subMatrix1) +\n        c * determinant(subMatrix2)\n      );\n    } else {\n      // general purpose determinant using the LU decomposition\n      return new LuDecomposition(matrix).determinant;\n    }\n  } else {\n    throw Error('determinant can only be calculated for a square matrix');\n  }\n}\n\nfunction xrange(n, exception) {\n  let range = [];\n  for (let i = 0; i < n; i++) {\n    if (i !== exception) {\n      range.push(i);\n    }\n  }\n  return range;\n}\n\nfunction dependenciesOneRow(\n  error,\n  matrix,\n  index,\n  thresholdValue = 10e-10,\n  thresholdError = 10e-10,\n) {\n  if (error > thresholdError) {\n    return new Array(matrix.rows + 1).fill(0);\n  } else {\n    let returnArray = matrix.addRow(index, [0]);\n    for (let i = 0; i < returnArray.rows; i++) {\n      if (Math.abs(returnArray.get(i, 0)) < thresholdValue) {\n        returnArray.set(i, 0, 0);\n      }\n    }\n    return returnArray.to1DArray();\n  }\n}\n\nfunction linearDependencies(matrix, options = {}) {\n  const { thresholdValue = 10e-10, thresholdError = 10e-10 } = options;\n  matrix = Matrix.checkMatrix(matrix);\n\n  let n = matrix.rows;\n  let results = new Matrix(n, n);\n\n  for (let i = 0; i < n; i++) {\n    let b = Matrix.columnVector(matrix.getRow(i));\n    let Abis = matrix.subMatrixRow(xrange(n, i)).transpose();\n    let svd = new SingularValueDecomposition(Abis);\n    let x = svd.solve(b);\n    let error = Matrix.sub(b, Abis.mmul(x)).abs().max();\n    results.setRow(\n      i,\n      dependenciesOneRow(error, x, i, thresholdValue, thresholdError),\n    );\n  }\n  return results;\n}\n\nfunction pseudoInverse(matrix, threshold = Number.EPSILON) {\n  matrix = Matrix.checkMatrix(matrix);\n  if (matrix.isEmpty()) {\n    // with a zero dimension, the pseudo-inverse is the transpose, since all 0xn and nx0 matrices are singular\n    // (0xn)*(nx0)*(0xn) = 0xn\n    // (nx0)*(0xn)*(nx0) = nx0\n    return matrix.transpose();\n  }\n  let svdSolution = new SingularValueDecomposition(matrix, { autoTranspose: true });\n\n  let U = svdSolution.leftSingularVectors;\n  let V = svdSolution.rightSingularVectors;\n  let s = svdSolution.diagonal;\n\n  for (let i = 0; i < s.length; i++) {\n    if (Math.abs(s[i]) > threshold) {\n      s[i] = 1.0 / s[i];\n    } else {\n      s[i] = 0.0;\n    }\n  }\n\n  return V.mmul(Matrix.diag(s).mmul(U.transpose()));\n}\n\nfunction covariance(xMatrix, yMatrix = xMatrix, options = {}) {\n  xMatrix = new Matrix(xMatrix);\n  let yIsSame = false;\n  if (\n    typeof yMatrix === 'object' &&\n    !Matrix.isMatrix(yMatrix) &&\n    !isAnyArray(yMatrix)\n  ) {\n    options = yMatrix;\n    yMatrix = xMatrix;\n    yIsSame = true;\n  } else {\n    yMatrix = new Matrix(yMatrix);\n  }\n  if (xMatrix.rows !== yMatrix.rows) {\n    throw new TypeError('Both matrices must have the same number of rows');\n  }\n  const { center = true } = options;\n  if (center) {\n    xMatrix = xMatrix.center('column');\n    if (!yIsSame) {\n      yMatrix = yMatrix.center('column');\n    }\n  }\n  const cov = xMatrix.transposeMultiply(yMatrix);\n  for (let i = 0; i < cov.rows; i++) {\n    for (let j = 0; j < cov.columns; j++) {\n      cov.set(i, j, cov.get(i, j) * (1 / (xMatrix.rows - 1)));\n    }\n  }\n  return cov;\n}\n\nfunction correlation(xMatrix, yMatrix = xMatrix, options = {}) {\n  xMatrix = new Matrix(xMatrix);\n  let yIsSame = false;\n  if (\n    typeof yMatrix === 'object' &&\n    !Matrix.isMatrix(yMatrix) &&\n    !isAnyArray(yMatrix)\n  ) {\n    options = yMatrix;\n    yMatrix = xMatrix;\n    yIsSame = true;\n  } else {\n    yMatrix = new Matrix(yMatrix);\n  }\n  if (xMatrix.rows !== yMatrix.rows) {\n    throw new TypeError('Both matrices must have the same number of rows');\n  }\n\n  const { center = true, scale = true } = options;\n  if (center) {\n    xMatrix.center('column');\n    if (!yIsSame) {\n      yMatrix.center('column');\n    }\n  }\n  if (scale) {\n    xMatrix.scale('column');\n    if (!yIsSame) {\n      yMatrix.scale('column');\n    }\n  }\n\n  const sdx = xMatrix.standardDeviation('column', { unbiased: true });\n  const sdy = yIsSame\n    ? sdx\n    : yMatrix.standardDeviation('column', { unbiased: true });\n\n  const corr = xMatrix.transposeMultiply(yMatrix);\n  for (let i = 0; i < corr.rows; i++) {\n    for (let j = 0; j < corr.columns; j++) {\n      corr.set(\n        i,\n        j,\n        corr.get(i, j) * (1 / (sdx[i] * sdy[j])) * (1 / (xMatrix.rows - 1)),\n      );\n    }\n  }\n  return corr;\n}\n\nclass EigenvalueDecomposition {\n  constructor(matrix, options = {}) {\n    const { assumeSymmetric = false } = options;\n\n    matrix = WrapperMatrix2D.checkMatrix(matrix);\n    if (!matrix.isSquare()) {\n      throw new Error('Matrix is not a square matrix');\n    }\n\n    if (matrix.isEmpty()) {\n      throw new Error('Matrix must be non-empty');\n    }\n\n    let n = matrix.columns;\n    let V = new Matrix(n, n);\n    let d = new Float64Array(n);\n    let e = new Float64Array(n);\n    let value = matrix;\n    let i, j;\n\n    let isSymmetric = false;\n    if (assumeSymmetric) {\n      isSymmetric = true;\n    } else {\n      isSymmetric = matrix.isSymmetric();\n    }\n\n    if (isSymmetric) {\n      // tred2/tql2 access V almost exclusively down columns (the row index\n      // varies in the hot loops). Storing V transposed turns those into\n      // sequential row scans of the row-major backing store; we transpose it\n      // back to the logical layout before returning. V.get(j, i) holds the\n      // logical V(i, j).\n      for (i = 0; i < n; i++) {\n        for (j = 0; j < n; j++) {\n          V.set(j, i, value.get(i, j));\n        }\n      }\n      tred2(n, e, d, V);\n      tql2(n, e, d, V);\n      // V is square; restore the logical layout in place (no allocation).\n      transposeSquareInPlace(V);\n    } else {\n      // The non-symmetric path (orthes/hqr2) has two O(n^3) phases with opposite\n      // memory-layout preferences (the QR sweep favours column-major eigenvectors\n      // while the back-transform favours row-major), so a single transposed\n      // storage cannot help both. It is left in the original row-major layout.\n      let H = new Matrix(n, n);\n      let ort = new Float64Array(n);\n      for (j = 0; j < n; j++) {\n        for (i = 0; i < n; i++) {\n          H.set(i, j, value.get(i, j));\n        }\n      }\n      orthes(n, H, ort, V);\n      hqr2(n, e, d, V, H);\n    }\n\n    this.n = n;\n    this.e = e;\n    this.d = d;\n    this.V = V;\n  }\n\n  get realEigenvalues() {\n    return Array.from(this.d);\n  }\n\n  get imaginaryEigenvalues() {\n    return Array.from(this.e);\n  }\n\n  get eigenvectorMatrix() {\n    return this.V;\n  }\n\n  get diagonalMatrix() {\n    let n = this.n;\n    let e = this.e;\n    let d = this.d;\n    let X = new Matrix(n, n);\n    let i, j;\n    for (i = 0; i < n; i++) {\n      for (j = 0; j < n; j++) {\n        X.set(i, j, 0);\n      }\n      X.set(i, i, d[i]);\n      if (e[i] > 0) {\n        X.set(i, i + 1, e[i]);\n      } else if (e[i] < 0) {\n        X.set(i, i - 1, e[i]);\n      }\n    }\n    return X;\n  }\n}\n\nfunction tred2(n, e, d, V) {\n  let f, g, h, i, j, k, hh, scale;\n\n  for (j = 0; j < n; j++) {\n    d[j] = V.get(j, n - 1);\n  }\n\n  for (i = n - 1; i > 0; i--) {\n    scale = 0;\n    h = 0;\n    for (k = 0; k < i; k++) {\n      scale = scale + Math.abs(d[k]);\n    }\n\n    if (scale === 0) {\n      e[i] = d[i - 1];\n      for (j = 0; j < i; j++) {\n        d[j] = V.get(j, i - 1);\n        V.set(j, i, 0);\n        V.set(i, j, 0);\n      }\n    } else {\n      for (k = 0; k < i; k++) {\n        d[k] /= scale;\n        h += d[k] * d[k];\n      }\n\n      f = d[i - 1];\n      g = Math.sqrt(h);\n      if (f > 0) {\n        g = -g;\n      }\n\n      e[i] = scale * g;\n      h = h - f * g;\n      d[i - 1] = f - g;\n      for (j = 0; j < i; j++) {\n        e[j] = 0;\n      }\n\n      for (j = 0; j < i; j++) {\n        f = d[j];\n        V.set(i, j, f);\n        g = e[j] + V.get(j, j) * f;\n        for (k = j + 1; k <= i - 1; k++) {\n          g += V.get(j, k) * d[k];\n          e[k] += V.get(j, k) * f;\n        }\n        e[j] = g;\n      }\n\n      f = 0;\n      for (j = 0; j < i; j++) {\n        e[j] /= h;\n        f += e[j] * d[j];\n      }\n\n      hh = f / (h + h);\n      for (j = 0; j < i; j++) {\n        e[j] -= hh * d[j];\n      }\n\n      for (j = 0; j < i; j++) {\n        f = d[j];\n        g = e[j];\n        for (k = j; k <= i - 1; k++) {\n          V.set(j, k, V.get(j, k) - (f * e[k] + g * d[k]));\n        }\n        d[j] = V.get(j, i - 1);\n        V.set(j, i, 0);\n      }\n    }\n    d[i] = h;\n  }\n\n  for (i = 0; i < n - 1; i++) {\n    V.set(i, n - 1, V.get(i, i));\n    V.set(i, i, 1);\n    h = d[i + 1];\n    if (h !== 0) {\n      for (k = 0; k <= i; k++) {\n        d[k] = V.get(i + 1, k) / h;\n      }\n\n      for (j = 0; j <= i; j++) {\n        g = 0;\n        for (k = 0; k <= i; k++) {\n          g += V.get(i + 1, k) * V.get(j, k);\n        }\n        for (k = 0; k <= i; k++) {\n          V.set(j, k, V.get(j, k) - g * d[k]);\n        }\n      }\n    }\n\n    for (k = 0; k <= i; k++) {\n      V.set(i + 1, k, 0);\n    }\n  }\n\n  for (j = 0; j < n; j++) {\n    d[j] = V.get(j, n - 1);\n    V.set(j, n - 1, 0);\n  }\n\n  V.set(n - 1, n - 1, 1);\n  e[0] = 0;\n}\n\nfunction tql2(n, e, d, V) {\n  let g, h, i, j, k, l, m, p, r, dl1, c, c2, c3, el1, s, s2;\n\n  for (i = 1; i < n; i++) {\n    e[i - 1] = e[i];\n  }\n\n  e[n - 1] = 0;\n\n  let f = 0;\n  let tst1 = 0;\n  let eps = Number.EPSILON;\n\n  for (l = 0; l < n; l++) {\n    tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l]));\n    m = l;\n    while (m < n) {\n      if (Math.abs(e[m]) <= eps * tst1) {\n        break;\n      }\n      m++;\n    }\n\n    if (m > l) {\n      do {\n\n        g = d[l];\n        p = (d[l + 1] - g) / (2 * e[l]);\n        r = hypotenuse(p, 1);\n        if (p < 0) {\n          r = -r;\n        }\n\n        d[l] = e[l] / (p + r);\n        d[l + 1] = e[l] * (p + r);\n        dl1 = d[l + 1];\n        h = g - d[l];\n        for (i = l + 2; i < n; i++) {\n          d[i] -= h;\n        }\n\n        f = f + h;\n\n        p = d[m];\n        c = 1;\n        c2 = c;\n        c3 = c;\n        el1 = e[l + 1];\n        s = 0;\n        s2 = 0;\n        for (i = m - 1; i >= l; i--) {\n          c3 = c2;\n          c2 = c;\n          s2 = s;\n          g = c * e[i];\n          h = c * p;\n          r = hypotenuse(p, e[i]);\n          e[i + 1] = s * r;\n          s = e[i] / r;\n          c = p / r;\n          p = c * d[i] - s * g;\n          d[i + 1] = h + s * (c * g + s * d[i]);\n\n          for (k = 0; k < n; k++) {\n            h = V.get(i + 1, k);\n            V.set(i + 1, k, s * V.get(i, k) + c * h);\n            V.set(i, k, c * V.get(i, k) - s * h);\n          }\n        }\n\n        p = (-s * s2 * c3 * el1 * e[l]) / dl1;\n        e[l] = s * p;\n        d[l] = c * p;\n      } while (Math.abs(e[l]) > eps * tst1);\n    }\n    d[l] = d[l] + f;\n    e[l] = 0;\n  }\n\n  for (i = 0; i < n - 1; i++) {\n    k = i;\n    p = d[i];\n    for (j = i + 1; j < n; j++) {\n      if (d[j] < p) {\n        k = j;\n        p = d[j];\n      }\n    }\n\n    if (k !== i) {\n      d[k] = d[i];\n      d[i] = p;\n      for (j = 0; j < n; j++) {\n        p = V.get(i, j);\n        V.set(i, j, V.get(k, j));\n        V.set(k, j, p);\n      }\n    }\n  }\n}\n\nfunction orthes(n, H, ort, V) {\n  let low = 0;\n  let high = n - 1;\n  let f, g, h, i, j, m;\n  let scale;\n\n  for (m = low + 1; m <= high - 1; m++) {\n    scale = 0;\n    for (i = m; i <= high; i++) {\n      scale = scale + Math.abs(H.get(i, m - 1));\n    }\n\n    if (scale !== 0) {\n      h = 0;\n      for (i = high; i >= m; i--) {\n        ort[i] = H.get(i, m - 1) / scale;\n        h += ort[i] * ort[i];\n      }\n\n      g = Math.sqrt(h);\n      if (ort[m] > 0) {\n        g = -g;\n      }\n\n      h = h - ort[m] * g;\n      ort[m] = ort[m] - g;\n\n      for (j = m; j < n; j++) {\n        f = 0;\n        for (i = high; i >= m; i--) {\n          f += ort[i] * H.get(i, j);\n        }\n\n        f = f / h;\n        for (i = m; i <= high; i++) {\n          H.set(i, j, H.get(i, j) - f * ort[i]);\n        }\n      }\n\n      for (i = 0; i <= high; i++) {\n        f = 0;\n        for (j = high; j >= m; j--) {\n          f += ort[j] * H.get(i, j);\n        }\n\n        f = f / h;\n        for (j = m; j <= high; j++) {\n          H.set(i, j, H.get(i, j) - f * ort[j]);\n        }\n      }\n\n      ort[m] = scale * ort[m];\n      H.set(m, m - 1, scale * g);\n    }\n  }\n\n  for (i = 0; i < n; i++) {\n    for (j = 0; j < n; j++) {\n      V.set(i, j, i === j ? 1 : 0);\n    }\n  }\n\n  for (m = high - 1; m >= low + 1; m--) {\n    if (H.get(m, m - 1) !== 0) {\n      for (i = m + 1; i <= high; i++) {\n        ort[i] = H.get(i, m - 1);\n      }\n\n      for (j = m; j <= high; j++) {\n        g = 0;\n        for (i = m; i <= high; i++) {\n          g += ort[i] * V.get(i, j);\n        }\n\n        g = g / ort[m] / H.get(m, m - 1);\n        for (i = m; i <= high; i++) {\n          V.set(i, j, V.get(i, j) + g * ort[i]);\n        }\n      }\n    }\n  }\n}\n\nfunction hqr2(nn, e, d, V, H) {\n  let n = nn - 1;\n  let low = 0;\n  let high = nn - 1;\n  let eps = Number.EPSILON;\n  let exshift = 0;\n  let norm = 0;\n  let p = 0;\n  let q = 0;\n  let r = 0;\n  let s = 0;\n  let z = 0;\n  let iter = 0;\n  let i, j, k, l, m, t, w, x, y;\n  let ra, sa, vr, vi;\n  let notlast, cdivres;\n\n  for (i = 0; i < nn; i++) {\n    if (i < low || i > high) {\n      d[i] = H.get(i, i);\n      e[i] = 0;\n    }\n\n    for (j = Math.max(i - 1, 0); j < nn; j++) {\n      norm = norm + Math.abs(H.get(i, j));\n    }\n  }\n\n  while (n >= low) {\n    l = n;\n    while (l > low) {\n      s = Math.abs(H.get(l - 1, l - 1)) + Math.abs(H.get(l, l));\n      if (s === 0) {\n        s = norm;\n      }\n      if (Math.abs(H.get(l, l - 1)) < eps * s) {\n        break;\n      }\n      l--;\n    }\n\n    if (l === n) {\n      H.set(n, n, H.get(n, n) + exshift);\n      d[n] = H.get(n, n);\n      e[n] = 0;\n      n--;\n      iter = 0;\n    } else if (l === n - 1) {\n      w = H.get(n, n - 1) * H.get(n - 1, n);\n      p = (H.get(n - 1, n - 1) - H.get(n, n)) / 2;\n      q = p * p + w;\n      z = Math.sqrt(Math.abs(q));\n      H.set(n, n, H.get(n, n) + exshift);\n      H.set(n - 1, n - 1, H.get(n - 1, n - 1) + exshift);\n      x = H.get(n, n);\n\n      if (q >= 0) {\n        z = p >= 0 ? p + z : p - z;\n        d[n - 1] = x + z;\n        d[n] = d[n - 1];\n        if (z !== 0) {\n          d[n] = x - w / z;\n        }\n        e[n - 1] = 0;\n        e[n] = 0;\n        x = H.get(n, n - 1);\n        s = Math.abs(x) + Math.abs(z);\n        p = x / s;\n        q = z / s;\n        r = Math.sqrt(p * p + q * q);\n        p = p / r;\n        q = q / r;\n\n        for (j = n - 1; j < nn; j++) {\n          z = H.get(n - 1, j);\n          H.set(n - 1, j, q * z + p * H.get(n, j));\n          H.set(n, j, q * H.get(n, j) - p * z);\n        }\n\n        for (i = 0; i <= n; i++) {\n          z = H.get(i, n - 1);\n          H.set(i, n - 1, q * z + p * H.get(i, n));\n          H.set(i, n, q * H.get(i, n) - p * z);\n        }\n\n        for (i = low; i <= high; i++) {\n          z = V.get(i, n - 1);\n          V.set(i, n - 1, q * z + p * V.get(i, n));\n          V.set(i, n, q * V.get(i, n) - p * z);\n        }\n      } else {\n        d[n - 1] = x + p;\n        d[n] = x + p;\n        e[n - 1] = z;\n        e[n] = -z;\n      }\n\n      n = n - 2;\n      iter = 0;\n    } else {\n      x = H.get(n, n);\n      y = 0;\n      w = 0;\n      if (l < n) {\n        y = H.get(n - 1, n - 1);\n        w = H.get(n, n - 1) * H.get(n - 1, n);\n      }\n\n      if (iter === 10) {\n        exshift += x;\n        for (i = low; i <= n; i++) {\n          H.set(i, i, H.get(i, i) - x);\n        }\n        s = Math.abs(H.get(n, n - 1)) + Math.abs(H.get(n - 1, n - 2));\n        // eslint-disable-next-line no-multi-assign\n        x = y = 0.75 * s;\n        w = -0.4375 * s * s;\n      }\n\n      if (iter === 30) {\n        s = (y - x) / 2;\n        s = s * s + w;\n        if (s > 0) {\n          s = Math.sqrt(s);\n          if (y < x) {\n            s = -s;\n          }\n          s = x - w / ((y - x) / 2 + s);\n          for (i = low; i <= n; i++) {\n            H.set(i, i, H.get(i, i) - s);\n          }\n          exshift += s;\n          // eslint-disable-next-line no-multi-assign\n          x = y = w = 0.964;\n        }\n      }\n\n      iter = iter + 1;\n\n      m = n - 2;\n      while (m >= l) {\n        z = H.get(m, m);\n        r = x - z;\n        s = y - z;\n        p = (r * s - w) / H.get(m + 1, m) + H.get(m, m + 1);\n        q = H.get(m + 1, m + 1) - z - r - s;\n        r = H.get(m + 2, m + 1);\n        s = Math.abs(p) + Math.abs(q) + Math.abs(r);\n        p = p / s;\n        q = q / s;\n        r = r / s;\n        if (m === l) {\n          break;\n        }\n        if (\n          Math.abs(H.get(m, m - 1)) * (Math.abs(q) + Math.abs(r)) <\n          eps *\n            (Math.abs(p) *\n              (Math.abs(H.get(m - 1, m - 1)) +\n                Math.abs(z) +\n                Math.abs(H.get(m + 1, m + 1))))\n        ) {\n          break;\n        }\n        m--;\n      }\n\n      for (i = m + 2; i <= n; i++) {\n        H.set(i, i - 2, 0);\n        if (i > m + 2) {\n          H.set(i, i - 3, 0);\n        }\n      }\n\n      for (k = m; k <= n - 1; k++) {\n        notlast = k !== n - 1;\n        if (k !== m) {\n          p = H.get(k, k - 1);\n          q = H.get(k + 1, k - 1);\n          r = notlast ? H.get(k + 2, k - 1) : 0;\n          x = Math.abs(p) + Math.abs(q) + Math.abs(r);\n          if (x !== 0) {\n            p = p / x;\n            q = q / x;\n            r = r / x;\n          }\n        }\n\n        if (x === 0) {\n          break;\n        }\n\n        s = Math.sqrt(p * p + q * q + r * r);\n        if (p < 0) {\n          s = -s;\n        }\n\n        if (s !== 0) {\n          if (k !== m) {\n            H.set(k, k - 1, -s * x);\n          } else if (l !== m) {\n            H.set(k, k - 1, -H.get(k, k - 1));\n          }\n\n          p = p + s;\n          x = p / s;\n          y = q / s;\n          z = r / s;\n          q = q / p;\n          r = r / p;\n\n          for (j = k; j < nn; j++) {\n            p = H.get(k, j) + q * H.get(k + 1, j);\n            if (notlast) {\n              p = p + r * H.get(k + 2, j);\n              H.set(k + 2, j, H.get(k + 2, j) - p * z);\n            }\n\n            H.set(k, j, H.get(k, j) - p * x);\n            H.set(k + 1, j, H.get(k + 1, j) - p * y);\n          }\n\n          for (i = 0; i <= Math.min(n, k + 3); i++) {\n            p = x * H.get(i, k) + y * H.get(i, k + 1);\n            if (notlast) {\n              p = p + z * H.get(i, k + 2);\n              H.set(i, k + 2, H.get(i, k + 2) - p * r);\n            }\n\n            H.set(i, k, H.get(i, k) - p);\n            H.set(i, k + 1, H.get(i, k + 1) - p * q);\n          }\n\n          for (i = low; i <= high; i++) {\n            p = x * V.get(i, k) + y * V.get(i, k + 1);\n            if (notlast) {\n              p = p + z * V.get(i, k + 2);\n              V.set(i, k + 2, V.get(i, k + 2) - p * r);\n            }\n\n            V.set(i, k, V.get(i, k) - p);\n            V.set(i, k + 1, V.get(i, k + 1) - p * q);\n          }\n        }\n      }\n    }\n  }\n\n  if (norm === 0) {\n    return;\n  }\n\n  for (n = nn - 1; n >= 0; n--) {\n    p = d[n];\n    q = e[n];\n\n    if (q === 0) {\n      l = n;\n      H.set(n, n, 1);\n      for (i = n - 1; i >= 0; i--) {\n        w = H.get(i, i) - p;\n        r = 0;\n        for (j = l; j <= n; j++) {\n          r = r + H.get(i, j) * H.get(j, n);\n        }\n\n        if (e[i] < 0) {\n          z = w;\n          s = r;\n        } else {\n          l = i;\n          if (e[i] === 0) {\n            H.set(i, n, w !== 0 ? -r / w : -r / (eps * norm));\n          } else {\n            x = H.get(i, i + 1);\n            y = H.get(i + 1, i);\n            q = (d[i] - p) * (d[i] - p) + e[i] * e[i];\n            t = (x * s - z * r) / q;\n            H.set(i, n, t);\n            H.set(\n              i + 1,\n              n,\n              Math.abs(x) > Math.abs(z) ? (-r - w * t) / x : (-s - y * t) / z,\n            );\n          }\n\n          t = Math.abs(H.get(i, n));\n          if (eps * t * t > 1) {\n            for (j = i; j <= n; j++) {\n              H.set(j, n, H.get(j, n) / t);\n            }\n          }\n        }\n      }\n    } else if (q < 0) {\n      l = n - 1;\n\n      if (Math.abs(H.get(n, n - 1)) > Math.abs(H.get(n - 1, n))) {\n        H.set(n - 1, n - 1, q / H.get(n, n - 1));\n        H.set(n - 1, n, -(H.get(n, n) - p) / H.get(n, n - 1));\n      } else {\n        cdivres = cdiv(0, -H.get(n - 1, n), H.get(n - 1, n - 1) - p, q);\n        H.set(n - 1, n - 1, cdivres[0]);\n        H.set(n - 1, n, cdivres[1]);\n      }\n\n      H.set(n, n - 1, 0);\n      H.set(n, n, 1);\n      for (i = n - 2; i >= 0; i--) {\n        ra = 0;\n        sa = 0;\n        for (j = l; j <= n; j++) {\n          ra = ra + H.get(i, j) * H.get(j, n - 1);\n          sa = sa + H.get(i, j) * H.get(j, n);\n        }\n\n        w = H.get(i, i) - p;\n\n        if (e[i] < 0) {\n          z = w;\n          r = ra;\n          s = sa;\n        } else {\n          l = i;\n          if (e[i] === 0) {\n            cdivres = cdiv(-ra, -sa, w, q);\n            H.set(i, n - 1, cdivres[0]);\n            H.set(i, n, cdivres[1]);\n          } else {\n            x = H.get(i, i + 1);\n            y = H.get(i + 1, i);\n            vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;\n            vi = (d[i] - p) * 2 * q;\n            if (vr === 0 && vi === 0) {\n              vr =\n                eps *\n                norm *\n                (Math.abs(w) +\n                  Math.abs(q) +\n                  Math.abs(x) +\n                  Math.abs(y) +\n                  Math.abs(z));\n            }\n            cdivres = cdiv(\n              x * r - z * ra + q * sa,\n              x * s - z * sa - q * ra,\n              vr,\n              vi,\n            );\n            H.set(i, n - 1, cdivres[0]);\n            H.set(i, n, cdivres[1]);\n            if (Math.abs(x) > Math.abs(z) + Math.abs(q)) {\n              H.set(\n                i + 1,\n                n - 1,\n                (-ra - w * H.get(i, n - 1) + q * H.get(i, n)) / x,\n              );\n              H.set(\n                i + 1,\n                n,\n                (-sa - w * H.get(i, n) - q * H.get(i, n - 1)) / x,\n              );\n            } else {\n              cdivres = cdiv(\n                -r - y * H.get(i, n - 1),\n                -s - y * H.get(i, n),\n                z,\n                q,\n              );\n              H.set(i + 1, n - 1, cdivres[0]);\n              H.set(i + 1, n, cdivres[1]);\n            }\n          }\n\n          t = Math.max(Math.abs(H.get(i, n - 1)), Math.abs(H.get(i, n)));\n          if (eps * t * t > 1) {\n            for (j = i; j <= n; j++) {\n              H.set(j, n - 1, H.get(j, n - 1) / t);\n              H.set(j, n, H.get(j, n) / t);\n            }\n          }\n        }\n      }\n    }\n  }\n\n  for (i = 0; i < nn; i++) {\n    if (i < low || i > high) {\n      for (j = i; j < nn; j++) {\n        V.set(i, j, H.get(i, j));\n      }\n    }\n  }\n\n  for (j = nn - 1; j >= low; j--) {\n    for (i = low; i <= high; i++) {\n      z = 0;\n      for (k = low; k <= Math.min(j, high); k++) {\n        z = z + V.get(i, k) * H.get(k, j);\n      }\n      V.set(i, j, z);\n    }\n  }\n}\n\nfunction cdiv(xr, xi, yr, yi) {\n  let r, d;\n  if (Math.abs(yr) > Math.abs(yi)) {\n    r = yi / yr;\n    d = yr + r * yi;\n    return [(xr + r * xi) / d, (xi - r * xr) / d];\n  } else {\n    r = yr / yi;\n    d = yi + r * yr;\n    return [(r * xr + xi) / d, (r * xi - xr) / d];\n  }\n}\n\nclass CholeskyDecomposition {\n  constructor(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n    if (!value.isSymmetric()) {\n      throw new Error('Matrix is not symmetric');\n    }\n\n    let a = value;\n    let dimension = a.rows;\n    let l = new Matrix(dimension, dimension);\n    let positiveDefinite = true;\n    let i, j, k;\n\n    for (j = 0; j < dimension; j++) {\n      let d = 0;\n      for (k = 0; k < j; k++) {\n        let s = 0;\n        for (i = 0; i < k; i++) {\n          s += l.get(k, i) * l.get(j, i);\n        }\n        s = (a.get(j, k) - s) / l.get(k, k);\n        l.set(j, k, s);\n        d = d + s * s;\n      }\n\n      d = a.get(j, j) - d;\n\n      positiveDefinite &&= d > 0;\n      l.set(j, j, Math.sqrt(Math.max(d, 0)));\n      for (k = j + 1; k < dimension; k++) {\n        l.set(j, k, 0);\n      }\n    }\n\n    this.L = l;\n    this.positiveDefinite = positiveDefinite;\n  }\n\n  isPositiveDefinite() {\n    return this.positiveDefinite;\n  }\n\n  solve(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let l = this.L;\n    let dimension = l.rows;\n\n    if (value.rows !== dimension) {\n      throw new Error('Matrix dimensions do not match');\n    }\n    if (this.isPositiveDefinite() === false) {\n      throw new Error('Matrix is not positive definite');\n    }\n\n    let count = value.columns;\n    let B = value.clone();\n    let i, j, k;\n\n    for (k = 0; k < dimension; k++) {\n      for (j = 0; j < count; j++) {\n        for (i = 0; i < k; i++) {\n          B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(k, i));\n        }\n        B.set(k, j, B.get(k, j) / l.get(k, k));\n      }\n    }\n\n    for (k = dimension - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        for (i = k + 1; i < dimension; i++) {\n          B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(i, k));\n        }\n        B.set(k, j, B.get(k, j) / l.get(k, k));\n      }\n    }\n\n    return B;\n  }\n\n  get lowerTriangularMatrix() {\n    return this.L;\n  }\n}\n\nclass nipals {\n  constructor(X, options = {}) {\n    X = WrapperMatrix2D.checkMatrix(X);\n    let { Y } = options;\n    const {\n      scaleScores = false,\n      maxIterations = 1000,\n      terminationCriteria = 1e-10,\n    } = options;\n\n    let u;\n    if (Y) {\n      if (isAnyArray(Y) && typeof Y[0] === 'number') {\n        Y = Matrix.columnVector(Y);\n      } else {\n        Y = WrapperMatrix2D.checkMatrix(Y);\n      }\n      if (Y.rows !== X.rows) {\n        throw new Error('Y should have the same number of rows as X');\n      }\n      u = Y.getColumnVector(0);\n    } else {\n      u = X.getColumnVector(0);\n    }\n\n    let diff = 1;\n    let t, q, w, tOld;\n\n    for (\n      let counter = 0;\n      counter < maxIterations && diff > terminationCriteria;\n      counter++\n    ) {\n      w = X.transpose().mmul(u).div(u.transpose().mmul(u).get(0, 0));\n      w = w.div(w.norm());\n\n      t = X.mmul(w).div(w.transpose().mmul(w).get(0, 0));\n\n      if (counter > 0) {\n        diff = t.clone().sub(tOld).pow(2).sum();\n      }\n      tOld = t.clone();\n\n      if (Y) {\n        q = Y.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n        q = q.div(q.norm());\n\n        u = Y.mmul(q).div(q.transpose().mmul(q).get(0, 0));\n      } else {\n        u = t;\n      }\n    }\n\n    if (Y) {\n      let p = X.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n      p = p.div(p.norm());\n      let xResidual = X.clone().sub(t.clone().mmul(p.transpose()));\n      let residual = u.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n      let yResidual = Y.clone().sub(\n        t.clone().mulS(residual.get(0, 0)).mmul(q.transpose()),\n      );\n\n      this.t = t;\n      this.p = p.transpose();\n      this.w = w.transpose();\n      this.q = q;\n      this.u = u;\n      this.s = t.transpose().mmul(t);\n      this.xResidual = xResidual;\n      this.yResidual = yResidual;\n      this.betas = residual;\n    } else {\n      this.w = w.transpose();\n      this.s = t.transpose().mmul(t).sqrt();\n      if (scaleScores) {\n        this.t = t.clone().div(this.s.get(0, 0));\n      } else {\n        this.t = t;\n      }\n      this.xResidual = X.sub(t.mmul(w.transpose()));\n    }\n  }\n}\n\nexports.AbstractMatrix = AbstractMatrix;\nexports.CHO = CholeskyDecomposition;\nexports.CholeskyDecomposition = CholeskyDecomposition;\nexports.DistanceMatrix = DistanceMatrix;\nexports.EVD = EigenvalueDecomposition;\nexports.EigenvalueDecomposition = EigenvalueDecomposition;\nexports.LU = LuDecomposition;\nexports.LuDecomposition = LuDecomposition;\nexports.Matrix = Matrix;\nexports.MatrixColumnSelectionView = MatrixColumnSelectionView;\nexports.MatrixColumnView = MatrixColumnView;\nexports.MatrixFlipColumnView = MatrixFlipColumnView;\nexports.MatrixFlipRowView = MatrixFlipRowView;\nexports.MatrixRowSelectionView = MatrixRowSelectionView;\nexports.MatrixRowView = MatrixRowView;\nexports.MatrixSelectionView = MatrixSelectionView;\nexports.MatrixSubView = MatrixSubView;\nexports.MatrixTransposeView = MatrixTransposeView;\nexports.NIPALS = nipals;\nexports.Nipals = nipals;\nexports.QR = QrDecomposition;\nexports.QrDecomposition = QrDecomposition;\nexports.SVD = SingularValueDecomposition;\nexports.SingularValueDecomposition = SingularValueDecomposition;\nexports.SymmetricMatrix = SymmetricMatrix;\nexports.WrapperMatrix1D = WrapperMatrix1D;\nexports.WrapperMatrix2D = WrapperMatrix2D;\nexports.correlation = correlation;\nexports.covariance = covariance;\nexports.default = Matrix;\nexports.determinant = determinant;\nexports.inverse = inverse;\nexports.linearDependencies = linearDependencies;\nexports.pseudoInverse = pseudoInverse;\nexports.solve = solve;\nexports.wrap = wrap;\n//# sourceMappingURL=matrix.js.map\n","import * as matrix from './matrix.js';\n\nexport const AbstractMatrix = matrix.AbstractMatrix;\nexport const CHO = matrix.CHO;\nexport const CholeskyDecomposition = matrix.CholeskyDecomposition;\nexport const DistanceMatrix = matrix.DistanceMatrix;\nexport const EVD = matrix.EVD;\nexport const EigenvalueDecomposition = matrix.EigenvalueDecomposition;\nexport const LU = matrix.LU;\nexport const LuDecomposition = matrix.LuDecomposition;\nexport const Matrix = matrix.Matrix;\nexport const MatrixColumnSelectionView = matrix.MatrixColumnSelectionView;\nexport const MatrixColumnView = matrix.MatrixColumnView;\nexport const MatrixFlipColumnView = matrix.MatrixFlipColumnView;\nexport const MatrixFlipRowView = matrix.MatrixFlipRowView;\nexport const MatrixRowSelectionView = matrix.MatrixRowSelectionView;\nexport const MatrixRowView = matrix.MatrixRowView;\nexport const MatrixSelectionView = matrix.MatrixSelectionView;\nexport const MatrixSubView = matrix.MatrixSubView;\nexport const MatrixTransposeView = matrix.MatrixTransposeView;\nexport const NIPALS = matrix.NIPALS;\nexport const Nipals = matrix.Nipals;\nexport const QR = matrix.QR;\nexport const QrDecomposition = matrix.QrDecomposition;\nexport const SVD = matrix.SVD;\nexport const SingularValueDecomposition = matrix.SingularValueDecomposition;\nexport const SymmetricMatrix = matrix.SymmetricMatrix;\nexport const WrapperMatrix1D = matrix.WrapperMatrix1D;\nexport const WrapperMatrix2D = matrix.WrapperMatrix2D;\nexport const correlation = matrix.correlation;\nexport const covariance = matrix.covariance;\nexport default matrix.default.Matrix ? matrix.default.Matrix : matrix.Matrix;\nexport const determinant = matrix.determinant;\nexport const inverse = matrix.inverse;\nexport const linearDependencies = matrix.linearDependencies;\nexport const pseudoInverse = matrix.pseudoInverse;\nexport const solve = matrix.solve;\nexport const wrap = matrix.wrap;\n","import { xCheck } from \"./xCheck.js\";\nimport { xGetFromToIndex } from \"./xGetFromToIndex.js\";\n/**\n * Computes the mean value of an array of values.\n * @param array - array of numbers\n * @param options - options\n */\nexport function xMean(array, options = {}) {\n    xCheck(array);\n    const { fromIndex, toIndex } = xGetFromToIndex(array, options);\n    let sumValue = array[fromIndex];\n    for (let i = fromIndex + 1; i <= toIndex; i++) {\n        sumValue += array[i];\n    }\n    return sumValue / (toIndex - fromIndex + 1);\n}\n//# sourceMappingURL=xMean.js.map","import { xCheck } from \"./xCheck.js\";\nimport { xGetFromToIndex } from \"./xGetFromToIndex.js\";\n/**\n * Computes the maximal value of an array of values\n * @param array - array of numbers\n * @param options - options\n */\nexport function xMaxValue(array, options = {}) {\n    xCheck(array);\n    const { fromIndex, toIndex } = xGetFromToIndex(array, options);\n    let maxValue = array[fromIndex];\n    for (let i = fromIndex + 1; i <= toIndex; i++) {\n        if (array[i] > maxValue) {\n            maxValue = array[i];\n        }\n    }\n    return maxValue;\n}\n//# sourceMappingURL=xMaxValue.js.map","import { xCheck } from \"./xCheck.js\";\nimport { xGetFromToIndex } from \"./xGetFromToIndex.js\";\n/**\n * Computes the minimal value of an array of values.\n * @param array - array of numbers\n * @param options - options\n */\nexport function xMinValue(array, options = {}) {\n    xCheck(array);\n    const { fromIndex, toIndex } = xGetFromToIndex(array, options);\n    let minValue = array[fromIndex];\n    for (let i = fromIndex + 1; i <= toIndex; i++) {\n        if (array[i] < minValue) {\n            minValue = array[i];\n        }\n    }\n    return minValue;\n}\n//# sourceMappingURL=xMinValue.js.map","import { xCheck } from \"./xCheck.js\";\nimport { xGetFromToIndex } from \"./xGetFromToIndex.js\";\n/**\n * Computes the maximal value of an array of values\n * @param array - array of numbers\n * @param options - options\n */\nexport function xMaxAbsoluteValue(array, options = {}) {\n    xCheck(array);\n    const { fromIndex, toIndex } = xGetFromToIndex(array, options);\n    let maxValue = Math.abs(array[fromIndex]);\n    for (let i = fromIndex + 1; i <= toIndex; i++) {\n        if (array[i] >= 0) {\n            if (array[i] > maxValue) {\n                maxValue = array[i];\n            }\n        }\n        else if (-array[i] > maxValue) {\n            maxValue = -array[i];\n        }\n    }\n    return maxValue;\n}\n//# sourceMappingURL=xMaxAbsoluteValue.js.map","import { xMedianAbsoluteDeviation } from \"./xMedianAbsoluteDeviation.js\";\n/**\n * Determine noise level using MAD https://en.wikipedia.org/wiki/Median_absolute_deviation\n * Constant to convert mad to sd calculated using https://www.wolframalpha.com/input?i=sqrt%282%29+inverse+erf%280.5%29\n * This assumes a gaussian distribution of the noise\n * @param array - array of numbers.\n * @returns noise level corresponding to one standard deviation.\n */\nexport function xNoiseStandardDeviation(array) {\n    const { mad, median } = xMedianAbsoluteDeviation(array);\n    return { sd: mad / 0.6744897501960817, mad, median };\n}\n//# sourceMappingURL=xNoiseStandardDeviation.js.map","import { xMedian } from \"./xMedian.js\";\n/**\n * This function calculates the median absolute deviation (MAD).\n * https://en.wikipedia.org/wiki/Median_absolute_deviation\n * @param array - array of numbers.\n */\nexport function xMedianAbsoluteDeviation(array) {\n    const median = xMedian(array);\n    const averageDeviations = new Float64Array(array.length);\n    for (let i = 0; i < array.length; i++) {\n        averageDeviations[i] = Math.abs(array[i] - median);\n    }\n    return {\n        median,\n        mad: xMedian(averageDeviations),\n    };\n}\n//# sourceMappingURL=xMedianAbsoluteDeviation.js.map","/**\n * This function calculate the norm of a vector.\n * @example xNorm([3, 4]) -> 5\n * @param array - array\n * @returns - calculated norm\n */\nexport function xNorm(array) {\n    let result = 0;\n    for (const element of array) {\n        result += element ** 2;\n    }\n    return Math.sqrt(result);\n}\n//# sourceMappingURL=xNorm.js.map","/**\n * Compute the `[left, right]` intervals around each peak by locating\n * consecutive extrema (minima / maxima) of the first derivative.\n * @param y - Spectrum y values.\n * @param x - Spectrum x values.\n * @param dY - First derivative of `y`.\n * @param dX - Sign of the x step (positive for increasing x, negative otherwise).\n * @returns `intervalL` / `intervalR` arrays defining each peak's inflection-point bounds.\n */\nexport function getMinMaxIntervalsDy(y, x, dY, dX) {\n    let lastMax = null;\n    let lastMin = null;\n    const intervalL = [];\n    const intervalR = [];\n    for (let i = 1; i < y.length - 1; ++i) {\n        if ((dY[i] < dY[i - 1] && dY[i] <= dY[i + 1]) ||\n            (dY[i] <= dY[i - 1] && dY[i] < dY[i + 1])) {\n            lastMin = {\n                x: x[i],\n                index: i,\n            };\n            if (dX > 0 && lastMax !== null) {\n                intervalL.push(lastMax);\n                intervalR.push(lastMin);\n            }\n        }\n        // Maximum in first derivative\n        if ((dY[i] >= dY[i - 1] && dY[i] > dY[i + 1]) ||\n            (dY[i] > dY[i - 1] && dY[i] >= dY[i + 1])) {\n            lastMax = {\n                x: x[i],\n                index: i,\n            };\n            if (dX < 0 && lastMin !== null) {\n                intervalL.push(lastMax);\n                intervalR.push(lastMin);\n            }\n        }\n    }\n    return { intervalL, intervalR };\n}\n//# sourceMappingURL=getMinMaxIntervals.js.map","/**\n * Find the `minData` index closest to the center of one interval,\n * above the intensity threshold and bounded by the half-width.\n * @param options - Interval, candidate indices and spectrum data.\n * @returns The picked candidate (`possible`) and the last visited index (`lastIndex`).\n */\nexport function tryMatchOneIntervalWithMinData(options) {\n    const { x, lastK, minData, yThreshold, intervalWidth, intervalCenter, yData, } = options;\n    let minDistance = Number.POSITIVE_INFINITY;\n    let possible = -1;\n    let newLastIndex = lastK;\n    for (let k = newLastIndex + 1; k < minData.length; k++) {\n        const centerIndex = minData[k];\n        if (yData[centerIndex] <= yThreshold) {\n            continue;\n        }\n        const deltaX = x[centerIndex];\n        const currentDistance = Math.abs(deltaX - intervalCenter);\n        if (currentDistance < intervalWidth) {\n            if (currentDistance < minDistance) {\n                possible = k;\n            }\n            newLastIndex = k;\n        }\n        if (currentDistance >= minDistance)\n            break;\n        minDistance = currentDistance;\n    }\n    return { lastIndex: newLastIndex, possible };\n}\n//# sourceMappingURL=tryMatchOneIntervalWithMinData.js.map","import { tryMatchOneIntervalWithMinData } from \"./tryMatchOneIntervalWithMinData.js\";\n/**\n * Build the list of detected peaks by matching each `[left, right]` interval\n * with the best candidate index from `minData`.\n * @param options - Intervals, candidate indices and spectrum data.\n * @returns The detected peaks.\n */\nexport function getPeakFromIntervals(options) {\n    let lastK = -1;\n    const peaks = [];\n    const { x, ddY, yData, yThreshold, intervalR, intervalL, minData } = options;\n    for (let i = 0; i < intervalL.length; i++) {\n        const intervalWidth = (intervalR[i].x - intervalL[i].x) / 2;\n        const intervalCenter = (intervalR[i].x + intervalL[i].x) / 2;\n        const { possible, lastIndex } = tryMatchOneIntervalWithMinData({\n            x,\n            lastK,\n            minData,\n            yThreshold,\n            intervalWidth,\n            intervalCenter,\n            yData,\n        });\n        if (possible !== -1) {\n            const centerIndex = minData[possible];\n            const width = Math.abs(intervalR[i].x - intervalL[i].x);\n            peaks.push({\n                id: crypto.randomUUID(),\n                x: x[centerIndex],\n                y: yData[centerIndex],\n                width,\n                index: centerIndex,\n                ddY: ddY[centerIndex],\n                inflectionPoints: {\n                    from: intervalL[i],\n                    to: intervalR[i],\n                },\n            });\n        }\n        lastK = lastIndex;\n    }\n    return peaks;\n}\n//# sourceMappingURL=getPeaksFromIntervals.js.map","/**\n * Finds the indices where the first derivative crosses zero (sign change),\n * which are potential peak positions. This function does not detect zero-crossings\n * in regions with consecutive zero values in the derivative (flat regions).\n * @param input - Object containing the y values and their first derivative (dY).\n * @returns Array of indices where the first derivative crosses zero (excluding consecutive zeros).\n */\nexport function xGetCrossZeroPoints(input) {\n    const { y, dY } = input;\n    const crossDy = [];\n    for (let i = 1; i < y.length - 1; ++i) {\n        if (isLessAndGreaterThanZero(dY[i], dY[i + 1])) {\n            // push the index of the element closer to zero\n            crossDy.push(Math.abs(dY[i]) < Math.abs(dY[i + 1]) ? i : i + 1);\n        }\n        else if (\n        // Handle exact zero\n        dY[i] === 0 &&\n            isLessAndGreaterThanZero(dY[i - 1], dY[i + 1])) {\n            crossDy.push(i);\n        }\n    }\n    return crossDy;\n}\nfunction isLessAndGreaterThanZero(back, next) {\n    return (back < 0 && next > 0) || (back > 0 && next < 0);\n}\n//# sourceMappingURL=xGetCrossZeroPoints.js.map","import { getMinMaxIntervalsDy } from \"./getMinMaxIntervals.js\";\nimport { getPeakFromIntervals } from \"./getPeaksFromIntervals.js\";\nimport { xGetCrossZeroPoints } from \"./xGetCrossZeroPoints.js\";\n/**\n * Detect peaks using zero-crossings of the first derivative.\n * @param input - Spectrum values and its first/second derivatives.\n * @returns The detected peaks.\n */\nexport function firstDerivative(input) {\n    const { y, x, dY, dX, yData, yThreshold, ddY } = input;\n    const crossDy = xGetCrossZeroPoints(input);\n    const { intervalL, intervalR } = getMinMaxIntervalsDy(y, x, dY, dX);\n    return getPeakFromIntervals({\n        minData: crossDy,\n        intervalL,\n        intervalR,\n        x,\n        yData,\n        yThreshold,\n        ddY,\n    });\n}\n//# sourceMappingURL=firstDerivative.js.map","export const GAUSSIAN_EXP_FACTOR = -4 * Math.LN2;\n/**\n * A pseudo-Voigt is a gaussian plus a lorentzian. Beyond 3.74 fwhm from the\n * centre the gaussian part is down to 1.4e-17 — too small to change a shape of\n * height one — while the lorentzian part is still 1.8e-2 and fades much more\n * slowly. Past that distance the gaussian is skipped: the `Math.exp` it costs\n * cannot change the result, and most points of a wide window lie out there.\n *\n * The limit is stored squared, so the test is `(x / fwhm)² > 14`, which saves a\n * square root. The same value works for every mixing ratio `mu`, except `mu = 1`\n * where the shape is a pure gaussian: with no lorentzian part left, skipping\n * would return zero instead of a very small number, so that case is never\n * skipped.\n */\nexport const GAUSSIAN_CUTOFF = 14;\nexport const ROOT_PI_OVER_LN2 = Math.sqrt(Math.PI / Math.LN2);\nexport const ROOT_LN2 = Math.sqrt(Math.LN2);\nexport const ROOT_THREE = Math.sqrt(3);\nexport const ROOT_2LN2 = Math.sqrt(2 * Math.LN2);\nexport const ROOT_2LN2_MINUS_ONE = Math.sqrt(2 * Math.LN2) - 1;\n//# sourceMappingURL=constants.js.map","import { GAUSSIAN_EXP_FACTOR, ROOT_2LN2, ROOT_LN2, ROOT_PI_OVER_LN2, } from \"../../../util/constants.js\";\nimport erfinv from \"../../../util/erfinv.js\";\nexport class Gaussian {\n    kind = 'gaussian';\n    /**\n     * Full width at half maximum.\n     * @default 500\n     */\n    fwhm;\n    constructor(options = {}) {\n        const { fwhm = 500, sd } = options;\n        this.fwhm = sd ? gaussianWidthToFWHM(2 * sd) : fwhm;\n    }\n    fwhmToWidth(fwhm = this.fwhm) {\n        return gaussianFwhmToWidth(fwhm);\n    }\n    widthToFWHM(width) {\n        return gaussianWidthToFWHM(width);\n    }\n    fct(x) {\n        return gaussianFct(x, this.fwhm);\n    }\n    getArea(height = calculateGaussianHeight({ fwhm: this.fwhm })) {\n        return getGaussianArea({ fwhm: this.fwhm, height });\n    }\n    getFactor(area) {\n        return getGaussianFactor(area);\n    }\n    getData(options = {}) {\n        return getGaussianData(this, options);\n    }\n    calculateHeight(area = 1) {\n        return calculateGaussianHeight({ fwhm: this.fwhm, area });\n    }\n    getParameters() {\n        return ['fwhm'];\n    }\n    /**\n     * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n     * @returns the shape descriptor.\n     */\n    toJSON() {\n        return { kind: this.kind, fwhm: this.fwhm };\n    }\n    derivative(x) {\n        const { fct, dx, dFwhm } = gaussianDerivative(x, this.fwhm);\n        return { fct, dx, parameters: [dFwhm] };\n    }\n}\n/**\n * Calculate the peak height for a given area and fwhm.\n * @param options - fwhm, area, and optional sd.\n * @returns the peak height.\n */\nexport function calculateGaussianHeight(options) {\n    const { area = 1, sd } = options;\n    let { fwhm = 500 } = options;\n    if (sd)\n        fwhm = gaussianWidthToFWHM(2 * sd);\n    return (2 * area) / ROOT_PI_OVER_LN2 / fwhm;\n}\n/**\n * Evaluate the gaussian function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @returns the intensity at x.\n */\nexport function gaussianFct(x, fwhm) {\n    return Math.exp(GAUSSIAN_EXP_FACTOR * (x / fwhm) ** 2);\n}\n/**\n * Analytical value and partial derivatives of the gaussian function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`) and `fwhm` (`dFwhm`).\n */\nexport function gaussianDerivative(x, fwhm) {\n    const fct = gaussianFct(x, fwhm);\n    const dx = ((2 * GAUSSIAN_EXP_FACTOR * x) / (fwhm * fwhm)) * fct;\n    const dFwhm = ((-2 * GAUSSIAN_EXP_FACTOR * x * x) / (fwhm * fwhm * fwhm)) * fct;\n    return { fct, dx, dFwhm };\n}\n/**\n * Convert inflection-point width to full width at half maximum.\n * @param width - width between inflection points.\n * @returns full width at half maximum.\n */\nexport function gaussianWidthToFWHM(width) {\n    return width * ROOT_2LN2;\n}\n/**\n * Convert full width at half maximum to inflection-point width.\n * @param fwhm - full width at half maximum.\n * @returns width between inflection points.\n */\nexport function gaussianFwhmToWidth(fwhm) {\n    return fwhm / ROOT_2LN2;\n}\n/**\n * Calculate the area under a gaussian peak.\n * @param options - fwhm, height, and optional sd.\n * @returns the area.\n */\nexport function getGaussianArea(options) {\n    const { sd, height = 1 } = options;\n    let { fwhm = 500 } = options;\n    if (sd)\n        fwhm = gaussianWidthToFWHM(2 * sd);\n    return (height * ROOT_PI_OVER_LN2 * fwhm) / 2;\n}\n/**\n * Calculate the width factor corresponding to a given area coverage fraction.\n * @param area - target area fraction (0–1). Defaults to `0.9999`.\n * @returns the factor by which to multiply fwhm to cover the given area.\n */\nexport function getGaussianFactor(area = 0.9999) {\n    if (area >= 1) {\n        throw new Error('area should be (0 - 1)');\n    }\n    return erfinv(area) / ROOT_LN2;\n}\n/**\n * Generate an intensity array for a gaussian shape.\n * @param shape - gaussian shape parameters (fwhm, sd).\n * @param options - sampling options (length, factor, height).\n * @returns Float64Array of intensity values.\n */\nexport function getGaussianData(shape = {}, options = {}) {\n    const { sd } = shape;\n    let { fwhm = 500 } = shape;\n    if (sd)\n        fwhm = gaussianWidthToFWHM(2 * sd);\n    const { factor = getGaussianFactor(), height = calculateGaussianHeight({ fwhm }), } = options;\n    let { length } = options;\n    if (!length) {\n        length = Math.min(Math.ceil(fwhm * factor), 2 ** 25 - 1);\n        if (length % 2 === 0)\n            length++;\n    }\n    const center = (length - 1) / 2;\n    const data = new Float64Array(length);\n    for (let i = 0; i <= center; i++) {\n        const value = gaussianFct(i - center, fwhm) * height;\n        data[i] = value;\n        data[length - 1 - i] = value;\n    }\n    return data;\n}\n//# sourceMappingURL=Gaussian.js.map","// https://en.wikipedia.org/wiki/Error_function#Inverse_functions\n// This code yields to a good approximation\n// If needed a better implementation using polynomial can be found on https://en.wikipedia.org/wiki/Error_function#Inverse_functions\n/**\n * Approximate inverse error function.\n * @param x - value in the range (-1, 1).\n * @returns erfinv(x).\n * @see https://en.wikipedia.org/wiki/Error_function#Inverse_functions\n */\nexport default function erfinv(x) {\n    const a = 0.147;\n    if (x === 0)\n        return 0;\n    const ln1MinusXSqrd = Math.log(1 - x * x);\n    const lnEtcBy2Plus2 = ln1MinusXSqrd / 2 + 2 / (Math.PI * a);\n    const firstSqrt = Math.sqrt(lnEtcBy2Plus2 ** 2 - ln1MinusXSqrd / a);\n    const secondSqrt = Math.sqrt(firstSqrt - lnEtcBy2Plus2);\n    return secondSqrt * (x > 0 ? 1 : -1);\n}\n//# sourceMappingURL=erfinv.js.map","import { ROOT_THREE } from \"../../../util/constants.js\";\nexport class Lorentzian {\n    kind = 'lorentzian';\n    /**\n     * Full width at half maximum.\n     * @default 500\n     */\n    fwhm;\n    constructor(options = {}) {\n        const { fwhm = 500 } = options;\n        this.fwhm = fwhm;\n    }\n    fwhmToWidth(fwhm = this.fwhm) {\n        return lorentzianFwhmToWidth(fwhm);\n    }\n    widthToFWHM(width) {\n        return lorentzianWidthToFWHM(width);\n    }\n    fct(x) {\n        return lorentzianFct(x, this.fwhm);\n    }\n    getArea(height = 1) {\n        return getLorentzianArea({ fwhm: this.fwhm, height });\n    }\n    getFactor(area) {\n        return getLorentzianFactor(area);\n    }\n    getData(options = {}) {\n        return getLorentzianData(this, options);\n    }\n    calculateHeight(area = 1) {\n        return calculateLorentzianHeight({ fwhm: this.fwhm, area });\n    }\n    getParameters() {\n        return ['fwhm'];\n    }\n    /**\n     * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n     * @returns the shape descriptor.\n     */\n    toJSON() {\n        return { kind: this.kind, fwhm: this.fwhm };\n    }\n    derivative(x) {\n        const { fct, dx, dFwhm } = lorentzianDerivative(x, this.fwhm);\n        return { fct, dx, parameters: [dFwhm] };\n    }\n}\nexport const calculateLorentzianHeight = ({ fwhm = 1, area = 1 }) => {\n    return (2 * area) / Math.PI / fwhm;\n};\nexport const getLorentzianArea = (options) => {\n    const { fwhm = 500, height = 1 } = options;\n    return (height * Math.PI * fwhm) / 2;\n};\nexport const lorentzianFct = (x, fwhm) => {\n    return fwhm ** 2 / (4 * x ** 2 + fwhm ** 2);\n};\n/**\n * Analytical value and partial derivatives of the lorentzian function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`) and `fwhm` (`dFwhm`).\n */\nexport function lorentzianDerivative(x, fwhm) {\n    const denominator = 4 * x * x + fwhm * fwhm;\n    const fct = (fwhm * fwhm) / denominator;\n    const dx = (-8 * x * fwhm * fwhm) / (denominator * denominator);\n    const dFwhm = (8 * fwhm * x * x) / (denominator * denominator);\n    return { fct, dx, dFwhm };\n}\nexport const lorentzianWidthToFWHM = (width) => {\n    return width * ROOT_THREE;\n};\nexport const lorentzianFwhmToWidth = (fwhm) => {\n    return fwhm / ROOT_THREE;\n};\nconst lorentzianQuantile = (p) => Math.tan(Math.PI * (p - 0.5));\nexport const getLorentzianFactor = (area = 0.9999) => {\n    if (area >= 1) {\n        throw new Error('area should be (0 - 1)');\n    }\n    const halfResidual = (1 - area) * 0.5;\n    return ((lorentzianQuantile(1 - halfResidual) - lorentzianQuantile(halfResidual)) /\n        2);\n};\nexport const getLorentzianData = (shape = {}, options = {}) => {\n    const { fwhm = 500 } = shape;\n    const { factor = getLorentzianFactor(), height = calculateLorentzianHeight({ fwhm, area: 1 }), } = options;\n    let { length } = options;\n    if (!length) {\n        length = Math.min(Math.ceil(fwhm * factor), 2 ** 25 - 1);\n        if (length % 2 === 0)\n            length++;\n    }\n    const center = (length - 1) / 2;\n    const data = new Float64Array(length);\n    for (let i = 0; i <= center; i++) {\n        const value = lorentzianFct(i - center, fwhm) * height;\n        data[i] = value;\n        data[length - 1 - i] = value;\n    }\n    return data;\n};\n//# sourceMappingURL=Lorentzian.js.map","import { calculateLorentzianHeight, getLorentzianFactor, lorentzianFwhmToWidth, lorentzianWidthToFWHM, } from \"../lorentzian/Lorentzian.js\";\nexport class LorentzianDispersive {\n    kind = 'lorentzianDispersive';\n    /**\n     * Full width at half maximum.\n     * @default 500\n     */\n    fwhm;\n    constructor(options = {}) {\n        const { fwhm = 500 } = options;\n        this.fwhm = fwhm;\n    }\n    fwhmToWidth(fwhm = this.fwhm) {\n        return lorentzianFwhmToWidth(fwhm);\n    }\n    widthToFWHM(width) {\n        return lorentzianWidthToFWHM(width);\n    }\n    fct(x) {\n        return lorentzianDispersiveFct(x, this.fwhm);\n    }\n    getArea() {\n        return 0;\n    }\n    getFactor(area) {\n        return getLorentzianFactor(area);\n    }\n    getData(options = {}) {\n        return getLorentzianDispersiveData(this, options);\n    }\n    calculateHeight(area = 1) {\n        return calculateLorentzianHeight({ fwhm: this.fwhm, area });\n    }\n    getParameters() {\n        return ['fwhm'];\n    }\n    /**\n     * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n     * @returns the shape descriptor.\n     */\n    toJSON() {\n        return { kind: this.kind, fwhm: this.fwhm };\n    }\n    derivative(x) {\n        const { fct, dx, dFwhm } = lorentzianDispersiveDerivative(x, this.fwhm);\n        return { fct, dx, parameters: [dFwhm] };\n    }\n}\nexport const lorentzianDispersiveFct = (x, fwhm) => {\n    return (2 * fwhm * x) / (4 * x ** 2 + fwhm ** 2);\n};\n/**\n * Analytical value and partial derivatives of the dispersive lorentzian function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`) and `fwhm` (`dFwhm`).\n */\nexport function lorentzianDispersiveDerivative(x, fwhm) {\n    const denominator = 4 * x * x + fwhm * fwhm;\n    const fct = (2 * fwhm * x) / denominator;\n    const dx = (2 * fwhm * (fwhm * fwhm - 4 * x * x)) / (denominator * denominator);\n    const dFwhm = (2 * x * (4 * x * x - fwhm * fwhm)) / (denominator * denominator);\n    return { fct, dx, dFwhm };\n}\nexport const getLorentzianDispersiveData = (shape = {}, options = {}) => {\n    const { fwhm = 500 } = shape;\n    const { factor = getLorentzianFactor(), height = calculateLorentzianHeight({ fwhm, area: 1 }), } = options;\n    let { length } = options;\n    if (!length) {\n        length = Math.min(Math.ceil(fwhm * factor), 2 ** 25 - 1);\n        if (length % 2 === 0)\n            length++;\n    }\n    const center = (length - 1) / 2;\n    const data = new Float64Array(length);\n    for (let i = 0; i <= center; i++) {\n        const value = lorentzianDispersiveFct(i - center, fwhm) * height;\n        data[i] = value;\n        data[length - 1 - i] = -value;\n    }\n    return data;\n};\n//# sourceMappingURL=LorentzianDispersive.js.map","import { getGaussianFactor } from \"../gaussian/Gaussian.js\";\nimport { getLorentzianFactor } from \"../lorentzian/Lorentzian.js\";\n/**\n * Find the k factor for a pseudo-Voigt distribution such that the\n * cumulative probability pPseudoVoigt(k, mu) equals `pTarget`.\n *\n * Uses a simple bisection search (with exponential bracketing) to\n * invert the pseudo-Voigt cumulative function. Special cases:\n * - mu === 1 -> reduces to the gaussian case\n * - mu === 0 -> reduces to the lorentzian case\n * @param pTarget - Target cumulative probability in (0,1)\n * @param mu - Gaussian fraction in [0,1]\n * @param tol - Convergence tolerance\n * @param maxIter - Maximum number of bisection iterations\n * @returns the factor k such that pPseudoVoigt(k, mu) ~= pTarget\n */\nexport function pseudoVoigtFindFactor(pTarget, mu, tol = 1e-9, maxIter = 200) {\n    if (pTarget <= 0 || pTarget >= 1) {\n        throw new RangeError('pTarget must be in (0,1)');\n    }\n    if (mu === 1) {\n        return getGaussianFactor(pTarget);\n    }\n    else if (mu === 0) {\n        return getLorentzianFactor(pTarget);\n    }\n    // bisection\n    let lo = 0;\n    let hi = 10;\n    let it = 0;\n    while (pPseudoVoigt(hi, mu) < pTarget && it++ < 200)\n        hi *= 2;\n    for (let i = 0; i < maxIter; i++) {\n        const mid = 0.5 * (lo + hi);\n        const val = pPseudoVoigt(mid, mu);\n        if (Math.abs(val - pTarget) < tol)\n            return mid;\n        if (val < pTarget) {\n            lo = mid;\n        }\n        else {\n            hi = mid;\n        }\n    }\n    return 0.5 * (lo + hi);\n}\nfunction erf(x) {\n    const sign = x < 0 ? -1 : 1;\n    x = Math.abs(x);\n    const a1 = 0.254829592;\n    const a2 = -0.284496736;\n    const a3 = 1.421413741;\n    const a4 = -1.453152027;\n    const a5 = 1.061405429;\n    const p = 0.3275911;\n    const t = 1 / (1 + p * x);\n    const y = 1 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.exp(-x * x);\n    return sign * y;\n}\nconst sqrtLn2 = Math.sqrt(Math.log(2));\nfunction pGaussian(k) {\n    return erf(k * sqrtLn2);\n}\nfunction pLorentz(k) {\n    return (2 / Math.PI) * Math.atan(k);\n}\nfunction pPseudoVoigt(k, mu) {\n    return (1 - mu) * pLorentz(k) + mu * pGaussian(k);\n}\n//# sourceMappingURL=computeFactor.js.map","import { GAUSSIAN_CUTOFF, GAUSSIAN_EXP_FACTOR, ROOT_2LN2_MINUS_ONE, ROOT_PI_OVER_LN2, } from \"../../../util/constants.js\";\nimport { gaussianFct } from \"../gaussian/Gaussian.js\";\nimport { lorentzianFct } from \"../lorentzian/Lorentzian.js\";\nimport { pseudoVoigtFindFactor } from \"./computeFactor.js\";\nexport class PseudoVoigt {\n    kind = 'pseudoVoigt';\n    fwhm;\n    /**\n     * Ratio of gaussian contribution in the shape\n     * @default 0.5\n     */\n    mu;\n    constructor(options = {}) {\n        const { fwhm = 500, mu = 0.5 } = options;\n        this.mu = mu;\n        this.fwhm = fwhm;\n    }\n    fwhmToWidth(fwhm = this.fwhm, mu = this.mu) {\n        return pseudoVoigtFwhmToWidth(fwhm, mu);\n    }\n    widthToFWHM(width, mu = this.mu) {\n        return pseudoVoigtWidthToFWHM(width, mu);\n    }\n    fct(x) {\n        return pseudoVoigtFct(x, this.fwhm, this.mu);\n    }\n    getArea(height = 1) {\n        return getPseudoVoigtArea({ fwhm: this.fwhm, height, mu: this.mu });\n    }\n    getFactor(area) {\n        return getPseudoVoigtFactor(area, this.mu);\n    }\n    getData(options = {}) {\n        const { length, factor, height = calculatePseudoVoigtHeight({\n            fwhm: this.fwhm,\n            mu: this.mu,\n            area: 1,\n        }), } = options;\n        return getPseudoVoigtData(this, { factor, length, height });\n    }\n    calculateHeight(area = 1) {\n        return calculatePseudoVoigtHeight({ fwhm: this.fwhm, mu: this.mu, area });\n    }\n    getParameters() {\n        return ['fwhm', 'mu'];\n    }\n    /**\n     * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n     * @returns the shape descriptor.\n     */\n    toJSON() {\n        return { kind: this.kind, fwhm: this.fwhm, mu: this.mu };\n    }\n    derivative(x) {\n        const { fct, dx, dFwhm, dMu } = pseudoVoigtDerivative(x, this.fwhm, this.mu);\n        return { fct, dx, parameters: [dFwhm, dMu] };\n    }\n}\nexport const calculatePseudoVoigtHeight = (options = {}) => {\n    const { fwhm = 1, mu = 0.5, area = 1 } = options;\n    return (2 * area) / (fwhm * (mu * ROOT_PI_OVER_LN2 + (1 - mu) * Math.PI));\n};\nexport const pseudoVoigtFct = (x, fwhm, mu) => {\n    // at mu = 1 the shape *is* the gaussian: there is no lorentzian half left to\n    // carry the tail, so the gaussian is evaluated however far out it is asked for\n    if (mu === 1)\n        return gaussianFct(x, fwhm);\n    const lorentzian = (1 - mu) * lorentzianFct(x, fwhm);\n    const z = x / fwhm;\n    if (z * z > GAUSSIAN_CUTOFF)\n        return lorentzian;\n    return lorentzian + mu * gaussianFct(x, fwhm);\n};\n/**\n * Analytical value and partial derivatives of the pseudo-Voigt function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @param mu - ratio of gaussian contribution in the shape.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`), `fwhm` (`dFwhm`) and `mu` (`dMu`).\n */\nexport function pseudoVoigtDerivative(x, fwhm, mu) {\n    // gaussian and lorentzian derivative math is inlined (rather than calling\n    // gaussianDerivative / lorentzianDerivative) to allocate a single object on\n    // this hot path; the sub-calls would allocate three.\n    //\n    // Past {@link GAUSSIAN_CUTOFF} the gaussian half has underflowed, so it is\n    // dropped here under the same condition as in `pseudoVoigtFct` — including its\n    // mu = 1 exemption, so the two stay consistent — which also settles what the\n    // derivatives are out there: `dx` and `dFwhm` keep only their lorentzian\n    // halves, and `dMu` becomes `-lorentz`, the value the shape loses by trading\n    // its lorentzian half for a gaussian one that contributes nothing.\n    const z = x / fwhm;\n    const e = mu !== 1 && z * z > GAUSSIAN_CUTOFF\n        ? 0\n        : Math.exp(GAUSSIAN_EXP_FACTOR * z * z);\n    const denominator = 4 * x * x + fwhm * fwhm;\n    const lorentz = (fwhm * fwhm) / denominator;\n    const dEdt = ((2 * GAUSSIAN_EXP_FACTOR * x) / (fwhm * fwhm)) * e;\n    const dLdt = (-8 * x * fwhm * fwhm) / (denominator * denominator);\n    const dEdfwhm = ((-2 * GAUSSIAN_EXP_FACTOR * x * x) / (fwhm * fwhm * fwhm)) * e;\n    const dLdfwhm = (8 * fwhm * x * x) / (denominator * denominator);\n    return {\n        fct: (1 - mu) * lorentz + mu * e,\n        dx: (1 - mu) * dLdt + mu * dEdt,\n        dFwhm: (1 - mu) * dLdfwhm + mu * dEdfwhm,\n        dMu: e - lorentz,\n    };\n}\nexport const pseudoVoigtWidthToFWHM = (width, mu = 0.5) => {\n    return width * (mu * ROOT_2LN2_MINUS_ONE + 1);\n};\nexport const pseudoVoigtFwhmToWidth = (fwhm, mu = 0.5) => {\n    return fwhm / (mu * ROOT_2LN2_MINUS_ONE + 1);\n};\nexport const getPseudoVoigtArea = (options) => {\n    const { fwhm = 500, height = 1, mu = 0.5 } = options;\n    return (fwhm * height * (mu * ROOT_PI_OVER_LN2 + (1 - mu) * Math.PI)) / 2;\n};\nexport const getPseudoVoigtFactor = (area = 0.9999, mu = 0.5) => {\n    return pseudoVoigtFindFactor(area, mu);\n};\nexport const getPseudoVoigtData = (shape = {}, options = {}) => {\n    const { fwhm = 500, mu = 0.5 } = shape;\n    const { factor = getPseudoVoigtFactor(0.999, mu) } = options;\n    let { length, height = calculatePseudoVoigtHeight({ fwhm, mu, area: 1 }) } = options;\n    if (!height) {\n        height =\n            1 /\n                ((mu / Math.sqrt(-GAUSSIAN_EXP_FACTOR / Math.PI)) * fwhm +\n                    ((1 - mu) * fwhm * Math.PI) / 2);\n    }\n    if (!length) {\n        length = Math.min(Math.ceil(fwhm * factor), 2 ** 25 - 1);\n        if (length % 2 === 0)\n            length++;\n    }\n    const center = (length - 1) / 2;\n    const data = new Float64Array(length);\n    for (let i = 0; i <= center; i++) {\n        const value = pseudoVoigtFct(i - center, fwhm, mu) * height;\n        data[i] = value;\n        data[length - 1 - i] = value;\n    }\n    return data;\n};\n//# sourceMappingURL=PseudoVoigt.js.map","import { GAUSSIAN_CUTOFF, GAUSSIAN_EXP_FACTOR, } from \"../../../util/constants.js\";\nimport { calculatePseudoVoigtHeight, getPseudoVoigtArea, getPseudoVoigtData, getPseudoVoigtFactor, pseudoVoigtFct, pseudoVoigtFwhmToWidth, pseudoVoigtWidthToFWHM, } from \"../pseudoVoigt/PseudoVoigt.js\";\n/**\n * TCH-style pseudo-Voigt where gaussian and lorentzian widths are independent.\n * The effective fwhm and mixing parameter mu are derived from fwhmG and fwhmL\n * via the Thompson–Cox–Hastings approximation.\n */\nexport class PseudoVoigtTCH {\n    kind = 'pseudoVoigtTCH';\n    _fwhmG;\n    _fwhmL;\n    _fwhm;\n    _mu;\n    _lorentzianWidthFraction;\n    constructor(options = {}) {\n        const { fwhmG, fwhmL, fwhm, mu = 0.5 } = options;\n        this._mu = mu;\n        this._fwhm = 0;\n        this._fwhmG = 0;\n        this._fwhmL = 0;\n        this._lorentzianWidthFraction = lorentzianWidthFraction(1 - mu);\n        if (fwhmG !== undefined && fwhmL !== undefined) {\n            this._fwhmG = fwhmG;\n            this.fwhmL = fwhmL;\n        }\n        else if (fwhm !== undefined) {\n            this.fwhm = fwhm;\n        }\n    }\n    set fwhmG(value) {\n        const effectiveFwhm = computeEffectiveWidth(value, this._fwhmL);\n        const lorentzianFraction = this._fwhmL / effectiveFwhm;\n        this._fwhm = effectiveFwhm;\n        this._mu =\n            1 -\n                (1.36603 * lorentzianFraction -\n                    0.47719 * lorentzianFraction * lorentzianFraction +\n                    0.11116 * lorentzianFraction * lorentzianFraction * lorentzianFraction);\n        this._fwhmG = value;\n        this._lorentzianWidthFraction = lorentzianFraction;\n    }\n    get fwhmG() {\n        return this._fwhmG;\n    }\n    set fwhmL(value) {\n        const effectiveFwhm = computeEffectiveWidth(this._fwhmG, value);\n        const lorentzianFraction = value / effectiveFwhm;\n        this._fwhm = effectiveFwhm;\n        this._mu =\n            1 -\n                (1.36603 * lorentzianFraction -\n                    0.47719 * lorentzianFraction * lorentzianFraction +\n                    0.11116 * lorentzianFraction * lorentzianFraction * lorentzianFraction);\n        this._fwhmL = value;\n        this._lorentzianWidthFraction = lorentzianFraction;\n    }\n    get fwhmL() {\n        return this._fwhmL;\n    }\n    set mu(value) {\n        const lorentzianFraction = lorentzianWidthFraction(1 - value);\n        this._lorentzianWidthFraction = lorentzianFraction;\n        this._fwhmL = this._fwhm * lorentzianFraction;\n        this._fwhmG = this._fwhm * gaussianWidthFraction(lorentzianFraction);\n        this._mu = value;\n    }\n    get mu() {\n        return this._mu;\n    }\n    set fwhm(value) {\n        const lorentzianFraction = this._lorentzianWidthFraction || lorentzianWidthFraction(1 - this._mu);\n        this._fwhmL = value * lorentzianFraction;\n        this._fwhmG = value * gaussianWidthFraction(lorentzianFraction);\n        this._fwhm = value;\n    }\n    get fwhm() {\n        return this._fwhm;\n    }\n    fwhmToWidth(fwhm = this._fwhm, mu = this._mu) {\n        return pseudoVoigtFwhmToWidth(fwhm, mu);\n    }\n    widthToFWHM(width, mu = this._mu) {\n        return pseudoVoigtWidthToFWHM(width, mu);\n    }\n    fct(x) {\n        return pseudoVoigtFct(x, this._fwhm, this._mu);\n    }\n    getArea(height = 1) {\n        return getPseudoVoigtArea({ fwhm: this._fwhm, height, mu: this._mu });\n    }\n    getFactor(area) {\n        return getPseudoVoigtFactor(area, this._mu);\n    }\n    getData(options = {}) {\n        const { length, factor, height = calculatePseudoVoigtHeight({\n            fwhm: this._fwhm,\n            mu: this._mu,\n            area: 1,\n        }), } = options;\n        return getPseudoVoigtData(this, { factor, length, height });\n    }\n    calculateHeight(area = 1) {\n        return calculatePseudoVoigtHeight({\n            fwhm: this._fwhm,\n            mu: this._mu,\n            area,\n        });\n    }\n    getParameters() {\n        return ['fwhmG', 'fwhmL'];\n    }\n    /**\n     * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n     * The component widths are emitted rather than `fwhm`/`mu`, because they are\n     * the state this shape is defined by: `getParameters` reports them and\n     * `derivative` differentiates with respect to them. The effective width and\n     * the mixing ratio are re-derived from them exactly.\n     * @returns the shape descriptor.\n     */\n    toJSON() {\n        return { kind: this.kind, fwhmG: this._fwhmG, fwhmL: this._fwhmL };\n    }\n    derivative(x) {\n        const { fct, dx, dFwhmG, dFwhmL } = pseudoVoigtTCHDerivative(x, this._fwhmG, this._fwhmL);\n        return { fct, dx, parameters: [dFwhmG, dFwhmL] };\n    }\n}\n/**\n * Analytical value and partial derivatives of the TCH pseudo-Voigt function centered at x=0.\n * The effective fwhm `F` and mixing `mu` are functions of `fwhmG` and `fwhmL`, so the\n * derivatives chain `∂fct/∂F` and `∂fct/∂mu` through `∂F/∂·` and `∂mu/∂·`.\n * @param x - position at which to evaluate.\n * @param fwhmG - full width at half maximum of the gaussian component.\n * @param fwhmL - full width at half maximum of the lorentzian component.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`), `fwhmG` (`dFwhmG`) and `fwhmL` (`dFwhmL`).\n */\nexport function pseudoVoigtTCHDerivative(x, fwhmG, fwhmL) {\n    const effectiveFwhm = computeEffectiveWidth(fwhmG, fwhmL);\n    const w = effectiveFwhm ** 5; // the polynomial under the 1/5 power\n    // ∂w/∂fwhmG and ∂w/∂fwhmL (derivatives of the TCH width polynomial).\n    const dwDfwhmG = 5 * fwhmG ** 4 +\n        10.77076 * fwhmG ** 3 * fwhmL +\n        7.28529 * fwhmG ** 2 * fwhmL ** 2 +\n        8.94326 * fwhmG * fwhmL ** 3 +\n        0.07842 * fwhmL ** 4;\n    const dwDfwhmL = 2.69269 * fwhmG ** 4 +\n        4.85686 * fwhmG ** 3 * fwhmL +\n        13.41489 * fwhmG ** 2 * fwhmL ** 2 +\n        0.31368 * fwhmG * fwhmL ** 3 +\n        5 * fwhmL ** 4;\n    // F = w^0.2  =>  ∂F/∂· = 0.2 * F / w * ∂w/∂·\n    const dFwhmDfwhmG = (0.2 * effectiveFwhm * dwDfwhmG) / w;\n    const dFwhmDfwhmL = (0.2 * effectiveFwhm * dwDfwhmL) / w;\n    // lorentzian width fraction L = fwhmL / F\n    const lorentzianFraction = fwhmL / effectiveFwhm;\n    const dLorentzianFractionDfwhmG = (-fwhmL / (effectiveFwhm * effectiveFwhm)) * dFwhmDfwhmG;\n    const dLorentzianFractionDfwhmL = 1 / effectiveFwhm - (fwhmL / (effectiveFwhm * effectiveFwhm)) * dFwhmDfwhmL;\n    // mu = 1 - (1.36603 L - 0.47719 L^2 + 0.11116 L^3)\n    const dPolyDfraction = 1.36603 -\n        0.95438 * lorentzianFraction +\n        0.33348 * lorentzianFraction * lorentzianFraction;\n    const dMuDfwhmG = -dPolyDfraction * dLorentzianFractionDfwhmG;\n    const dMuDfwhmL = -dPolyDfraction * dLorentzianFractionDfwhmL;\n    const mu = 1 -\n        (1.36603 * lorentzianFraction -\n            0.47719 * lorentzianFraction * lorentzianFraction +\n            0.11116 * lorentzianFraction * lorentzianFraction * lorentzianFraction);\n    // pseudoVoigt value and its ∂/∂x, ∂/∂F (dFwhm), ∂/∂mu (dMu) at the effective\n    // fwhm, inlined to allocate a single object on this hot path.\n    //\n    // Past {@link GAUSSIAN_CUTOFF} the gaussian half has underflowed and is\n    // dropped under the same condition as in `pseudoVoigtFct` — which this shape's\n    // own `fct` delegates to, so the value and its derivatives stay consistent out\n    // there. `fwhmL = 0` gives `mu = 1`, the pure gaussian that is never dropped.\n    const z = x / effectiveFwhm;\n    const e = mu !== 1 && z * z > GAUSSIAN_CUTOFF\n        ? 0\n        : Math.exp(GAUSSIAN_EXP_FACTOR * z * z);\n    const denominator2 = 4 * x * x + effectiveFwhm * effectiveFwhm;\n    const lorentz = (effectiveFwhm * effectiveFwhm) / denominator2;\n    const dEdt = ((2 * GAUSSIAN_EXP_FACTOR * x) / (effectiveFwhm * effectiveFwhm)) * e;\n    const dLdt = (-8 * x * effectiveFwhm * effectiveFwhm) / (denominator2 * denominator2);\n    const dEdfwhm = ((-2 * GAUSSIAN_EXP_FACTOR * x * x) /\n        (effectiveFwhm * effectiveFwhm * effectiveFwhm)) *\n        e;\n    const dLdfwhm = (8 * effectiveFwhm * x * x) / (denominator2 * denominator2);\n    const dFwhm = (1 - mu) * dLdfwhm + mu * dEdfwhm;\n    const dMu = e - lorentz;\n    return {\n        fct: (1 - mu) * lorentz + mu * e,\n        dx: (1 - mu) * dLdt + mu * dEdt,\n        dFwhmG: dFwhm * dFwhmDfwhmG + dMu * dMuDfwhmG,\n        dFwhmL: dFwhm * dFwhmDfwhmL + dMu * dMuDfwhmL,\n    };\n}\n/**\n * Compute the effective FWHM from gaussian and lorentzian component widths\n * using the Thompson–Cox–Hastings approximation.\n * @param fwhmG - gaussian component FWHM.\n * @param fwhmL - lorentzian component FWHM.\n * @returns effective combined FWHM.\n */\nfunction computeEffectiveWidth(fwhmG, fwhmL) {\n    return ((fwhmG ** 5 +\n        2.69269 * fwhmG ** 4 * fwhmL +\n        2.42843 * fwhmG ** 3 * fwhmL ** 2 +\n        4.47163 * fwhmG ** 2 * fwhmL ** 3 +\n        0.07842 * fwhmG * fwhmL ** 4 +\n        fwhmL ** 5) **\n        0.2);\n}\n/**\n * Solve for the lorentzian width fraction fwhmL/fwhm given lorentzianFraction = 1 - mu,\n * using Newton's method on: 1.36603·x - 0.47719·x² + 0.11116·x³ = lorentzianFraction.\n * @param lorentzianFraction - TCH lorentzian mixing parameter (= 1 - mu).\n * @returns the lorentzian width fraction fwhmL/fwhm.\n */\nfunction lorentzianWidthFraction(lorentzianFraction) {\n    let fraction = lorentzianFraction;\n    for (let i = 0; i < 6; i++) {\n        const f = 1.36603 * fraction -\n            0.47719 * fraction * fraction +\n            0.11116 * fraction * fraction * fraction -\n            lorentzianFraction;\n        const df = 1.36603 - 2 * 0.47719 * fraction + 3 * 0.11116 * fraction * fraction;\n        fraction -= f / df;\n    }\n    return fraction;\n}\n/**\n * Solve for the gaussian width fraction fwhmG/fwhm that pairs with a given\n * lorentzian width fraction fwhmL/fwhm. Writing fwhmG = g·fwhm and\n * fwhmL = q·fwhm in {@link computeEffectiveWidth} makes fwhm cancel, so `g` is\n * the root of the TCH width polynomial evaluated at 1. Solving it — rather than\n * taking `1 - q` — is what keeps `computeEffectiveWidth(fwhmG, fwhmL)` equal to\n * `fwhm`, and therefore keeps `fct` and `derivative` describing one same curve.\n * @param lorentzianFraction - the lorentzian width fraction fwhmL/fwhm.\n * @returns the gaussian width fraction fwhmG/fwhm.\n */\nfunction gaussianWidthFraction(lorentzianFraction) {\n    const q = lorentzianFraction;\n    let g = 1 - q;\n    for (let i = 0; i < 8; i++) {\n        const f = g ** 5 +\n            2.69269 * g ** 4 * q +\n            2.42843 * g ** 3 * q ** 2 +\n            4.47163 * g ** 2 * q ** 3 +\n            0.07842 * g * q ** 4 +\n            q ** 5 -\n            1;\n        const df = 5 * g ** 4 +\n            10.77076 * g ** 3 * q +\n            7.28529 * g ** 2 * q ** 2 +\n            8.94326 * g * q ** 3 +\n            0.07842 * q ** 4;\n        if (df === 0)\n            break;\n        g -= f / df;\n    }\n    return g;\n}\n//# sourceMappingURL=PseudoVoigtTCH.js.map","import { ROOT_THREE } from \"../../../util/constants.js\";\n/**\n * This shape is a linear combination of rational function (n|n+2), for n = 0 (lorentzian function) and n = 2\n * the parameter that combines those two functions is `gamma` and it is called the kurtosis parameter, it is an\n * implementation of generalized lorentzian shape published by Stanislav Sykora in the SMASH 2010. DOI:10.3247/SL3nmr10.006\n * {@link https://www.ebyte.it/stan/Talk_ML_UserMeeting_SMASH_2010_GeneralizedLorentzian.html}\n */\nexport class GeneralizedLorentzian {\n    kind = 'generalizedLorentzian';\n    /**\n     * Full width at half maximum.\n     * @default 500\n     */\n    fwhm;\n    /**\n     * kurtosis parameter of the shape, between -1 to 2\n     * @default 1\n     */\n    gamma;\n    constructor(options = {}) {\n        const { fwhm = 500, gamma = 0.5 } = options;\n        this.fwhm = fwhm;\n        this.gamma = gamma;\n    }\n    fwhmToWidth(fwhm = this.fwhm) {\n        return generalizedLorentzianFwhmToWidth(fwhm);\n    }\n    widthToFWHM(width) {\n        return generalizedLorentzianWidthToFWHM(width);\n    }\n    fct(x) {\n        return generalizedLorentzianFct(x, this.fwhm, this.gamma);\n    }\n    getArea(height = 1) {\n        return getGeneralizedLorentzianArea({\n            fwhm: this.fwhm,\n            height,\n            gamma: this.gamma,\n        });\n    }\n    getFactor(area) {\n        return getGeneralizedLorentzianFactor(area);\n    }\n    getData(options = {}) {\n        return getGeneralizedLorentzianData(this, options);\n    }\n    calculateHeight(area = 1) {\n        const { gamma, fwhm } = this;\n        return calculateGeneralizedLorentzianHeight({ fwhm, area, gamma });\n    }\n    getParameters() {\n        return ['fwhm', 'gamma'];\n    }\n    /**\n     * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n     * @returns the shape descriptor.\n     */\n    toJSON() {\n        return { kind: this.kind, fwhm: this.fwhm, gamma: this.gamma };\n    }\n    derivative(x) {\n        const { fct, dx, dFwhm, dGamma } = generalizedLorentzianDerivative(x, this.fwhm, this.gamma);\n        return { fct, dx, parameters: [dFwhm, dGamma] };\n    }\n}\nexport const calculateGeneralizedLorentzianHeight = ({ fwhm = 1, gamma = 1, area = 1, }) => {\n    return (area / fwhm / (3.14159 - 0.420894 * gamma)) * 2;\n};\n/**\n * Calculate the area under a generalized Lorentzian peak (integral from Mathematica).\n * @param options - shape parameters including fwhm, height, and gamma.\n * @returns the area under the peak.\n */\nexport const getGeneralizedLorentzianArea = (options) => {\n    const { fwhm = 500, height = 1, gamma = 1 } = options;\n    return (height * fwhm * (3.14159 - 0.420894 * gamma)) / 2;\n};\nexport const generalizedLorentzianFct = (x, fwhm, gamma) => {\n    const u = ((2 * x) / fwhm) ** 2;\n    return (1 - gamma) / (1 + u) + (gamma * (1 + u / 2)) / (1 + u + u ** 2);\n};\n/**\n * Analytical value and partial derivatives of the generalized lorentzian function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @param gamma - kurtosis parameter of the shape.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`), `fwhm` (`dFwhm`) and `gamma` (`dGamma`).\n */\nexport function generalizedLorentzianDerivative(x, fwhm, gamma) {\n    const u = ((2 * x) / fwhm) ** 2;\n    const lorentzian = 1 / (1 + u); // A\n    const rational = (1 + u / 2) / (1 + u + u * u); // B\n    const fct = (1 - gamma) * lorentzian + gamma * rational;\n    // dA/du and dB/du\n    const dLorentzianDu = -1 / ((1 + u) * (1 + u));\n    const denominator = 1 + u + u * u;\n    const dRationalDu = -(0.5 + 2 * u + 0.5 * u * u) / (denominator * denominator);\n    const dFctDu = (1 - gamma) * dLorentzianDu + gamma * dRationalDu;\n    const duDx = (8 * x) / (fwhm * fwhm);\n    const duDfwhm = (-8 * x * x) / (fwhm * fwhm * fwhm);\n    const dx = dFctDu * duDx;\n    const dFwhm = dFctDu * duDfwhm;\n    const dGamma = rational - lorentzian; // B - A\n    return { fct, dx, dFwhm, dGamma };\n}\nexport const generalizedLorentzianWidthToFWHM = (width) => {\n    return width * ROOT_THREE;\n};\nexport const generalizedLorentzianFwhmToWidth = (fwhm) => {\n    return fwhm / ROOT_THREE;\n};\nconst generalizedLorentzianQuantile = (p) => Math.tan(Math.PI * (p - 0.5));\nexport const getGeneralizedLorentzianFactor = (area = 0.9999) => {\n    if (area >= 1) {\n        throw new Error('area should be (0 - 1)');\n    }\n    const halfResidual = (1 - area) * 0.5;\n    return ((generalizedLorentzianQuantile(1 - halfResidual) -\n        generalizedLorentzianQuantile(halfResidual)) /\n        2);\n};\nexport const getGeneralizedLorentzianData = (shape = {}, options = {}) => {\n    const { fwhm = 500, gamma = 1 } = shape;\n    const { factor = getGeneralizedLorentzianFactor(), height = calculateGeneralizedLorentzianHeight({ fwhm, area: 1, gamma }), } = options;\n    let { length } = options;\n    if (!length) {\n        length = Math.min(Math.ceil(fwhm * factor), 2 ** 25 - 1);\n        if (length % 2 === 0)\n            length++;\n    }\n    const center = (length - 1) / 2;\n    const data = new Float64Array(length);\n    for (let i = 0; i <= center; i++) {\n        const value = generalizedLorentzianFct(i - center, fwhm, gamma) * height;\n        data[i] = value;\n        data[length - 1 - i] = value;\n    }\n    return data;\n};\n//# sourceMappingURL=GeneralizedLorentzian.js.map","import { ROOT_PI_OVER_LN2 } from \"../../../util/constants.js\";\nimport { gaussianDerivative, gaussianFct, gaussianFwhmToWidth, gaussianWidthToFWHM, getGaussianFactor, } from \"../gaussian/Gaussian.js\";\nexport class SplitGaussian {\n    kind = 'splitGaussian';\n    /**\n     * Full width at half maximum of the lower-x half (x <= 0).\n     * @default 500\n     */\n    fwhmLow;\n    /**\n     * Full width at half maximum of the higher-x half (x > 0).\n     * @default 500\n     */\n    fwhmHigh;\n    constructor(options = {}) {\n        const { fwhmLow = 500, fwhmHigh = 500 } = options;\n        this.fwhmLow = fwhmLow;\n        this.fwhmHigh = fwhmHigh;\n    }\n    /**\n     * Full width at half maximum of the peak. The half-maximum crossings are at\n     * `-fwhmLow / 2` and `fwhmHigh / 2`, so the width between them is the mean of\n     * both halves.\n     * @returns the full width at half maximum.\n     */\n    get fwhm() {\n        return (this.fwhmLow + this.fwhmHigh) / 2;\n    }\n    /**\n     * Set the full width at half maximum. Both halves are scaled by the same\n     * ratio, so their mean becomes `value` while the asymmetry between them is\n     * preserved. A peak with no width has no ratio to preserve, so both halves\n     * take `value` and the peak stays symmetric.\n     * @param value - the new full width at half maximum.\n     */\n    set fwhm(value) {\n        const { fwhm } = this;\n        if (fwhm === 0) {\n            this.fwhmLow = value;\n            this.fwhmHigh = value;\n            return;\n        }\n        const ratio = value / fwhm;\n        this.fwhmLow *= ratio;\n        this.fwhmHigh *= ratio;\n    }\n    /**\n     * Convert a full width at half maximum to the width between the inflection\n     * points. For this peak's own fwhm the result is exactly `σlow + σhigh`.\n     * @param fwhm - full width at half maximum. Defaults to the peak's fwhm.\n     * @returns the width between the inflection points.\n     */\n    fwhmToWidth(fwhm = this.fwhm) {\n        return gaussianFwhmToWidth(fwhm);\n    }\n    /**\n     * Convert a width between the inflection points back to a full width at half\n     * maximum. A single width does not encode the asymmetry, so it cannot recover\n     * `fwhmLow` and `fwhmHigh` individually.\n     * @param width - width between the inflection points.\n     * @returns the corresponding full width at half maximum.\n     */\n    widthToFWHM(width) {\n        return gaussianWidthToFWHM(width);\n    }\n    fct(x) {\n        return splitGaussianFct(x, this.fwhmLow, this.fwhmHigh);\n    }\n    getArea(height = calculateSplitGaussianHeight({\n        fwhmLow: this.fwhmLow,\n        fwhmHigh: this.fwhmHigh,\n    })) {\n        return getSplitGaussianArea({\n            fwhmLow: this.fwhmLow,\n            fwhmHigh: this.fwhmHigh,\n            height,\n        });\n    }\n    getFactor(area) {\n        return getGaussianFactor(area);\n    }\n    getData(options = {}) {\n        return getSplitGaussianData(this, options);\n    }\n    calculateHeight(area = 1) {\n        return calculateSplitGaussianHeight({\n            fwhmLow: this.fwhmLow,\n            fwhmHigh: this.fwhmHigh,\n            area,\n        });\n    }\n    getParameters() {\n        return ['fwhmLow', 'fwhmHigh'];\n    }\n    /**\n     * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n     * @returns the shape descriptor.\n     */\n    toJSON() {\n        return {\n            kind: this.kind,\n            fwhmLow: this.fwhmLow,\n            fwhmHigh: this.fwhmHigh,\n        };\n    }\n    derivative(x) {\n        const { fct, dx, dFwhmLow, dFwhmHigh } = splitGaussianDerivative(x, this.fwhmLow, this.fwhmHigh);\n        return { fct, dx, parameters: [dFwhmLow, dFwhmHigh] };\n    }\n}\n/**\n * Calculate the peak height for a given area and both half-widths.\n * @param options - fwhmLow, fwhmHigh and area.\n * @returns the peak height.\n */\nexport function calculateSplitGaussianHeight(options) {\n    const { fwhmLow = 500, fwhmHigh = 500, area = 1 } = options;\n    return (4 * area) / ROOT_PI_OVER_LN2 / (fwhmLow + fwhmHigh);\n}\n/**\n * Evaluate the split (asymmetric) gaussian function centered at x=0.\n * The lower-x half (x <= 0) uses `fwhmLow`, the higher-x half (x > 0) uses `fwhmHigh`.\n * @param x - position at which to evaluate.\n * @param fwhmLow - full width at half maximum of the lower-x half.\n * @param fwhmHigh - full width at half maximum of the higher-x half.\n * @returns the intensity at x.\n */\nexport function splitGaussianFct(x, fwhmLow, fwhmHigh) {\n    return x <= 0 ? gaussianFct(x, fwhmLow) : gaussianFct(x, fwhmHigh);\n}\n/**\n * Analytical value and partial derivatives of the split gaussian function centered at x=0.\n * Each half's fwhm only affects its own side, so the off-side derivative is 0.\n * @param x - position at which to evaluate.\n * @param fwhmLow - full width at half maximum of the lower-x half.\n * @param fwhmHigh - full width at half maximum of the higher-x half.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`), `fwhmLow` (`dFwhmLow`) and `fwhmHigh` (`dFwhmHigh`).\n */\nexport function splitGaussianDerivative(x, fwhmLow, fwhmHigh) {\n    if (x <= 0) {\n        const { fct, dx, dFwhm } = gaussianDerivative(x, fwhmLow);\n        return { fct, dx, dFwhmLow: dFwhm, dFwhmHigh: 0 };\n    }\n    const { fct, dx, dFwhm } = gaussianDerivative(x, fwhmHigh);\n    return { fct, dx, dFwhmLow: 0, dFwhmHigh: dFwhm };\n}\n/**\n * Calculate the area under a split gaussian peak.\n * @param options - fwhmLow, fwhmHigh and height.\n * @returns the area.\n */\nexport function getSplitGaussianArea(options) {\n    const { fwhmLow = 500, fwhmHigh = 500, height = 1 } = options;\n    return (height * ROOT_PI_OVER_LN2 * (fwhmLow + fwhmHigh)) / 4;\n}\n/**\n * Generate an intensity array for a split gaussian shape.\n * @param shape - split gaussian shape parameters (fwhmLow, fwhmHigh).\n * @param options - sampling options (length, factor, height).\n * @returns Float64Array of intensity values.\n */\nexport function getSplitGaussianData(shape = {}, options = {}) {\n    const { fwhmLow = 500, fwhmHigh = 500 } = shape;\n    const { factor = getGaussianFactor(), height = calculateSplitGaussianHeight({ fwhmLow, fwhmHigh }), } = options;\n    let { length } = options;\n    if (!length) {\n        length = Math.min(Math.ceil(Math.max(fwhmLow, fwhmHigh) * factor), 2 ** 25 - 1);\n        if (length % 2 === 0)\n            length++;\n    }\n    const center = (length - 1) / 2;\n    const data = new Float64Array(length);\n    for (let i = 0; i < length; i++) {\n        data[i] = splitGaussianFct(i - center, fwhmLow, fwhmHigh) * height;\n    }\n    return data;\n}\n//# sourceMappingURL=SplitGaussian.js.map","import { Gaussian } from \"./gaussian/Gaussian.js\";\nimport { GeneralizedLorentzian } from \"./generalizedLorentzian/GeneralizedLorentzian.js\";\nimport { Lorentzian } from \"./lorentzian/Lorentzian.js\";\nimport { LorentzianDispersive } from \"./lorentzianDispersive/LorentzianDispersive.js\";\nimport { PseudoVoigt } from \"./pseudoVoigt/PseudoVoigt.js\";\nimport { PseudoVoigtTCH } from \"./pseudoVoigtTCH/PseudoVoigtTCH.js\";\nimport { SplitGaussian } from \"./splitGaussian/SplitGaussian.js\";\nexport function getShape1D(shape) {\n    const { kind } = shape;\n    switch (kind) {\n        case 'gaussian':\n            return new Gaussian(shape);\n        case 'lorentzian':\n            return new Lorentzian(shape);\n        case 'pseudoVoigt':\n            return new PseudoVoigt(shape);\n        case 'pseudoVoigtTCH':\n            return new PseudoVoigtTCH(shape);\n        case 'lorentzianDispersive':\n            return new LorentzianDispersive(shape);\n        case 'generalizedLorentzian':\n            return new GeneralizedLorentzian(shape);\n        case 'splitGaussian':\n            return new SplitGaussian(shape);\n        default:\n            throw new Error(`Unknown distribution ${kind}`);\n    }\n}\n//# sourceMappingURL=getShape1D.js.map","/**\n * Asserts that value is truthy.\n * @param value - Value to check.\n * @param message - Optional error message to throw.\n */\nexport function assert(value, message) {\n    if (!value) {\n        throw new Error(message || 'unreachable');\n    }\n}\n//# sourceMappingURL=assert.js.map","import { xMean } from 'ml-spectra-processing';\nimport { assert } from \"./assert.js\";\n/**\n * Build an optimization layout mapping actual per-peak parameter slots\n * to optimizer variables. The layout describes slots, grouped/shared\n * variables, variable bounds/initials, and provides a helper to\n * materialize actual peak parameter values from a variable vector.\n * @param internalPeaks - normalized internal peaks with parameter indices\n * @param peaks - original peak objects (for per-peak optimize flags)\n * @param options - user `OptimizeOptions`, may contain `linkedParameters`\n * @param yScale - y normalization factor (used when converting offsets)\n * @returns an `OptimizationLayout` describing variables and slots\n */\nexport function buildOptimizationLayout(internalPeaks, peaks, options, yScale = 1) {\n    const slots = buildParameterSlots(internalPeaks, peaks, options);\n    const variables = buildOptimizationVariables(slots, options.linkedParameters, yScale);\n    const variableMin = new Float64Array(variables.length);\n    const variableMax = new Float64Array(variables.length);\n    const variableInit = new Float64Array(variables.length);\n    const variableGrad = new Float64Array(variables.length);\n    const freeIndices = [];\n    for (let i = 0; i < variables.length; i++) {\n        const variable = variables[i];\n        variableMin[i] = variable.min;\n        variableMax[i] = variable.max;\n        variableInit[i] = variable.init;\n        variableGrad[i] = variable.gradientDifference;\n        if (variable.optimize) {\n            freeIndices.push(i);\n        }\n    }\n    return {\n        slots,\n        variables,\n        freeIndices,\n        variableMin,\n        variableMax,\n        variableInit,\n        variableGrad,\n        variableToPeakValues(variableValues) {\n            const actualValues = new Array(slots.length);\n            for (let i = 0; i < variables.length; i++) {\n                const variableValue = variableValues[i];\n                const members = variables[i].members;\n                for (const member of members) {\n                    actualValues[member.actualIndex] =\n                        variableValue * member.factor + member.offset;\n                }\n            }\n            return actualValues;\n        },\n    };\n}\n/**\n * Builds concrete parameter slots for each peak parameter.\n * @param internalPeaks - normalized peaks containing parameter metadata\n * @param peaks - original peaks used to resolve optimize flags\n * @param options - optimization options with parameter settings\n * @returns flattened parameter slots across all peaks\n */\nfunction buildParameterSlots(internalPeaks, peaks, options) {\n    const slots = [];\n    for (let peakIndex = 0; peakIndex < internalPeaks.length; peakIndex++) {\n        const internalPeak = internalPeaks[peakIndex];\n        for (let i = 0; i < internalPeak.parameters.length; i++) {\n            const parameter = internalPeak.parameters[i];\n            slots.push({\n                actualIndex: internalPeak.fromIndex + i,\n                peakIndex,\n                peakId: internalPeak.id,\n                parameter,\n                init: internalPeak.propertiesValues.init[i],\n                min: internalPeak.propertiesValues.min[i],\n                max: internalPeak.propertiesValues.max[i],\n                gradientDifference: internalPeak.propertiesValues.gradientDifference[i],\n                optimize: getOptimizeFlag(peaks[peakIndex], parameter, options),\n            });\n        }\n    }\n    return slots;\n}\n/**\n * Builds optimization variables from concrete parameter slots.\n * @param slots - flattened per-peak parameter slots\n * @param linkedParameters - optional linked parameter groups\n * @param yScale - y normalization factor for y-offset conversion\n * @returns sorted optimization variables ready for the optimizer\n */\nfunction buildOptimizationVariables(slots, linkedParameters, yScale) {\n    const groupedActualIndices = new Set();\n    const variables = [];\n    const slotLookup = new Map();\n    const idToIndices = new Map();\n    for (const slot of slots) {\n        slotLookup.set(getSlotKey(slot.peakIndex, slot.parameter), slot);\n        if (slot.peakId) {\n            const indices = idToIndices.get(slot.peakId) ?? [];\n            if (!indices.includes(slot.peakIndex)) {\n                indices.push(slot.peakIndex);\n            }\n            idToIndices.set(slot.peakId, indices);\n        }\n    }\n    for (const linkedParameter of linkedParameters ?? []) {\n        variables.push(buildLinkedVariable(linkedParameter, slotLookup, groupedActualIndices, idToIndices, yScale));\n    }\n    for (const slot of slots) {\n        if (groupedActualIndices.has(slot.actualIndex)) {\n            continue;\n        }\n        variables.push({\n            sortKey: slot.actualIndex,\n            parameter: slot.parameter,\n            init: slot.init,\n            min: slot.min,\n            max: slot.max,\n            gradientDifference: slot.gradientDifference,\n            optimize: slot.optimize,\n            members: [\n                {\n                    actualIndex: slot.actualIndex,\n                    peakIndex: slot.peakIndex,\n                    parameter: slot.parameter,\n                    factor: 1,\n                    offset: 0,\n                },\n            ],\n        });\n    }\n    variables.sort((a, b) => a.sortKey - b.sortKey);\n    return variables.map(({ sortKey: _sortKey, ...variable }) => variable);\n}\nfunction buildLinkedVariable(linkedParameter, slotLookup, groupedActualIndices, idToIndices, yScale) {\n    if (linkedParameter.peaks.length === 0) {\n        throw new Error(`Linked parameter for ${linkedParameter.parameter} must contain at least one peak`);\n    }\n    const resolvedMembers = linkedParameter.peaks.map((peak) => {\n        const slot = resolveLinkedSlot(peak, linkedParameter.parameter, slotLookup, idToIndices);\n        if (groupedActualIndices.has(slot.actualIndex)) {\n            throw new Error(`Peak ${String(peak.id)} parameter ${linkedParameter.parameter} is already linked`);\n        }\n        return {\n            slot,\n            factor: getFactor(peak, linkedParameter.parameter),\n            offset: getOffset(peak, linkedParameter.parameter, yScale),\n        };\n    });\n    const memberActualIndices = new Set();\n    for (const member of resolvedMembers) {\n        if (memberActualIndices.has(member.slot.actualIndex)) {\n            throw new Error(`Linked parameter for ${linkedParameter.parameter} contains the same peak more than once`);\n        }\n        memberActualIndices.add(member.slot.actualIndex);\n    }\n    const firstMember = resolvedMembers[0];\n    let sharedMin = Number.NEGATIVE_INFINITY;\n    let sharedMax = Number.POSITIVE_INFINITY;\n    const optimize = firstMember.slot.optimize;\n    const sharedInitCandidates = [];\n    for (const member of resolvedMembers) {\n        if (member.slot.optimize !== optimize) {\n            throw new Error(`Linked parameter ${linkedParameter.parameter} must use a consistent optimize flag across all members`);\n        }\n        if (member.slot.min > member.slot.max) {\n            throw new Error(`Linked parameter ${linkedParameter.parameter} has incompatible bounds across its members`);\n        }\n        const variableBounds = getMemberVariableBounds(member);\n        sharedMin = Math.max(sharedMin, variableBounds.min);\n        sharedMax = Math.min(sharedMax, variableBounds.max);\n        sharedInitCandidates.push((member.slot.init - member.offset) / member.factor);\n    }\n    if (sharedMin > sharedMax) {\n        throw new Error(`Linked parameter ${linkedParameter.parameter} has incompatible bounds across its members`);\n    }\n    for (const member of resolvedMembers) {\n        groupedActualIndices.add(member.slot.actualIndex);\n    }\n    return {\n        sortKey: Math.min(...resolvedMembers.map((member) => member.slot.actualIndex)),\n        parameter: linkedParameter.parameter,\n        init: xMean(sharedInitCandidates),\n        min: sharedMin,\n        max: sharedMax,\n        gradientDifference: Math.min(...resolvedMembers.map((m) => Math.abs(m.slot.gradientDifference))),\n        optimize,\n        members: resolvedMembers.map((member) => ({\n            actualIndex: member.slot.actualIndex,\n            peakIndex: member.slot.peakIndex,\n            parameter: member.slot.parameter,\n            factor: member.factor,\n            offset: member.offset,\n        })),\n    };\n}\nfunction resolveLinkedSlot(peak, parameter, slotLookup, idToIndices) {\n    const peakIndex = typeof peak.id === 'number'\n        ? peak.id\n        : resolvePeakIndexById(peak.id, idToIndices);\n    if (!Number.isInteger(peakIndex) || peakIndex < 0) {\n        throw new Error(`Invalid peak reference ${String(peak.id)}`);\n    }\n    const slot = slotLookup.get(getSlotKey(peakIndex, parameter));\n    if (!slot) {\n        throw new Error(`Unknown parameter ${parameter} for peak ${String(peak.id)}`);\n    }\n    return slot;\n}\nfunction resolvePeakIndexById(peakId, idToIndices) {\n    const indices = idToIndices.get(peakId);\n    if (!indices || indices.length === 0) {\n        throw new Error(`Unknown peak id ${peakId}`);\n    }\n    if (new Set(indices).size > 1) {\n        throw new Error(`Peak id ${peakId} is ambiguous because it is used by multiple peaks`);\n    }\n    return indices[0];\n}\nfunction getFactor(peak, parameter) {\n    const factor = peak.factor ?? 1;\n    if (!Number.isFinite(factor) || factor === 0) {\n        throw new Error(`Linked parameter ${parameter} must use a non-zero finite factor`);\n    }\n    return factor;\n}\nfunction getOffset(peak, parameter, yScale) {\n    const offset = peak.offset ?? 0;\n    if (!Number.isFinite(offset)) {\n        throw new Error(`Linked parameter ${parameter} must use a finite offset`);\n    }\n    if (parameter === 'y') {\n        return offset / yScale;\n    }\n    return offset;\n}\nfunction getMemberVariableBounds(member) {\n    const transformedMin = (member.slot.min - member.offset) / member.factor;\n    const transformedMax = (member.slot.max - member.offset) / member.factor;\n    return {\n        min: Math.min(transformedMin, transformedMax),\n        max: Math.max(transformedMin, transformedMax),\n    };\n}\nfunction getOptimizeFlag(peak, parameter, options) {\n    assert(peak);\n    let optimizeFlag = true;\n    const perPeakParam = peak.parameters?.[parameter];\n    const globalParam = options.parameters?.[parameter];\n    if (perPeakParam?.optimize !== undefined) {\n        if (typeof perPeakParam.optimize === 'function') {\n            optimizeFlag = perPeakParam.optimize(peak);\n        }\n        else {\n            const { optimize = true } = perPeakParam;\n            optimizeFlag = optimize;\n        }\n    }\n    else if (globalParam?.optimize !== undefined) {\n        if (typeof globalParam.optimize === 'function') {\n            optimizeFlag = globalParam.optimize(peak);\n        }\n        else {\n            const { optimize = true } = globalParam;\n            optimizeFlag = optimize;\n        }\n    }\n    return optimizeFlag;\n}\nfunction getSlotKey(peakIndex, parameter) {\n    return `${peakIndex}:${parameter}`;\n}\n//# sourceMappingURL=buildOptimizationLayout.js.map","/**\n * Reconstruct user-facing peak objects from internal peaks and a full\n * actual-parameter vector.\n * @template T - original Peak type\n * @param internalPeaks - internal peaks produced by `getInternalPeaks`\n * @param actualValues - flattened actual parameter values (not normalized for Y)\n * @param yScale - normalization factor previously applied to Y values\n * @returns array of optimized peaks with reconstructed shapes and ids\n */\nexport function reconstructPeaks(internalPeaks, actualValues, yScale) {\n    const newPeaks = [];\n    for (const peak of internalPeaks) {\n        const { id, shape, parameters, fromIndex } = peak;\n        let newPeak = { x: 0, y: 0, shape };\n        if (id) {\n            newPeak = { ...newPeak, id };\n        }\n        newPeak.x = actualValues[fromIndex];\n        newPeak.y = actualValues[fromIndex + 1] * yScale;\n        for (let i = 2; i < parameters.length; i++) {\n            //@ts-expect-error should be fixed once\n            newPeak.shape[parameters[i]] = actualValues[fromIndex + i];\n        }\n        newPeaks.push(newPeak);\n    }\n    return newPeaks;\n}\n//# sourceMappingURL=reconstructPeaks.js.map","export const DefaultParameters = {\n    x: {\n        init: (peak) => peak.x,\n        min: (peak, peakShape) => peak.x - peakShape.fwhm * 2,\n        max: (peak, peakShape) => peak.x + peakShape.fwhm * 2,\n        gradientDifference: (peak, peakShape) => peakShape.fwhm * 2e-3,\n    },\n    y: {\n        init: (peak) => peak.y,\n        min: (peak) => (peak.y < 0 ? -1.1 : 0),\n        max: (peak) => (peak.y < 0 ? 0 : 1.1),\n        gradientDifference: () => 1e-3,\n    },\n    fwhm: {\n        init: (peak, peakShape) => peakShape.fwhm,\n        min: (peak, peakShape) => peakShape.fwhm * 0.25,\n        max: (peak, peakShape) => peakShape.fwhm * 4,\n        gradientDifference: (peak, peakShape) => peakShape.fwhm * 2e-3,\n    },\n    fwhmG: {\n        init: (peak, peakShape) => peakShape.fwhm * 0.6,\n        min: (peak, peakShape) => peakShape.fwhm * 0.6 * 0.25,\n        max: (peak, peakShape) => peakShape.fwhm * 0.6 * 4,\n        gradientDifference: (peak, peakShape) => peakShape.fwhm * 0.6 * 2e-3,\n    },\n    fwhmL: {\n        init: (peak, peakShape) => peakShape.fwhm * 0.4,\n        min: (peak, peakShape) => peakShape.fwhm * 0.4 * 0.25,\n        max: (peak, peakShape) => peakShape.fwhm * 0.4 * 4,\n        gradientDifference: (peak, peakShape) => peakShape.fwhm * 0.4 * 2e-3,\n    },\n    mu: {\n        init: (peak, peakShape) => peakShape.mu,\n        min: () => 0,\n        max: () => 1,\n        gradientDifference: () => 0.01,\n    },\n    gamma: {\n        init: (peak, peakShape) => peakShape.gamma || 0.5,\n        min: () => -1,\n        max: () => 2,\n        gradientDifference: () => 0.01,\n    },\n};\n//# sourceMappingURL=DefaultParameters.js.map","import { getShape1D } from 'ml-peak-shape-generator';\nimport { assert } from \"../assert.js\";\nimport { DefaultParameters } from \"./DefaultParameters.js\";\nconst properties = ['init', 'min', 'max', 'gradientDifference'];\n/**\n * Return an array of internalPeaks that contains the exact init, min, max values based on the options\n * @param peaks\n * @param minMaxY\n * @param yScale\n * @param options\n * @returns\n */\nexport function getInternalPeaks(peaks, yScale, options = {}) {\n    let index = 0;\n    const internalPeaks = [];\n    for (const originalPeak of peaks) {\n        const normalizedPeak = {\n            ...originalPeak,\n            y: originalPeak.y / yScale,\n        };\n        const peak = normalizedPeak;\n        const { id, shape = options.shape || { kind: 'gaussian' } } = peak;\n        const shapeFct = getShape1D(shape);\n        const parameters = ['x', 'y', ...shapeFct.getParameters()];\n        const propertiesValuesInternal = {\n            min: [],\n            max: [],\n            init: [],\n            gradientDifference: [],\n        };\n        for (const parameter of parameters) {\n            for (const property of properties) {\n                // check if the property is specified in the peak\n                let propertyValue = peak?.parameters?.[parameter]?.[property];\n                if (propertyValue !== undefined) {\n                    propertyValue = getNormalizedValue(propertyValue, parameter, property, yScale);\n                    propertiesValuesInternal[property].push(propertyValue);\n                    continue;\n                }\n                // check if there are some global option, it could be a number or a callback\n                let generalParameterValue = options?.parameters?.[parameter]?.[property];\n                if (generalParameterValue !== undefined) {\n                    if (typeof generalParameterValue === 'number') {\n                        generalParameterValue = getNormalizedValue(generalParameterValue, parameter, property, yScale);\n                        propertiesValuesInternal[property].push(generalParameterValue);\n                        continue;\n                    }\n                    else {\n                        // callbacks receive user-provided peak values (not Y-normalized)\n                        let value = generalParameterValue(originalPeak);\n                        value = getNormalizedValue(value, parameter, property, yScale);\n                        propertiesValuesInternal[property].push(value);\n                        continue;\n                    }\n                }\n                // we just need to take the default parameters\n                assert(DefaultParameters[parameter], `No default parameter for ${parameter}`);\n                const defaultParameterValues = DefaultParameters[parameter][property];\n                propertiesValuesInternal[property].push(\n                //@ts-expect-error parameters and shape instance are guaranteed to be present in the defaultParameterValues function\n                defaultParameterValues(peak, shapeFct));\n            }\n        }\n        const fromIndex = index;\n        const toIndex = fromIndex + parameters.length - 1;\n        index += toIndex - fromIndex + 1;\n        const propertiesValues = {\n            min: propertiesValuesInternal.min,\n            max: propertiesValuesInternal.max,\n            init: propertiesValuesInternal.init,\n            gradientDifference: propertiesValuesInternal.gradientDifference,\n        };\n        internalPeaks.push({\n            id,\n            shape,\n            shapeFct,\n            parameters,\n            propertiesValues,\n            fromIndex,\n            toIndex,\n        });\n    }\n    return internalPeaks;\n}\nfunction getNormalizedValue(value, parameter, property, yScale) {\n    if (parameter === 'y') {\n        if (property === 'gradientDifference') {\n            return value;\n        }\n        else {\n            return value / yScale;\n        }\n    }\n    return value;\n}\n//# sourceMappingURL=getInternalPeaks.js.map","// eslint-disable-next-line @typescript-eslint/unbound-method\nconst toString = Object.prototype.toString;\n/**\n * Checks if an object is an instance of an Array (array or typed array, except those that contain bigint values).\n * @param value - Object to check.\n * @returns True if the object is an array or a typed array.\n */\nexport function isAnyArray(value) {\n    const tag = toString.call(value);\n    return tag.endsWith('Array]') && !tag.includes('Big');\n}\n//# sourceMappingURL=index.js.map","import { isAnyArray } from 'is-any-array';\nexport default function checkOptions(data, options) {\n    const { timeout, initialValues, weights = 1, damping = 1e-2, dampingStepUp = 11, dampingStepDown = 9, maxIterations = 100, errorTolerance = 1e-7, centralDifference = false, gradientDifference = 10e-2, improvementThreshold = 1e-3, } = options;\n    let { minValues, maxValues } = options;\n    if (damping <= 0) {\n        throw new Error('The damping option must be a positive number');\n    }\n    else if (!data.x || !data.y) {\n        throw new Error('The data parameter must have x and y elements');\n    }\n    else if (!isAnyArray(data.x) ||\n        data.x.length < 2 ||\n        !isAnyArray(data.y) ||\n        data.y.length < 2) {\n        throw new Error('The data parameter elements must be an array with more than 2 points');\n    }\n    else if (data.x.length !== data.y.length) {\n        throw new Error('The data parameter elements must have the same size');\n    }\n    if (!(initialValues && initialValues.length > 0)) {\n        throw new Error('The initialValues option is mandatory and must be an array');\n    }\n    const parameters = Array.from(initialValues);\n    const parLen = parameters.length;\n    maxValues = maxValues || new Array(parLen).fill(Number.MAX_SAFE_INTEGER);\n    minValues = minValues || new Array(parLen).fill(Number.MIN_SAFE_INTEGER);\n    if (maxValues.length !== minValues.length) {\n        throw new Error('minValues and maxValues must be the same size');\n    }\n    const gradientDifferenceArray = getGradientDifferenceArray(gradientDifference, parameters);\n    const filler = getFiller(weights, data.x.length);\n    const checkTimeout = getCheckTimeout(timeout);\n    const weightSquare = Array.from({ length: data.x.length }, (_, i) => filler(i));\n    return {\n        checkTimeout,\n        minValues,\n        maxValues,\n        parameters,\n        weightSquare,\n        damping,\n        dampingStepUp,\n        dampingStepDown,\n        maxIterations,\n        errorTolerance,\n        centralDifference,\n        gradientDifference: gradientDifferenceArray,\n        improvementThreshold,\n    };\n}\nfunction getGradientDifferenceArray(gradientDifference, parameters) {\n    if (typeof gradientDifference === 'number') {\n        return new Array(parameters.length).fill(gradientDifference);\n    }\n    else if (isAnyArray(gradientDifference)) {\n        const parLen = parameters.length;\n        if (gradientDifference.length !== parLen) {\n            return new Array(parLen).fill(gradientDifference[0]);\n        }\n        return Array.from(gradientDifference);\n    }\n    throw new Error('gradientDifference should be a number or array with length equal to the number of parameters');\n}\nfunction getFiller(weights, dataLength) {\n    if (typeof weights === 'number') {\n        const value = 1 / weights ** 2;\n        return () => value;\n    }\n    else if (isAnyArray(weights)) {\n        if (weights.length < dataLength) {\n            const value = 1 / weights[0] ** 2;\n            return () => value;\n        }\n        return (i) => 1 / weights[i] ** 2;\n    }\n    throw new Error('weights should be a number or array with length equal to the number of data points');\n}\nfunction getCheckTimeout(timeout) {\n    if (timeout !== undefined) {\n        if (typeof timeout !== 'number') {\n            throw new Error('timeout should be a number');\n        }\n        const endTime = Date.now() + timeout * 1000;\n        return () => Date.now() > endTime;\n    }\n    else {\n        return () => false;\n    }\n}\n//# sourceMappingURL=check_options.js.map","/**\n * the sum of the weighted squares of the errors (or weighted residuals) between the data.y\n * and the curve-fit function.\n *\n * @param data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param parameters - Array of current parameter values\n * @param parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @param weightSquare - Square of weights (must be same length as data.x)\n */\nexport default function errorCalculation(data, parameters, parameterizedFunction, weightSquare) {\n    let error = 0;\n    const func = parameterizedFunction(parameters);\n    for (let i = 0; i < data.x.length; i++) {\n        error += (data.y[i] - func(data.x[i])) ** 2 / weightSquare[i];\n    }\n    return error;\n}\n//# sourceMappingURL=error_calculation.js.map","import { CholeskyDecomposition, Matrix, inverse } from 'ml-matrix';\nimport gradientFunction from \"./gradient_function.js\";\n/**\n * Builds the (nbParams x nbPoints) Jacobian of the residuals from an analytical\n * model gradient. The residual is `y - model`, so the residual Jacobian is the\n * negative of the model gradient — matching the sign convention produced by the\n * finite-difference `gradientFunction`.\n * @param data - points to fit\n * @param params - current parameter values\n * @param jacobianFunction - returns, for an x, the model partials over params\n */\nfunction analyticalGradient(data, params, jacobianFunction) {\n    const nbParams = params.length;\n    const nbPoints = data.x.length;\n    const ans = Matrix.zeros(nbParams, nbPoints);\n    const gradient = jacobianFunction(params);\n    for (let point = 0; point < nbPoints; point++) {\n        const partials = gradient(data.x[point]);\n        for (let param = 0; param < nbParams; param++) {\n            ans.set(param, point, -partials[param]);\n        }\n    }\n    return ans;\n}\n/**\n * Matrix function over the samples\n *\n * @param data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param evaluatedData - Array of previous evaluated function values\n */\nfunction matrixFunction(data, evaluatedData) {\n    const m = data.x.length;\n    const ans = new Matrix(m, 1);\n    for (let point = 0; point < m; point++) {\n        ans.set(point, 0, data.y[point] - evaluatedData[point]);\n    }\n    return ans;\n}\n/**\n * Iteration for Levenberg-Marquardt\n *\n * @param data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param params - Array of previous parameter values\n * @param damping - Levenberg-Marquardt parameter\n * @param gradientDifference - The step size to approximate the jacobian matrix\n * @param centralDifference - If true the jacobian matrix is approximated by central differences otherwise by forward differences\n * @param parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @param weights - scale the gradient and residual error by weights\n * @param jacobianFunction - optional analytical Jacobian, replaces finite differences when provided\n */\nexport default function step(data, params, damping, gradientDifference, parameterizedFunction, centralDifference, weights, jacobianFunction) {\n    const func = parameterizedFunction(params);\n    const evaluatedData = new Float64Array(data.x.length);\n    for (let i = 0; i < data.x.length; i++) {\n        evaluatedData[i] = func(data.x[i]);\n    }\n    const gradientFunc = jacobianFunction\n        ? analyticalGradient(data, params, jacobianFunction)\n        : gradientFunction(data, evaluatedData, params, gradientDifference, parameterizedFunction, centralDifference);\n    const residualError = matrixFunction(data, evaluatedData);\n    const hessianApproximation = gradientFunc.mmulByTranspose(weights);\n    for (let i = 0; i < params.length; i++) {\n        hessianApproximation.set(i, i, hessianApproximation.get(i, i) + damping);\n    }\n    const jacobianWeightResidualError = gradientFunc.mmul(residualError.scale('row', { scale: weights }));\n    // (damping * I + Jᵀ W J) is symmetric positive-definite for damping > 0, so a\n    // Cholesky solve is faster and more numerically stable than forming the full\n    // inverse and multiplying. Fall back to the inverse only in the rare case the\n    // approximated Hessian is not positive-definite.\n    const cholesky = new CholeskyDecomposition(hessianApproximation);\n    const perturbations = cholesky.isPositiveDefinite()\n        ? cholesky.solve(jacobianWeightResidualError)\n        : inverse(hessianApproximation).mmul(jacobianWeightResidualError);\n    return {\n        perturbations,\n        jacobianWeightResidualError,\n    };\n}\n//# sourceMappingURL=step.js.map","import { Matrix } from 'ml-matrix';\n/**\n * Difference of the matrix function over the parameters\n * @param data Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param evaluatedData - Array of previous evaluated function values\n * @param params - Array of previous parameter values\n * @param gradientDifference - The step size to approximate the jacobian matrix\n * @param centralDifference - If true the jacobian matrix is approximated by central differences otherwise by forward differences\n * @param paramFunction - The parameters and returns a function with the independent variable as a parameter\n */\nexport default function gradientFunction(data, evaluatedData, params, gradientDifference, paramFunction, centralDifference) {\n    const nbParams = params.length;\n    const nbPoints = data.x.length;\n    const ans = Matrix.zeros(nbParams, nbPoints);\n    let rowIndex = 0;\n    for (let param = 0; param < nbParams; param++) {\n        if (gradientDifference[param] === 0)\n            continue;\n        let delta = gradientDifference[param];\n        let auxParams = params.slice();\n        auxParams[param] += delta;\n        const funcParam = paramFunction(auxParams);\n        if (!centralDifference) {\n            for (let point = 0; point < nbPoints; point++) {\n                ans.set(rowIndex, point, (evaluatedData[point] - funcParam(data.x[point])) / delta);\n            }\n        }\n        else {\n            auxParams = params.slice();\n            auxParams[param] -= delta;\n            delta *= 2;\n            const funcParam2 = paramFunction(auxParams);\n            for (let point = 0; point < nbPoints; point++) {\n                ans.set(rowIndex, point, (funcParam2(data.x[point]) - funcParam(data.x[point])) / delta);\n            }\n        }\n        rowIndex++;\n    }\n    return ans;\n}\n//# sourceMappingURL=gradient_function.js.map","import checkOptions from \"./check_options.js\";\nimport errorCalculation from \"./error_calculation.js\";\nimport step from \"./step.js\";\n/**\n * Curve fitting algorithm\n * @param data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param parameterizedFunction - Takes an array of parameters and returns a function with the independent variable as its sole argument\n * @param options - Options object\n */\nexport function levenbergMarquardt(data, parameterizedFunction, options) {\n    const checkedOptions = checkOptions(data, options);\n    const { checkTimeout, minValues, maxValues, parameters, weightSquare, dampingStepUp, dampingStepDown, maxIterations, errorTolerance, centralDifference, gradientDifference, improvementThreshold, } = checkedOptions;\n    let damping = checkedOptions.damping;\n    const { jacobianFunction } = options;\n    let error = errorCalculation(data, parameters, parameterizedFunction, weightSquare);\n    let optimalError = error;\n    let optimalParameters = parameters.slice();\n    let converged = error <= errorTolerance;\n    let iteration = 0;\n    for (; iteration < maxIterations && !converged; iteration++) {\n        const previousError = error;\n        const { perturbations, jacobianWeightResidualError } = step(data, parameters, damping, gradientDifference, parameterizedFunction, centralDifference, weightSquare, jacobianFunction);\n        for (let k = 0; k < parameters.length; k++) {\n            parameters[k] = Math.min(Math.max(minValues[k], parameters[k] - perturbations.get(k, 0)), maxValues[k]);\n        }\n        error = errorCalculation(data, parameters, parameterizedFunction, weightSquare);\n        if (isNaN(error))\n            break;\n        if (error < optimalError - errorTolerance) {\n            optimalError = error;\n            optimalParameters = parameters.slice();\n        }\n        const improvementMetric = (previousError - error) /\n            perturbations\n                .transpose()\n                .mmul(perturbations.mul(damping).add(jacobianWeightResidualError))\n                .get(0, 0);\n        if (improvementMetric > improvementThreshold) {\n            damping = Math.max(damping / dampingStepDown, 1e-7);\n        }\n        else {\n            damping = Math.min(damping * dampingStepUp, 1e7);\n        }\n        if (checkTimeout()) {\n            throw new Error(`The execution time is over to ${options.timeout} seconds`);\n        }\n        converged = error <= errorTolerance;\n    }\n    return {\n        parameterValues: optimalParameters,\n        parameterError: optimalError,\n        iterations: iteration,\n    };\n}\n//# sourceMappingURL=levenberg_marquardt.js.map","/**\n * Preparata, F. P., & Shamos, M. I. (2012). Computational geometry: an introduction. Springer Science & Business Media.\n * @param {Array} x - The array with x coordinates of the points.\n * @param {Array} y - The array with y coordinates of the points.\n * @return {Array} The indices of the points of anticlockwise lower convex hull\n * @private\n */\nexport default function antiLowerConvexHull(x, y) {\n  if (x.length !== y.length) {\n    throw new RangeError('X and Y vectors has different dimensions');\n  }\n\n  const nbPoints = x.length - 1;\n  if (nbPoints === 0) return [0];\n  if (nbPoints === 1) return [0, 1];\n\n  let currentPoint = 0;\n  let result = new Array(x.length).fill(true);\n  while (true) {\n    const a = currentPoint;\n    const b = moveOn(currentPoint, nbPoints, result);\n    const c = moveOn(moveOn(currentPoint, nbPoints, result), nbPoints, result);\n\n    const det =\n      x[c] * (y[a] - y[b]) + x[a] * (y[b] - y[c]) + x[b] * (y[c] - y[a]);\n\n    const leftTurn = det >= 0;\n\n    if (leftTurn) {\n      currentPoint = b;\n    } else {\n      result[b] = false;\n      currentPoint = moveBack(currentPoint, nbPoints, result);\n    }\n    if (c === nbPoints) break;\n  }\n\n  return result\n    .map((item, index) => (item === false ? false : index))\n    .filter((item) => item !== false);\n}\n\n/**\n * @param {number} currentPoint - The index of the current point to make the move\n * @param {number} nbPoints - The total number of points in the array\n * @param {Array} vector - The array with the points\n * @return {number} the index of the point after the move\n * @private\n */\n\nfunction moveBack(currentPoint, nbPoints, vector) {\n  let counter = currentPoint - 1;\n  while (vector[counter] === false) counter--;\n  return currentPoint === 0 ? nbPoints : counter;\n}\n\nfunction moveOn(currentPoint, nbPoints, vector) {\n  let counter = currentPoint + 1;\n  while (vector[counter] === false) counter++;\n  return currentPoint === nbPoints ? 0 : counter;\n}\n","import { xNorm, xMaxValue, xMinValue } from 'ml-spectra-processing';\n\nimport antiLowerConvexHull from './util/antiLowerConvexHull';\n\n/**\n * Performs a global optimization of required parameters\n * It will return an object containing:\n * - `minFunctionValue`: The minimum value found for the objetive function\n * - `optima`: Array of Array of values for all the variables where the function reach its minimum value\n * - `iterations`: Number of iterations performed in the process\n * - `finalState`: Internal state allowing to continue optimization (initialState)\n * @param {function} objectiveFunction Function to evaluate. It should accept an array of variables\n * @param {Array} lowerBoundaries Array containing for each variable the lower boundary\n * @param {Array} upperBoundaries Array containing for each variable the higher boundary\n * @param {Object} [options={}]\n * @param {number} [options.iterations] - Number of iterations.\n * @param {number} [options.epsilon] - Tolerance to choose best current value.\n * @param {number} [options.tolerance] - Minimum tollerance of the function.\n * @param {number} [options.tolerance2] - Minimum tollerance of the function.\n * @param {Object} [options.initialState={}}] - finalState of previous optimization.\n * @return {Object} {finalState, iterations, minFunctionValue}\n * */\n\nexport default function direct(\n  objectiveFunction,\n  lowerBoundaries,\n  upperBoundaries,\n  options = {},\n) {\n  const {\n    iterations = 50,\n    epsilon = 1e-4,\n    tolerance = 1e-16,\n    tolerance2 = 1e-12,\n    initialState = {},\n  } = options;\n\n  if (\n    objectiveFunction === undefined ||\n    lowerBoundaries === undefined ||\n    upperBoundaries === undefined\n  ) {\n    throw new RangeError('There is something undefined');\n  }\n\n  lowerBoundaries = new Float64Array(lowerBoundaries);\n  upperBoundaries = new Float64Array(upperBoundaries);\n\n  if (lowerBoundaries.length !== upperBoundaries.length) {\n    throw new Error(\n      'Lower bounds and Upper bounds for x are not of the same length',\n    );\n  }\n\n  //-------------------------------------------------------------------------\n  //                        STEP 1. Initialization\n  //-------------------------------------------------------------------------\n  let n = lowerBoundaries.length;\n  let diffBorders = upperBoundaries.map((x, i) => x - lowerBoundaries[i]);\n\n  let {\n    numberOfRectangles = 0,\n    totalIterations = 0,\n    unitaryCoordinates = [new Float64Array(n).fill(0.5)],\n    middlePoint = new Float64Array(n).map((value, index) => {\n      return (\n        lowerBoundaries[index] +\n        unitaryCoordinates[0][index] * diffBorders[index]\n      );\n    }),\n    bestCurrentValue = objectiveFunction(middlePoint),\n    fCalls = 1,\n    smallerDistance = 0,\n    edgeSizes = [new Float64Array(n).fill(0.5)],\n    diagonalDistances = [Math.sqrt(n * 0.5 ** 2)],\n    functionValues = [bestCurrentValue],\n    differentDistances = diagonalDistances,\n    smallerValuesByDistance = [bestCurrentValue],\n    choiceLimit = undefined,\n  } = initialState;\n  if (\n    initialState.originalCoordinates &&\n    initialState.originalCoordinates.length > 0\n  ) {\n    bestCurrentValue = xMinValue(functionValues);\n    choiceLimit =\n      epsilon * Math.abs(bestCurrentValue) > 1e-8\n        ? epsilon * Math.abs(bestCurrentValue)\n        : 1e-8;\n\n    smallerDistance = getMinIndex(\n      functionValues,\n      diagonalDistances,\n      choiceLimit,\n      bestCurrentValue,\n    );\n\n    unitaryCoordinates = initialState.originalCoordinates.slice();\n    for (let j = 0; j < unitaryCoordinates.length; j++) {\n      for (let i = 0; i < lowerBoundaries.length; i++) {\n        unitaryCoordinates[j][i] =\n          (unitaryCoordinates[j][i] - lowerBoundaries[i]) / diffBorders[i];\n      }\n    }\n  }\n\n  let iteration = 0;\n  //-------------------------------------------------------------------------\n  //                          Iteration loop\n  //-------------------------------------------------------------------------\n\n  while (iteration < iterations) {\n    //----------------------------------------------------------------------\n    //  STEP 2. Identify the set S of all potentially optimal rectangles\n    //----------------------------------------------------------------------\n\n    let S1 = [];\n    let idx = differentDistances.findIndex(\n      // eslint-disable-next-line no-loop-func\n      (e) => e === diagonalDistances[smallerDistance],\n    );\n    let counter = 0;\n    for (let i = idx; i < differentDistances.length; i++) {\n      for (let f = 0; f < functionValues.length; f++) {\n        if (\n          (functionValues[f] === smallerValuesByDistance[i]) &\n          (diagonalDistances[f] === differentDistances[i])\n        ) {\n          S1[counter++] = f;\n        }\n      }\n    }\n\n    let optimumValuesIndex, S3;\n    if (differentDistances.length - idx > 1) {\n      let a1 = diagonalDistances[smallerDistance];\n      let b1 = functionValues[smallerDistance];\n      let a2 = differentDistances[differentDistances.length - 1];\n      let b2 = smallerValuesByDistance[differentDistances.length - 1];\n      let slope = (b2 - b1) / (a2 - a1);\n      let constant = b1 - slope * a1;\n      let S2 = new Uint32Array(counter);\n      counter = 0;\n      for (let i = 0; i < S2.length; i++) {\n        let j = S1[i];\n        if (\n          functionValues[j] <=\n          slope * diagonalDistances[j] + constant + tolerance2\n        ) {\n          S2[counter++] = j;\n        }\n      }\n\n      let xHull = [];\n      let yHull = [];\n      for (let i = 0; i < counter; i++) {\n        xHull.push(diagonalDistances[S2[i]]);\n        yHull.push(functionValues[S2[i]]);\n      }\n\n      let lowerIndexHull = antiLowerConvexHull(xHull, yHull);\n\n      S3 = [];\n      for (let i = 0; i < lowerIndexHull.length; i++) {\n        S3.push(S2[lowerIndexHull[i]]);\n      }\n    } else {\n      S3 = S1.slice(0, counter);\n    }\n    optimumValuesIndex = S3;\n    //--------------------------------------------------------------\n    // STEPS 3,5: Select any rectangle j in S\n    //--------------------------------------------------------------\n    for (let k = 0; k < optimumValuesIndex.length; k++) {\n      let j = optimumValuesIndex[k];\n      let largerSide = xMaxValue(edgeSizes[j]);\n      let largeSidesIndex = new Uint32Array(edgeSizes[j].length);\n      counter = 0;\n      for (let i = 0; i < edgeSizes[j].length; i++) {\n        if (Math.abs(edgeSizes[j][i] - largerSide) < tolerance) {\n          largeSidesIndex[counter++] = i;\n        }\n      }\n      let delta = (2 * largerSide) / 3;\n      let bestFunctionValues = [];\n      for (let r = 0; r < counter; r++) {\n        let i = largeSidesIndex[r];\n        let firstMiddleCenter = unitaryCoordinates[j].slice();\n        let secondMiddleCenter = unitaryCoordinates[j].slice();\n        firstMiddleCenter[i] += delta;\n        secondMiddleCenter[i] -= delta;\n        let firstMiddleValue = new Float64Array(firstMiddleCenter.length);\n        let secondMiddleValue = new Float64Array(secondMiddleCenter.length);\n        for (let i = 0; i < firstMiddleCenter.length; i++) {\n          firstMiddleValue[i] =\n            lowerBoundaries[i] + firstMiddleCenter[i] * diffBorders[i];\n          secondMiddleValue[i] =\n            lowerBoundaries[i] + secondMiddleCenter[i] * diffBorders[i];\n        }\n        let firstMinValue = objectiveFunction(firstMiddleValue);\n        let secondMinValue = objectiveFunction(secondMiddleValue);\n        fCalls += 2;\n        bestFunctionValues.push({\n          minValue: Math.min(firstMinValue, secondMinValue),\n          index: r,\n        });\n        // [Math.min(firstMinValue, secondMinValue), r];\n        unitaryCoordinates.push(firstMiddleCenter, secondMiddleCenter);\n        functionValues.push(firstMinValue, secondMinValue);\n      }\n\n      let b = bestFunctionValues.sort((a, b) => a.minValue - b.minValue);\n      for (let r = 0; r < counter; r++) {\n        let u = largeSidesIndex[b[r].index];\n        let ix1 = numberOfRectangles + 2 * (b[r].index + 1) - 1;\n        let ix2 = numberOfRectangles + 2 * (b[r].index + 1);\n        edgeSizes[j][u] = delta / 2;\n        edgeSizes[ix1] = edgeSizes[j].slice();\n        edgeSizes[ix2] = edgeSizes[j].slice();\n        diagonalDistances[j] = xNorm(edgeSizes[j]);\n        diagonalDistances[ix1] = diagonalDistances[j];\n        diagonalDistances[ix2] = diagonalDistances[j];\n      }\n      numberOfRectangles += 2 * counter;\n    }\n\n    //--------------------------------------------------------------\n    //                  Update\n    //--------------------------------------------------------------\n\n    bestCurrentValue = xMinValue(functionValues);\n\n    choiceLimit =\n      epsilon * Math.abs(bestCurrentValue) > 1e-8\n        ? epsilon * Math.abs(bestCurrentValue)\n        : 1e-8;\n\n    smallerDistance = getMinIndex(\n      functionValues,\n      diagonalDistances,\n      choiceLimit,\n      bestCurrentValue,\n      iteration,\n    );\n\n    differentDistances = Array.from(new Set(diagonalDistances));\n    differentDistances = differentDistances.sort((a, b) => a - b);\n\n    smallerValuesByDistance = [];\n    for (let i = 0; i < differentDistances.length; i++) {\n      let minIndex;\n      let minValue = Number.POSITIVE_INFINITY;\n      for (let k = 0; k < diagonalDistances.length; k++) {\n        if (diagonalDistances[k] === differentDistances[i]) {\n          if (functionValues[k] < minValue) {\n            minValue = functionValues[k];\n            minIndex = k;\n          }\n        }\n      }\n      smallerValuesByDistance.push(functionValues[minIndex]);\n    }\n\n    let currentMin = [];\n    for (let j = 0; j < functionValues.length; j++) {\n      if (functionValues[j] === bestCurrentValue) {\n        let temp = [];\n        for (let i = 0; i < lowerBoundaries.length; i++) {\n          temp.push(\n            lowerBoundaries[i] + unitaryCoordinates[j][i] * diffBorders[i],\n          );\n        }\n        currentMin.push(temp);\n      }\n    }\n    iteration += 1;\n  }\n  //--------------------------------------------------------------\n  //                  Saving results\n  //--------------------------------------------------------------\n\n  let result = {};\n  result.minFunctionValue = bestCurrentValue;\n  result.iterations = iteration;\n  let originalCoordinates = [];\n  for (let j = 0; j < numberOfRectangles + 1; j++) {\n    let pair = [];\n    for (let i = 0; i < lowerBoundaries.length; i++) {\n      pair.push(lowerBoundaries[i] + unitaryCoordinates[j][i] * diffBorders[i]);\n    }\n    originalCoordinates.push(pair);\n  }\n\n  result.finalState = {\n    numberOfRectangles,\n    totalIterations: (totalIterations += iterations),\n    originalCoordinates,\n    middlePoint,\n    fCalls,\n    smallerDistance,\n    edgeSizes,\n    diagonalDistances,\n    functionValues,\n    differentDistances,\n    smallerValuesByDistance,\n    choiceLimit,\n  };\n\n  let minimizer = [];\n  for (let i = 0; i < functionValues.length; i++) {\n    if (functionValues[i] === bestCurrentValue) {\n      minimizer.push(originalCoordinates[i]);\n    }\n  }\n\n  result.optima = minimizer;\n  return result;\n}\n\nfunction getMinIndex(\n  functionValues,\n  diagonalDistances,\n  choiceLimit,\n  bestCurrentValue,\n) {\n  let item = [];\n  for (let i = 0; i < functionValues.length; i++) {\n    item[i] =\n      Math.abs(functionValues[i] - (bestCurrentValue + choiceLimit)) /\n      diagonalDistances[i];\n  }\n  const min = xMinValue(item);\n  let result = item.findIndex((x) => x === min);\n  return result;\n}\n","import direct from 'ml-direct';\n/**\n * Run a direct optimization on the provided data using a sum-of-shapes model.\n * @param data - The observed x/y data to fit.\n * @param sumOfShapes - A function returning the model prediction for a given parameter vector.\n * @param options - Optimization bounds and solver options.\n * @returns The optimized parameter values, the final objective error, and the number of iterations.\n */\nexport function directOptimization(data, sumOfShapes, options) {\n    const { minValues, maxValues, maxIterations, epsilon, tolerance, tolerance2, initialState, } = options;\n    const objectiveFunction = getObjectiveFunction(data, sumOfShapes);\n    const result = direct(objectiveFunction, \n    // direct internally converts ArrayLike to Float64Array,\n    // so we can safely cast minValues and maxValues to number[]\n    minValues, maxValues, {\n        iterations: maxIterations,\n        epsilon,\n        tolerance,\n        tolerance2,\n        initialState,\n    });\n    const { optima, minFunctionValue, iterations } = result;\n    return {\n        parameterError: minFunctionValue,\n        iterations,\n        parameterValues: optima[0],\n    };\n}\nfunction getObjectiveFunction(data, sumOfShapes) {\n    const { x, y } = data;\n    const nbPoints = x.length;\n    return (parameters) => {\n        const fct = sumOfShapes(parameters);\n        let error = 0;\n        for (let i = 0; i < nbPoints; i++) {\n            error += (y[i] - fct(x[i])) ** 2;\n        }\n        return error;\n    };\n}\n//# sourceMappingURL=directOptimization.js.map","import { xMaxAbsoluteValue } from 'ml-spectra-processing';\nimport { getSumOfShapes } from \"./shapes/getSumOfShapes.js\";\nimport { buildOptimizationLayout } from \"./util/buildOptimizationLayout.js\";\nimport { getFixedParametersResult } from \"./util/getFixedParametersResult.js\";\nimport { getInternalPeaks } from \"./util/internalPeaks/getInternalPeaks.js\";\nimport { reconstructPeaks } from \"./util/reconstructPeaks.js\";\nimport { selectMethod } from \"./util/selectMethod.js\";\n/**\n * Fits a set of points to the sum of a set of bell functions.\n * @param data - An object containing the x and y data to be fitted.\n * @param peaks - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].\n * @param options - Options for optimize\n * @returns - An object with fitting error and the list of optimized parameters { parameters: [ {x, y, width} ], error } if the kind of shape is pseudoVoigt mu parameter is optimized.\n */\nexport function optimize(data, peaks, options = {}) {\n    // rescale data so the maximum Y value becomes 1\n    const max = xMaxAbsoluteValue(data.y);\n    const yScale = max === 0 ? 1 : max;\n    const internalPeaks = getInternalPeaks(peaks, yScale, options);\n    // need to rescale what is related to Y\n    const normalizedY = new Float64Array(data.y.length);\n    for (let i = 0; i < data.y.length; i++) {\n        normalizedY[i] = data.y[i] / yScale;\n    }\n    const optimizationLayout = buildOptimizationLayout(internalPeaks, peaks, options, yScale);\n    const { freeIndices, variableMin, variableMax, variableInit, variableGrad, variables, } = optimizationLayout;\n    const { algorithm, optimizationOptions } = selectMethod(options.optimization);\n    const baseSumOfShapes = getSumOfShapes(internalPeaks);\n    const sumOfShapesForVariables = (variableValues) => {\n        return baseSumOfShapes(optimizationLayout.variableToPeakValues(variableValues));\n    };\n    if (freeIndices.length === 0) {\n        return getFixedParametersResult(internalPeaks, normalizedY, data.x, optimizationLayout.variableToPeakValues(variableInit), baseSumOfShapes, yScale);\n    }\n    // prepare arrays to pass to the algorithm (reduced if needed)\n    let minValues;\n    let maxValues;\n    let initialValues;\n    let gradientDifferences;\n    let sumOfShapesToUse = sumOfShapesForVariables;\n    if (freeIndices.length === variables.length) {\n        // nothing to reduce\n        minValues = variableMin;\n        maxValues = variableMax;\n        initialValues = variableInit;\n        gradientDifferences = variableGrad;\n    }\n    else {\n        // wrapper that maps reduced (free) parameters into the full parameter vector\n        const sumOfShapesForReduced = (reducedParameters) => {\n            const full = new Float64Array(variables.length);\n            full.set(variableInit);\n            for (let k = 0; k < freeIndices.length; k++) {\n                full[freeIndices[k]] = reducedParameters[k];\n            }\n            return sumOfShapesForVariables(full);\n        };\n        minValues = new Float64Array(freeIndices.length);\n        maxValues = new Float64Array(freeIndices.length);\n        initialValues = new Float64Array(freeIndices.length);\n        gradientDifferences = new Float64Array(freeIndices.length);\n        for (let j = 0; j < freeIndices.length; j++) {\n            const i = freeIndices[j];\n            minValues[j] = variableMin[i];\n            maxValues[j] = variableMax[i];\n            initialValues[j] = variableInit[i];\n            gradientDifferences[j] = variableGrad[i];\n        }\n        sumOfShapesToUse = sumOfShapesForReduced;\n    }\n    const fitted = algorithm({ x: data.x, y: normalizedY }, sumOfShapesToUse, {\n        minValues,\n        maxValues,\n        initialValues,\n        gradientDifference: gradientDifferences,\n        ...optimizationOptions,\n    });\n    let fittedVariableValues;\n    if (freeIndices.length === variables.length) {\n        fittedVariableValues = fitted.parameterValues;\n    }\n    else {\n        const full = variableInit.slice();\n        for (let k = 0; k < freeIndices.length; k++) {\n            full[freeIndices[k]] = fitted.parameterValues[k];\n        }\n        fittedVariableValues = full;\n    }\n    const fittedValues = optimizationLayout.variableToPeakValues(fittedVariableValues);\n    return {\n        error: fitted.parameterError,\n        iterations: fitted.iterations,\n        peaks: reconstructPeaks(internalPeaks, fittedValues, yScale),\n    };\n}\n//# sourceMappingURL=index.js.map","import { levenbergMarquardt } from 'ml-levenberg-marquardt';\nimport { directOptimization } from \"./wrappers/directOptimization.js\";\n/**\n * Algorithm to select the method.\n * @param optimizationOptions - Optimization options\n * @returns - The algorithm and optimization options\n */\nexport function selectMethod(optimizationOptions = {}) {\n    const { kind = 'lm', options } = optimizationOptions;\n    switch (kind) {\n        case 'lm':\n        case 'levenbergMarquardt':\n            return {\n                algorithm: levenbergMarquardt,\n                optimizationOptions: {\n                    damping: 1.5,\n                    maxIterations: 100,\n                    errorTolerance: 1e-8,\n                    ...options,\n                },\n            };\n        case 'direct': {\n            return {\n                algorithm: directOptimization,\n                optimizationOptions: {\n                    maxIterations: 20,\n                    epsilon: 1e-4,\n                    tolerance: 1e-16,\n                    tolerance2: 1e-12,\n                    initialState: {},\n                    ...options,\n                },\n            };\n        }\n        default:\n            throw new Error(`Unknown fitting algorithm`);\n    }\n}\n//# sourceMappingURL=selectMethod.js.map","/**\n * This function returns the sumOfShapes function\n * This function gives sumOfShapes access to the peak list and the associated data\n * @param internalPeaks\n */\nexport function getSumOfShapes(internalPeaks) {\n    return function sumOfShapes(parameters) {\n        for (const peak of internalPeaks) {\n            for (let i = 2; i < peak.parameters.length; i++) {\n                const shapeFctKey = peak.parameters[i];\n                peak.shapeFct[shapeFctKey] = parameters[peak.fromIndex + i];\n            }\n        }\n        return (x) => {\n            let totalY = 0;\n            for (const peak of internalPeaks) {\n                const peakX = parameters[peak.fromIndex];\n                const y = parameters[peak.fromIndex + 1];\n                totalY += y * peak.shapeFct.fct(x - peakX);\n            }\n            return totalY;\n        };\n    };\n}\n//# sourceMappingURL=getSumOfShapes.js.map","import { reconstructPeaks } from \"./reconstructPeaks.js\";\n/**\n * Build result when no parameters are free to optimize.\n * Computes the fit error using the provided `globalInit` parameter vector\n * and reconstructs the output peak objects from `internalPeaks`.\n * @template T - input Peak type\n * @param internalPeaks - internal representation of peaks (with parameter indices)\n * @param normalizedY - observed Y values normalized by the global scale\n * @param x - X axis values\n * @param globalInit - full parameter vector (actual-space) used to evaluate the model\n * @param baseSumOfShapes - function that returns the spectrum function given parameters\n * @param yScale - the scale factor used to normalize Y (used to reconstruct peak amplitudes)\n * @returns an object containing `error`, `iterations` (0) and the reconstructed `peaks`\n */\nexport function getFixedParametersResult(internalPeaks, normalizedY, x, globalInit, baseSumOfShapes, yScale) {\n    const fct = baseSumOfShapes(globalInit);\n    let error = 0;\n    for (let i = 0; i < normalizedY.length; i++) {\n        error += (normalizedY[i] - fct(x[i])) ** 2;\n    }\n    return {\n        error,\n        iterations: 0,\n        peaks: reconstructPeaks(internalPeaks, globalInit, yScale),\n    };\n}\n//# sourceMappingURL=getFixedParametersResult.js.map","import { getShape1D } from 'ml-peak-shape-generator';\n/**\n * Add a `shape` property to peaks that do not have one.\n * If a peak already has a `shape` but no `fwhm`, the FWHM is computed from `peak.width`.\n * @param peaks - Peaks with a `width` property.\n * @param options - Shape options.\n * @returns A peak list where every peak has a `shape` property.\n */\nexport function addMissingShape(peaks, options = {}) {\n    const { shape = { kind: 'gaussian' }, output = structuredClone(peaks) } = options;\n    const defaultShapeInstance = getShape1D(shape);\n    return output.map((peak) => {\n        if (hasShape(peak)) {\n            if (!('fwhm' in peak.shape)) {\n                peak.shape.fwhm = getShape1D(peak.shape).widthToFWHM(peak.width);\n            }\n            return peak;\n        }\n        return {\n            ...peak,\n            shape: {\n                fwhm: defaultShapeInstance.widthToFWHM(peak.width),\n                ...shape,\n            },\n        };\n    });\n}\nfunction hasShape(peak) {\n    return 'shape' in peak;\n}\n//# sourceMappingURL=addMissingShape.js.map","/**\n * Splits a group of peaks into smaller subgroups whose size is approximately\n * limited by `maxNumberOfPeaks`.\n *\n * If the group contains more peaks than allowed, the function identifies the\n * largest normalized gaps between adjacent peaks and uses them as split points.\n *\n * The gap score is computed as:\n *\n * `score = distanceBetweenCenters / averagePeakWidth`\n *\n * where:\n * - `distanceBetweenCenters = peak[i + 1].x - peak[i].x`\n * - `averagePeakWidth = (peak[i].width + peak[i + 1].width) / 2`\n *\n * Larger scores indicate that two neighboring peaks are well separated\n * relative to their widths, making them good candidates for dividing the\n * fitting problem into independent subgroups.\n *\n * The algorithm:\n * 1. Computes the number of cuts required.\n * 2. Scores every boundary between adjacent peaks.\n * 3. Selects the boundaries with the largest scores.\n * 4. Splits the original group at those boundaries.\n * @param group - Ordered collection of peaks. Peaks should be sorted by\n * increasing `x` position before calling this function.\n * @param maxNumberOfPeaks - Desired maximum number of peaks per subgroup.\n * If the group size is less than or equal to this value, no splitting occurs.\n * @returns An array of peak subgroups. The returned groups preserve the\n * original peak ordering.\n */\nexport function splitGroup(group, maxNumberOfPeaks) {\n    const groups = [group];\n    while (true) {\n        const index = groups.findIndex((g) => g.length > maxNumberOfPeaks);\n        if (index === -1)\n            break;\n        const current = groups[index];\n        const cut = findBestCut(current);\n        groups.splice(index, 1, current.slice(0, cut), current.slice(cut));\n    }\n    return groups;\n}\nfunction findBestCut(group) {\n    let bestScore = -Infinity;\n    const candidates = [];\n    for (let i = 1; i < group.length; i++) {\n        const score = (group[i].x - group[i - 1].x) /\n            ((group[i].width + group[i - 1].width) / 2);\n        if (score > bestScore) {\n            bestScore = score;\n            candidates.length = 0;\n        }\n        if (score === bestScore) {\n            candidates.push({\n                index: i,\n                balance: Math.abs(i - (group.length - i)),\n            });\n        }\n    }\n    let bestScored = candidates[0];\n    for (const candidate of candidates) {\n        if (candidate.balance < bestScored.balance) {\n            bestScored = candidate;\n        }\n    }\n    return bestScored.index;\n}\n//# sourceMappingURL=splitGroup.js.map","import { splitGroup } from \"./splitGroup.js\";\n/**\n * Group peaks based on a width-aware factor.\n * Only `x` and `width` are used, so the current implementation does not take\n * peak asymmetry into account.\n * @param peaks - Peaks with `x` and `width` properties.\n * @param options - Grouping options.\n * @returns Groups of peaks sorted by ascending `x`.\n */\nexport function groupPeaks(peaks, options = {}) {\n    if (peaks.length === 0)\n        return [];\n    const { groupingFactor = 1, maxNumberOfPeaks = 15 } = options;\n    const sortedPeaks = peaks.toSorted((a, b) => a.x - b.x);\n    let previousPeak = sortedPeaks[0];\n    let currentGroup = [previousPeak];\n    const groups = [currentGroup];\n    for (let i = 1; i < sortedPeaks.length; i++) {\n        const peak = sortedPeaks[i];\n        if ((peak.x - previousPeak.x) / ((peak.width + previousPeak.width) / 2) <=\n            groupingFactor) {\n            currentGroup.push(peak);\n        }\n        else {\n            currentGroup = [peak];\n            groups.push(currentGroup);\n        }\n        previousPeak = peak;\n    }\n    if (maxNumberOfPeaks !== undefined) {\n        return groups.flatMap((group) => group.length > maxNumberOfPeaks\n            ? splitGroup(group, maxNumberOfPeaks)\n            : [group]);\n    }\n    return groups;\n}\n//# sourceMappingURL=groupPeaks.js.map","import { getShape1D } from 'ml-peak-shape-generator';\nimport { optimize } from 'ml-spectra-fitting';\nimport { xGetFromToIndex } from 'ml-spectra-processing';\nimport { addMissingShape } from \"../utils/addMissingShape.js\";\nimport { groupPeaks } from \"../utils/groupPeaks.js\";\n/**\n * Optimize the position (x), max intensity (y), full width at half maximum (fwhm)\n * and the ratio of gaussian contribution (mu) if it's required.\n * It currently supports three kind of shapes: gaussian, lorentzian and pseudovoigt.\n * Returns both the optimized peaks and per-group diagnostic logs.\n * @param data - An object containing the x and y data to be fitted.\n * @param peakList - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].\n * @param options - Optimization options.\n * @returns An object with the optimized peaks and the per-group logs.\n */\nexport function optimizePeaksWithLogs(data, peakList, options = {}) {\n    const { fromTo = {}, shape = { kind: 'gaussian' }, maxNumberOfPeaks, groupingFactor, factorLimits = 2, parameters, optimization = {\n        kind: 'lm',\n        options: {\n            timeout: 10,\n        },\n    }, } = options;\n    // Optimize peaks in groups: fitting everything at once would be too slow and\n    // have too many free parameters.\n    const groups = groupPeaks(peakList, {\n        groupingFactor,\n        maxNumberOfPeaks,\n    });\n    const logs = [];\n    const results = [];\n    for (const peakGroup of groups) {\n        const start = Date.now();\n        const peaks = addMissingShape(peakGroup, { shape });\n        const firstPeak = peaks[0];\n        const lastPeak = peaks.at(-1);\n        const { from = firstPeak.x - firstPeak.width * factorLimits, to = lastPeak.x + lastPeak.width * factorLimits, } = fromTo;\n        const { fromIndex, toIndex } = xGetFromToIndex(data.x, { from, to });\n        const x = data.x instanceof Float64Array\n            ? data.x.subarray(fromIndex, toIndex)\n            : data.x.slice(fromIndex, toIndex);\n        const y = data.y instanceof Float64Array\n            ? data.y.subarray(fromIndex, toIndex)\n            : data.y.slice(fromIndex, toIndex);\n        const log = {\n            range: { from, to },\n            parameters: { optimization, parameters },\n            groupSize: peakGroup.length,\n            time: Date.now() - start,\n        };\n        if (x.length > 5) {\n            const { iterations, error, peaks: optimizedPeaks, } = optimize({ x, y }, peaks, {\n                shape,\n                parameters,\n                optimization,\n            });\n            for (let i = 0; i < peaks.length; i++) {\n                results.push({\n                    ...optimizedPeaks[i],\n                    width: getShape1D(peaks[i].shape).fwhmToWidth(optimizedPeaks[i].shape.fwhm),\n                });\n            }\n            logs.push({\n                ...log,\n                iterations,\n                error,\n                message: 'optimization successful',\n            });\n        }\n        else {\n            results.push(...peaks);\n            logs.push({\n                ...log,\n                iterations: 0,\n                message: 'x length too small for optimization',\n            });\n        }\n    }\n    return { logs, optimizedPeaks: results };\n}\n//# sourceMappingURL=optimizePeaksWithLogs.js.map","/**\n * Assign a random UUID to every peak that does not already have an `id`.\n * @param peaks - Peaks possibly missing an `id`.\n * @param options - Options.\n * @param options.output - Destination array. Defaults to a deep clone of `peaks`.\n * @returns A peak list where every peak has an `id`.\n */\nexport function addMissingIDs(peaks, options = {}) {\n    const { output = structuredClone(peaks) } = options;\n    for (const peak of output) {\n        if (!('id' in peak)) {\n            peak.id = crypto.randomUUID();\n        }\n    }\n    return output;\n}\n//# sourceMappingURL=addMissingIDs.js.map","import { addMissingIDs } from \"../utils/addMissingIDs.js\";\nimport { addMissingShape } from \"../utils/addMissingShape.js\";\nimport { optimizePeaksWithLogs } from \"./optimizePeaksWithLogs.js\";\n/**\n * Join peaks that seem to belong to a broad signal into a single broad peak.\n * @param peakList - Detected peaks, possibly containing fragments of a broad signal.\n * @param options - Join options.\n * @returns The peak list with broad fragments fitted as a single peak.\n */\nexport function joinBroadPeaks(peakList, options = {}) {\n    const { shape = { kind: 'gaussian' }, optimization = { kind: 'lm', options: { timeout: 10 } }, broadWidth = 0.25, broadRatio = 0.0025, } = options;\n    let max = 0;\n    let maxI = 0;\n    let count = 1;\n    const broadLines = [];\n    if (peakList.length < 2) {\n        return addMissingIDs(addMissingShape(peakList.map(getGSDPeakOptimizedStructure), { shape }));\n    }\n    let maxDdy = Math.abs(peakList[0].ddY);\n    for (let i = 1; i < peakList.length; i++) {\n        const absDdy = Math.abs(peakList[i].ddY);\n        if (absDdy > maxDdy)\n            maxDdy = absDdy;\n    }\n    const newPeaks = [];\n    for (const peak of peakList) {\n        if (Math.abs(peak.ddY) <= broadRatio * maxDdy) {\n            broadLines.push(peak);\n        }\n        else {\n            newPeaks.push(getGSDPeakOptimizedStructure(peak));\n        }\n    }\n    // Sentinel: forces the final group to be flushed by the `else` branch below.\n    //@ts-expect-error Sentinel peak, x=+Infinity guarantees the distance check fails.\n    broadLines.push({ x: Number.MAX_VALUE, y: 0 });\n    let candidates = {\n        x: [broadLines[0].x],\n        y: [broadLines[0].y],\n    };\n    let indexes = [0];\n    for (let i = 1; i < broadLines.length; i++) {\n        if (Math.abs(broadLines[i - 1].x - broadLines[i].x) < broadWidth) {\n            candidates.x.push(broadLines[i].x);\n            candidates.y.push(broadLines[i].y);\n            if (broadLines[i].y > max) {\n                max = broadLines[i].y;\n                maxI = i;\n            }\n            indexes.push(i);\n            count++;\n        }\n        else {\n            if (count > 2) {\n                const initialWidth = Math.abs(candidates.x.at(-1) - candidates.x[0]);\n                const { logs, optimizedPeaks } = optimizePeaksWithLogs(candidates, [\n                    {\n                        id: crypto.randomUUID(),\n                        x: broadLines[maxI].x,\n                        y: max,\n                        width: initialWidth,\n                        parameters: {\n                            width: { max: initialWidth * 4, min: initialWidth * 0.8 },\n                        },\n                    },\n                ], { shape: { kind: 'pseudoVoigt' }, optimization });\n                max = 0;\n                maxI = 0;\n                const log = logs.find((l) => l.message === 'optimization successful');\n                if (log?.error !== undefined && log.error < 0.2) {\n                    newPeaks.push(optimizedPeaks[0]);\n                }\n                else {\n                    pushBackPeaks(broadLines, indexes, newPeaks);\n                }\n            }\n            else {\n                pushBackPeaks(broadLines, indexes, newPeaks);\n            }\n            candidates = { x: [broadLines[i].x], y: [broadLines[i].y] };\n            indexes = [i];\n            max = broadLines[i].y;\n            maxI = i;\n            count = 1;\n        }\n    }\n    newPeaks.sort((a, b) => a.x - b.x);\n    return addMissingIDs(newPeaks, { output: newPeaks });\n}\nfunction pushBackPeaks(broadLines, indexes, peaks) {\n    for (const index of indexes) {\n        peaks.push(getGSDPeakOptimizedStructure(broadLines[index]));\n    }\n}\nfunction getGSDPeakOptimizedStructure(peak) {\n    const { id, shape, x, y, width } = peak;\n    const newPeak = {\n        x,\n        y,\n        width,\n        shape,\n    };\n    if (id)\n        newPeak.id = id;\n    return newPeak;\n}\n//# sourceMappingURL=joinBroadPeaks.js.map","import { getShape1D } from 'ml-peak-shape-generator';\n/**\n * Enlarge peaks while preventing overlap between them.\n * A typical application is chromatography peak picking.\n * We should not make the hypothesis that `x` is equidistant, because peaks\n * may not be symmetric once we add the `from` and `to` properties.\n * @param peakList - Peaks to broaden.\n * @param options - Broadening options.\n * @returns The broadened peaks.\n */\nexport function broadenPeaks(peakList, options = {}) {\n    const { factor = 2, overlap = false } = options;\n    const peaks = mapPeaks(peakList, factor);\n    if (!overlap) {\n        for (let i = 0; i < peaks.length - 1; i++) {\n            const peak = peaks[i];\n            const nextPeak = peaks[i + 1];\n            if (peak.to.x > nextPeak.from.x) {\n                // we do it proportional to the width of the peaks\n                peak.to.x =\n                    (peak.width / (nextPeak.width + peak.width)) * (nextPeak.x - peak.x) +\n                        peak.x;\n                nextPeak.from.x = peak.to.x;\n            }\n        }\n    }\n    for (const peak of peaks) {\n        peak.width = peak.to.x - peak.from.x;\n        if (peak.shape) {\n            const { shape, width } = peak;\n            if (shape.fwhm !== undefined) {\n                const shapeFct = getShape1D(shape);\n                shape.fwhm = shapeFct.widthToFWHM(width);\n            }\n        }\n    }\n    return peaks;\n}\nfunction mapPeaks(peaks, factor) {\n    return peaks.map((peak) => {\n        const { id, shape, x, y, index, inflectionPoints } = peak;\n        const xFrom = x - (x - inflectionPoints.from.x) * factor;\n        const xTo = x + (inflectionPoints.to.x - x) * factor;\n        let result = {\n            x,\n            y,\n            index,\n            width: xTo - xFrom,\n            from: { x: xFrom },\n            to: { x: xTo },\n        };\n        if (id) {\n            result = { ...result, id };\n        }\n        if (shape) {\n            result = { ...result, shape };\n        }\n        return result;\n    });\n}\n//# sourceMappingURL=broadenPeaks.js.map","import { sgg } from 'ml-savitzky-golay-generalized';\nimport { xIsEquallySpaced, xIsMonotonic, xMaxAbsoluteValue, xMaxValue, xMinMaxValues, xNoiseStandardDeviation, } from 'ml-spectra-processing';\nimport { autoAlgorithm } from \"./algorithms/autoAlgorithm.js\";\nimport { firstDerivative } from \"./algorithms/firstDerivative.js\";\nimport { secondDerivative } from \"./algorithms/secondDerivative.js\";\nimport { optimizeTop } from \"./utils/optimizeTop.js\";\n/**\n * Global spectra deconvolution.\n * @param data - Object with `x` and `y` arrays. `x` must be monotone increasing.\n * @param options - Peak detection options.\n * @returns The detected peaks, sorted by ascending `x`.\n */\nexport function gsd(data, options = {}) {\n    let { noiseLevel } = options;\n    const { sgOptions = {\n        windowSize: 9,\n        polynomial: 3,\n    }, smoothY = false, maxCriteria = true, maxAbsoluteRatio = 0, minMaxRatio = 0.00025, realTopDetection = false, peakDetectionAlgorithm = 'second', } = options;\n    if (maxAbsoluteRatio < 0 || maxAbsoluteRatio > 1) {\n        throw new Error('maxAbsoluteRatio must be between 0 and 1');\n    }\n    const { x } = data;\n    let { y } = data;\n    if (xIsMonotonic(x) !== 1) {\n        throw new Error('GSD only accepts monotone increasing x values');\n    }\n    // Copy so the `maxCriteria` / clipping loops below don't mutate the caller's array.\n    y = y.slice();\n    // If the max difference between delta x is less than 5%, then,\n    // we can assume it to be equally spaced variable\n    const isEquallySpaced = xIsEquallySpaced(x);\n    if (noiseLevel === undefined) {\n        if (isEquallySpaced) {\n            const noiseInfo = xNoiseStandardDeviation(y);\n            if (maxCriteria) {\n                noiseLevel = noiseInfo.median + 1.5 * noiseInfo.sd;\n            }\n            else {\n                noiseLevel = -noiseInfo.median + 1.5 * noiseInfo.sd;\n            }\n        }\n        else {\n            noiseLevel = 0;\n        }\n    }\n    else if (!maxCriteria) {\n        noiseLevel *= -1;\n    }\n    if (!maxCriteria) {\n        for (let i = 0; i < y.length; i++) {\n            y[i] = -y[i];\n        }\n    }\n    const maxAbsoluteValue = maxAbsoluteRatio > 0 ? maxAbsoluteRatio * xMaxAbsoluteValue(y) : noiseLevel;\n    if (noiseLevel !== undefined) {\n        for (let i = 0; i < y.length; i++) {\n            if (y[i] < noiseLevel) {\n                y[i] = noiseLevel;\n            }\n        }\n    }\n    const xValue = isEquallySpaced ? x[1] - x[0] : x;\n    const yData = smoothY\n        ? sgg(y, xValue, {\n            ...sgOptions,\n            derivative: 0,\n        })\n        : y;\n    const { min: minY, max: maxY } = xMinMaxValues(yData);\n    if (minY > maxY || minY === maxY)\n        return [];\n    const dY = sgg(y, xValue, {\n        ...sgOptions,\n        derivative: 1,\n    });\n    const ddY = sgg(y, xValue, {\n        ...sgOptions,\n        derivative: 2,\n    });\n    const yThreshold = xMaxValue([\n        noiseLevel,\n        minY + (maxY - minY) * minMaxRatio,\n        maxAbsoluteValue,\n    ]);\n    const dX = x[1] - x[0];\n    const peakData = { x, y, yData, dY, ddY, dX, yThreshold };\n    let peaks = [];\n    if (peakDetectionAlgorithm === 'first') {\n        peaks = firstDerivative(peakData);\n    }\n    else if (peakDetectionAlgorithm === 'second') {\n        peaks = secondDerivative(peakData);\n    }\n    else {\n        peaks = autoAlgorithm(peakData);\n    }\n    if (realTopDetection) {\n        optimizeTop({ x, y: yData }, peaks);\n    }\n    for (const peak of peaks) {\n        if (!maxCriteria) {\n            peak.y *= -1;\n            peak.ddY = peak.ddY * -1;\n        }\n    }\n    peaks.sort((a, b) => {\n        return a.x - b.x;\n    });\n    return peaks;\n}\n//# sourceMappingURL=gsd.js.map","/**\n * Returns true if x is monotonic.\n * @param array - array of numbers.\n * @returns 1 if monotonic increasing, -1 if monotonic decreasing, 0 if not monotonic.\n */\nexport function xIsMonotonic(array) {\n    if (array.length <= 2) {\n        return 1;\n    }\n    if (array[0] === array[1]) {\n        // maybe a constant series\n        for (let i = 1; i < array.length - 1; i++) {\n            if (array[i] !== array[i + 1])\n                return 0;\n        }\n        return 1;\n    }\n    if (array[0] < array.at(-1)) {\n        for (let i = 0; i < array.length - 1; i++) {\n            if (array[i] >= array[i + 1])\n                return 0;\n        }\n        return 1;\n    }\n    else {\n        for (let i = 0; i < array.length - 1; i++) {\n            if (array[i] <= array[i + 1])\n                return 0;\n        }\n        return -1;\n    }\n}\n//# sourceMappingURL=xIsMonotonic.js.map","/**\n * Check if the values are separated always by the same difference\n * @param array - monotone growing array of number.\n * @param options - options.\n */\nexport function xIsEquallySpaced(array, options = {}) {\n    if (array.length < 3)\n        return true;\n    const { tolerance = 0.05 } = options;\n    let maxDx = 0;\n    let minDx = Number.MAX_SAFE_INTEGER;\n    for (let i = 0; i < array.length - 1; ++i) {\n        const absoluteDifference = array[i + 1] - array[i];\n        if (absoluteDifference < minDx) {\n            minDx = absoluteDifference;\n        }\n        if (absoluteDifference > maxDx) {\n            maxDx = absoluteDifference;\n        }\n    }\n    return (maxDx - minDx) / maxDx < tolerance;\n}\n//# sourceMappingURL=xIsEquallySpaced.js.map","import { xCheck } from \"./xCheck.js\";\n/**\n * Return min and max values of an array.\n * @param array - array of number\n * @returns object with 2 properties, min and max.\n */\nexport function xMinMaxValues(array) {\n    xCheck(array);\n    let min = array[0];\n    let max = array[0];\n    for (const value of array) {\n        if (value < min)\n            min = value;\n        if (value > max)\n            max = value;\n    }\n    return { min, max };\n}\n//# sourceMappingURL=xMinMaxValues.js.map","import { getMinMaxIntervalsDy } from \"./getMinMaxIntervals.js\";\nimport { getPeakFromIntervals } from \"./getPeaksFromIntervals.js\";\n/**\n * Detect peaks using local minima of the second derivative (inflection points).\n * @param input - Spectrum values and its first/second derivatives.\n * @returns The detected peaks.\n */\nexport function secondDerivative(input) {\n    const { x, y, yData, dY, ddY, dX, yThreshold } = input;\n    const minddY = [];\n    const { intervalL, intervalR } = getMinMaxIntervalsDy(y, x, dY, dX);\n    // By the intermediate value theorem We cannot find 2 consecutive maximum or minimum\n    for (let i = 1; i < y.length - 1; ++i) {\n        // Minimum in second derivative\n        if (ddY[i] < ddY[i - 1] && ddY[i] < ddY[i + 1]) {\n            minddY.push(i);\n        }\n    }\n    return getPeakFromIntervals({\n        minData: minddY,\n        intervalL,\n        intervalR,\n        x,\n        yData,\n        yThreshold,\n        ddY,\n    });\n}\n//# sourceMappingURL=secondDerivative.js.map","import { getMinMaxIntervalsDy } from \"./getMinMaxIntervals.js\";\nimport { tryMatchOneIntervalWithMinData } from \"./tryMatchOneIntervalWithMinData.js\";\n/**\n * Peak detection that combines first-derivative zero-crossings and\n * second-derivative local minima to find peaks inside each interval.\n * @param input - Spectrum values together with its first and second derivatives.\n * @returns The detected peaks.\n */\nexport function autoAlgorithm(input) {\n    const { x, y, yData, dY, ddY, dX, yThreshold } = input;\n    const minddY = [];\n    const crossDy = [];\n    const { intervalL, intervalR } = getMinMaxIntervalsDy(y, x, dY, dX);\n    for (let i = 1; i < y.length - 1; ++i) {\n        if ((dY[i] < 0 && dY[i + 1] > 0) || (dY[i] > 0 && dY[i + 1] < 0)) {\n            // push the index of the element closer to zero\n            crossDy.push(Math.abs(dY[i]) < Math.abs(dY[i + 1]) ? i : i + 1);\n        }\n        // Handle exact zero\n        if (dY[i] === 0 &&\n            dY[i] < Math.abs(dY[i + 1]) &&\n            dY[i] < Math.abs(dY[i - 1])) {\n            crossDy.push(i);\n        }\n        // Minimum in second derivative\n        if (ddY[i] < ddY[i - 1] && ddY[i] < ddY[i + 1]) {\n            minddY.push(i);\n        }\n    }\n    const peaks = [];\n    let lastK = -1;\n    let lastJ = -1;\n    for (let i = 0; i < intervalL.length; i++) {\n        const intervalWidth = (intervalR[i].x - intervalL[i].x) / 2;\n        const intervalCenter = (intervalR[i].x + intervalL[i].x) / 2;\n        let yIndex = -1;\n        let match = tryMatchOneIntervalWithMinData({\n            x,\n            yData,\n            lastK,\n            yThreshold,\n            intervalWidth,\n            intervalCenter,\n            minData: crossDy,\n        });\n        lastK = match.lastIndex;\n        if (match.possible !== -1) {\n            yIndex = crossDy[match.possible];\n        }\n        else {\n            match = tryMatchOneIntervalWithMinData({\n                x,\n                yData,\n                yThreshold,\n                lastK: lastJ,\n                intervalWidth,\n                intervalCenter,\n                minData: minddY,\n            });\n            if (match.possible !== -1) {\n                yIndex = minddY[match.possible];\n            }\n            lastJ = match.lastIndex;\n        }\n        if (yIndex !== -1) {\n            const width = Math.abs(intervalR[i].x - intervalL[i].x);\n            peaks.push({\n                id: crypto.randomUUID(),\n                x: x[yIndex],\n                y: y[yIndex],\n                width,\n                index: yIndex,\n                ddY: ddY[yIndex],\n                inflectionPoints: {\n                    from: intervalL[i],\n                    to: intervalR[i],\n                },\n            });\n        }\n    }\n    return peaks;\n}\n//# sourceMappingURL=autoAlgorithm.js.map","/**\n * Refine the `x` and `y` coordinates of each peak by running a quadratic\n * interpolation over the peak and its 3 closest neighbors.\n * The correction is performed in place.\n * @param data - Object with `x` and `y` arrays.\n * @param peaks - Peaks to refine (mutated in place).\n */\nexport function optimizeTop(data, peaks) {\n    const { x, y } = data;\n    for (const peak of peaks) {\n        let currentIndex = peak.index;\n        // The detected peak could be moved 1 or 2 units to left or right.\n        if (y[currentIndex - 1] >= y[currentIndex - 2] &&\n            y[currentIndex - 1] >= y[currentIndex]) {\n            currentIndex--;\n        }\n        else if (y[currentIndex + 1] >= y[currentIndex] &&\n            y[currentIndex + 1] >= y[currentIndex + 2]) {\n            currentIndex++;\n        }\n        else if (y[currentIndex - 2] >= y[currentIndex - 3] &&\n            y[currentIndex - 2] >= y[currentIndex - 1]) {\n            currentIndex -= 2;\n        }\n        else if (y[currentIndex + 2] >= y[currentIndex + 1] &&\n            y[currentIndex + 2] >= y[currentIndex + 3]) {\n            currentIndex += 2;\n        }\n        // Quadratic interpolation on log-intensities to refine the peak top.\n        if (y[currentIndex - 1] > 0 &&\n            y[currentIndex + 1] > 0 &&\n            y[currentIndex] >= y[currentIndex - 1] &&\n            y[currentIndex] >= y[currentIndex + 1] &&\n            (y[currentIndex] !== y[currentIndex - 1] ||\n                y[currentIndex] !== y[currentIndex + 1])) {\n            const alpha = Math.log10(y[currentIndex - 1]);\n            const beta = Math.log10(y[currentIndex]);\n            const gamma = Math.log10(y[currentIndex + 1]);\n            const p = (0.5 * (alpha - gamma)) / (alpha - 2 * beta + gamma);\n            const xCurrent = x[currentIndex];\n            const xPrevious = x[currentIndex - 1];\n            peak.x = xCurrent + (xCurrent - xPrevious) * p;\n            peak.y =\n                y[currentIndex] -\n                    0.25 * (y[currentIndex - 1] - y[currentIndex + 1]) * p;\n        }\n    }\n}\n//# sourceMappingURL=optimizeTop.js.map","import { optimizePeaksWithLogs } from \"./optimizePeaksWithLogs.js\";\n/**\n * Optimize the position (x), max intensity (y), full width at half maximum (fwhm)\n * and the ratio of gaussian contribution (mu) if it's required.\n * @param data - An object containing the x and y data to be fitted.\n * @param peakList - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].\n * @param options - Optimization options.\n * @returns The optimized peaks.\n */\nexport function optimizePeaks(data, peakList, options = {}) {\n    return optimizePeaksWithLogs(data, peakList, options).optimizedPeaks;\n}\n//# sourceMappingURL=optimizePeaks.js.map","import { getShape1D } from 'ml-peak-shape-generator';\n/**\n * Append a `shape` property (including `fwhm`) to every peak.\n * @param peaks - Peaks with a `width` property.\n * @param options - Shape options.\n * @returns A peak list where every peak has a `shape` property.\n */\nexport function setShape(peaks, options = {}) {\n    const { shape = { kind: 'gaussian' }, output = structuredClone(peaks) } = options;\n    const shapeInstance = getShape1D(shape);\n    return output.map((peak) => ({\n        ...peak,\n        shape: { ...shape, fwhm: shapeInstance.widthToFWHM(peak.width) },\n    }));\n}\n//# sourceMappingURL=setShape.js.map"],"names":["toString","Object","prototype","isAnyArray","value","tag","call","endsWith","includes","sgg","ys","xs","options","windowSize","derivative","polynomial","Number","isInteger","RangeError","TypeError","undefined","length","console","warn","half","Math","floor","np","ans","Float64Array","weights","m","n","s","Array","t","j","weight","fullWeights","hs","constantH","i","wg1","wg2","d1","d2","l","getHs","wg","d","h","center","count","gramPoly","k","Grampoly","genFact","a","b","gf","sum","xMedian","input","exact","fromIndex","toIndex","array","slice","middleIndex","calcMiddle","median","quickSelect","low","high","middle","currentLow","currentHigh","swap","temp","xCheck","minLength","Error","xFindClosestIndex","target","sorted","abs","index","diff","POSITIVE_INFINITY","currentDiff","xGetFromToIndex","x","from","to","CholeskyDecomposition","matrix","Matrix","inverse","xMean","sumValue","xMaxValue","maxValue","xMinValue","minValue","xMaxAbsoluteValue","xNoiseStandardDeviation","mad","averageDeviations","xMedianAbsoluteDeviation","sd","xNorm","result","element","sqrt","getMinMaxIntervalsDy","y","dY","dX","lastMax","lastMin","intervalL","intervalR","push","tryMatchOneIntervalWithMinData","lastK","minData","yThreshold","intervalWidth","intervalCenter","yData","minDistance","possible","newLastIndex","centerIndex","deltaX","currentDistance","lastIndex","getPeakFromIntervals","peaks","ddY","width","id","crypto","randomUUID","inflectionPoints","isLessAndGreaterThanZero","back","next","firstDerivative","crossDy","xGetCrossZeroPoints","GAUSSIAN_EXP_FACTOR","LN2","ROOT_PI_OVER_LN2","PI","ROOT_LN2","ROOT_THREE","ROOT_2LN2","ROOT_2LN2_MINUS_ONE","Gaussian","kind","fwhm","constructor","this","gaussianWidthToFWHM","fwhmToWidth","gaussianFwhmToWidth","widthToFWHM","fct","gaussianFct","getArea","height","calculateGaussianHeight","getGaussianArea","getFactor","area","getGaussianFactor","getData","shape","factor","min","ceil","data","getGaussianData","calculateHeight","getParameters","toJSON","dx","dFwhm","gaussianDerivative","parameters","exp","ln1MinusXSqrd","log","lnEtcBy2Plus2","firstSqrt","erfinv","Lorentzian","lorentzianFwhmToWidth","lorentzianWidthToFWHM","lorentzianFct","getLorentzianArea","getLorentzianFactor","getLorentzianData","calculateLorentzianHeight","denominator","lorentzianDerivative","lorentzianQuantile","p","tan","halfResidual","LorentzianDispersive","lorentzianDispersiveFct","getLorentzianDispersiveData","lorentzianDispersiveDerivative","sqrtLn2","pGaussian","sign","erf","pPseudoVoigt","mu","atan","pLorentz","PseudoVoigt","pseudoVoigtFwhmToWidth","pseudoVoigtWidthToFWHM","pseudoVoigtFct","getPseudoVoigtArea","getPseudoVoigtFactor","calculatePseudoVoigtHeight","getPseudoVoigtData","dMu","z","e","lorentz","dEdt","dLdt","dEdfwhm","dLdfwhm","pseudoVoigtDerivative","lorentzian","pTarget","tol","maxIter","lo","hi","it","mid","val","pseudoVoigtFindFactor","PseudoVoigtTCH","_fwhmG","_fwhmL","_fwhm","_mu","_lorentzianWidthFraction","fwhmG","fwhmL","lorentzianWidthFraction","effectiveFwhm","computeEffectiveWidth","lorentzianFraction","gaussianWidthFraction","dFwhmG","dFwhmL","w","dFwhmDfwhmG","dFwhmDfwhmL","dPolyDfraction","dMuDfwhmG","dMuDfwhmL","denominator2","pseudoVoigtTCHDerivative","fraction","q","g","f","df","GeneralizedLorentzian","gamma","generalizedLorentzianFwhmToWidth","generalizedLorentzianWidthToFWHM","generalizedLorentzianFct","getGeneralizedLorentzianArea","getGeneralizedLorentzianFactor","getGeneralizedLorentzianData","calculateGeneralizedLorentzianHeight","dGamma","u","rational","dFctDu","duDx","duDfwhm","generalizedLorentzianDerivative","generalizedLorentzianQuantile","SplitGaussian","fwhmLow","fwhmHigh","ratio","splitGaussianFct","calculateSplitGaussianHeight","getSplitGaussianArea","max","getSplitGaussianData","dFwhmLow","dFwhmHigh","splitGaussianDerivative","getShape1D","assert","message","buildOptimizationLayout","internalPeaks","yScale","slots","peakIndex","internalPeak","parameter","actualIndex","peakId","init","propertiesValues","gradientDifference","optimize","getOptimizeFlag","buildParameterSlots","variables","linkedParameters","groupedActualIndices","Set","slotLookup","Map","idToIndices","slot","set","getSlotKey","indices","get","linkedParameter","buildLinkedVariable","has","sortKey","members","offset","sort","map","_sortKey","variable","buildOptimizationVariables","variableMin","variableMax","variableInit","variableGrad","freeIndices","variableToPeakValues","variableValues","actualValues","variableValue","member","resolvedMembers","peak","size","resolvePeakIndexById","String","resolveLinkedSlot","getOffset","memberActualIndices","add","firstMember","sharedMin","NEGATIVE_INFINITY","sharedMax","sharedInitCandidates","variableBounds","getMemberVariableBounds","isFinite","transformedMin","transformedMax","optimizeFlag","perPeakParam","globalParam","reconstructPeaks","newPeaks","newPeak","DefaultParameters","peakShape","properties","getNormalizedValue","property","checkOptions","timeout","initialValues","damping","dampingStepUp","dampingStepDown","maxIterations","errorTolerance","centralDifference","improvementThreshold","minValues","maxValues","parLen","fill","MAX_SAFE_INTEGER","MIN_SAFE_INTEGER","gradientDifferenceArray","getGradientDifferenceArray","filler","dataLength","getFiller","checkTimeout","endTime","Date","now","getCheckTimeout","weightSquare","_","errorCalculation","parameterizedFunction","error","func","step","params","jacobianFunction","evaluatedData","gradientFunc","nbParams","nbPoints","zeros","gradient","point","partials","param","analyticalGradient","paramFunction","rowIndex","delta","auxParams","funcParam","funcParam2","gradientFunction","residualError","matrixFunction","hessianApproximation","mmulByTranspose","jacobianWeightResidualError","mmul","scale","cholesky","perturbations","isPositiveDefinite","solve","levenbergMarquardt","checkedOptions","optimalError","optimalParameters","converged","iteration","previousError","isNaN","transpose","mul","parameterValues","parameterError","iterations","antiLowerConvexHull","currentPoint","moveOn","c","moveBack","item","filter","vector","counter","getMinIndex","functionValues","diagonalDistances","choiceLimit","bestCurrentValue","findIndex","directOptimization","sumOfShapes","epsilon","tolerance","tolerance2","initialState","objectiveFunction","getObjectiveFunction","lowerBoundaries","upperBoundaries","diffBorders","numberOfRectangles","totalIterations","unitaryCoordinates","middlePoint","fCalls","smallerDistance","edgeSizes","differentDistances","smallerValuesByDistance","originalCoordinates","optimumValuesIndex","S3","S1","idx","a1","b1","a2","slope","constant","S2","Uint32Array","xHull","yHull","lowerIndexHull","largerSide","largeSidesIndex","bestFunctionValues","r","firstMiddleCenter","secondMiddleCenter","firstMiddleValue","secondMiddleValue","firstMinValue","secondMinValue","ix1","ix2","minIndex","minFunctionValue","pair","finalState","minimizer","optima","direct","originalPeak","shapeFct","propertiesValuesInternal","propertyValue","generalParameterValue","defaultParameterValues","getInternalPeaks","normalizedY","optimizationLayout","algorithm","optimizationOptions","selectMethod","optimization","baseSumOfShapes","shapeFctKey","totalY","peakX","getSumOfShapes","sumOfShapesForVariables","globalInit","getFixedParametersResult","gradientDifferences","sumOfShapesToUse","sumOfShapesForReduced","reducedParameters","full","fitted","fittedVariableValues","fittedValues","addMissingShape","output","structuredClone","defaultShapeInstance","hasShape","findBestCut","group","bestScore","Infinity","candidates","score","balance","bestScored","candidate","groupPeaks","groupingFactor","maxNumberOfPeaks","sortedPeaks","toSorted","previousPeak","currentGroup","groups","flatMap","current","cut","splice","splitGroup","optimizePeaksWithLogs","peakList","fromTo","factorLimits","logs","results","peakGroup","start","firstPeak","lastPeak","at","subarray","range","groupSize","time","optimizedPeaks","addMissingIDs","pushBackPeaks","broadLines","indexes","getGSDPeakOptimizedStructure","overlap","xFrom","xTo","mapPeaks","nextPeak","noiseLevel","sgOptions","smoothY","maxCriteria","maxAbsoluteRatio","minMaxRatio","realTopDetection","peakDetectionAlgorithm","xIsMonotonic","isEquallySpaced","maxDx","minDx","absoluteDifference","xIsEquallySpaced","noiseInfo","maxAbsoluteValue","xValue","minY","maxY","xMinMaxValues","peakData","minddY","secondDerivative","lastJ","yIndex","match","autoAlgorithm","currentIndex","alpha","log10","beta","xCurrent","xPrevious","optimizeTop","broadWidth","broadRatio","maxI","maxDdy","absDdy","MAX_VALUE","initialWidth","find","shapeInstance"],"mappings":";0OACA,MAAMA,EAAWC,OAAOC,UAAUF,SAmB5B,SAAUG,EAAWC,GACzB,MAAMC,EAAML,EAASM,KAAKF,GAC1B,OAAOC,EAAIE,SAAS,YAAcF,EAAIG,SAAS,MACjD,CCEM,SAAUC,EACdC,EACAC,EACAC,EAAsB,CAAA,GAEtB,MAAMC,WAAEA,EAAa,EAACC,WAAEA,EAAa,EAACC,WAAEA,EAAa,GAAMH,EAE3D,GAAIC,EAAa,GAAM,GAAKA,EAAa,IAAMG,OAAOC,UAAUJ,GAC9D,MAAM,IAAIK,WACR,qEAGJ,IAAKf,EAAWO,GACd,MAAM,IAAIS,UAAU,6BAEtB,QAAWC,IAAPT,EACF,MAAM,IAAIQ,UAAU,qBAEtB,GAAIN,EAAaH,EAAGW,OAClB,MAAM,IAAIH,WACR,8CAA8CL,KAAcH,EAAGW,UAGnE,GAAIP,EAAa,IAAME,OAAOC,UAAUH,GACtC,MAAM,IAAII,WAAW,2CAEvB,GAAIH,EAAa,IAAMC,OAAOC,UAAUF,GACtC,MAAM,IAAIG,WAAW,2CAEnBH,GAAc,GAEhBO,QAAQC,KACN,2JAKJ,MAAMC,EAAOC,KAAKC,MAAMb,EAAa,GAC/Bc,EAAKjB,EAAGW,OACRO,EAAM,IAAIC,aAAaF,GACvBG,EAyGR,SAAqBC,EAAWC,EAAWC,GACzC,MAAMH,EAAU,IAAII,MAAMH,GACpBJ,EAAKF,KAAKC,MAAMK,EAAI,GAC1B,IAAK,IAAII,GAAKR,EAAIQ,GAAKR,EAAIQ,IAAK,CAC9BL,EAAQK,EAAIR,GAAM,IAAIE,aAAaE,GACnC,IAAK,IAAIK,GAAKT,EAAIS,GAAKT,EAAIS,IACzBN,EAAQK,EAAIR,GAAIS,EAAIT,GAAMU,EAAOD,EAAGD,EAAGR,EAAIK,EAAGC,EAElD,CACA,OAAOH,CACT,CAnHkBQ,CAAYzB,EAAYE,EAAYD,GACpD,IAAIyB,EAAK,EACLC,GAAY,EACZrC,EAAWQ,GACb6B,GAAY,EAEZD,EAAK5B,GAAMG,EAIb,IAAK,IAAI2B,EAAI,EAAGA,EAAIjB,EAAMiB,IAAK,CAC7B,MAAMC,EAAMZ,EAAQN,EAAOiB,EAAI,GACzBE,EAAMb,EAAQN,EAAOiB,EAAI,GAC/B,IAAIG,EAAK,EACLC,EAAK,EACT,IAAK,IAAIC,EAAI,EAAGA,EAAIjC,EAAYiC,IAC9BF,GAAMF,EAAII,GAAKpC,EAAGoC,GAClBD,GAAMF,EAAIG,GAAKpC,EAAGiB,EAAKd,EAAaiC,GAElCN,GACFZ,EAAIJ,EAAOiB,EAAI,GAAKG,EAAKL,EACzBX,EAAID,EAAKH,EAAOiB,GAAKI,EAAKN,IAE1BA,EAAKQ,EAAMpC,EAAmBa,EAAOiB,EAAI,EAAGjB,EAAMV,GAClDc,EAAIJ,EAAOiB,EAAI,GAAKG,EAAKL,EACzBA,EAAKQ,EAAMpC,EAAmBgB,EAAKH,EAAOiB,EAAGjB,EAAMV,GACnDc,EAAID,EAAKH,EAAOiB,GAAKI,EAAKN,EAE9B,CAGA,MAAMS,EAAKlB,EAAQN,GACnB,IAAK,IAAIiB,EAAI5B,EAAY4B,GAAKd,EAAIc,IAAK,CACrC,IAAIQ,EAAI,EACR,IAAK,IAAIH,EAAI,EAAGA,EAAIjC,EAAYiC,IAAKG,GAAKD,EAAGF,GAAKpC,EAAGoC,EAAIL,EAAI5B,GACxD2B,IACHD,EAAKQ,EAAMpC,EAAmB8B,EAAIjB,EAAO,EAAGA,EAAMV,IAEpDc,EAAIa,EAAIjB,EAAO,GAAKyB,EAAIV,CAC1B,CACA,OAAOX,CACT,CAEA,SAASmB,EACPG,EACAC,EACA3B,EACAV,GAEA,IAAIyB,EAAK,EACLa,EAAQ,EACZ,IAAK,IAAIX,EAAIU,EAAS3B,EAAMiB,EAAIU,EAAS3B,EAAMiB,IACzCA,GAAK,GAAKA,EAAIS,EAAE7B,OAAS,IAC3BkB,GAAMW,EAAET,EAAI,GAAKS,EAAET,GACnBW,KAGJ,OAAQb,EAAKa,IAAUtC,CACzB,CAEA,SAASuC,EAASZ,EAAWV,EAAWuB,EAAWrB,GACjD,IAAIsB,EAAW,EAYf,OAVEA,EADED,EAAI,GAEF,EAAIA,EAAI,IAAMA,GAAK,EAAIvB,EAAIuB,EAAI,KAC9Bb,EAAIY,EAASZ,EAAGV,EAAGuB,EAAI,EAAGrB,GAAKA,EAAIoB,EAASZ,EAAGV,EAAGuB,EAAI,EAAGrB,EAAI,KAC7DqB,EAAI,IAAM,EAAIvB,EAAIuB,IAAOA,GAAK,EAAIvB,EAAIuB,EAAI,IAC3CD,EAASZ,EAAGV,EAAGuB,EAAI,EAAGrB,GACX,IAANqB,GAAiB,IAANrB,EACT,EAEA,EAENsB,CACT,CAEA,SAASC,EAAQC,EAAWC,GAC1B,IAAIC,EAAK,EACT,GAAIF,GAAKC,EACP,IAAK,IAAItB,EAAIqB,EAAIC,EAAI,EAAGtB,GAAKqB,EAAGrB,IAC9BuB,GAAMvB,EAGV,OAAOuB,CACT,CAEA,SAAStB,EAAOI,EAAWN,EAAWJ,EAAWC,EAAWC,GAC1D,IAAI2B,EAAM,EACV,IAAK,IAAIN,EAAI,EAAGA,GAAKtB,EAAGsB,IACtBM,IACG,EAAIN,EAAI,IACRE,EAAQ,EAAIzB,EAAGuB,GAAKE,EAAQ,EAAIzB,EAAIuB,EAAI,EAAGA,EAAI,IAChDD,EAASZ,EAAGV,EAAGuB,EAAG,GAClBD,EAASlB,EAAGJ,EAAGuB,EAAGrB,GAEtB,OAAO2B,CACT,CChKA,MAAM5D,EAAWC,OAAOC,UAAUF,SAmB5B,SAAUG,EAAWC,GACzB,MAAMC,EAAML,EAASM,KAAKF,GAC1B,OAAOC,EAAIE,SAAS,YAAcF,EAAIG,SAAS,MACjD,CCOM,SAAUqD,EACdC,EACAlD,EAA0B,IAE1B,IAAKT,EAAW2D,GACd,MAAM,IAAI3C,UAAU,0BAGtB,MAAM4C,MACJA,GAAQ,EAAKC,UACbA,EAAY,EAACC,QACbA,EAAUH,EAAMzC,QACdT,GAAW,CAAA,EACTsD,EAAQJ,EAAMK,MAAMH,EAAWC,GAErC,GAAqB,IAAjBC,EAAM7C,OACR,MAAM,IAAIF,UAAU,2BAGtB,MAAMiD,EAAcC,EAAW,EAAGH,EAAM7C,OAAS,GAE3CiD,EAASC,EAAYL,EAAOE,GAClC,GAAIF,EAAM7C,OAAS,GAAM,IAAM0C,EAC7B,OAAOO,EAGT,OAAQA,EADWC,EAAYL,EAAOE,EAAc,IACrB,CACjC,CAEA,SAASG,EAAYL,EAAoBE,GACvC,IAAII,EAAM,EACNC,EAAOP,EAAM7C,OAAS,EACtBqD,EAAS,EACTC,EAAa,EACbC,EAAc,EAClB,OAAa,CACX,GAAIH,GAAQD,EACV,OAAON,EAAME,GAGf,GAAIK,IAASD,EAAM,EAIjB,OAHIN,EAAMM,GAAON,EAAMO,IACrBI,EAAKX,EAAOM,EAAKC,GAEZP,EAAME,GAef,IAXAM,EAASL,EAAWG,EAAKC,GACrBP,EAAMQ,GAAUR,EAAMO,IAAOI,EAAKX,EAAOQ,EAAQD,GACjDP,EAAMM,GAAON,EAAMO,IAAOI,EAAKX,EAAOM,EAAKC,GAC3CP,EAAMQ,GAAUR,EAAMM,IAAMK,EAAKX,EAAOQ,EAAQF,GAGpDK,EAAKX,EAAOQ,EAAQF,EAAM,GAG1BG,EAAaH,EAAM,EACnBI,EAAcH,IACD,CACX,GAAGE,UACIT,EAAMM,GAAON,EAAMS,IAC1B,GAAGC,UACIV,EAAMU,GAAeV,EAAMM,IAElC,GAAII,EAAcD,EAChB,MAGFE,EAAKX,EAAOS,EAAYC,EAC1B,CAGAC,EAAKX,EAAOM,EAAKI,GAGbA,GAAeR,IACjBI,EAAMG,GAEJC,GAAeR,IACjBK,EAAOG,EAAc,EAEzB,CACF,CAEA,SAASC,EAAKX,EAAoBzB,EAAWL,GAC3C,MAAM0C,EAAOZ,EAAM9B,GACnB8B,EAAM9B,GAAK8B,EAAMzB,GACjByB,EAAMzB,GAAKqC,CACb,CAEA,SAAST,EAAW5B,EAAWL,GAC7B,OAAOX,KAAKC,OAAOe,EAAIL,GAAK,EAC9B,CCzGM,SAAU2C,EACdjB,EACAlD,EAAyB,IAEzB,MAAMoE,UAAEA,EAAY,GAAMpE,EAC1B,IAAKT,EAAW2D,GACd,MAAM,IAAI3C,UAAU,0BAEtB,GAAqB,IAAjB2C,EAAMzC,OACR,MAAM,IAAIF,UAAU,2BAEtB,GAAwB,iBAAb2C,EAAM,GACf,MAAM,IAAI3C,UAAU,8BAEtB,GAAI2C,EAAMzC,OAAS2D,EACjB,MAAM,IAAIC,MAAM,wCAAwCD,IAE5D,CClBM,SAAUE,EACdhB,EACAiB,EACAvE,EAAoC,CAAA,GAEpC,MAAMwE,OAAEA,GAAS,GAASxE,EAC1B,GAAIwE,EAAQ,CACV,IAAIZ,EAAM,EACNC,EAAOP,EAAM7C,OAAS,EACtBqD,EAAS,EACb,KAAOD,EAAOD,EAAM,GAElB,GADAE,EAASF,GAAQC,EAAOD,GAAQ,GAC5BN,EAAMQ,GAAUS,EAClBX,EAAME,MACD,MAAIR,EAAMQ,GAAUS,GAGzB,OAAOT,EAFPD,EAAOC,CAGT,CAGF,OAAIF,EAAMN,EAAM7C,OAAS,EACnBI,KAAK4D,IAAIF,EAASjB,EAAMM,IAAQ/C,KAAK4D,IAAInB,EAAMM,EAAM,GAAKW,GACrDX,EAEAA,EAAM,EAGRA,CAEX,CAAO,CACL,IAAIc,EAAQ,EACRC,EAAOvE,OAAOwE,kBAClB,IAAK,IAAI/C,EAAI,EAAGA,EAAIyB,EAAM7C,OAAQoB,IAAK,CACrC,MAAMgD,EAAchE,KAAK4D,IAAInB,EAAMzB,GAAK0C,GACpCM,EAAcF,IAChBA,EAAOE,EACPH,EAAQ7C,EAEZ,CACA,OAAO6C,CACT,CACF,CCxBM,SAAUI,EACdC,EACA/E,EAAkC,IAElC,IAAIoD,UAAEA,EAASC,QAAEA,GAAYrD,EAC7B,MAAMgF,KAAEA,EAAIC,GAAEA,GAAOjF,EAsBrB,YApBkBQ,IAAd4C,IAEAA,OADW5C,IAATwE,EACUV,EAAkBS,EAAGC,GAErB,QAGAxE,IAAZ6C,IAEAA,OADS7C,IAAPyE,EACQX,EAAkBS,EAAGE,GAErBF,EAAEtE,OAAS,GAGrB2C,EAAY,IAAGA,EAAY,GAC3BC,EAAU,IAAGA,EAAU,GACvBD,GAAa2B,EAAEtE,SAAQ2C,EAAY2B,EAAEtE,OAAS,GAC9C4C,GAAW0B,EAAEtE,SAAQ4C,EAAU0B,EAAEtE,OAAS,GAE1C2C,EAAYC,KAAUD,EAAWC,GAAW,CAACA,EAASD,IACnD,CAAEA,YAAWC,UACtB,6FCxDA,SAAA9D,EAAAC,ugnECHO,MAAM0F,GAAwBC,GAMxBC,GAASD,GAqBPA,GAAeC,QAASD,GAAeC,OAE/C,MAAMC,GAAUF,GCtBjB,SAAUG,GACdhC,EACAtD,EAAkC,IAElCmE,EAAOb,GACP,MAAMF,UAAEA,EAASC,QAAEA,GAAYyB,EAAgBxB,EAAOtD,GAEtD,IAAIuF,EAAWjC,EAAMF,GAErB,IAAK,IAAIvB,EAAIuB,EAAY,EAAGvB,GAAKwB,EAASxB,IACxC0D,GAAYjC,EAAMzB,GAEpB,OAAO0D,GAAYlC,EAAUD,EAAY,EAC3C,CCbM,SAAUoC,GACdlC,EACAtD,EAAkC,IAElCmE,EAAOb,GACP,MAAMF,UAAEA,EAASC,QAAEA,GAAYyB,EAAgBxB,EAAOtD,GACtD,IAAIyF,EAAWnC,EAAMF,GAErB,IAAK,IAAIvB,EAAIuB,EAAY,EAAGvB,GAAKwB,EAASxB,IACpCyB,EAAMzB,GAAK4D,IACbA,EAAWnC,EAAMzB,IAGrB,OAAO4D,CACT,CCdM,SAAUC,GACdpC,EACAtD,EAAkC,IAElCmE,EAAOb,GACP,MAAMF,UAAEA,EAASC,QAAEA,GAAYyB,EAAgBxB,EAAOtD,GACtD,IAAI2F,EAAWrC,EAAMF,GACrB,IAAK,IAAIvB,EAAIuB,EAAY,EAAGvB,GAAKwB,EAASxB,IACpCyB,EAAMzB,GAAK8D,IACbA,EAAWrC,EAAMzB,IAGrB,OAAO8D,CACT,CCbM,SAAUC,GACdtC,EACAtD,EAAkC,IAElCmE,EAAOb,GACP,MAAMF,UAAEA,EAASC,QAAEA,GAAYyB,EAAgBxB,EAAOtD,GACtD,IAAIyF,EAAW5E,KAAK4D,IAAInB,EAAMF,IAE9B,IAAK,IAAIvB,EAAIuB,EAAY,EAAGvB,GAAKwB,EAASxB,IACpCyB,EAAMzB,IAAM,EACVyB,EAAMzB,GAAK4D,IACbA,EAAWnC,EAAMzB,KAETyB,EAAMzB,GAAK4D,IACrBA,GAAYnC,EAAMzB,IAGtB,OAAO4D,CACT,CCDM,SAAUI,GACdvC,GAEA,MAAMwC,IAAEA,EAAGpC,OAAEA,GCVT,SACJJ,GAEA,MAAMI,EAAST,EAAQK,GACjByC,EAAoB,IAAI9E,aAAaqC,EAAM7C,QACjD,IAAK,IAAIoB,EAAI,EAAGA,EAAIyB,EAAM7C,OAAQoB,IAChCkE,EAAkBlE,GAAKhB,KAAK4D,IAAInB,EAAMzB,GAAK6B,GAE7C,MAAO,CACLA,SACAoC,IAAK7C,EAAQ8C,GAEjB,CDF0BC,CAAyB1C,GACjD,MAAO,CAAE2C,GAAIH,EAAM,kBAAoBA,MAAKpC,SAC9C,CEzBM,SAAUwC,GAAM5C,GACpB,IAAI6C,EAAS,EACb,IAAK,MAAMC,KAAW9C,EACpB6C,GAAUC,GAAW,EAEvB,OAAOvF,KAAKwF,KAAKF,EACnB,CCDM,SAAUG,GACdC,EACAxB,EACAyB,EACAC,GAEA,IAAIC,EAAyB,KACzBC,EAAyB,KAC7B,MAAMC,EAAsB,GACtBC,EAAsB,GAC5B,IAAK,IAAIhF,EAAI,EAAGA,EAAI0E,EAAE9F,OAAS,IAAKoB,GAE/B2E,EAAG3E,GAAK2E,EAAG3E,EAAI,IAAM2E,EAAG3E,IAAM2E,EAAG3E,EAAI,IACrC2E,EAAG3E,IAAM2E,EAAG3E,EAAI,IAAM2E,EAAG3E,GAAK2E,EAAG3E,EAAI,MAEtC8E,EAAU,CACR5B,EAAGA,EAAElD,GACL6C,MAAO7C,GAEL4E,EAAK,GAAiB,OAAZC,IACZE,EAAUE,KAAKJ,GACfG,EAAUC,KAAKH,MAMhBH,EAAG3E,IAAM2E,EAAG3E,EAAI,IAAM2E,EAAG3E,GAAK2E,EAAG3E,EAAI,IACrC2E,EAAG3E,GAAK2E,EAAG3E,EAAI,IAAM2E,EAAG3E,IAAM2E,EAAG3E,EAAI,MAEtC6E,EAAU,CACR3B,EAAGA,EAAElD,GACL6C,MAAO7C,GAEL4E,EAAK,GAAiB,OAAZE,IACZC,EAAUE,KAAKJ,GACfG,EAAUC,KAAKH,KAKrB,MAAO,CAAEC,YAAWC,YACtB,CCrCM,SAAUE,GACd/G,GAEA,MAAM+E,EACJA,EAACiC,MACDA,EAAKC,QACLA,EAAOC,WACPA,EAAUC,cACVA,EAAaC,eACbA,EAAcC,MACdA,GACErH,EAEJ,IAAIsH,EAAclH,OAAOwE,kBACrB2C,GAAW,EACXC,EAAeR,EACnB,IAAK,IAAItE,EAAI8E,EAAe,EAAG9E,EAAIuE,EAAQxG,OAAQiC,IAAK,CACtD,MAAM+E,EAAcR,EAAQvE,GAC5B,GAAI2E,EAAMI,IAAgBP,EACxB,SAGF,MAAMQ,EAAS3C,EAAE0C,GACXE,EAAkB9G,KAAK4D,IAAIiD,EAASN,GAS1C,GAPIO,EAAkBR,IAChBQ,EAAkBL,IACpBC,EAAW7E,GAEb8E,EAAe9E,GAGbiF,GAAmBL,EAAa,MACpCA,EAAcK,CAChB,CAEA,MAAO,CAAEC,UAAWJ,EAAcD,WACpC,CC/BM,SAAUM,GAAqB7H,GACnC,IAAIgH,GAAQ,EACZ,MAAMc,EAAqB,IACrB/C,EAAEA,EAACgD,IAAEA,EAAGV,MAAEA,EAAKH,WAAEA,EAAUL,UAAEA,EAASD,UAAEA,EAASK,QAAEA,GAAYjH,EAErE,IAAK,IAAI6B,EAAI,EAAGA,EAAI+E,EAAUnG,OAAQoB,IAAK,CACzC,MAAMsF,GAAiBN,EAAUhF,GAAGkD,EAAI6B,EAAU/E,GAAGkD,GAAK,EACpDqC,GAAkBP,EAAUhF,GAAGkD,EAAI6B,EAAU/E,GAAGkD,GAAK,GACrDwC,SAAEA,EAAQK,UAAEA,GAAcb,GAA+B,CAC7DhC,IACAiC,QACAC,UACAC,aACAC,gBACAC,iBACAC,UAGF,IAAiB,IAAbE,EAAiB,CACnB,MAAME,EAAcR,EAAQM,GACtBS,EAAQnH,KAAK4D,IAAIoC,EAAUhF,GAAGkD,EAAI6B,EAAU/E,GAAGkD,GACrD+C,EAAMhB,KAAK,CACTmB,GAAIC,OAAOC,aACXpD,EAAGA,EAAE0C,GACLlB,EAAGc,EAAMI,GACTO,QACAtD,MAAO+C,EACPM,IAAKA,EAAIN,GACTW,iBAAkB,CAChBpD,KAAM4B,EAAU/E,GAChBoD,GAAI4B,EAAUhF,KAGpB,CACAmF,EAAQY,CACV,CAEA,OAAOE,CACT,CC/BA,SAASO,GAAyBC,EAAcC,GAC9C,OAAQD,EAAO,GAAKC,EAAO,GAAOD,EAAO,GAAKC,EAAO,CACvD,CCvBM,SAAUC,GAAgBtF,GAC9B,MAAMqD,EAAEA,EAACxB,EAAEA,EAACyB,GAAEA,EAAEC,GAAEA,EAAEY,MAAEA,EAAKH,WAAEA,EAAUa,IAAEA,GAAQ7E,EAC3CuF,EDDF,SAA8BvF,GAClC,MAAMqD,EAAEA,EAACC,GAAEA,GAAOtD,EAEZuF,EAAoB,GAE1B,IAAK,IAAI5G,EAAI,EAAGA,EAAI0E,EAAE9F,OAAS,IAAKoB,EAC9BwG,GAAyB7B,EAAG3E,GAAI2E,EAAG3E,EAAI,IAEzC4G,EAAQ3B,KAAKjG,KAAK4D,IAAI+B,EAAG3E,IAAMhB,KAAK4D,IAAI+B,EAAG3E,EAAI,IAAMA,EAAIA,EAAI,GAGnD,IAAV2E,EAAG3E,IACHwG,GAAyB7B,EAAG3E,EAAI,GAAI2E,EAAG3E,EAAI,KAE3C4G,EAAQ3B,KAAKjF,GAGjB,OAAO4G,CACT,CCjBkBC,CAAoBxF,IAC9B0D,UAAEA,EAASC,UAAEA,GAAcP,GAAqBC,EAAGxB,EAAGyB,EAAIC,GAEhE,OAAOoB,GAAqB,CAC1BZ,QAASwB,EACT7B,YACAC,YACA9B,IACAsC,QACAH,aACAa,OAEJ,CCxBO,MAAMY,IAAsB,EAAK9H,KAAK+H,IAgBhCC,GAAmBhI,KAAKwF,KAAKxF,KAAKiI,GAAKjI,KAAK+H,KAC5CG,GAAWlI,KAAKwF,KAAKxF,KAAK+H,KAC1BI,GAAanI,KAAKwF,KAAK,GACvB4C,GAAYpI,KAAKwF,KAAK,EAAIxF,KAAK+H,KAC/BM,GAAsBrI,KAAKwF,KAAK,EAAIxF,KAAK+H,KAAO,ECkCvD,MAAOO,GACKC,KAAO,WAKhBC,KAEPC,WAAAA,CAAmBtJ,EAAgC,IACjD,MAAMqJ,KAAEA,EAAO,IAAGpD,GAAEA,GAAOjG,EAE3BuJ,KAAKF,KAAOpD,EAAKuD,GAAoB,EAAIvD,GAAMoD,CACjD,CAEOI,WAAAA,CAAYJ,EAAOE,KAAKF,MAC7B,OAAOK,GAAoBL,EAC7B,CAEOM,WAAAA,CAAY3B,GACjB,OAAOwB,GAAoBxB,EAC7B,CAEO4B,GAAAA,CAAI7E,GACT,OAAO8E,GAAY9E,EAAGwE,KAAKF,KAC7B,CAEOS,OAAAA,CAAQC,EAASC,GAAwB,CAAEX,KAAME,KAAKF,QAC3D,OAmGE,SAA0BrJ,GAC9B,MAAMiG,GAAEA,EAAE8D,OAAEA,EAAS,GAAM/J,EAC3B,IAAIqJ,KAAEA,EAAO,KAAQrJ,EAEjBiG,IAAIoD,EAAOG,GAAoB,EAAIvD,IAEvC,OAAQ8D,EAASlB,GAAmBQ,EAAQ,CAC9C,CA1GWY,CAAgB,CAAEZ,KAAME,KAAKF,KAAMU,UAC5C,CAEOG,SAAAA,CAAUC,GACf,OAAOC,GAAkBD,EAC3B,CAEOE,OAAAA,CAAQrK,EAA4B,IACzC,OAsHE,SACJsK,EAA8B,GAC9BtK,EAA4B,CAAA,GAE5B,MAAMiG,GAAEA,GAAOqE,EACf,IAAIjB,KAAEA,EAAO,KAAQiB,EACjBrE,IAAIoD,EAAOG,GAAoB,EAAIvD,IAEvC,MAAMsE,OACJA,EAASH,KAAmBL,OAC5BA,EAASC,GAAwB,CAAEX,UACjCrJ,EACJ,IAAIS,OAAEA,GAAWT,EAEZS,IACHA,EAASI,KAAK2J,IAAI3J,KAAK4J,KAAKpB,EAAOkB,GAAS,GAAK,GAAK,GAClD9J,EAAS,GAAM,GAAGA,KAGxB,MAAM8B,GAAU9B,EAAS,GAAK,EACxBiK,EAAO,IAAIzJ,aAAaR,GAC9B,IAAK,IAAIoB,EAAI,EAAGA,GAAKU,EAAQV,IAAK,CAChC,MAAMrC,EAAQqK,GAAYhI,EAAIU,EAAQ8G,GAAQU,EAC9CW,EAAK7I,GAAKrC,EACVkL,EAAKjK,EAAS,EAAIoB,GAAKrC,CACzB,CAEA,OAAOkL,CACT,CAlJWC,CAAgBpB,KAAMvJ,EAC/B,CAEO4K,eAAAA,CAAgBT,EAAO,GAC5B,OAAOH,GAAwB,CAAEX,KAAME,KAAKF,KAAMc,QACpD,CAEOU,aAAAA,GACL,MAAO,CAAC,OACV,CAMOC,MAAAA,GACL,MAAO,CAAE1B,KAAMG,KAAKH,KAAMC,KAAME,KAAKF,KACvC,CAEOnJ,UAAAA,CAAW6E,GAChB,MAAM6E,IAAEA,EAAGmB,GAAEA,EAAEC,MAAEA,GAAUC,GAAmBlG,EAAGwE,KAAKF,MACtD,MAAO,CAAEO,MAAKmB,KAAIG,WAAY,CAACF,GACjC,EAWI,SAAUhB,GACdhK,GAEA,MAAMmK,KAAEA,EAAO,EAAClE,GAAEA,GAAOjG,EACzB,IAAIqJ,KAAEA,EAAO,KAAQrJ,EAIrB,OAFIiG,IAAIoD,EAAOG,GAAoB,EAAIvD,IAE/B,EAAIkE,EAAQtB,GAAmBQ,CACzC,CAQM,SAAUQ,GAAY9E,EAAWsE,GACrC,OAAOxI,KAAKsK,IAAIxC,IAAuB5D,EAAIsE,IAAS,EACtD,CAQM,SAAU4B,GAAmBlG,EAAWsE,GAC5C,MAAMO,EAAMC,GAAY9E,EAAGsE,GAI3B,MAAO,CAAEO,MAAKmB,GAHD,EAAIpC,GAAsB5D,GAAMsE,EAAOA,GAASO,EAG3CoB,SADTrC,GAAsB5D,EAAIA,GAAMsE,EAAOA,EAAOA,GAASO,EAElE,CAOM,SAAUJ,GAAoBxB,GAClC,OAAOA,EAAQiB,EACjB,CAOM,SAAUS,GAAoBL,GAClC,OAAOA,EAAOJ,EAChB,CAqBM,SAAUmB,GAAkBD,EAAO,OACvC,GAAIA,GAAQ,EACV,MAAM,IAAI9F,MAAM,0BAElB,OC3LY,SAAiBU,GAE7B,GAAU,IAANA,EAAS,OAAO,EACpB,MAAMqG,EAAgBvK,KAAKwK,IAAI,EAAItG,EAAIA,GACjCuG,EAAgBF,EAAgB,EAAI,GAHhC,KAGqCvK,KAAKiI,IAC9CyC,EAAY1K,KAAKwF,KAAKiF,GAAiB,EAAIF,EAJvC,MAMV,OADmBvK,KAAKwF,KAAKkF,EAAYD,IACpBvG,EAAI,EAAI,GAAI,EACnC,CDmLSyG,CAAOrB,GAAQpB,EACxB,CE7KM,MAAO0C,GACKrC,KAAO,aAKhBC,KAEPC,WAAAA,CAAmBtJ,EAAkC,IACnD,MAAMqJ,KAAEA,EAAO,KAAQrJ,EAEvBuJ,KAAKF,KAAOA,CACd,CAEOI,WAAAA,CAAYJ,EAAOE,KAAKF,MAC7B,OAAOqC,GAAsBrC,EAC/B,CAEOM,WAAAA,CAAY3B,GACjB,OAAO2D,GAAsB3D,EAC/B,CAEO4B,GAAAA,CAAI7E,GACT,OAAO6G,GAAc7G,EAAGwE,KAAKF,KAC/B,CAEOS,OAAAA,CAAQC,EAAS,GACtB,OAAO8B,GAAkB,CAAExC,KAAME,KAAKF,KAAMU,UAC9C,CAEOG,SAAAA,CAAUC,GACf,OAAO2B,GAAoB3B,EAC7B,CAEOE,OAAAA,CAAQrK,EAA4B,IACzC,OAAO+L,GAAkBxC,KAAMvJ,EACjC,CAEO4K,eAAAA,CAAgBT,EAAO,GAC5B,OAAO6B,GAA0B,CAAE3C,KAAME,KAAKF,KAAMc,QACtD,CAEOU,aAAAA,GACL,MAAO,CAAC,OACV,CAMOC,MAAAA,GACL,MAAO,CAAE1B,KAAMG,KAAKH,KAAMC,KAAME,KAAKF,KACvC,CAEOnJ,UAAAA,CAAW6E,GAChB,MAAM6E,IAAEA,EAAGmB,GAAEA,EAAEC,MAAEA,GA2Bf,SAA+BjG,EAAWsE,GAC9C,MAAM4C,EAAc,EAAIlH,EAAIA,EAAIsE,EAAOA,EACjCO,EAAOP,EAAOA,EAAQ4C,EACtBlB,GAAM,EAAKhG,EAAIsE,EAAOA,GAAS4C,EAAcA,GAC7CjB,EAAS,EAAI3B,EAAOtE,EAAIA,GAAMkH,EAAcA,GAClD,MAAO,CAAErC,MAAKmB,KAAIC,QACpB,CAjC+BkB,CAAqBnH,EAAGwE,KAAKF,MACxD,MAAO,CAAEO,MAAKmB,KAAIG,WAAY,CAACF,GACjC,EAMK,MAAMgB,GAA4BA,EAAG3C,OAAO,EAAGc,OAAO,KACnD,EAAIA,EAAQtJ,KAAKiI,GAAKO,EAGnBwC,GAAqB7L,IAChC,MAAMqJ,KAAEA,EAAO,IAAGU,OAAEA,EAAS,GAAM/J,EACnC,OAAQ+J,EAASlJ,KAAKiI,GAAKO,EAAQ,GAGxBuC,GAAgBA,CAAC7G,EAAWsE,IAChCA,GAAQ,GAAK,EAAItE,GAAK,EAAIsE,GAAQ,GAiBpC,MAAMsC,GAAyB3D,GAC7BA,EAAQgB,GAGJ0C,GAAyBrC,GAC7BA,EAAOL,GAGVmD,GAAsBC,GAAcvL,KAAKwL,IAAIxL,KAAKiI,IAAMsD,EAAI,KAErDN,GAAsBA,CAAC3B,EAAO,SACzC,GAAIA,GAAQ,EACV,MAAM,IAAI9F,MAAM,0BAElB,MAAMiI,EAA4B,IAAZ,EAAInC,GAC1B,OACGgC,GAAmB,EAAIG,GAAgBH,GAAmBG,IAC3D,GAISP,GAAoBA,CAC/BzB,EAAgC,GAChCtK,EAA4B,CAAA,KAE5B,MAAMqJ,KAAEA,EAAO,KAAQiB,GACjBC,OACJA,EAASuB,KAAqB/B,OAC9BA,EAASiC,GAA0B,CAAE3C,OAAMc,KAAM,KAC/CnK,EACJ,IAAIS,OAAEA,GAAWT,EAEZS,IACHA,EAASI,KAAK2J,IAAI3J,KAAK4J,KAAKpB,EAAOkB,GAAS,GAAK,GAAK,GAClD9J,EAAS,GAAM,GAAGA,KAGxB,MAAM8B,GAAU9B,EAAS,GAAK,EACxBiK,EAAO,IAAIzJ,aAAaR,GAC9B,IAAK,IAAIoB,EAAI,EAAGA,GAAKU,EAAQV,IAAK,CAChC,MAAMrC,EAAQoM,GAAc/J,EAAIU,EAAQ8G,GAAQU,EAChDW,EAAK7I,GAAKrC,EACVkL,EAAKjK,EAAS,EAAIoB,GAAKrC,CACzB,CAEA,OAAOkL,GCtJH,MAAO6B,GACKnD,KAAO,uBAKhBC,KAEPC,WAAAA,CAAmBtJ,EAAkC,IACnD,MAAMqJ,KAAEA,EAAO,KAAQrJ,EAEvBuJ,KAAKF,KAAOA,CACd,CAEOI,WAAAA,CAAYJ,EAAOE,KAAKF,MAC7B,OAAOqC,GAAsBrC,EAC/B,CAEOM,WAAAA,CAAY3B,GACjB,OAAO2D,GAAsB3D,EAC/B,CAEO4B,GAAAA,CAAI7E,GACT,OAAOyH,GAAwBzH,EAAGwE,KAAKF,KACzC,CAEOS,OAAAA,GACL,OAAO,CACT,CAEOI,SAAAA,CAAUC,GACf,OAAO2B,GAAoB3B,EAC7B,CAEOE,OAAAA,CAAQrK,EAA4B,IACzC,OAAOyM,GAA4BlD,KAAMvJ,EAC3C,CAEO4K,eAAAA,CAAgBT,EAAO,GAC5B,OAAO6B,GAA0B,CAAE3C,KAAME,KAAKF,KAAMc,QACtD,CAEOU,aAAAA,GACL,MAAO,CAAC,OACV,CAMOC,MAAAA,GACL,MAAO,CAAE1B,KAAMG,KAAKH,KAAMC,KAAME,KAAKF,KACvC,CAEOnJ,UAAAA,CAAW6E,GAChB,MAAM6E,IAAEA,EAAGmB,GAAEA,EAAEC,MAAEA,GAkBf,SAAyCjG,EAAWsE,GACxD,MAAM4C,EAAc,EAAIlH,EAAIA,EAAIsE,EAAOA,EACjCO,EAAO,EAAIP,EAAOtE,EAAKkH,EACvBlB,EACH,EAAI1B,GAAQA,EAAOA,EAAO,EAAItE,EAAIA,IAAOkH,EAAcA,GACpDjB,EACH,EAAIjG,GAAK,EAAIA,EAAIA,EAAIsE,EAAOA,IAAU4C,EAAcA,GACvD,MAAO,CAAErC,MAAKmB,KAAIC,QACpB,CA1B+B0B,CAA+B3H,EAAGwE,KAAKF,MAClE,MAAO,CAAEO,MAAKmB,KAAIG,WAAY,CAACF,GACjC,EAMK,MAAMwB,GAA0BA,CAACzH,EAAWsE,IACzC,EAAIA,EAAOtE,GAAM,EAAIA,GAAK,EAAIsE,GAAQ,GAmBzC,MAAMoD,GAA8BA,CACzCnC,EAAgC,GAChCtK,EAA4B,CAAA,KAE5B,MAAMqJ,KAAEA,EAAO,KAAQiB,GACjBC,OACJA,EAASuB,KAAqB/B,OAC9BA,EAASiC,GAA0B,CAAE3C,OAAMc,KAAM,KAC/CnK,EACJ,IAAIS,OAAEA,GAAWT,EAEZS,IACHA,EAASI,KAAK2J,IAAI3J,KAAK4J,KAAKpB,EAAOkB,GAAS,GAAK,GAAK,GAClD9J,EAAS,GAAM,GAAGA,KAGxB,MAAM8B,GAAU9B,EAAS,GAAK,EACxBiK,EAAO,IAAIzJ,aAAaR,GAC9B,IAAK,IAAIoB,EAAI,EAAGA,GAAKU,EAAQV,IAAK,CAChC,MAAMrC,EAAQgN,GAAwB3K,EAAIU,EAAQ8G,GAAQU,EAC1DW,EAAK7I,GAAKrC,EACVkL,EAAKjK,EAAS,EAAIoB,IAAMrC,CAC1B,CAEA,OAAOkL,GCpDT,MAAMiC,GAAU9L,KAAKwF,KAAKxF,KAAKwK,IAAI,IACnC,SAASuB,GAAUlK,GACjB,OAjBF,SAAaqC,GACX,MAAM8H,EAAO9H,EAAI,GAAI,EAAK,EAQpBxD,EAAI,GAAK,EADL,UANVwD,EAAIlE,KAAK4D,IAAIM,KAUb,OAAO8H,GADL,MAJS,YAIItL,EALJ,aAKcA,EANd,aAMwBA,EAPxB,YAOkCA,EARlC,YAQ4CA,EAAIV,KAAKsK,KAAKpG,EAAIA,GAE3E,CAIS+H,CAAIpK,EAAIiK,GACjB,CAIA,SAASI,GAAarK,EAAWsK,GAC/B,OAAQ,EAAIA,GAJd,SAAkBtK,GAChB,OAAQ,EAAI7B,KAAKiI,GAAMjI,KAAKoM,KAAKvK,EACnC,CAEoBwK,CAASxK,GAAKsK,EAAKJ,GAAUlK,EACjD,CCfM,MAAOyK,GACK/D,KAAO,cAChBC,KAKA2D,GAEP1D,WAAAA,CAAmBtJ,EAAmC,IACpD,MAAMqJ,KAAEA,EAAO,IAAG2D,GAAEA,EAAK,IAAQhN,EAEjCuJ,KAAKyD,GAAKA,EACVzD,KAAKF,KAAOA,CACd,CAEOI,WAAAA,CAAYJ,EAAOE,KAAKF,KAAM2D,EAAKzD,KAAKyD,IAC7C,OAAOI,GAAuB/D,EAAM2D,EACtC,CAEOrD,WAAAA,CAAY3B,EAAegF,EAAazD,KAAKyD,IAClD,OAAOK,GAAuBrF,EAAOgF,EACvC,CAEOpD,GAAAA,CAAI7E,GACT,OAAOuI,GAAevI,EAAGwE,KAAKF,KAAME,KAAKyD,GAC3C,CAEOlD,OAAAA,CAAQC,EAAS,GACtB,OAAOwD,GAAmB,CAAElE,KAAME,KAAKF,KAAMU,SAAQiD,GAAIzD,KAAKyD,IAChE,CAEO9C,SAAAA,CAAUC,GACf,OAAOqD,GAAqBrD,EAAMZ,KAAKyD,GACzC,CAEO3C,OAAAA,CAAQrK,EAA4B,IACzC,MAAMS,OACJA,EAAM8J,OACNA,EAAMR,OACNA,EAAS0D,GAA2B,CAClCpE,KAAME,KAAKF,KACX2D,GAAIzD,KAAKyD,GACT7C,KAAM,KAENnK,EACJ,OAAO0N,GAAmBnE,KAAM,CAAEgB,SAAQ9J,SAAQsJ,UACpD,CAEOa,eAAAA,CAAgBT,EAAO,GAC5B,OAAOsD,GAA2B,CAAEpE,KAAME,KAAKF,KAAM2D,GAAIzD,KAAKyD,GAAI7C,QACpE,CAEOU,aAAAA,GACL,MAAO,CAAC,OAAQ,KAClB,CAMOC,MAAAA,GACL,MAAO,CAAE1B,KAAMG,KAAKH,KAAMC,KAAME,KAAKF,KAAM2D,GAAIzD,KAAKyD,GACtD,CAEO9M,UAAAA,CAAW6E,GAChB,MAAM6E,IAAEA,EAAGmB,GAAEA,EAAEC,MAAEA,EAAK2C,IAAEA,GAoCtB,SAAgC5I,EAAWsE,EAAc2D,GAW7D,MAAMY,EAAI7I,EAAIsE,EACRwE,EACG,IAAPb,GAAYY,EAAIA,ENhKW,GMiKvB,EACA/M,KAAKsK,IAAIxC,GAAsBiF,EAAIA,GACnC3B,EAAc,EAAIlH,EAAIA,EAAIsE,EAAOA,EACjCyE,EAAWzE,EAAOA,EAAQ4C,EAC1B8B,EAAS,EAAIpF,GAAsB5D,GAAMsE,EAAOA,GAASwE,EACzDG,GAAQ,EAAKjJ,EAAIsE,EAAOA,GAAS4C,EAAcA,GAC/CgC,KACGtF,GAAsB5D,EAAIA,GAAMsE,EAAOA,EAAOA,GAASwE,EAC1DK,EAAW,EAAI7E,EAAOtE,EAAIA,GAAMkH,EAAcA,GACpD,MAAO,CACLrC,KAAM,EAAIoD,GAAMc,EAAUd,EAAKa,EAC/B9C,IAAK,EAAIiC,GAAMgB,EAAOhB,EAAKe,EAC3B/C,OAAQ,EAAIgC,GAAMkB,EAAUlB,EAAKiB,EACjCN,IAAKE,EAAIC,EAEb,CAjEoCK,CAC9BpJ,EACAwE,KAAKF,KACLE,KAAKyD,IAEP,MAAO,CAAEpD,MAAKmB,KAAIG,WAAY,CAACF,EAAO2C,GACxC,EAMK,MAAMF,GAA6BA,CACxCzN,EAA8C,MAE9C,MAAMqJ,KAAEA,EAAO,EAAC2D,GAAEA,EAAK,GAAG7C,KAAEA,EAAO,GAAMnK,EACzC,OAAQ,EAAImK,GAASd,GAAQ2D,EAAKnE,IAAoB,EAAImE,GAAMnM,KAAKiI,MAG1DwE,GAAiBA,CAACvI,EAAWsE,EAAc2D,KAGtD,GAAW,IAAPA,EAAU,OAAOnD,GAAY9E,EAAGsE,GACpC,MAAM+E,GAAc,EAAIpB,GAAMpB,GAAc7G,EAAGsE,GACzCuE,EAAI7I,EAAIsE,EACd,OAAIuE,EAAIA,ENxIqB,GMwIOQ,EAC7BA,EAAapB,EAAKnD,GAAY9E,EAAGsE,IAyCnC,MAAMgE,GAAyBA,CAACrF,EAAegF,EAAK,KAClDhF,GAASgF,EAAK9D,GAAsB,GAGhCkE,GAAyBA,CAAC/D,EAAc2D,EAAK,KACjD3D,GAAQ2D,EAAK9D,GAAsB,GAG/BqE,GAAsBvN,IACjC,MAAMqJ,KAAEA,EAAO,IAAGU,OAAEA,EAAS,EAACiD,GAAEA,EAAK,IAAQhN,EAC7C,OAAQqJ,EAAOU,GAAUiD,EAAKnE,IAAoB,EAAImE,GAAMnM,KAAKiI,IAAO,GAG7D0E,GAAuBA,CAACrD,EAAO,MAAQ6C,EAAK,KD7LnD,SACJqB,EACArB,EACAsB,EAAM,KACNC,EAAU,KAEV,GAAIF,GAAW,GAAKA,GAAW,EAC7B,MAAM,IAAI/N,WAAW,4BAGvB,GAAW,IAAP0M,EACF,OAAO5C,GAAkBiE,GACpB,GAAW,IAAPrB,EACT,OAAOlB,GAAoBuC,GAI7B,IAAIG,EAAK,EACLC,EAAK,GACLC,EAAK,EACT,KAAO3B,GAAa0B,EAAIzB,GAAMqB,GAAWK,IAAO,KAAKD,GAAM,EAC3D,IAAK,IAAI5M,EAAI,EAAGA,EAAI0M,EAAS1M,IAAK,CAChC,MAAM8M,EAAM,IAAOH,EAAKC,GAClBG,EAAM7B,GAAa4B,EAAK3B,GAC9B,GAAInM,KAAK4D,IAAImK,EAAMP,GAAWC,EAAK,OAAOK,EACtCC,EAAMP,EACRG,EAAKG,EAELF,EAAKE,CAET,CACA,MAAO,IAAOH,EAAKC,EACrB,CC8JSI,CAAsB1E,EAAM6C,GAGxBU,GAAqBA,CAChCpD,EAAiC,GACjCtK,EAA4B,CAAA,KAE5B,MAAMqJ,KAAEA,EAAO,IAAG2D,GAAEA,EAAK,IAAQ1C,GAC3BC,OAAEA,EAASiD,GAAqB,KAAOR,IAAQhN,EACrD,IAAIS,OAAEA,EAAMsJ,OAAEA,EAAS0D,GAA2B,CAAEpE,OAAM2D,KAAI7C,KAAM,KAClEnK,EAEG+J,IACHA,EACE,GACEiD,EAAKnM,KAAKwF,MAAMsC,GAAsB9H,KAAKiI,IAAOO,GAChD,EAAI2D,GAAM3D,EAAOxI,KAAKiI,GAAM,IAG/BrI,IACHA,EAASI,KAAK2J,IAAI3J,KAAK4J,KAAKpB,EAAOkB,GAAS,GAAK,GAAK,GAClD9J,EAAS,GAAM,GAAGA,KAGxB,MAAM8B,GAAU9B,EAAS,GAAK,EACxBiK,EAAO,IAAIzJ,aAAaR,GAC9B,IAAK,IAAIoB,EAAI,EAAGA,GAAKU,EAAQV,IAAK,CAChC,MAAMrC,EAAQ8N,GAAezL,EAAIU,EAAQ8G,EAAM2D,GAAMjD,EACrDW,EAAK7I,GAAKrC,EACVkL,EAAKjK,EAAS,EAAIoB,GAAKrC,CACzB,CAEA,OAAOkL,GClMH,MAAOoE,GACK1F,KAAO,iBACf2F,OACAC,OACAC,MACAC,IACAC,yBAER7F,WAAAA,CAAmBtJ,EAAsC,IACvD,MAAMoP,MAAEA,EAAKC,MAAEA,EAAKhG,KAAEA,EAAI2D,GAAEA,EAAK,IAAQhN,EAEzCuJ,KAAK2F,IAAMlC,EACXzD,KAAK0F,MAAQ,EACb1F,KAAKwF,OAAS,EACdxF,KAAKyF,OAAS,EACdzF,KAAK4F,yBAA2BG,GAAwB,EAAItC,QAE9CxM,IAAV4O,QAAiC5O,IAAV6O,GACzB9F,KAAKwF,OAASK,EACd7F,KAAK8F,MAAQA,QACK7O,IAAT6I,IACTE,KAAKF,KAAOA,EAEhB,CAEA,SAAW+F,CAAM5P,GACf,MAAM+P,EAAgBC,GAAsBhQ,EAAO+J,KAAKyF,QAClDS,EAAqBlG,KAAKyF,OAASO,EACzChG,KAAK0F,MAAQM,EACbhG,KAAK2F,IACH,GACC,QAAUO,EACT,OAAUA,EAAqBA,EAC/B,OAAUA,EAAqBA,EAAqBA,GACxDlG,KAAKwF,OAASvP,EACd+J,KAAK4F,yBAA2BM,CAClC,CAEA,SAAWL,GACT,OAAO7F,KAAKwF,MACd,CAEA,SAAWM,CAAM7P,GACf,MAAM+P,EAAgBC,GAAsBjG,KAAKwF,OAAQvP,GACnDiQ,EAAqBjQ,EAAQ+P,EACnChG,KAAK0F,MAAQM,EACbhG,KAAK2F,IACH,GACC,QAAUO,EACT,OAAUA,EAAqBA,EAC/B,OAAUA,EAAqBA,EAAqBA,GACxDlG,KAAKyF,OAASxP,EACd+J,KAAK4F,yBAA2BM,CAClC,CAEA,SAAWJ,GACT,OAAO9F,KAAKyF,MACd,CAEA,MAAWhC,CAAGxN,GACZ,MAAMiQ,EAAqBH,GAAwB,EAAI9P,GACvD+J,KAAK4F,yBAA2BM,EAChClG,KAAKyF,OAASzF,KAAK0F,MAAQQ,EAC3BlG,KAAKwF,OAASxF,KAAK0F,MAAQS,GAAsBD,GACjDlG,KAAK2F,IAAM1P,CACb,CAEA,MAAWwN,GACT,OAAOzD,KAAK2F,GACd,CAEA,QAAW7F,CAAK7J,GACd,MAAMiQ,EACJlG,KAAK4F,0BAA4BG,GAAwB,EAAI/F,KAAK2F,KACpE3F,KAAKyF,OAASxP,EAAQiQ,EACtBlG,KAAKwF,OAASvP,EAAQkQ,GAAsBD,GAC5ClG,KAAK0F,MAAQzP,CACf,CAEA,QAAW6J,GACT,OAAOE,KAAK0F,KACd,CAEOxF,WAAAA,CAAYJ,EAAOE,KAAK0F,MAAOjC,EAAKzD,KAAK2F,KAC9C,OAAO9B,GAAuB/D,EAAM2D,EACtC,CAEOrD,WAAAA,CAAY3B,EAAegF,EAAazD,KAAK2F,KAClD,OAAO7B,GAAuBrF,EAAOgF,EACvC,CAEOpD,GAAAA,CAAI7E,GACT,OAAOuI,GAAevI,EAAGwE,KAAK0F,MAAO1F,KAAK2F,IAC5C,CAEOpF,OAAAA,CAAQC,EAAS,GACtB,OAAOwD,GAAmB,CAAElE,KAAME,KAAK0F,MAAOlF,SAAQiD,GAAIzD,KAAK2F,KACjE,CAEOhF,SAAAA,CAAUC,GACf,OAAOqD,GAAqBrD,EAAMZ,KAAK2F,IACzC,CAEO7E,OAAAA,CAAQrK,EAA4B,IACzC,MAAMS,OACJA,EAAM8J,OACNA,EAAMR,OACNA,EAAS0D,GAA2B,CAClCpE,KAAME,KAAK0F,MACXjC,GAAIzD,KAAK2F,IACT/E,KAAM,KAENnK,EACJ,OAAO0N,GAAmBnE,KAAM,CAAEgB,SAAQ9J,SAAQsJ,UACpD,CAEOa,eAAAA,CAAgBT,EAAO,GAC5B,OAAOsD,GAA2B,CAChCpE,KAAME,KAAK0F,MACXjC,GAAIzD,KAAK2F,IACT/E,QAEJ,CAEOU,aAAAA,GACL,MAAO,CAAC,QAAS,QACnB,CAUOC,MAAAA,GACL,MAAO,CAAE1B,KAAMG,KAAKH,KAAMgG,MAAO7F,KAAKwF,OAAQM,MAAO9F,KAAKyF,OAC5D,CAEO9O,UAAAA,CAAW6E,GAChB,MAAM6E,IAAEA,EAAGmB,GAAEA,EAAE4E,OAAEA,EAAMC,OAAEA,GAqBvB,SACJ7K,EACAqK,EACAC,GAEA,MAAME,EAAgBC,GAAsBJ,EAAOC,GAC7CQ,EAAIN,GAAiB,EAiBrBO,EAAe,GAAMP,GAbzB,EAAIH,GAAS,EACb,SAAWA,GAAS,EAAIC,EACxB,QAAUD,GAAS,EAAIC,GAAS,EAChC,QAAUD,EAAQC,GAAS,EAC3B,OAAUA,GAAS,GASkCQ,EACjDE,EAAe,GAAMR,GARzB,QAAUH,GAAS,EACnB,QAAUA,GAAS,EAAIC,EACvB,SAAWD,GAAS,EAAIC,GAAS,EACjC,OAAUD,EAAQC,GAAS,EAC3B,EAAIA,GAAS,GAIwCQ,EAGjDJ,EAAqBJ,EAAQE,EAO7BS,EACJ,QACA,OAAUP,EACV,OAAUA,EAAqBA,EAC3BQ,GATFZ,GAASE,EAAgBA,GAAkBO,GAS5BE,EACbE,GAAaF,GARjB,EAAIT,EAAiBF,GAASE,EAAgBA,GAAkBQ,GAU5D/C,EACJ,GACC,QAAUyC,EACT,OAAUA,EAAqBA,EAC/B,OAAUA,EAAqBA,EAAqBA,GASlD7B,EAAI7I,EAAIwK,EACR1B,EACG,IAAPb,GAAYY,EAAIA,EPxPW,GOyPvB,EACA/M,KAAKsK,IAAIxC,GAAsBiF,EAAIA,GACnCuC,EAAe,EAAIpL,EAAIA,EAAIwK,EAAgBA,EAC3CzB,EAAWyB,EAAgBA,EAAiBY,EAC5CpC,EACF,EAAIpF,GAAsB5D,GAAMwK,EAAgBA,GAAkB1B,EAChEG,GACH,EAAKjJ,EAAIwK,EAAgBA,GAAkBY,EAAeA,GACvDlC,KACGtF,GAAsB5D,EAAIA,GAC9BwK,EAAgBA,EAAgBA,GACnC1B,EACIK,EAAW,EAAIqB,EAAgBxK,EAAIA,GAAMoL,EAAeA,GACxDnF,GAAS,EAAIgC,GAAMkB,EAAUlB,EAAKiB,EAClCN,EAAME,EAAIC,EAChB,MAAO,CACLlE,KAAM,EAAIoD,GAAMc,EAAUd,EAAKa,EAC/B9C,IAAK,EAAIiC,GAAMgB,EAAOhB,EAAKe,EAC3B4B,OAAQ3E,EAAQ8E,EAAcnC,EAAMsC,EACpCL,OAAQ5E,EAAQ+E,EAAcpC,EAAMuC,EAExC,CAnGwCE,CAClCrL,EACAwE,KAAKwF,OACLxF,KAAKyF,QAEP,MAAO,CAAEpF,MAAKmB,KAAIG,WAAY,CAACyE,EAAQC,GACzC,EAsGF,SAASJ,GAAsBJ,EAAeC,GAC5C,OACGD,GAAS,EACR,QAAUA,GAAS,EAAIC,EACvB,QAAUD,GAAS,EAAIC,GAAS,EAChC,QAAUD,GAAS,EAAIC,GAAS,EAChC,OAAUD,EAAQC,GAAS,EAC3BA,GAAS,IACX,EAEJ,CAQA,SAASC,GAAwBG,GAC/B,IAAIY,EAAWZ,EACf,IAAK,IAAI5N,EAAI,EAAGA,EAAI,EAAGA,IAAK,CAQ1BwO,IANE,QAAUA,EACV,OAAUA,EAAWA,EACrB,OAAUA,EAAWA,EAAWA,EAChCZ,IAEA,QAAU,OAAcY,EAAW,OAAcA,EAAWA,EAEhE,CACA,OAAOA,CACT,CAYA,SAASX,GAAsBD,GAC7B,MAAMa,EAAIb,EACV,IAAIc,EAAI,EAAID,EACZ,IAAK,IAAIzO,EAAI,EAAGA,EAAI,EAAGA,IAAK,CAC1B,MAAM2O,EACJD,GAAK,EACL,QAAUA,GAAK,EAAID,EACnB,QAAUC,GAAK,EAAID,GAAK,EACxB,QAAUC,GAAK,EAAID,GAAK,EACxB,OAAUC,EAAID,GAAK,EACnBA,GAAK,EACL,EACIG,EACJ,EAAIF,GAAK,EACT,SAAWA,GAAK,EAAID,EACpB,QAAUC,GAAK,EAAID,GAAK,EACxB,QAAUC,EAAID,GAAK,EACnB,OAAUA,GAAK,EACjB,GAAW,IAAPG,EAAU,MACdF,GAAKC,EAAIC,CACX,CACA,OAAOF,CACT,CChUM,MAAOG,GACKtH,KAAO,wBAKhBC,KAKAsH,MAEPrH,WAAAA,CAAmBtJ,EAA6C,IAC9D,MAAMqJ,KAAEA,EAAO,IAAGsH,MAAEA,EAAQ,IAAQ3Q,EAEpCuJ,KAAKF,KAAOA,EACZE,KAAKoH,MAAQA,CACf,CAEOlH,WAAAA,CAAYJ,EAAOE,KAAKF,MAC7B,OAAOuH,GAAiCvH,EAC1C,CAEOM,WAAAA,CAAY3B,GACjB,OAAO6I,GAAiC7I,EAC1C,CAEO4B,GAAAA,CAAI7E,GACT,OAAO+L,GAAyB/L,EAAGwE,KAAKF,KAAME,KAAKoH,MACrD,CAEO7G,OAAAA,CAAQC,EAAS,GACtB,OAAOgH,GAA6B,CAClC1H,KAAME,KAAKF,KACXU,SACA4G,MAAOpH,KAAKoH,OAEhB,CAEOzG,SAAAA,CAAUC,GACf,OAAO6G,GAA+B7G,EACxC,CAEOE,OAAAA,CAAQrK,EAA4B,IACzC,OAAOiR,GAA6B1H,KAAMvJ,EAC5C,CAEO4K,eAAAA,CAAgBT,EAAO,GAC5B,MAAMwG,MAAEA,EAAKtH,KAAEA,GAASE,KACxB,OAAO2H,GAAqC,CAAE7H,OAAMc,OAAMwG,SAC5D,CAEO9F,aAAAA,GACL,MAAO,CAAC,OAAQ,QAClB,CAMOC,MAAAA,GACL,MAAO,CAAE1B,KAAMG,KAAKH,KAAMC,KAAME,KAAKF,KAAMsH,MAAOpH,KAAKoH,MACzD,CAEOzQ,UAAAA,CAAW6E,GAChB,MAAM6E,IAAEA,EAAGmB,GAAEA,EAAEC,MAAEA,EAAKmG,OAAEA,GAgDtB,SACJpM,EACAsE,EACAsH,GAEA,MAAMS,GAAM,EAAIrM,EAAKsE,IAAS,EACxB+E,EAAa,GAAK,EAAIgD,GACtBC,GAAY,EAAID,EAAI,IAAM,EAAIA,EAAIA,EAAIA,GACtCxH,GAAO,EAAI+G,GAASvC,EAAauC,EAAQU,EAIzCpF,EAAc,EAAImF,EAAIA,EAAIA,EAG1BE,GAJgB,IAAO,EAAIF,IAAM,EAAIA,KAI3B,EAAIT,GAAyBA,KADzC,GAAM,EAAIS,EAAI,GAAMA,EAAIA,IAAMnF,EAAcA,IAG1CsF,EAAQ,EAAIxM,GAAMsE,EAAOA,GACzBmI,GAAW,EAAKzM,EAAIA,GAAMsE,EAAOA,EAAOA,GAExC0B,EAAKuG,EAASC,EACdvG,EAAQsG,EAASE,EACjBL,EAASE,EAAWjD,EAC1B,MAAO,CAAExE,MAAKmB,KAAIC,QAAOmG,SAC3B,CAxEuCM,CACjC1M,EACAwE,KAAKF,KACLE,KAAKoH,OAEP,MAAO,CAAE/G,MAAKmB,KAAIG,WAAY,CAACF,EAAOmG,GACxC,EAMK,MAAMD,GAAuCA,EAClD7H,OAAO,EACPsH,QAAQ,EACRxG,OAAO,KAECA,EAAOd,GAAQ,QAAU,QAAWsH,GAAU,EAQ3CI,GACX/Q,IAEA,MAAMqJ,KAAEA,EAAO,IAAGU,OAAEA,EAAS,EAAC4G,MAAEA,EAAQ,GAAM3Q,EAC9C,OAAQ+J,EAASV,GAAQ,QAAU,QAAWsH,GAAU,GAG7CG,GAA2BA,CACtC/L,EACAsE,EACAsH,KAEA,MAAMS,GAAM,EAAIrM,EAAKsE,IAAS,EAC9B,OAAQ,EAAIsH,IAAU,EAAIS,GAAMT,GAAS,EAAIS,EAAI,IAAO,EAAIA,EAAIA,GAAK,IAoChE,MAAMP,GAAoC7I,GACxCA,EAAQgB,GAGJ4H,GAAoCvH,GACxCA,EAAOL,GAGV0I,GAAiCtF,GACrCvL,KAAKwL,IAAIxL,KAAKiI,IAAMsD,EAAI,KAEb4E,GAAiCA,CAAC7G,EAAO,SACpD,GAAIA,GAAQ,EACV,MAAM,IAAI9F,MAAM,0BAElB,MAAMiI,EAA4B,IAAZ,EAAInC,GAC1B,OACGuH,GAA8B,EAAIpF,GACjCoF,GAA8BpF,IAChC,GAQS2E,GAA+BA,CAC1C3G,EAA2C,GAC3CtK,EAAwC,CAAA,KAExC,MAAMqJ,KAAEA,EAAO,IAAGsH,MAAEA,EAAQ,GAAMrG,GAC5BC,OACJA,EAASyG,KAAgCjH,OACzCA,EAASmH,GAAqC,CAAE7H,OAAMc,KAAM,EAAGwG,WAC7D3Q,EACJ,IAAIS,OAAEA,GAAWT,EAEZS,IACHA,EAASI,KAAK2J,IAAI3J,KAAK4J,KAAKpB,EAAOkB,GAAS,GAAK,GAAK,GAClD9J,EAAS,GAAM,GAAGA,KAGxB,MAAM8B,GAAU9B,EAAS,GAAK,EACxBiK,EAAO,IAAIzJ,aAAaR,GAC9B,IAAK,IAAIoB,EAAI,EAAGA,GAAKU,EAAQV,IAAK,CAChC,MAAMrC,EAAQsR,GAAyBjP,EAAIU,EAAQ8G,EAAMsH,GAAS5G,EAClEW,EAAK7I,GAAKrC,EACVkL,EAAKjK,EAAS,EAAIoB,GAAKrC,CACzB,CAEA,OAAOkL,GC1KH,MAAOiH,GACKvI,KAAO,gBAKhBwI,QAKAC,SAEPvI,WAAAA,CAAmBtJ,EAAqC,IACtD,MAAM4R,QAAEA,EAAU,IAAGC,SAAEA,EAAW,KAAQ7R,EAE1CuJ,KAAKqI,QAAUA,EACfrI,KAAKsI,SAAWA,CAClB,CAQA,QAAWxI,GACT,OAAQE,KAAKqI,QAAUrI,KAAKsI,UAAY,CAC1C,CASA,QAAWxI,CAAK7J,GACd,MAAM6J,KAAEA,GAASE,KAEjB,GAAa,IAATF,EAGF,OAFAE,KAAKqI,QAAUpS,OACf+J,KAAKsI,SAAWrS,GAIlB,MAAMsS,EAAQtS,EAAQ6J,EACtBE,KAAKqI,SAAWE,EAChBvI,KAAKsI,UAAYC,CACnB,CAQOrI,WAAAA,CAAYJ,EAAOE,KAAKF,MAC7B,OAAOK,GAAoBL,EAC7B,CASOM,WAAAA,CAAY3B,GACjB,OAAOwB,GAAoBxB,EAC7B,CAEO4B,GAAAA,CAAI7E,GACT,OAAOgN,GAAiBhN,EAAGwE,KAAKqI,QAASrI,KAAKsI,SAChD,CAEO/H,OAAAA,CACLC,EAASiI,GAA6B,CACpCJ,QAASrI,KAAKqI,QACdC,SAAUtI,KAAKsI,YAGjB,OAsGE,SAA+B7R,GACnC,MAAM4R,QAAEA,EAAU,IAAGC,SAAEA,EAAW,IAAG9H,OAAEA,EAAS,GAAM/J,EACtD,OAAQ+J,EAASlB,IAAoB+I,EAAUC,GAAa,CAC9D,CAzGWI,CAAqB,CAC1BL,QAASrI,KAAKqI,QACdC,SAAUtI,KAAKsI,SACf9H,UAEJ,CAEOG,SAAAA,CAAUC,GACf,OAAOC,GAAkBD,EAC3B,CAEOE,OAAAA,CAAQrK,EAA4B,IACzC,OAqGE,SACJsK,EAAmC,GACnCtK,EAA4B,CAAA,GAE5B,MAAM4R,QAAEA,EAAU,IAAGC,SAAEA,EAAW,KAAQvH,GAEpCC,OACJA,EAASH,KAAmBL,OAC5BA,EAASiI,GAA6B,CAAEJ,UAASC,cAC/C7R,EACJ,IAAIS,OAAEA,GAAWT,EAEZS,IACHA,EAASI,KAAK2J,IACZ3J,KAAK4J,KAAK5J,KAAKqR,IAAIN,EAASC,GAAYtH,GACxC,GAAK,GAAK,GAER9J,EAAS,GAAM,GAAGA,KAGxB,MAAM8B,GAAU9B,EAAS,GAAK,EACxBiK,EAAO,IAAIzJ,aAAaR,GAC9B,IAAK,IAAIoB,EAAI,EAAGA,EAAIpB,EAAQoB,IAC1B6I,EAAK7I,GAAKkQ,GAAiBlQ,EAAIU,EAAQqP,EAASC,GAAY9H,EAG9D,OAAOW,CACT,CAhIWyH,CAAqB5I,KAAMvJ,EACpC,CAEO4K,eAAAA,CAAgBT,EAAO,GAC5B,OAAO6H,GAA6B,CAClCJ,QAASrI,KAAKqI,QACdC,SAAUtI,KAAKsI,SACf1H,QAEJ,CAEOU,aAAAA,GACL,MAAO,CAAC,UAAW,WACrB,CAMOC,MAAAA,GACL,MAAO,CACL1B,KAAMG,KAAKH,KACXwI,QAASrI,KAAKqI,QACdC,SAAUtI,KAAKsI,SAEnB,CAEO3R,UAAAA,CAAW6E,GAChB,MAAM6E,IAAEA,EAAGmB,GAAEA,EAAEqH,SAAEA,EAAQC,UAAEA,GA4CzB,SACJtN,EACA6M,EACAC,GAEA,GAAI9M,GAAK,EAAG,CACV,MAAM6E,IAAEA,EAAGmB,GAAEA,EAAEC,MAAEA,GAAUC,GAAmBlG,EAAG6M,GACjD,MAAO,CAAEhI,MAAKmB,KAAIqH,SAAUpH,EAAOqH,UAAW,EAChD,CACA,MAAMzI,IAAEA,EAAGmB,GAAEA,EAAEC,MAAEA,GAAUC,GAAmBlG,EAAG8M,GACjD,MAAO,CAAEjI,MAAKmB,KAAIqH,SAAU,EAAGC,UAAWrH,EAC5C,CAvD6CsH,CACvCvN,EACAwE,KAAKqI,QACLrI,KAAKsI,UAEP,MAAO,CAAEjI,MAAKmB,KAAIG,WAAY,CAACkH,EAAUC,GAC3C,EAWI,SAAUL,GACdhS,GAEA,MAAM4R,QAAEA,EAAU,IAAGC,SAAEA,EAAW,IAAG1H,KAAEA,EAAO,GAAMnK,EACpD,OAAQ,EAAImK,EAAQtB,IAAoB+I,EAAUC,EACpD,CAUM,SAAUE,GAAiBhN,EAAW6M,EAAiBC,GAC3D,OAAgBhI,GAAY9E,EAArBA,GAAK,EAAmB6M,EAA0BC,EAC3D,CCtMM,SAAUU,GAAWjI,GACzB,MAAMlB,KAAEA,GAASkB,EACjB,OAAQlB,GACN,IAAK,WACH,OAAO,IAAID,GAASmB,GACtB,IAAK,aACH,OAAO,IAAImB,GAAWnB,GACxB,IAAK,cACH,OAAO,IAAI6C,GAAY7C,GACzB,IAAK,iBACH,OAAO,IAAIwE,GAAexE,GAC5B,IAAK,uBACH,OAAO,IAAIiC,GAAqBjC,GAClC,IAAK,wBACH,OAAO,IAAIoG,GAAsBpG,GACnC,IAAK,gBACH,OAAO,IAAIqH,GAAcrH,GAC3B,QACE,MAAM,IAAIjG,MAAM,wBAAwB+E,KAE9C,CCjCM,SAAUoJ,GAAOhT,EAAgBiT,GACrC,IAAKjT,EACH,MAAM,IAAI6E,MAAMoO,GAAW,cAE/B,CCiGM,SAAUC,GACdC,EACA7K,EACA9H,EACA4S,EAAS,GAET,MAAMC,EAsDR,SACEF,EACA7K,EACA9H,GAEA,MAAM6S,EAAyB,GAE/B,IAAK,IAAIC,EAAY,EAAGA,EAAYH,EAAclS,OAAQqS,IAAa,CACrE,MAAMC,EAAeJ,EAAcG,GACnC,IAAK,IAAIjR,EAAI,EAAGA,EAAIkR,EAAa7H,WAAWzK,OAAQoB,IAAK,CACvD,MAAMmR,EAAYD,EAAa7H,WAAWrJ,GAC1CgR,EAAM/L,KAAK,CACTmM,YAAaF,EAAa3P,UAAYvB,EACtCiR,YACAI,OAAQH,EAAa9K,GACrB+K,YACAG,KAAMJ,EAAaK,iBAAiBD,KAAKtR,GACzC2I,IAAKuI,EAAaK,iBAAiB5I,IAAI3I,GACvCqQ,IAAKa,EAAaK,iBAAiBlB,IAAIrQ,GACvCwR,mBAAoBN,EAAaK,iBAAiBC,mBAAmBxR,GACrEyR,SAAUC,GAAgBzL,EAAMgL,GAAYE,EAAWhT,IAE3D,CACF,CAEA,OAAO6S,CACT,CAhFgBW,CAAoBb,EAAe7K,EAAO9H,GAClDyT,EAwFR,SACEZ,EACAa,EACAd,GAEA,MAAMe,EAAuB,IAAIC,IAC3BH,EAAgC,GAChCI,EAAa,IAAIC,IACjBC,EAAc,IAAID,IACxB,IAAK,MAAME,KAAQnB,EAEjB,GADAgB,EAAWI,IAAIC,GAAWF,EAAKlB,UAAWkB,EAAKhB,WAAYgB,GACvDA,EAAKd,OAAQ,CACf,MAAMiB,EAAUJ,EAAYK,IAAIJ,EAAKd,SAAW,GAC3CiB,EAAQvU,SAASoU,EAAKlB,YACzBqB,EAAQrN,KAAKkN,EAAKlB,WAEpBiB,EAAYE,IAAID,EAAKd,OAAQiB,EAC/B,CAGF,IAAK,MAAME,KAAmBX,GAAoB,GAChDD,EAAU3M,KACRwN,GACED,EACAR,EACAF,EACAI,EACAnB,IAKN,IAAK,MAAMoB,KAAQnB,EACbc,EAAqBY,IAAIP,EAAKf,cAIlCQ,EAAU3M,KAAK,CACb0N,QAASR,EAAKf,YACdD,UAAWgB,EAAKhB,UAChBG,KAAMa,EAAKb,KACX3I,IAAKwJ,EAAKxJ,IACV0H,IAAK8B,EAAK9B,IACVmB,mBAAoBW,EAAKX,mBACzBC,SAAUU,EAAKV,SACfmB,QAAS,CACP,CACExB,YAAae,EAAKf,YAClBH,UAAWkB,EAAKlB,UAChBE,UAAWgB,EAAKhB,UAChBzI,OAAQ,EACRmK,OAAQ,MAOhB,OADAjB,EAAUkB,KAAK,CAAC9R,EAAGC,IAAMD,EAAE2R,QAAU1R,EAAE0R,SAChCf,EAAUmB,IAAI,EAAGJ,QAASK,KAAaC,KAAeA,EAC/D,CAnJoBC,CAChBlC,EACA7S,EAAQ0T,iBACRd,GAGIoC,EAAc,IAAI/T,aAAawS,EAAUhT,QACzCwU,EAAc,IAAIhU,aAAawS,EAAUhT,QACzCyU,EAAe,IAAIjU,aAAawS,EAAUhT,QAC1C0U,EAAe,IAAIlU,aAAawS,EAAUhT,QAC1C2U,EAAwB,GAE9B,IAAK,IAAIvT,EAAI,EAAGA,EAAI4R,EAAUhT,OAAQoB,IAAK,CACzC,MAAMiT,EAAWrB,EAAU5R,GAC3BmT,EAAYnT,GAAKiT,EAAStK,IAC1ByK,EAAYpT,GAAKiT,EAAS5C,IAC1BgD,EAAarT,GAAKiT,EAAS3B,KAC3BgC,EAAatT,GAAKiT,EAASzB,mBACvByB,EAASxB,UACX8B,EAAYtO,KAAKjF,EAErB,CAEA,MAAO,CACLgR,QACAY,YACA2B,cACAJ,cACAC,cACAC,eACAC,eACAE,oBAAAA,CAAqBC,GACnB,MAAMC,EAAe,IAAIjU,MAAcuR,EAAMpS,QAC7C,IAAK,IAAIoB,EAAI,EAAGA,EAAI4R,EAAUhT,OAAQoB,IAAK,CACzC,MAAM2T,EAAgBF,EAAezT,GAC/B4S,EAAUhB,EAAU5R,GAAG4S,QAC7B,IAAK,MAAMgB,KAAUhB,EACnBc,EAAaE,EAAOxC,aAClBuC,EAAgBC,EAAOlL,OAASkL,EAAOf,MAE7C,CACA,OAAOa,CACT,EAEJ,CAyGA,SAASjB,GACPD,EACAR,EACAF,EACAI,EACAnB,GAEA,GAAqC,IAAjCyB,EAAgBvM,MAAMrH,OACxB,MAAM,IAAI4D,MACR,wBAAwBgQ,EAAgBrB,4CAI5C,MAAM0C,EAAkBrB,EAAgBvM,MAAM8M,IAAKe,IACjD,MAAM3B,EAuFV,SACE2B,EACA3C,EACAa,EACAE,GAEA,MAAMjB,EACe,iBAAZ6C,EAAK1N,GACR0N,EAAK1N,GAiBb,SACEiL,EACAa,GAEA,MAAMI,EAAUJ,EAAYK,IAAIlB,GAChC,IAAKiB,GAA8B,IAAnBA,EAAQ1T,OACtB,MAAM,IAAI4D,MAAM,mBAAmB6O,KAErC,GAAI,IAAIU,IAAIO,GAASyB,KAAO,EAC1B,MAAM,IAAIvR,MACR,WAAW6O,uDAIf,OAAOiB,EAAQ,EACjB,CA/BQ0B,CAAqBF,EAAK1N,GAAI8L,GAEpC,IAAK3T,OAAOC,UAAUyS,IAAcA,EAAY,EAC9C,MAAM,IAAIzO,MAAM,0BAA0ByR,OAAOH,EAAK1N,OAGxD,MAAM+L,EAAOH,EAAWO,IAAIF,GAAWpB,EAAWE,IAClD,IAAKgB,EACH,MAAM,IAAI3P,MACR,qBAAqB2O,cAAsB8C,OAAOH,EAAK1N,OAI3D,OAAO+L,CACT,CA9GiB+B,CACXJ,EACAtB,EAAgBrB,UAChBa,EACAE,GAEF,GAAIJ,EAAqBY,IAAIP,EAAKf,aAChC,MAAM,IAAI5O,MACR,QAAQyR,OAAOH,EAAK1N,iBAAiBoM,EAAgBrB,+BAGzD,MAAO,CACLgB,OACAzJ,OAAQL,GAAUyL,EAAMtB,EAAgBrB,WACxC0B,OAAQsB,GAAUL,EAAMtB,EAAgBrB,UAAWJ,MAIjDqD,EAAsB,IAAIrC,IAChC,IAAK,MAAM6B,KAAUC,EAAiB,CACpC,GAAIO,EAAoB1B,IAAIkB,EAAOzB,KAAKf,aACtC,MAAM,IAAI5O,MACR,wBAAwBgQ,EAAgBrB,mDAG5CiD,EAAoBC,IAAIT,EAAOzB,KAAKf,YACtC,CAEA,MAAMkD,EAAcT,EAAgB,GACpC,IAAIU,EAAYhW,OAAOiW,kBACnBC,EAAYlW,OAAOwE,kBACvB,MAAM0O,EAAW6C,EAAYnC,KAAKV,SAC5BiD,EAAiC,GAEvC,IAAK,MAAMd,KAAUC,EAAiB,CACpC,GAAID,EAAOzB,KAAKV,WAAaA,EAC3B,MAAM,IAAIjP,MACR,oBAAoBgQ,EAAgBrB,oEAIxC,GAAIyC,EAAOzB,KAAKxJ,IAAMiL,EAAOzB,KAAK9B,IAChC,MAAM,IAAI7N,MACR,oBAAoBgQ,EAAgBrB,wDAIxC,MAAMwD,EAAiBC,GAAwBhB,GAC/CW,EAAYvV,KAAKqR,IAAIkE,EAAWI,EAAehM,KAC/C8L,EAAYzV,KAAK2J,IAAI8L,EAAWE,EAAetE,KAC/CqE,EAAqBzP,MAClB2O,EAAOzB,KAAKb,KAAOsC,EAAOf,QAAUe,EAAOlL,OAEhD,CAEA,GAAI6L,EAAYE,EACd,MAAM,IAAIjS,MACR,oBAAoBgQ,EAAgBrB,wDAIxC,IAAK,MAAMyC,KAAUC,EACnB/B,EAAqBuC,IAAIT,EAAOzB,KAAKf,aAGvC,MAAO,CACLuB,QAAS3T,KAAK2J,OACTkL,EAAgBd,IAAKa,GAAWA,EAAOzB,KAAKf,cAEjDD,UAAWqB,EAAgBrB,UAC3BG,KAAM7N,GAAMiR,GACZ/L,IAAK4L,EACLlE,IAAKoE,EACLjD,mBAAoBxS,KAAK2J,OACpBkL,EAAgBd,IAAKzT,GAAMN,KAAK4D,IAAItD,EAAE6S,KAAKX,sBAEhDC,WACAmB,QAASiB,EAAgBd,IAAKa,IAAM,CAClCxC,YAAawC,EAAOzB,KAAKf,YACzBH,UAAW2C,EAAOzB,KAAKlB,UACvBE,UAAWyC,EAAOzB,KAAKhB,UACvBzI,OAAQkL,EAAOlL,OACfmK,OAAQe,EAAOf,UAGrB,CA4CA,SAASxK,GAAUyL,EAA2B3C,GAC5C,MAAMzI,EAASoL,EAAKpL,QAAU,EAC9B,IAAKnK,OAAOsW,SAASnM,IAAsB,IAAXA,EAC9B,MAAM,IAAIlG,MACR,oBAAoB2O,uCAGxB,OAAOzI,CACT,CAEA,SAASyL,GACPL,EACA3C,EACAJ,GAEA,MAAM8B,EAASiB,EAAKjB,QAAU,EAC9B,IAAKtU,OAAOsW,SAAShC,GACnB,MAAM,IAAIrQ,MAAM,oBAAoB2O,8BAEtC,MAAkB,MAAdA,EACK0B,EAAS9B,EAEX8B,CACT,CAEA,SAAS+B,GAAwBhB,GAK/B,MAAMkB,GAAkBlB,EAAOzB,KAAKxJ,IAAMiL,EAAOf,QAAUe,EAAOlL,OAC5DqM,GAAkBnB,EAAOzB,KAAK9B,IAAMuD,EAAOf,QAAUe,EAAOlL,OAElE,MAAO,CACLC,IAAK3J,KAAK2J,IAAImM,EAAgBC,GAC9B1E,IAAKrR,KAAKqR,IAAIyE,EAAgBC,GAElC,CAEA,SAASrD,GACPoC,EACA3C,EACAhT,GAEAwS,GAAOmD,GACP,IAAIkB,GAAe,EACnB,MAAMC,EAAenB,EAAKzK,aAAa8H,GACjC+D,EAAc/W,EAAQkL,aAAa8H,GAEzC,QAA+BxS,IAA3BsW,GAAcxD,SAChB,GAAqC,mBAA1BwD,EAAaxD,SACtBuD,EAAeC,EAAaxD,SAASqC,OAChC,CACL,MAAMrC,SAAEA,GAAW,GAASwD,EAC5BD,EAAevD,CACjB,MACK,QAA8B9S,IAA1BuW,GAAazD,SACtB,GAAoC,mBAAzByD,EAAYzD,SACrBuD,EAAeE,EAAYzD,SAASqC,OAC/B,CACL,MAAMrC,SAAEA,GAAW,GAASyD,EAC5BF,EAAevD,CACjB,CAGF,OAAOuD,CACT,CAEA,SAAS3C,GAAWpB,EAAmBE,GACrC,MAAO,GAAGF,KAAaE,GACzB,CC3cM,SAAUgE,GACdrE,EACA4C,EACA3C,GAEA,MAAMqE,EAA2C,GAEjD,IAAK,MAAMtB,KAAQhD,EAAe,CAChC,MAAM1K,GAAEA,EAAEqC,MAAEA,EAAKY,WAAEA,EAAU9H,UAAEA,GAAcuS,EAE7C,IAAIuB,EAAU,CAAEnS,EAAG,EAAGwB,EAAG,EAAG+D,SAExBrC,IACFiP,EAAU,IAAKA,EAASjP,OAG1BiP,EAAQnS,EAAIwQ,EAAanS,GACzB8T,EAAQ3Q,EAAIgP,EAAanS,EAAY,GAAKwP,EAC1C,IAAK,IAAI/Q,EAAI,EAAGA,EAAIqJ,EAAWzK,OAAQoB,IAErCqV,EAAQ5M,MAAMY,EAAWrJ,IAAM0T,EAAanS,EAAYvB,GAE1DoV,EAASnQ,KAAKoQ,EAChB,CAEA,OAAOD,CACT,CClCO,MAAME,GAAoB,CAC/BpS,EAAG,CACDoO,KAAOwC,GAAeA,EAAK5Q,EAC3ByF,IAAKA,CAACmL,EAAYyB,IAChBzB,EAAK5Q,EAAqB,EAAjBqS,EAAU/N,KACrB6I,IAAKA,CAACyD,EAAYyB,IAChBzB,EAAK5Q,EAAqB,EAAjBqS,EAAU/N,KACrBgK,mBAAoBA,CAACsC,EAAYyB,IACd,KAAjBA,EAAU/N,MAEd9C,EAAG,CACD4M,KAAOwC,GAAeA,EAAKpP,EAC3BiE,IAAMmL,GAAgBA,EAAKpP,EAAI,GAAI,IAAO,EAC1C2L,IAAMyD,GAAgBA,EAAKpP,EAAI,EAAI,EAAI,IACvC8M,mBAAoBA,IAAM,MAE5BhK,KAAM,CACJ8J,KAAMA,CAACwC,EAAYyB,IAA+BA,EAAU/N,KAC5DmB,IAAKA,CAACmL,EAAYyB,IAAgD,IAAjBA,EAAU/N,KAC3D6I,IAAKA,CAACyD,EAAYyB,IAAgD,EAAjBA,EAAU/N,KAC3DgK,mBAAoBA,CAACsC,EAAYyB,IACd,KAAjBA,EAAU/N,MAEd+F,MAAO,CACL+D,KAAMA,CAACwC,EAAYyB,IAAgD,GAAjBA,EAAU/N,KAC5DmB,IAAKA,CAACmL,EAAYyB,IACC,GAAjBA,EAAU/N,KAAa,IACzB6I,IAAKA,CAACyD,EAAYyB,IAAgD,GAAjBA,EAAU/N,KAAa,EACxEgK,mBAAoBA,CAACsC,EAAYyB,IACd,GAAjBA,EAAU/N,KAAa,MAE3BgG,MAAO,CACL8D,KAAMA,CAACwC,EAAYyB,IAAgD,GAAjBA,EAAU/N,KAC5DmB,IAAKA,CAACmL,EAAYyB,IACC,GAAjBA,EAAU/N,KAAa,IACzB6I,IAAKA,CAACyD,EAAYyB,IAAgD,GAAjBA,EAAU/N,KAAa,EACxEgK,mBAAoBA,CAACsC,EAAYyB,IACd,GAAjBA,EAAU/N,KAAa,MAE3B2D,GAAI,CACFmG,KAAMA,CAACwC,EAAYyB,IAA2BA,EAAUpK,GACxDxC,IAAKA,IAAM,EACX0H,IAAKA,IAAM,EACXmB,mBAAoBA,IAAM,KAE5B1C,MAAO,CACLwC,KAAMA,CAACwC,EAAYyB,IACjBA,EAAUzG,OAAS,GACrBnG,IAAKA,KAAM,EACX0H,IAAKA,IAAM,EACXmB,mBAAoBA,IAAM,MC9CxBgE,GAAyB,CAAC,OAAQ,MAAO,MAAO,sBAwHtD,SAASC,GACP9X,EACAwT,EACAuE,EACA3E,GAEA,MAAkB,MAAdI,EACe,uBAAbuE,EACK/X,EAEAA,EAAQoT,EAGZpT,CACT,CCjJA,MAAMJ,GAAWC,OAAOC,UAAUF,SAmB5B,SAAUG,GAAWC,GACzB,MAAMC,EAAML,GAASM,KAAKF,GAC1B,OAAOC,EAAIE,SAAS,YAAcF,EAAIG,SAAS,MACjD,CCHc,SAAU4X,GACtB9M,EACA1K,GAEA,MAAMyX,QACJA,EAAOC,cACPA,EAAaxW,QACbA,EAAU,EAACyW,QACXA,EAAU,IAAIC,cACdA,EAAgB,GAAEC,gBAClBA,EAAkB,EAACC,cACnBA,EAAgB,IAAGC,eACnBA,EAAiB,KAAIC,kBACrBA,GAAoB,EAAK3E,mBACzBA,EAAqB,GAAK4E,qBAC1BA,EAAuB,MACrBjY,EACJ,IAAIkY,UAAEA,EAASC,UAAEA,GAAcnY,EAE/B,GAAI2X,GAAW,EACb,MAAM,IAAItT,MAAM,gDACX,IAAKqG,EAAK3F,IAAM2F,EAAKnE,EAC1B,MAAM,IAAIlC,MAAM,iDACX,IACJ9E,GAAWmL,EAAK3F,IACjB2F,EAAK3F,EAAEtE,OAAS,IACflB,GAAWmL,EAAKnE,IACjBmE,EAAKnE,EAAE9F,OAAS,EAEhB,MAAM,IAAI4D,MACR,wEAEG,GAAIqG,EAAK3F,EAAEtE,SAAWiK,EAAKnE,EAAE9F,OAClC,MAAM,IAAI4D,MAAM,uDAGlB,KAAMqT,GAAiBA,EAAcjX,OAAS,GAC5C,MAAM,IAAI4D,MACR,8DAGJ,MAAM6G,EAAa5J,MAAM0D,KAAK0S,GAExBU,EAASlN,EAAWzK,OAI1B,GAHA0X,EAAYA,GAAa,IAAI7W,MAAM8W,GAAQC,KAAKjY,OAAOkY,kBACvDJ,EAAYA,GAAa,IAAI5W,MAAM8W,GAAQC,KAAKjY,OAAOmY,kBAEnDJ,EAAU1X,SAAWyX,EAAUzX,OACjC,MAAM,IAAI4D,MAAM,iDAGlB,MAAMmU,EA6BR,SACEnF,EACAnI,GAEA,GAAkC,iBAAvBmI,EACT,OAAO,IAAI/R,MAAM4J,EAAWzK,QAAQ4X,KAAKhF,GACpC,GAAI9T,GAAW8T,GAAqB,CACzC,MAAM+E,EAASlN,EAAWzK,OAC1B,OAAI4S,EAAmB5S,SAAW2X,EACzB,IAAI9W,MAAM8W,GAAQC,KAAKhF,EAAmB,IAE5C/R,MAAM0D,KAAKqO,EACpB,CAEA,MAAM,IAAIhP,MACR,+FAEJ,CA9CkCoU,CAC9BpF,EACAnI,GAGIwN,EA2CR,SACExX,EACAyX,GAEA,GAAuB,iBAAZzX,EAAsB,CAC/B,MAAM1B,EAAQ,EAAI0B,GAAW,EAC7B,MAAO,IAAM1B,CACf,CAAO,GAAID,GAAW2B,GAAU,CAC9B,GAAIA,EAAQT,OAASkY,EAAY,CAC/B,MAAMnZ,EAAQ,EAAI0B,EAAQ,IAAM,EAChC,MAAO,IAAM1B,CACf,CAEA,OAAQqC,GAAc,EAAIX,EAAQW,IAAM,CAC1C,CAEA,MAAM,IAAIwC,MACR,qFAEJ,CA9DiBuU,CAAU1X,EAASwJ,EAAK3F,EAAEtE,QACnCoY,EA+DR,SAAyBpB,GACvB,QAAgBjX,IAAZiX,EAAuB,CACzB,GAAuB,iBAAZA,EACT,MAAM,IAAIpT,MAAM,8BAElB,MAAMyU,EAAUC,KAAKC,MAAkB,IAAVvB,EAC7B,MAAO,IAAMsB,KAAKC,MAAQF,CAC5B,CACE,MAAO,KAAM,CAEjB,CAzEuBG,CAAgBxB,GAMrC,MAAO,CACLoB,eACAX,YACAC,YACAjN,aACAgO,aATmB5X,MAAM0D,KAAK,CAAEvE,OAAQiK,EAAK3F,EAAEtE,QAAU,CAAC0Y,EAAGtX,IAC7D6W,EAAO7W,IASP8V,UACAC,gBACAC,kBACAC,gBACAC,iBACAC,oBACA3E,mBAAoBmF,EACpBP,uBAEJ,CCvFc,SAAUmB,GACtB1O,EACAQ,EACAmO,EACAH,GAEA,IAAII,EAAQ,EACZ,MAAMC,EAAOF,EAAsBnO,GACnC,IAAK,IAAIrJ,EAAI,EAAGA,EAAI6I,EAAK3F,EAAEtE,OAAQoB,IACjCyX,IAAU5O,EAAKnE,EAAE1E,GAAK0X,EAAK7O,EAAK3F,EAAElD,MAAQ,EAAIqX,EAAarX,GAG7D,OAAOyX,CACT,CCyCc,SAAUE,GACtB9O,EACA+O,EACA9B,EACAtE,EACAgG,EACArB,EACA9W,EACAwY,GAEA,MAAMH,EAAOF,EAAsBI,GAE7BE,EAAgB,IAAI1Y,aAAayJ,EAAK3F,EAAEtE,QAC9C,IAAK,IAAIoB,EAAI,EAAGA,EAAI6I,EAAK3F,EAAEtE,OAAQoB,IACjC8X,EAAc9X,GAAK0X,EAAK7O,EAAK3F,EAAElD,IAGjC,MAAM+X,EAAeF,EAhEvB,SACEhP,EACA+O,EACAC,GAEA,MAAMG,EAAWJ,EAAOhZ,OAClBqZ,EAAWpP,EAAK3F,EAAEtE,OAClBO,EAAMoE,GAAO2U,MAAMF,EAAUC,GAC7BE,EAAWN,EAAiBD,GAClC,IAAK,IAAIQ,EAAQ,EAAGA,EAAQH,EAAUG,IAAS,CAC7C,MAAMC,EAAWF,EAAStP,EAAK3F,EAAEkV,IACjC,IAAK,IAAIE,EAAQ,EAAGA,EAAQN,EAAUM,IACpCnZ,EAAIiT,IAAIkG,EAAOF,GAAQC,EAASC,GAEpC,CACA,OAAOnZ,CACT,CAiDMoZ,CAAmB1P,EAAM+O,EAAQC,GCtEzB,SACZhP,EACAiP,EACAF,EACApG,EACAgH,EACArC,GAEA,MAAM6B,EAAWJ,EAAOhZ,OAClBqZ,EAAWpP,EAAK3F,EAAEtE,OAClBO,EAAMoE,GAAO2U,MAAMF,EAAUC,GAEnC,IAAIQ,EAAW,EACf,IAAK,IAAIH,EAAQ,EAAGA,EAAQN,EAAUM,IAAS,CAC7C,GAAkC,IAA9B9G,EAAmB8G,GAAc,SACrC,IAAII,EAAQlH,EAAmB8G,GAC3BK,EAAYf,EAAOlW,QACvBiX,EAAUL,IAAUI,EACpB,MAAME,EAAYJ,EAAcG,GAChC,GAAKxC,EAQE,CACLwC,EAAYf,EAAOlW,QACnBiX,EAAUL,IAAUI,EACpBA,GAAS,EACT,MAAMG,EAAaL,EAAcG,GACjC,IAAK,IAAIP,EAAQ,EAAGA,EAAQH,EAAUG,IACpCjZ,EAAIiT,IACFqG,EACAL,GACCS,EAAWhQ,EAAK3F,EAAEkV,IAAUQ,EAAU/P,EAAK3F,EAAEkV,KAAWM,EAG/D,MAnBE,IAAK,IAAIN,EAAQ,EAAGA,EAAQH,EAAUG,IACpCjZ,EAAIiT,IACFqG,EACAL,GACCN,EAAcM,GAASQ,EAAU/P,EAAK3F,EAAEkV,KAAWM,GAgB1DD,GACF,CAEA,OAAOtZ,CACT,CD2BM2Z,CACEjQ,EACAiP,EACAF,EACApG,EACAgG,EACArB,GAEA4C,EAlDR,SAAwBlQ,EAAciP,GACpC,MAAMxY,EAAIuJ,EAAK3F,EAAEtE,OAEXO,EAAM,IAAIoE,GAAOjE,EAAG,GAE1B,IAAK,IAAI8Y,EAAQ,EAAGA,EAAQ9Y,EAAG8Y,IAC7BjZ,EAAIiT,IAAIgG,EAAO,EAAGvP,EAAKnE,EAAE0T,GAASN,EAAcM,IAElD,OAAOjZ,CACT,CAyCwB6Z,CAAenQ,EAAMiP,GAErCmB,EAAuBlB,EAAamB,gBAAgB7Z,GAC1D,IAAK,IAAIW,EAAI,EAAGA,EAAI4X,EAAOhZ,OAAQoB,IACjCiZ,EAAqB7G,IAAIpS,EAAGA,EAAGiZ,EAAqB1G,IAAIvS,EAAGA,GAAK8V,GAElE,MAAMqD,EAA8BpB,EAAaqB,KAC/CL,EAAcM,MAAM,MAAO,CAAEA,MAAOha,KAOhCia,EAAW,IAAIjW,GAAsB4V,GAK3C,MAAO,CACLM,cALoBD,EAASE,qBAC3BF,EAASG,MAAMN,GACf3V,GAAQyV,GAAsBG,KAAKD,GAIrCA,8BAEJ,CEnGM,SAAUO,GACd7Q,EACA2O,EACArZ,GAEA,MAAMwb,EAAiBhE,GAAa9M,EAAM1K,IACpC6Y,aACJA,EAAYX,UACZA,EAASC,UACTA,EAASjN,WACTA,EAAUgO,aACVA,EAAYtB,cACZA,EAAaC,gBACbA,EAAeC,cACfA,EAAaC,eACbA,EAAcC,kBACdA,EAAiB3E,mBACjBA,EAAkB4E,qBAClBA,GACEuD,EACJ,IAAI7D,EAAU6D,EAAe7D,QAC7B,MAAM+B,iBAAEA,GAAqB1Z,EAE7B,IAAIsZ,EAAQF,GACV1O,EACAQ,EACAmO,EACAH,GAEEuC,EAAenC,EACfoC,EAAoBxQ,EAAW3H,QAE/BoY,EAAYrC,GAASvB,EAErB6D,EAAY,EAChB,KAAOA,EAAY9D,IAAkB6D,EAAWC,IAAa,CAC3D,MAAMC,EAAgBvC,GAEhB8B,cAAEA,EAAaJ,4BAAEA,GAAgCxB,GACrD9O,EACAQ,EACAyM,EACAtE,EACAgG,EACArB,EACAkB,EACAQ,GAGF,IAAK,IAAIhX,EAAI,EAAGA,EAAIwI,EAAWzK,OAAQiC,IACrCwI,EAAWxI,GAAK7B,KAAK2J,IACnB3J,KAAKqR,IAAIgG,EAAUxV,GAAIwI,EAAWxI,GAAK0Y,EAAchH,IAAI1R,EAAG,IAC5DyV,EAAUzV,IAWd,GAPA4W,EAAQF,GACN1O,EACAQ,EACAmO,EACAH,GAGE4C,MAAMxC,GAAQ,MAEdA,EAAQmC,EAAe1D,IACzB0D,EAAenC,EACfoC,EAAoBxQ,EAAW3H,SAgBjC,GALEoU,GAPCkE,EAAgBvC,GACjB8B,EACGW,YACAd,KAAKG,EAAcY,IAAIrE,GAASzB,IAAI8E,IACpC5G,IAAI,EAAG,GAEY6D,EACZpX,KAAKqR,IAAIyF,EAAUE,EAAiB,MAEpChX,KAAK2J,IAAImN,EAAUC,EAAe,KAG1CiB,IACF,MAAM,IAAIxU,MACR,iCAAiCrE,EAAQyX,mBAI7CkE,EAAYrC,GAASvB,CACvB,CAEA,MAAO,CACLkE,gBAAiBP,EACjBQ,eAAgBT,EAChBU,WAAYP,EAEhB,CC1Ge,SAASQ,GAAoBrX,EAAGwB,GAC7C,GAAIxB,EAAEtE,SAAW8F,EAAE9F,OACjB,MAAM,IAAIH,WAAW,4CAGvB,MAAMwZ,EAAW/U,EAAEtE,OAAS,EAC5B,GAAiB,IAAbqZ,EAAgB,MAAO,CAAC,GAC5B,GAAiB,IAAbA,EAAgB,MAAO,CAAC,EAAG,GAE/B,IAAIuC,EAAe,EACflW,EAAS,IAAI7E,MAAMyD,EAAEtE,QAAQ4X,MAAK,GACtC,OAAa,CACX,MAAMxV,EAAIwZ,EACJvZ,EAAIwZ,GAAOD,EAAcvC,EAAU3T,GACnCoW,EAAID,GAAOA,GAAOD,EAAcvC,EAAU3T,GAAS2T,EAAU3T,GAanE,GAVEpB,EAAEwX,IAAMhW,EAAE1D,GAAK0D,EAAEzD,IAAMiC,EAAElC,IAAM0D,EAAEzD,GAAKyD,EAAEgW,IAAMxX,EAAEjC,IAAMyD,EAAEgW,GAAKhW,EAAE1D,KAEzC,EAGtBwZ,EAAevZ,GAEfqD,EAAOrD,IAAK,EACZuZ,EAAeG,GAASH,EAAcvC,EAAU3T,IAE9CoW,IAAMzC,EAAU,KACtB,CAEA,OAAO3T,EACJyO,IAAI,CAAC6H,EAAM/X,KAAoB,IAAT+X,GAAyB/X,GAC/CgY,OAAQD,IAAkB,IAATA,EACtB,CAUA,SAASD,GAASH,EAAcvC,EAAU6C,GACxC,IAAIC,EAAUP,EAAe,EAC7B,MAA2B,IAApBM,EAAOC,IAAoBA,IAClC,OAAwB,IAAjBP,EAAqBvC,EAAW8C,CACzC,CAEA,SAASN,GAAOD,EAAcvC,EAAU6C,GACtC,IAAIC,EAAUP,EAAe,EAC7B,MAA2B,IAApBM,EAAOC,IAAoBA,IAClC,OAAOP,IAAiBvC,EAAW,EAAI8C,CACzC,CCmQA,SAASC,GACPC,EACAC,EACAC,EACAC,GAEA,IAAIR,EAAO,GACX,IAAK,IAAI5a,EAAI,EAAGA,EAAIib,EAAerc,OAAQoB,IACzC4a,EAAK5a,GACHhB,KAAK4D,IAAIqY,EAAejb,IAAMob,EAAmBD,IACjDD,EAAkBlb,GAEtB,MAAM2I,EAAM9E,GAAU+W,GAEtB,OADaA,EAAKS,UAAWnY,GAAMA,IAAMyF,EAE3C,CC1TM,SAAU2S,GACdzS,EACA0S,EACApd,GAEA,MAAMkY,UACJA,EAASC,UACTA,EAASL,cACTA,EAAauF,QACbA,EAAOC,UACPA,EAASC,WACTA,EAAUC,aACVA,GACExd,EACEyd,EAyBR,SACE/S,EACA0S,GAEA,MAAMrY,EAAEA,EAACwB,EAAEA,GAAMmE,EACXoP,EAAW/U,EAAEtE,OACnB,OAAQyK,IACN,MAAMtB,EAAMwT,EAAYlS,GACxB,IAAIoO,EAAQ,EACZ,IAAK,IAAIzX,EAAI,EAAGA,EAAIiY,EAAUjY,IAC5ByX,IAAU/S,EAAE1E,GAAK+H,EAAI7E,EAAElD,MAAQ,EAEjC,OAAOyX,EAEX,CAvC4BoE,CAAqBhT,EAAM0S,GAC/CjX,EDZO,SACbsX,EACAE,EACAC,EACA5d,EAAU,CAAA,GAEV,MAAMmc,WACJA,EAAa,GAAEkB,QACfA,EAAU,KAAIC,UACdA,EAAY,MAAKC,WACjBA,EAAa,MAAKC,aAClBA,EAAe,CAAA,GACbxd,EAEJ,QACwBQ,IAAtBid,QACoBjd,IAApBmd,QACoBnd,IAApBod,EAEA,MAAM,IAAItd,WAAW,gCAMvB,GAHAqd,EAAkB,IAAI1c,aAAa0c,GACnCC,EAAkB,IAAI3c,aAAa2c,GAE/BD,EAAgBld,SAAWmd,EAAgBnd,OAC7C,MAAM,IAAI4D,MACR,kEAOJ,IAAIjD,EAAIuc,EAAgBld,OACpBod,EAAcD,EAAgBhJ,IAAI,CAAC7P,EAAGlD,IAAMkD,EAAI4Y,EAAgB9b,KAEhEic,mBACFA,EAAqB,EAACC,gBACtBA,EAAkB,EAACC,mBACnBA,EAAqB,CAAC,IAAI/c,aAAaG,GAAGiX,KAAK,KAAK4F,YACpDA,EAAc,IAAIhd,aAAaG,GAAGwT,IAAI,CAACpV,EAAOkF,IAE1CiZ,EAAgBjZ,GAChBsZ,EAAmB,GAAGtZ,GAASmZ,EAAYnZ,IAE7CuY,iBACFA,EAAmBQ,EAAkBQ,GAAYC,OACjDA,EAAS,EAACC,gBACVA,EAAkB,EAACC,UACnBA,EAAY,CAAC,IAAInd,aAAaG,GAAGiX,KAAK,KAAK0E,kBAC3CA,EAAoB,CAAClc,KAAKwF,KAAS,IAAJjF,IAAc0b,eAC7CA,EAAiB,CAACG,GAAiBoB,mBACnCA,EAAqBtB,EAAiBuB,wBACtCA,EAA0B,CAACrB,GAAiBD,YAC5CA,GACEQ,EACJ,GACEA,EAAae,qBACbf,EAAae,oBAAoB9d,OAAS,EAC1C,CACAwc,EAAmBvX,GAAUoX,GAC7BE,EACEK,EAAUxc,KAAK4D,IAAIwY,GAAoB,KACnCI,EAAUxc,KAAK4D,IAAIwY,GACnB,KAENkB,EAAkBtB,GAChBC,EACAC,EACAC,EACAC,GAGFe,EAAqBR,EAAae,oBAAoBhb,QACtD,IAAK,IAAI/B,EAAI,EAAGA,EAAIwc,EAAmBvd,OAAQe,IAC7C,IAAK,IAAIK,EAAI,EAAGA,EAAI8b,EAAgBld,OAAQoB,IAC1Cmc,EAAmBxc,GAAGK,IACnBmc,EAAmBxc,GAAGK,GAAK8b,EAAgB9b,IAAMgc,EAAYhc,EAGtE,CAEA,IAAI+Z,EAAY,EAKhB,KAAOA,EAAYO,GAAY,CAK7B,IAiBIqC,EAAoBC,EAjBpBC,EAAK,GACLC,EAAMN,EAAmBnB,UAE1BrP,GAAMA,IAAMkP,EAAkBoB,IAE7BvB,EAAU,EACd,IAAK,IAAI/a,EAAI8c,EAAK9c,EAAIwc,EAAmB5d,OAAQoB,IAC/C,IAAK,IAAI2O,EAAI,EAAGA,EAAIsM,EAAerc,OAAQ+P,IAEtCsM,EAAetM,KAAO8N,EAAwBzc,GAC9Ckb,EAAkBvM,KAAO6N,EAAmBxc,KAE7C6c,EAAG9B,KAAapM,GAMtB,GAAI6N,EAAmB5d,OAASke,EAAM,EAAG,CACvC,IAAIC,EAAK7B,EAAkBoB,GACvBU,EAAK/B,EAAeqB,GACpBW,EAAKT,EAAmBA,EAAmB5d,OAAS,GAEpDse,GADKT,EAAwBD,EAAmB5d,OAAS,GAC3Coe,IAAOC,EAAKF,GAC1BI,EAAWH,EAAKE,EAAQH,EACxBK,EAAK,IAAIC,YAAYtC,GACzBA,EAAU,EACV,IAAK,IAAI/a,EAAI,EAAGA,EAAIod,EAAGxe,OAAQoB,IAAK,CAClC,IAAIL,EAAIkd,EAAG7c,GAETib,EAAetb,IACfud,EAAQhC,EAAkBvb,GAAKwd,EAAWzB,IAE1C0B,EAAGrC,KAAapb,EAEpB,CAEA,IAAI2d,EAAQ,GACRC,EAAQ,GACZ,IAAK,IAAIvd,EAAI,EAAGA,EAAI+a,EAAS/a,IAC3Bsd,EAAMrY,KAAKiW,EAAkBkC,EAAGpd,KAChCud,EAAMtY,KAAKgW,EAAemC,EAAGpd,KAG/B,IAAIwd,EAAiBjD,GAAoB+C,EAAOC,GAEhDX,EAAK,GACL,IAAK,IAAI5c,EAAI,EAAGA,EAAIwd,EAAe5e,OAAQoB,IACzC4c,EAAG3X,KAAKmY,EAAGI,EAAexd,IAE9B,MACE4c,EAAKC,EAAGnb,MAAM,EAAGqZ,GAEnB4B,EAAqBC,EAIrB,IAAK,IAAI/b,EAAI,EAAGA,EAAI8b,EAAmB/d,OAAQiC,IAAK,CAClD,IAAIlB,EAAIgd,EAAmB9b,GACvB4c,EAAa9Z,GAAU4Y,EAAU5c,IACjC+d,EAAkB,IAAIL,YAAYd,EAAU5c,GAAGf,QACnDmc,EAAU,EACV,IAAK,IAAI/a,EAAI,EAAGA,EAAIuc,EAAU5c,GAAGf,OAAQoB,IACnChB,KAAK4D,IAAI2Z,EAAU5c,GAAGK,GAAKyd,GAAchC,IAC3CiC,EAAgB3C,KAAa/a,GAGjC,IAAI0Y,EAAS,EAAI+E,EAAc,EAC3BE,EAAqB,GACzB,IAAK,IAAIC,EAAI,EAAGA,EAAI7C,EAAS6C,IAAK,CAChC,IAAI5d,EAAI0d,EAAgBE,GACpBC,EAAoB1B,EAAmBxc,GAAG+B,QAC1Coc,EAAqB3B,EAAmBxc,GAAG+B,QAC/Cmc,EAAkB7d,IAAM0Y,EACxBoF,EAAmB9d,IAAM0Y,EACzB,IAAIqF,EAAmB,IAAI3e,aAAaye,EAAkBjf,QACtDof,EAAoB,IAAI5e,aAAa0e,EAAmBlf,QAC5D,IAAK,IAAIoB,EAAI,EAAGA,EAAI6d,EAAkBjf,OAAQoB,IAC5C+d,EAAiB/d,GACf8b,EAAgB9b,GAAK6d,EAAkB7d,GAAKgc,EAAYhc,GAC1Dge,EAAkBhe,GAChB8b,EAAgB9b,GAAK8d,EAAmB9d,GAAKgc,EAAYhc,GAE7D,IAAIie,EAAgBrC,EAAkBmC,GAClCG,EAAiBtC,EAAkBoC,GACvC3B,GAAU,EACVsB,EAAmB1Y,KAAK,CACtBnB,SAAU9E,KAAK2J,IAAIsV,EAAeC,GAClCrb,MAAO+a,IAGTzB,EAAmBlX,KAAK4Y,EAAmBC,GAC3C7C,EAAehW,KAAKgZ,EAAeC,EACrC,CAEA,IAAIjd,EAAI0c,EAAmB7K,KAAK,CAAC9R,EAAGC,IAAMD,EAAE8C,SAAW7C,EAAE6C,UACzD,IAAK,IAAI8Z,EAAI,EAAGA,EAAI7C,EAAS6C,IAAK,CAChC,IAAIrO,EAAImO,EAAgBzc,EAAE2c,GAAG/a,OACzBsb,EAAMlC,EAAqB,GAAKhb,EAAE2c,GAAG/a,MAAQ,GAAK,EAClDub,EAAMnC,EAAqB,GAAKhb,EAAE2c,GAAG/a,MAAQ,GACjD0Z,EAAU5c,GAAG4P,GAAKmJ,EAAQ,EAC1B6D,EAAU4B,GAAO5B,EAAU5c,GAAG+B,QAC9B6a,EAAU6B,GAAO7B,EAAU5c,GAAG+B,QAC9BwZ,EAAkBvb,GAAK0E,GAAMkY,EAAU5c,IACvCub,EAAkBiD,GAAOjD,EAAkBvb,GAC3Cub,EAAkBkD,GAAOlD,EAAkBvb,EAC7C,CACAsc,GAAsB,EAAIlB,CAC5B,CAMAK,EAAmBvX,GAAUoX,GAE7BE,EACEK,EAAUxc,KAAK4D,IAAIwY,GAAoB,KACnCI,EAAUxc,KAAK4D,IAAIwY,GACnB,KAENkB,EAAkBtB,GAChBC,EACAC,EACAC,EACAC,GAIFoB,EAAqB/c,MAAM0D,KAAK,IAAI4O,IAAImJ,IACxCsB,EAAqBA,EAAmB1J,KAAK,CAAC9R,EAAGC,IAAMD,EAAIC,GAE3Dwb,EAA0B,GAC1B,IAAK,IAAIzc,EAAI,EAAGA,EAAIwc,EAAmB5d,OAAQoB,IAAK,CAClD,IAAIqe,EACAva,EAAWvF,OAAOwE,kBACtB,IAAK,IAAIlC,EAAI,EAAGA,EAAIqa,EAAkBtc,OAAQiC,IACxCqa,EAAkBra,KAAO2b,EAAmBxc,IAC1Cib,EAAepa,GAAKiD,IACtBA,EAAWmX,EAAepa,GAC1Bwd,EAAWxd,GAIjB4b,EAAwBxX,KAAKgW,EAAeoD,GAC9C,CAGA,IAAK,IAAI1e,EAAI,EAAGA,EAAIsb,EAAerc,OAAQe,IACzC,GAAIsb,EAAetb,KAAOyb,EAAkB,CAC1C,IAAI/Y,EAAO,GACX,IAAK,IAAIrC,EAAI,EAAGA,EAAI8b,EAAgBld,OAAQoB,IAC1CqC,EAAK4C,KACH6W,EAAgB9b,GAAKmc,EAAmBxc,GAAGK,GAAKgc,EAAYhc,GAIlE,CAEF+Z,GAAa,CACf,CAKA,IAAIzV,EAAS,CAAA,EACbA,EAAOga,iBAAmBlD,EAC1B9W,EAAOgW,WAAaP,EACpB,IAAI2C,EAAsB,GAC1B,IAAK,IAAI/c,EAAI,EAAGA,EAAIsc,EAAqB,EAAGtc,IAAK,CAC/C,IAAI4e,EAAO,GACX,IAAK,IAAIve,EAAI,EAAGA,EAAI8b,EAAgBld,OAAQoB,IAC1Cue,EAAKtZ,KAAK6W,EAAgB9b,GAAKmc,EAAmBxc,GAAGK,GAAKgc,EAAYhc,IAExE0c,EAAoBzX,KAAKsZ,EAC3B,CAEAja,EAAOka,WAAa,CAClBvC,qBACAC,gBAAkBA,GAAmB5B,EACrCoC,sBACAN,cACAC,SACAC,kBACAC,YACArB,oBACAD,iBACAuB,qBACAC,0BACAtB,eAGF,IAAIsD,EAAY,GAChB,IAAK,IAAIze,EAAI,EAAGA,EAAIib,EAAerc,OAAQoB,IACrCib,EAAejb,KAAOob,GACxBqD,EAAUxZ,KAAKyX,EAAoB1c,IAKvC,OADAsE,EAAOoa,OAASD,EACTna,CACT,CC1RiBqa,CACb/C,EAGAvF,EACAC,EACA,CACEgE,WAAYrE,EACZuF,UACAC,YACAC,aACAC,kBAIE+C,OAAEA,EAAMJ,iBAAEA,EAAgBhE,WAAEA,GAAehW,EAEjD,MAAO,CACL+V,eAAgBiE,EAChBhE,aACAF,gBAAiBsE,EAAO,GAE5B,CC2FM,SAAUjN,GACd5I,EACA5C,EACA9H,EAA2B,CAAA,GAO3B,MAAMkS,EAAMtM,GAAkB8E,EAAKnE,GAC7BqM,EAAiB,IAARV,EAAY,EAAIA,EAEzBS,EVlIF,SACJ7K,EACA8K,EACA5S,EAA2B,CAAA,GAE3B,IAAI0E,EAAQ,EACZ,MAAMiO,EAAgC,GACtC,IAAK,MAAM8N,KAAgB3Y,EAAO,CAChC,MAIM6N,EAJiB,IAClB8K,EACHla,EAAGka,EAAala,EAAIqM,IAGhB3K,GAAEA,EAAEqC,MAAEA,EAAQtK,EAAQsK,OAAS,CAAElB,KAAM,aAAiBuM,EAExD+K,EAA4BnO,GAAWjI,GAEvCY,EAA0B,CAAC,IAAK,OAAQwV,EAAS7V,iBAEjD8V,EAAuD,CAC3DnW,IAAK,GACL0H,IAAK,GACLiB,KAAM,GACNE,mBAAoB,IAGtB,IAAK,MAAML,KAAa9H,EACtB,IAAK,MAAMqM,KAAYF,GAAY,CAEjC,IAAIuJ,EAAgBjL,GAAMzK,aAAa8H,KAAauE,GACpD,QAAsB/W,IAAlBogB,EAA6B,CAC/BA,EAAgBtJ,GACdsJ,EACA5N,EACAuE,EACA3E,GAGF+N,EAAyBpJ,GAAUzQ,KAAK8Z,GACxC,QACF,CAGA,IAAIC,EACF7gB,GAASkL,aAAa8H,KAAauE,GACrC,QAA8B/W,IAA1BqgB,EAAqC,CACvC,GAAqC,iBAA1BA,EAAoC,CAC7CA,EAAwBvJ,GACtBuJ,EACA7N,EACAuE,EACA3E,GAEF+N,EAAyBpJ,GAAUzQ,KAAK+Z,GACxC,QACF,CAAO,CAEL,IAAIrhB,EAAQqhB,EAAsBJ,GAClCjhB,EAAQ8X,GAAmB9X,EAAOwT,EAAWuE,EAAU3E,GACvD+N,EAAyBpJ,GAAUzQ,KAAKtH,GACxC,QACF,CACF,CAGAgT,GACE2E,GAAkBnE,GAClB,4BAA4BA,KAE9B,MAAM8N,EAAyB3J,GAAkBnE,GAAWuE,GAC5DoJ,EAAyBpJ,GAAUzQ,KAEjCga,EAAuBnL,EAAM+K,GAEjC,CAGF,MAAMtd,EAAYsB,EACZrB,EAAUD,EAAY8H,EAAWzK,OAAS,EAChDiE,GAASrB,EAAUD,EAAY,EAE/B,MAAMgQ,EAAkD,CACtD5I,IAAKmW,EAAyBnW,IAC9B0H,IAAKyO,EAAyBzO,IAC9BiB,KAAMwN,EAAyBxN,KAC/BE,mBAAoBsN,EAAyBtN,oBAG/CV,EAAc7L,KAAK,CACjBmB,KACAqC,QACAoW,WACAxV,aACAkI,mBACAhQ,YACAC,WAEJ,CACA,OAAOsP,CACT,CU+BwBoO,CAAiBjZ,EAAO8K,EAAQ5S,GAEhDghB,EAAc,IAAI/f,aAAayJ,EAAKnE,EAAE9F,QAC5C,IAAK,IAAIoB,EAAI,EAAGA,EAAI6I,EAAKnE,EAAE9F,OAAQoB,IACjCmf,EAAYnf,GAAK6I,EAAKnE,EAAE1E,GAAK+Q,EAG/B,MAAMqO,EAAqBvO,GACzBC,EACA7K,EACA9H,EACA4S,IAGIwC,YACJA,EAAWJ,YACXA,EAAWC,YACXA,EAAWC,aACXA,EAAYC,aACZA,EAAY1B,UACZA,GACEwN,GAEEC,UAAEA,EAASC,oBAAEA,GC7Kf,SAAuBA,EAA2C,IACtE,MAAM/X,KAAEA,EAAO,KAAIpJ,QAAEA,GAAYmhB,EAEjC,OAAQ/X,GACN,IAAK,KACL,IAAK,qBACH,MAAO,CACL8X,UAAW3F,GACX4F,oBAAqB,CACnBxJ,QAAS,IACTG,cAAe,IACfC,eAAgB,QACb/X,IAGT,IAAK,SACH,MAAO,CACLkhB,UAAW/D,GACXgE,oBAAqB,CACnBrJ,cAAe,GACfuF,QAAS,KACTC,UAAW,MACXC,WAAY,MACZC,aAAc,CAAA,KACXxd,IAIT,QACE,MAAM,IAAIqE,MAAM,6BAEtB,CD8I6C+c,CAAaphB,EAAQqhB,cAE1DC,EEhLF,SAAyB3O,GAC7B,OAAO,SAAqBzH,GAC1B,IAAK,MAAMyK,KAAQhD,EACjB,IAAK,IAAI9Q,EAAI,EAAGA,EAAI8T,EAAKzK,WAAWzK,OAAQoB,IAAK,CAE/C,MAAM0f,EAAc5L,EAAKzK,WAAWrJ,GAIpC8T,EAAK+K,SAASa,GAAerW,EAAWyK,EAAKvS,UAAYvB,EAC3D,CAEF,OAAQkD,IACN,IAAIyc,EAAS,EACb,IAAK,MAAM7L,KAAQhD,EAAe,CAChC,MAAM8O,EAAQvW,EAAWyK,EAAKvS,WAE9Boe,GADUtW,EAAWyK,EAAKvS,UAAY,GACxBuS,EAAK+K,SAAS9W,IAAI7E,EAAI0c,EACtC,CACA,OAAOD,EAEX,CACF,CF0J0BE,CAAe/O,GACjCgP,EAA2BrM,GACxBgM,EACLL,EAAmB5L,qBAAqBC,IAI5C,GAA2B,IAAvBF,EAAY3U,OACd,OG9KE,SACJkS,EACAqO,EACAjc,EACA6c,EACAN,EACA1O,GAMA,MAAMhJ,EAAM0X,EAAgBM,GAC5B,IAAItI,EAAQ,EACZ,IAAK,IAAIzX,EAAI,EAAGA,EAAImf,EAAYvgB,OAAQoB,IACtCyX,IAAU0H,EAAYnf,GAAK+H,EAAI7E,EAAElD,MAAQ,EAG3C,MAAO,CACLyX,QACA6C,WAAY,EACZrU,MAAOkP,GAAoBrE,EAAeiP,EAAYhP,GAE1D,CHuJWiP,CACLlP,EACAqO,EACAtW,EAAK3F,EACLkc,EAAmB5L,qBAAqBH,GACxCoM,EACA1O,GAKJ,IAAIsF,EACAC,EACAT,EACAoK,EACAC,EAAmBJ,EAEvB,GAAIvM,EAAY3U,SAAWgT,EAAUhT,OAEnCyX,EAAYlD,EACZmD,EAAYlD,EACZyC,EAAgBxC,EAChB4M,EAAsB3M,MACjB,CAEL,MAAM6M,EAAyBC,IAC7B,MAAMC,EAAO,IAAIjhB,aAAawS,EAAUhT,QACxCyhB,EAAKjO,IAAIiB,GACT,IAAK,IAAIxS,EAAI,EAAGA,EAAI0S,EAAY3U,OAAQiC,IACtCwf,EAAK9M,EAAY1S,IAAMuf,EAAkBvf,GAE3C,OAAOif,EAAwBO,IAGjChK,EAAY,IAAIjX,aAAamU,EAAY3U,QACzC0X,EAAY,IAAIlX,aAAamU,EAAY3U,QACzCiX,EAAgB,IAAIzW,aAAamU,EAAY3U,QAC7CqhB,EAAsB,IAAI7gB,aAAamU,EAAY3U,QACnD,IAAK,IAAIe,EAAI,EAAGA,EAAI4T,EAAY3U,OAAQe,IAAK,CAC3C,MAAMK,EAAIuT,EAAY5T,GACtB0W,EAAU1W,GAAKwT,EAAYnT,GAC3BsW,EAAU3W,GAAKyT,EAAYpT,GAC3B6V,EAAclW,GAAK0T,EAAarT,GAChCigB,EAAoBtgB,GAAK2T,EAAatT,EACxC,CACAkgB,EAAmBC,CACrB,CAEA,MAAMG,EAASjB,EAAU,CAAEnc,EAAG2F,EAAK3F,EAAGwB,EAAGya,GAAee,EAAkB,CACxE7J,YACAC,YACAT,gBACArE,mBAAoByO,KACjBX,IAGL,IAAIiB,EACJ,GAAIhN,EAAY3U,SAAWgT,EAAUhT,OACnC2hB,EAAuBD,EAAOlG,oBACzB,CACL,MAAMiG,EAAOhN,EAAa3R,QAC1B,IAAK,IAAIb,EAAI,EAAGA,EAAI0S,EAAY3U,OAAQiC,IACtCwf,EAAK9M,EAAY1S,IAAMyf,EAAOlG,gBAAgBvZ,GAEhD0f,EAAuBF,CACzB,CAEA,MAAMG,EACJpB,EAAmB5L,qBAAqB+M,GAE1C,MAAO,CACL9I,MAAO6I,EAAOjG,eACdC,WAAYgG,EAAOhG,WACnBrU,MAAOkP,GAAoBrE,EAAe0P,EAAczP,GAE5D,CItPM,SAAU0P,GACdxa,EACA9H,EAAqC,IAErC,MAAMsK,MAAEA,EAAQ,CAAElB,KAAM,YAAYmZ,OAAEA,EAASC,gBAAgB1a,IAC7D9H,EACIyiB,EAAuBlQ,GAAWjI,GACxC,OAAOiY,EAAO3N,IAAKe,GAiBrB,SACEA,GAEA,MAAO,UAAWA,CACpB,CApBQ+M,CAAS/M,IACL,SAAUA,EAAKrL,QACnBqL,EAAKrL,MAAMjB,KAAOkJ,GAAWoD,EAAKrL,OAAOX,YAAYgM,EAAK3N,QAErD2N,GAEF,IACFA,EACHrL,MAAO,CACLjB,KAAMoZ,EAAqB9Y,YAAYgM,EAAK3N,UACzCsC,IAIX,CCOA,SAASqY,GAAYC,GACnB,IAAIC,GAAaC,IACjB,MAAMC,EAAwD,GAE9D,IAAK,IAAIlhB,EAAI,EAAGA,EAAI+gB,EAAMniB,OAAQoB,IAAK,CACrC,MAAMmhB,GACHJ,EAAM/gB,GAAGkD,EAAI6d,EAAM/gB,EAAI,GAAGkD,KACzB6d,EAAM/gB,GAAGmG,MAAQ4a,EAAM/gB,EAAI,GAAGmG,OAAS,GAEvCgb,EAAQH,IACVA,EAAYG,EACZD,EAAWtiB,OAAS,GAGlBuiB,IAAUH,GACZE,EAAWjc,KAAK,CACdpC,MAAO7C,EACPohB,QAASpiB,KAAK4D,IAAI5C,GAAK+gB,EAAMniB,OAASoB,KAG5C,CAEA,IAAIqhB,EAAaH,EAAW,GAC5B,IAAK,MAAMI,KAAaJ,EAClBI,EAAUF,QAAUC,EAAWD,UACjCC,EAAaC,GAIjB,OAAOD,EAAWxe,KACpB,CCzDM,SAAU0e,GACdtb,EACA9H,EAA6B,IAE7B,GAAqB,IAAjB8H,EAAMrH,OAAc,MAAO,GAE/B,MAAM4iB,eAAEA,EAAiB,EAACC,iBAAEA,EAAmB,IAAOtjB,EAEhDujB,EAAczb,EAAM0b,SAAS,CAAC3gB,EAAGC,IAAMD,EAAEkC,EAAIjC,EAAEiC,GAErD,IAAI0e,EAAeF,EAAY,GAC3BG,EAAoB,CAACD,GACzB,MAAME,EAAgB,CAACD,GAEvB,IAAK,IAAI7hB,EAAI,EAAGA,EAAI0hB,EAAY9iB,OAAQoB,IAAK,CAC3C,MAAM8T,EAAO4N,EAAY1hB,IAEtB8T,EAAK5Q,EAAI0e,EAAa1e,KAAO4Q,EAAK3N,MAAQyb,EAAazb,OAAS,IACjEqb,EAEAK,EAAa5c,KAAK6O,IAElB+N,EAAe,CAAC/N,GAChBgO,EAAO7c,KAAK4c,IAEdD,EAAe9N,CACjB,CAEA,YAAyBnV,IAArB8iB,EACKK,EAAOC,QAAShB,GACrBA,EAAMniB,OAAS6iB,EDvBf,SACJV,EACAU,GAEA,MAAMK,EAAgB,CAACf,GAEvB,OAAa,CACX,MAAMle,EAAQif,EAAOzG,UAAW3M,GAAMA,EAAE9P,OAAS6iB,GAEjD,IAAc,IAAV5e,EAAc,MAElB,MAAMmf,EAAUF,EAAOjf,GACjBof,EAAMnB,GAAYkB,GAExBF,EAAOI,OAAOrf,EAAO,EAAGmf,EAAQtgB,MAAM,EAAGugB,GAAMD,EAAQtgB,MAAMugB,GAC/D,CAEA,OAAOH,CACT,CCMUK,CAAWpB,EAAOU,GAClB,CAACV,IAIFe,CACT,CCPM,SAAUM,GACdvZ,EACAwZ,EACAlkB,EAAgC,CAAA,GAKhC,MAAMmkB,OACJA,EAAS,CAAA,EAAE7Z,MACXA,EAAQ,CAAElB,KAAM,YAAYka,iBAC5BA,EAAgBD,eAChBA,EAAce,aACdA,EAAe,EAAClZ,WAChBA,EAAUmW,aACVA,EAAe,CACbjY,KAAM,KACNpJ,QAAS,CACPyX,QAAS,MAGXzX,EAIE2jB,EAASP,GAAWc,EAAU,CAClCb,iBACAC,qBAEIe,EAA2B,GAC3BC,EAA6C,GACnD,IAAK,MAAMC,KAAaZ,EAAQ,CAC9B,MAAMa,EAAQzL,KAAKC,MACblR,EAAQwa,GAAgBiC,EAAW,CAAEja,UAErCma,EAAY3c,EAAM,GAClB4c,EAAW5c,EAAM6c,QAEjB3f,KACJA,EAAOyf,EAAU1f,EAAI0f,EAAUzc,MAAQoc,EAAYnf,GACnDA,EAAKyf,EAAS3f,EAAI2f,EAAS1c,MAAQoc,GACjCD,GACE/gB,UAAEA,EAASC,QAAEA,GAAYyB,EAAgB4F,EAAK3F,EAAG,CAAEC,OAAMC,OAEzDF,EACJ2F,EAAK3F,aAAa9D,aACdyJ,EAAK3F,EAAE6f,SAASxhB,EAAWC,GAC3BqH,EAAK3F,EAAExB,MAAMH,EAAWC,GACxBkD,EACJmE,EAAKnE,aAAatF,aACdyJ,EAAKnE,EAAEqe,SAASxhB,EAAWC,GAC3BqH,EAAKnE,EAAEhD,MAAMH,EAAWC,GAExBgI,EAAM,CACVwZ,MAAO,CAAE7f,OAAMC,MACfiG,WAAY,CAAEmW,eAAcnW,cAC5B4Z,UAAWP,EAAU9jB,OACrBskB,KAAMhM,KAAKC,MAAQwL,GAGrB,GAAIzf,EAAEtE,OAAS,EAAG,CAChB,MAAM0b,WACJA,EAAU7C,MACVA,EACAxR,MAAOkd,GACL1R,GAAS,CAAEvO,IAAGwB,KAAKuB,EAAO,CAC5BwC,QACAY,aACAmW,iBAGF,IAAK,IAAIxf,EAAI,EAAGA,EAAIiG,EAAMrH,OAAQoB,IAChCyiB,EAAQxd,KAAK,IACRke,EAAenjB,GAClBmG,MAAOuK,GAAWzK,EAAMjG,GAAGyI,OAAOb,YAChCub,EAAenjB,GAAGyI,MAAMjB,QAI9Bgb,EAAKvd,KAAK,IACLuE,EACH8Q,aACA7C,QACA7G,QAAS,2BAEb,MACE6R,EAAQxd,QAASgB,GACjBuc,EAAKvd,KAAK,IACLuE,EACH8Q,WAAY,EACZ1J,QAAS,uCAGf,CAEA,MAAO,CAAE4R,OAAMW,eAAgBV,EACjC,CChJM,SAAUW,GACdnd,EACA9H,EAA4B,IAE5B,MAAMuiB,OAAEA,EAASC,gBAAgB1a,IAAW9H,EAC5C,IAAK,MAAM2V,KAAQ4M,EACX,OAAQ5M,IACZA,EAAK1N,GAAKC,OAAOC,cAIrB,OAAOoa,CACT,CC4HA,SAAS2C,GACPC,EACAC,EACAtd,GAEA,IAAK,MAAMpD,KAAS0gB,EAClBtd,EAAMhB,KAAKue,GAA6BF,EAAWzgB,IAEvD,CACA,SAAS2gB,GAA6B1P,GACpC,MAAM1N,GAAEA,EAAEqC,MAAEA,EAAKvF,EAAEA,EAACwB,EAAEA,EAACyB,MAAEA,GAAU2N,EAE7BuB,EAAU,CACdnS,IACAwB,IACAyB,QACAsC,SAKF,OAFIrC,IAAIiP,EAAQjP,GAAKA,GAEdiP,CACT,qCC1HM,SACJgN,EACAlkB,EAA+B,IAE/B,MAAMuK,OAAEA,EAAS,EAAC+a,QAAEA,GAAU,GAAUtlB,EAElC8H,EA8BR,SACEA,EACAyC,GAEA,OAAOzC,EAAM8M,IAAKe,IAChB,MAAM1N,GAAEA,EAAEqC,MAAEA,EAAKvF,EAAEA,EAACwB,EAAEA,EAAC7B,MAAEA,EAAK0D,iBAAEA,GAAqBuN,EAC/C4P,EAAQxgB,GAAKA,EAAIqD,EAAiBpD,KAAKD,GAAKwF,EAC5Cib,EAAMzgB,GAAKqD,EAAiBnD,GAAGF,EAAIA,GAAKwF,EAE9C,IAAIpE,EAAS,CACXpB,IACAwB,IACA7B,QACAsD,MAAOwd,EAAMD,EACbvgB,KAAM,CAAED,EAAGwgB,GACXtgB,GAAI,CAAEF,EAAGygB,IAWX,OARIvd,IACF9B,EAAS,IAAKA,EAAQ8B,OAGpBqC,IACFnE,EAAS,IAAKA,EAAQmE,UAGjBnE,GAEX,CA1DgBsf,CAASvB,EAAU3Z,GAEjC,IAAK+a,EACH,IAAK,IAAIzjB,EAAI,EAAGA,EAAIiG,EAAMrH,OAAS,EAAGoB,IAAK,CACzC,MAAM8T,EAAO7N,EAAMjG,GACb6jB,EAAW5d,EAAMjG,EAAI,GACvB8T,EAAK1Q,GAAGF,EAAI2gB,EAAS1gB,KAAKD,IAE5B4Q,EAAK1Q,GAAGF,EACL4Q,EAAK3N,OAAS0d,EAAS1d,MAAQ2N,EAAK3N,QAAW0d,EAAS3gB,EAAI4Q,EAAK5Q,GAClE4Q,EAAK5Q,EACP2gB,EAAS1gB,KAAKD,EAAI4Q,EAAK1Q,GAAGF,EAE9B,CAGF,IAAK,MAAM4Q,KAAQ7N,EAEjB,GADA6N,EAAK3N,MAAQ2N,EAAK1Q,GAAGF,EAAI4Q,EAAK3Q,KAAKD,EAC/B4Q,EAAKrL,MAAO,CACd,MAAMA,MAAEA,EAAKtC,MAAEA,GAAU2N,EACzB,QAAmBnV,IAAf8J,EAAMjB,KAAoB,CAC5B,MAAMqX,EAAWnO,GAAWjI,GAC5BA,EAAMjB,KAAOqX,EAAS/W,YAAY3B,EACpC,CACF,CAGF,OAAOF,CACT,QCKM,SAAc4C,EAAc1K,EAAsB,IACtD,IAAI2lB,WAAEA,GAAe3lB,EACrB,MAAM4lB,UACJA,EAAY,CACV3lB,WAAY,EACZE,WAAY,GACb0lB,QACDA,GAAU,EAAKC,YACfA,GAAc,EAAIC,iBAClBA,EAAmB,EAACC,YACpBA,EAAc,MAAOC,iBACrBA,GAAmB,EAAKC,uBACxBA,EAAyB,UACvBlmB,EACJ,GAAI+lB,EAAmB,GAAKA,EAAmB,EAC7C,MAAM,IAAI1hB,MAAM,4CAElB,MAAMU,EAAEA,GAAM2F,EACd,IAAInE,EAAEA,GAAMmE,EACZ,GAAwB,IC9FpB,SAAuBpH,GAC3B,GAAIA,EAAM7C,QAAU,EAClB,OAAO,EAET,GAAI6C,EAAM,KAAOA,EAAM,GAAI,CAEzB,IAAK,IAAIzB,EAAI,EAAGA,EAAIyB,EAAM7C,OAAS,EAAGoB,IACpC,GAAIyB,EAAMzB,KAAOyB,EAAMzB,EAAI,GAAI,OAAO,EAExC,OAAO,CACT,CAEA,GAAIyB,EAAM,GAAMA,EAAMqhB,IAAG,GAAgB,CACvC,IAAK,IAAI9iB,EAAI,EAAGA,EAAIyB,EAAM7C,OAAS,EAAGoB,IACpC,GAAIyB,EAAMzB,IAAMyB,EAAMzB,EAAI,GAAI,OAAO,EAEvC,OAAO,CACT,CACE,IAAK,IAAIA,EAAI,EAAGA,EAAIyB,EAAM7C,OAAS,EAAGoB,IACpC,GAAIyB,EAAMzB,IAAMyB,EAAMzB,EAAI,GAAI,OAAO,EAEvC,OAAO,CAEX,CDuEMskB,CAAaphB,GACf,MAAM,IAAIV,MAAM,iDAGlBkC,EAAIA,EAAEhD,QAIN,MAAM6iB,EE9FF,SACJ9iB,EACAtD,EAAmC,IAEnC,GAAIsD,EAAM7C,OAAS,EAAG,OAAO,EAC7B,MAAM6c,UAAEA,EAAY,KAAStd,EAC7B,IAAIqmB,EAAQ,EACRC,EAAQlmB,OAAOkY,iBACnB,IAAK,IAAIzW,EAAI,EAAGA,EAAIyB,EAAM7C,OAAS,IAAKoB,EAAG,CACzC,MAAM0kB,EAAqBjjB,EAAMzB,EAAI,GAAKyB,EAAMzB,GAC5C0kB,EAAqBD,IACvBA,EAAQC,GAENA,EAAqBF,IACvBA,EAAQE,EAEZ,CACA,OAAQF,EAAQC,GAASD,EAAQ/I,CACnC,CF4E0BkJ,CAAiBzhB,GAEzC,QAAmBvE,IAAfmlB,EACF,GAAIS,EAAiB,CACnB,MAAMK,EAAY5gB,GAAwBU,GAExCof,EADEG,EACWW,EAAU/iB,OAAS,IAAM+iB,EAAUxgB,IAElCwgB,EAAU/iB,OAAS,IAAM+iB,EAAUxgB,EAErD,MACE0f,EAAa,OAELG,IACVH,IAAc,GAGhB,IAAKG,EACH,IAAK,IAAIjkB,EAAI,EAAGA,EAAI0E,EAAE9F,OAAQoB,IAC5B0E,EAAE1E,IAAM0E,EAAE1E,GAId,MAAM6kB,EACJX,EAAmB,EAAIA,EAAmBngB,GAAkBW,GAAKof,EAEnE,QAAmBnlB,IAAfmlB,EACF,IAAK,IAAI9jB,EAAI,EAAGA,EAAI0E,EAAE9F,OAAQoB,IACxB0E,EAAE1E,GAAK8jB,IACTpf,EAAE1E,GAAK8jB,GAKb,MAAMgB,EAASP,EAAkBrhB,EAAE,GAAKA,EAAE,GAAKA,EAEzCsC,EAAQwe,EACVhmB,EAAI0G,EAAGogB,EAAQ,IACVf,EACH1lB,WAAY,IAEdqG,GACIiE,IAAKoc,EAAM1U,IAAK2U,GG9IpB,SAAwBvjB,GAI5Ba,EAAOb,GAEP,IAAIkH,EAAMlH,EAAM,GACZ4O,EAAM5O,EAAM,GAEhB,IAAK,MAAM9D,KAAS8D,EACd9D,EAAQgL,IAAKA,EAAMhL,GACnBA,EAAQ0S,IAAKA,EAAM1S,GAGzB,MAAO,CAAEgL,MAAK0H,MAChB,CH+HmC4U,CAAczf,GAE/C,GAAIuf,EAAOC,GAAQD,IAASC,EAAM,MAAO,GAEzC,MAAMrgB,EAAK3G,EAAI0G,EAAGogB,EAAQ,IACrBf,EACH1lB,WAAY,IAGR6H,EAAMlI,EAAI0G,EAAGogB,EAAQ,IACtBf,EACH1lB,WAAY,IAGRgH,EAAa1B,GAAU,CAC3BmgB,EACAiB,GAAQC,EAAOD,GAAQZ,EACvBU,IAIIK,EAAW,CAAEhiB,IAAGwB,IAAGc,QAAOb,KAAIuB,MAAKtB,GAD9B1B,EAAE,GAAKA,EAAE,GACyBmC,cAC7C,IAAIY,EAAqB,GAEvBA,EAD6B,UAA3Boe,EACM1d,GAAgBue,GACY,WAA3Bb,EIvKP,SAA2BhjB,GAC/B,MAAM6B,EAAEA,EAACwB,EAAEA,EAACc,MAAEA,EAAKb,GAAEA,EAAEuB,IAAEA,EAAGtB,GAAEA,EAAES,WAAEA,GAAehE,EAE3C8jB,EAAmB,IACnBpgB,UAAEA,EAASC,UAAEA,GAAcP,GAAqBC,EAAGxB,EAAGyB,EAAIC,GAGhE,IAAK,IAAI5E,EAAI,EAAGA,EAAI0E,EAAE9F,OAAS,IAAKoB,EAE9BkG,EAAIlG,GAAKkG,EAAIlG,EAAI,IAAMkG,EAAIlG,GAAKkG,EAAIlG,EAAI,IAC1CmlB,EAAOlgB,KAAKjF,GAIhB,OAAOgG,GAAqB,CAC1BZ,QAAS+f,EACTpgB,YACAC,YACA9B,IACAsC,QACAH,aACAa,OAEJ,CJiJYkf,CAAiBF,GKrKvB,SAAwB7jB,GAC5B,MAAM6B,EAAEA,EAACwB,EAAEA,EAACc,MAAEA,EAAKb,GAAEA,EAAEuB,IAAEA,EAAGtB,GAAEA,EAAES,WAAEA,GAAehE,EAE3C8jB,EAAmB,GACnBve,EAAoB,IACpB7B,UAAEA,EAASC,UAAEA,GAAcP,GAAqBC,EAAGxB,EAAGyB,EAAIC,GAEhE,IAAK,IAAI5E,EAAI,EAAGA,EAAI0E,EAAE9F,OAAS,IAAKoB,GAC7B2E,EAAG3E,GAAK,GAAK2E,EAAG3E,EAAI,GAAK,GAAO2E,EAAG3E,GAAK,GAAK2E,EAAG3E,EAAI,GAAK,IAE5D4G,EAAQ3B,KAAKjG,KAAK4D,IAAI+B,EAAG3E,IAAMhB,KAAK4D,IAAI+B,EAAG3E,EAAI,IAAMA,EAAIA,EAAI,GAInD,IAAV2E,EAAG3E,IACH2E,EAAG3E,GAAKhB,KAAK4D,IAAI+B,EAAG3E,EAAI,KACxB2E,EAAG3E,GAAKhB,KAAK4D,IAAI+B,EAAG3E,EAAI,KAExB4G,EAAQ3B,KAAKjF,GAIXkG,EAAIlG,GAAKkG,EAAIlG,EAAI,IAAMkG,EAAIlG,GAAKkG,EAAIlG,EAAI,IAC1CmlB,EAAOlgB,KAAKjF,GAIhB,MAAMiG,EAAqB,GAC3B,IAAId,GAAQ,EACRkgB,GAAQ,EACZ,IAAK,IAAIrlB,EAAI,EAAGA,EAAI+E,EAAUnG,OAAQoB,IAAK,CACzC,MAAMsF,GAAiBN,EAAUhF,GAAGkD,EAAI6B,EAAU/E,GAAGkD,GAAK,EACpDqC,GAAkBP,EAAUhF,GAAGkD,EAAI6B,EAAU/E,GAAGkD,GAAK,EAE3D,IAAIoiB,GAAS,EACTC,EAAQrgB,GAA+B,CACzChC,IACAsC,QACAL,QACAE,aACAC,gBACAC,iBACAH,QAASwB,IAqBX,GAnBAzB,EAAQogB,EAAMxf,eACVwf,EAAM7f,SACR4f,EAAS1e,EAAQ2e,EAAM7f,WAEvB6f,EAAQrgB,GAA+B,CACrChC,IACAsC,QACAH,aACAF,MAAOkgB,EACP/f,gBACAC,iBACAH,QAAS+f,SAEPI,EAAM7f,WACR4f,EAASH,EAAOI,EAAM7f,WAExB2f,EAAQE,EAAMxf,YAGD,IAAXuf,EAAe,CACjB,MAAMnf,EAAQnH,KAAK4D,IAAIoC,EAAUhF,GAAGkD,EAAI6B,EAAU/E,GAAGkD,GACrD+C,EAAMhB,KAAK,CACTmB,GAAIC,OAAOC,aACXpD,EAAGA,EAAEoiB,GACL5gB,EAAGA,EAAE4gB,GACLnf,QACAtD,MAAOyiB,EACPpf,IAAKA,EAAIof,GACT/e,iBAAkB,CAChBpD,KAAM4B,EAAU/E,GAChBoD,GAAI4B,EAAUhF,KAGpB,CACF,CAEA,OAAOiG,CACT,CLsFYuf,CAAcN,GAGpBd,GM7KA,SACJvb,EACA5C,GAEA,MAAM/C,EAAEA,EAACwB,EAAEA,GAAMmE,EAEjB,IAAK,MAAMiL,KAAQ7N,EAAO,CACxB,IAAIwf,EAAe3R,EAAKjR,MAwBxB,GArBE6B,EAAE+gB,EAAe,IAAM/gB,EAAE+gB,EAAe,IACxC/gB,EAAE+gB,EAAe,IAAM/gB,EAAE+gB,GAEzBA,IAEA/gB,EAAE+gB,EAAe,IAAM/gB,EAAE+gB,IACzB/gB,EAAE+gB,EAAe,IAAM/gB,EAAE+gB,EAAe,GAExCA,IAEA/gB,EAAE+gB,EAAe,IAAM/gB,EAAE+gB,EAAe,IACxC/gB,EAAE+gB,EAAe,IAAM/gB,EAAE+gB,EAAe,GAExCA,GAAgB,EAEhB/gB,EAAE+gB,EAAe,IAAM/gB,EAAE+gB,EAAe,IACxC/gB,EAAE+gB,EAAe,IAAM/gB,EAAE+gB,EAAe,KAExCA,GAAgB,GAIhB/gB,EAAE+gB,EAAe,GAAK,GACtB/gB,EAAE+gB,EAAe,GAAK,GACtB/gB,EAAE+gB,IAAiB/gB,EAAE+gB,EAAe,IACpC/gB,EAAE+gB,IAAiB/gB,EAAE+gB,EAAe,KACnC/gB,EAAE+gB,KAAkB/gB,EAAE+gB,EAAe,IACpC/gB,EAAE+gB,KAAkB/gB,EAAE+gB,EAAe,IACvC,CACA,MAAMC,EAAQ1mB,KAAK2mB,MAAMjhB,EAAE+gB,EAAe,IACpCG,EAAO5mB,KAAK2mB,MAAMjhB,EAAE+gB,IACpB3W,EAAQ9P,KAAK2mB,MAAMjhB,EAAE+gB,EAAe,IACpClb,EAAK,IAAOmb,EAAQ5W,IAAW4W,EAAQ,EAAIE,EAAO9W,GAClD+W,EAAmB3iB,EAAEuiB,GACrBK,EAAoB5iB,EAAEuiB,EAAe,GAC3C3R,EAAK5Q,EAAI2iB,GAAYA,EAAWC,GAAavb,EAC7CuJ,EAAKpP,EACHA,EAAE+gB,GACF,KAAQ/gB,EAAE+gB,EAAe,GAAK/gB,EAAE+gB,EAAe,IAAMlb,CACzD,CACF,CACF,CN2HIwb,CAAY,CAAE7iB,IAAGwB,EAAGc,GAASS,GAG/B,IAAK,MAAM6N,KAAQ7N,EACZge,IACHnQ,EAAKpP,IAAK,EACVoP,EAAK5N,KAAiB,EAAX4N,EAAK5N,KAQpB,OAJAD,EAAM6M,KAAK,CAAC9R,EAAGC,IACND,EAAEkC,EAAIjC,EAAEiC,GAGV+C,CACT,mBF1JM,SACJoc,EACAlkB,EAAiC,IAEjC,MAAMsK,MACJA,EAAQ,CAAElB,KAAM,YAAYiY,aAC5BA,EAAe,CAAEjY,KAAM,KAAMpJ,QAAS,CAAEyX,QAAS,KAAMoQ,WACvDA,EAAa,IAAIC,WACjBA,EAAa,OACX9nB,EAEJ,IAAIkS,EAAM,EACN6V,EAAO,EACPvlB,EAAQ,EACZ,MAAM2iB,EAAqC,GAE3C,GAAIjB,EAASzjB,OAAS,EACpB,OAAOwkB,GACL3C,GAAgB4B,EAAStP,IAAIyQ,IAA+B,CAAE/a,WAIlE,IAAI0d,EAASnnB,KAAK4D,IAAIyf,EAAS,GAAGnc,KAClC,IAAK,IAAIlG,EAAI,EAAGA,EAAIqiB,EAASzjB,OAAQoB,IAAK,CACxC,MAAMomB,EAASpnB,KAAK4D,IAAIyf,EAASriB,GAAGkG,KAChCkgB,EAASD,IAAQA,EAASC,EAChC,CAEA,MAAMhR,EAA+B,GACrC,IAAK,MAAMtB,KAAQuO,EACbrjB,KAAK4D,IAAIkR,EAAK5N,MAAQ+f,EAAaE,EACrC7C,EAAWre,KAAK6O,GAEhBsB,EAASnQ,KAAKue,GAA6B1P,IAM/CwP,EAAWre,KAAK,CAAE/B,EAAG3E,OAAO8nB,UAAW3hB,EAAG,IAC1C,IAAIwc,EAA2C,CAC7Che,EAAG,CAACogB,EAAW,GAAGpgB,GAClBwB,EAAG,CAAC4e,EAAW,GAAG5e,IAEhB6e,EAAoB,CAAC,GACzB,IAAK,IAAIvjB,EAAI,EAAGA,EAAIsjB,EAAW1kB,OAAQoB,IACrC,GAAIhB,KAAK4D,IAAI0gB,EAAWtjB,EAAI,GAAGkD,EAAIogB,EAAWtjB,GAAGkD,GAAK8iB,EACpD9E,EAAWhe,EAAE+B,KAAKqe,EAAWtjB,GAAGkD,GAChCge,EAAWxc,EAAEO,KAAKqe,EAAWtjB,GAAG0E,GAC5B4e,EAAWtjB,GAAG0E,EAAI2L,IACpBA,EAAMiT,EAAWtjB,GAAG0E,EACpBwhB,EAAOlmB,GAETujB,EAAQte,KAAKjF,GACbW,QACK,CACL,GAAIA,EAAQ,EAAG,CACb,MAAM2lB,EAAetnB,KAAK4D,IACvBse,EAAWhe,EAAE4f,IAAG,GAAiB5B,EAAWhe,EAAE,KAE3Csf,KAAEA,EAAIW,eAAEA,GAAmBf,GAC/BlB,EACA,CACE,CACE9a,GAAIC,OAAOC,aACXpD,EAAGogB,EAAW4C,GAAMhjB,EACpBwB,EAAG2L,EACHlK,MAAOmgB,EACPjd,WAAY,CACVlD,MAAO,CAAEkK,IAAoB,EAAfiW,EAAkB3d,IAAoB,GAAf2d,MAI3C,CAAE7d,MAAO,CAAElB,KAAM,eAAiBiY,iBAEpCnP,EAAM,EACN6V,EAAO,EACP,MAAM1c,EAAMgZ,EAAK+D,KAAMlmB,GAAoB,4BAAdA,EAAEuQ,cACZjS,IAAf6K,GAAKiO,OAAuBjO,EAAIiO,MAAQ,GAC1CrC,EAASnQ,KAAKke,EAAe,IAE7BE,GAAcC,EAAYC,EAASnO,EAEvC,MACEiO,GAAcC,EAAYC,EAASnO,GAGrC8L,EAAa,CAAEhe,EAAG,CAACogB,EAAWtjB,GAAGkD,GAAIwB,EAAG,CAAC4e,EAAWtjB,GAAG0E,IACvD6e,EAAU,CAACvjB,GACXqQ,EAAMiT,EAAWtjB,GAAG0E,EACpBwhB,EAAOlmB,EACPW,EAAQ,CACV,CAIF,OAFAyU,EAAStC,KAAK,CAAC9R,EAAGC,IAAMD,EAAEkC,EAAIjC,EAAEiC,GAEzBkgB,GAAchO,EAAU,CAAEsL,OAAQtL,GAC3C,kBSjFM,SACJvM,EACAwZ,EACAlkB,EAAgC,CAAA,GAEhC,OAAOikB,GAAsBvZ,EAAMwZ,EAAUlkB,GAASglB,cACxD,wCC5CM,SACJld,EACA9H,EAA8B,IAE9B,MAAMsK,MAAEA,EAAQ,CAAElB,KAAM,YAAYmZ,OAAEA,EAASC,gBAAgB1a,IAC7D9H,EACIqoB,EAAgB9V,GAAWjI,GACjC,OAAOiY,EAAO3N,IAAKe,IAAI,IAClBA,EACHrL,MAAO,IAAKA,EAAOjB,KAAMgf,EAAc1e,YAAYgM,EAAK3N,UAE5D","x_google_ignoreList":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,58,59,60]}