{"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/xMaxValue.js","../node_modules/ml-spectra-processing/lib/x/xMinValue.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-esm/util/constants.js","../node_modules/ml-peak-shape-generator/lib-esm/shapes/1d/gaussian/Gaussian.js","../node_modules/ml-peak-shape-generator/lib-esm/util/erfinv.js","../node_modules/ml-peak-shape-generator/lib-esm/shapes/1d/lorentzian/Lorentzian.js","../node_modules/ml-peak-shape-generator/lib-esm/shapes/1d/lorentzianDispersive/LorentzianDispersive.js","../node_modules/ml-peak-shape-generator/lib-esm/shapes/1d/pseudoVoigt/PseudoVoigt.js","../node_modules/ml-peak-shape-generator/lib-esm/shapes/1d/generalizedLorentzian/GeneralizedLorentzian.js","../node_modules/ml-peak-shape-generator/lib-esm/shapes/1d/getShape1D.js","../node_modules/ml-spectra-fitting/lib/util/assert.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-processing/lib/x/xMaxAbsoluteValue.js","../node_modules/ml-spectra-fitting/lib/util/getGlobalParameterVectors.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/post/optimizePeaksWithLogs.js","../lib/utils/groupPeaks.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\n * @returns - median\n */\nexport function xMedian(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 { exact = false } = options || {};\n    const array = input.slice();\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 } = 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 (minLength && 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\n * @param 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  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\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    let swapped = false;\n    let a;\n    if (m < n) {\n      if (!autoTranspose) {\n        a = value.clone();\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      } else {\n        a = value.transpose();\n        m = a.rows;\n        n = a.columns;\n        swapped = true;\n        let aux = wantu;\n        wantu = wantv;\n        wantv = aux;\n      }\n    } else {\n      a = value.clone();\n    }\n\n    let nu = Math.min(m, n);\n    let ni = Math.min(m + 1, n);\n    let s = new Float64Array(ni);\n    let U = new Matrix(m, nu);\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], a.get(i, k));\n        }\n        if (s[k] !== 0) {\n          if (a.get(k, k) < 0) {\n            s[k] = -s[k];\n          }\n          for (let i = k; i < m; i++) {\n            a.set(i, k, a.get(i, k) / s[k]);\n          }\n          a.set(k, k, a.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 += a.get(i, k) * a.get(i, j);\n          }\n          t = -t / a.get(k, k);\n          for (let i = k; i < m; i++) {\n            a.set(i, j, a.get(i, j) + t * a.get(i, k));\n          }\n        }\n        e[j] = a.get(k, j);\n      }\n\n      if (wantu && k < nct) {\n        for (let i = k; i < m; i++) {\n          U.set(i, k, a.get(i, k));\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] * a.get(i, j);\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              a.set(i, j, a.get(i, j) + t * work[i]);\n            }\n          }\n        }\n        if (wantv) {\n          for (let i = k + 1; i < n; i++) {\n            V.set(i, k, e[i]);\n          }\n        }\n      }\n    }\n\n    let p = Math.min(n, m + 1);\n    if (nct < n) {\n      s[nct] = a.get(nct, nct);\n    }\n    if (m < p) {\n      s[p - 1] = 0;\n    }\n    if (nrt + 1 < p) {\n      e[nrt] = a.get(nrt, p - 1);\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(i, j, 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(i, k) * U.get(i, j);\n            }\n            t = -t / U.get(k, k);\n            for (let i = k; i < m; i++) {\n              U.set(i, j, U.get(i, j) + t * U.get(i, k));\n            }\n          }\n          for (let i = k; i < m; i++) {\n            U.set(i, k, -U.get(i, k));\n          }\n          U.set(k, k, 1 + U.get(k, k));\n          for (let i = 0; i < k - 1; i++) {\n            U.set(i, k, 0);\n          }\n        } else {\n          for (let i = 0; i < m; i++) {\n            U.set(i, k, 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(i, k) * V.get(i, j);\n            }\n            t = -t / V.get(k + 1, k);\n            for (let i = k + 1; i < n; i++) {\n              V.set(i, j, V.get(i, j) + t * V.get(i, k));\n            }\n          }\n        }\n        for (let i = 0; i < n; i++) {\n          V.set(i, k, 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(i, j) + sn * V.get(i, p - 1);\n                V.set(i, p - 1, -sn * V.get(i, j) + cs * V.get(i, p - 1));\n                V.set(i, j, 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(i, j) + sn * U.get(i, k - 1);\n                U.set(i, k - 1, -sn * U.get(i, j) + cs * U.get(i, k - 1));\n                U.set(i, j, 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(i, j) + sn * V.get(i, j + 1);\n                V.set(i, j + 1, -sn * V.get(i, j) + cs * V.get(i, j + 1));\n                V.set(i, j, 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(i, j) + sn * U.get(i, j + 1);\n                U.set(i, j + 1, -sn * U.get(i, j) + cs * U.get(i, j + 1));\n                U.set(i, j, 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(i, k, -V.get(i, k));\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(i, k + 1);\n                V.set(i, k + 1, V.get(i, k));\n                V.set(i, k, t);\n              }\n            }\n            if (wantu && k < m - 1) {\n              for (let i = 0; i < m; i++) {\n                t = U.get(i, k + 1);\n                U.set(i, k + 1, U.get(i, k));\n                U.set(i, k, t);\n              }\n            }\n            k++;\n          }\n          p--;\n          break;\n        }\n        // no default\n      }\n    }\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.transpose().mmul(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.transpose().mmul(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      for (i = 0; i < n; i++) {\n        for (j = 0; j < n; j++) {\n          V.set(i, j, value.get(i, j));\n        }\n      }\n      tred2(n, e, d, V);\n      tql2(n, e, d, V);\n    } else {\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(n - 1, j);\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(i - 1, j);\n        V.set(i, j, 0);\n        V.set(j, i, 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(j, i, f);\n        g = e[j] + V.get(j, j) * f;\n        for (k = j + 1; k <= i - 1; k++) {\n          g += V.get(k, j) * d[k];\n          e[k] += V.get(k, j) * 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(k, j, V.get(k, j) - (f * e[k] + g * d[k]));\n        }\n        d[j] = V.get(i - 1, j);\n        V.set(i, j, 0);\n      }\n    }\n    d[i] = h;\n  }\n\n  for (i = 0; i < n - 1; i++) {\n    V.set(n - 1, i, 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(k, i + 1) / h;\n      }\n\n      for (j = 0; j <= i; j++) {\n        g = 0;\n        for (k = 0; k <= i; k++) {\n          g += V.get(k, i + 1) * V.get(k, j);\n        }\n        for (k = 0; k <= i; k++) {\n          V.set(k, j, V.get(k, j) - g * d[k]);\n        }\n      }\n    }\n\n    for (k = 0; k <= i; k++) {\n      V.set(k, i + 1, 0);\n    }\n  }\n\n  for (j = 0; j < n; j++) {\n    d[j] = V.get(n - 1, j);\n    V.set(n - 1, j, 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(k, i + 1);\n            V.set(k, i + 1, s * V.get(k, i) + c * h);\n            V.set(k, i, c * V.get(k, i) - 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(j, i);\n        V.set(j, i, V.get(j, k));\n        V.set(j, k, 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 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 { 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\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\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;\nexport const ROOT_PI_OVER_LN2 = Math.sqrt(Math.PI / 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 { ROOT_2LN2, GAUSSIAN_EXP_FACTOR, ROOT_PI_OVER_LN2, } from '../../../util/constants';\nimport erfinv from '../../../util/erfinv';\nexport class Gaussian {\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}\nexport function calculateGaussianHeight(options) {\n    let { fwhm = 500, area = 1, sd } = options;\n    if (sd)\n        fwhm = gaussianWidthToFWHM(2 * sd);\n    return (2 * area) / ROOT_PI_OVER_LN2 / fwhm;\n}\n/**\n * Calculate the height of the gaussian function of a specific width (fwhm) at a speicifc\n * x position (the gaussian is centered on x=0)\n * @param x\n * @param fwhm\n * @returns y\n */\nexport function gaussianFct(x, fwhm) {\n    return Math.exp(GAUSSIAN_EXP_FACTOR * Math.pow(x / fwhm, 2));\n}\nexport function gaussianWidthToFWHM(width) {\n    return width * ROOT_2LN2;\n}\nexport function gaussianFwhmToWidth(fwhm) {\n    return fwhm / ROOT_2LN2;\n}\nexport function getGaussianArea(options) {\n    let { fwhm = 500, sd, height = 1 } = options;\n    if (sd)\n        fwhm = gaussianWidthToFWHM(2 * sd);\n    return (height * ROOT_PI_OVER_LN2 * fwhm) / 2;\n}\nexport function getGaussianFactor(area = 0.9999) {\n    return Math.sqrt(2) * erfinv(area);\n}\nexport function getGaussianData(shape = {}, options = {}) {\n    let { fwhm = 500, sd } = shape;\n    if (sd)\n        fwhm = gaussianWidthToFWHM(2 * sd);\n    let { length, factor = getGaussianFactor(), height = calculateGaussianHeight({ fwhm }), } = options;\n    if (!length) {\n        length = Math.min(Math.ceil(fwhm * factor), Math.pow(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        data[i] = gaussianFct(i - center, fwhm) * height;\n        data[length - 1 - i] = data[i];\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\nexport default function erfinv(x) {\n    let a = 0.147;\n    if (x === 0)\n        return 0;\n    let ln1MinusXSqrd = Math.log(1 - x * x);\n    let lnEtcBy2Plus2 = ln1MinusXSqrd / 2 + 2 / (Math.PI * a);\n    let firstSqrt = Math.sqrt(lnEtcBy2Plus2 ** 2 - ln1MinusXSqrd / a);\n    let secondSqrt = Math.sqrt(firstSqrt - lnEtcBy2Plus2);\n    return secondSqrt * (x > 0 ? 1 : -1);\n}\n//# sourceMappingURL=erfinv.js.map","import { ROOT_THREE } from '../../../util/constants';\nexport class Lorentzian {\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}\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};\nexport const lorentzianWidthToFWHM = (width) => {\n    return width * ROOT_THREE;\n};\nexport const lorentzianFwhmToWidth = (fwhm) => {\n    return fwhm / ROOT_THREE;\n};\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    const quantileFunction = (p) => Math.tan(Math.PI * (p - 0.5));\n    return ((quantileFunction(1 - halfResidual) - quantileFunction(halfResidual)) / 2);\n};\nexport const getLorentzianData = (shape = {}, options = {}) => {\n    let { fwhm = 500 } = shape;\n    let { length, factor = getLorentzianFactor(), height = calculateLorentzianHeight({ fwhm, area: 1 }), } = options;\n    if (!length) {\n        length = Math.min(Math.ceil(fwhm * factor), Math.pow(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        data[i] = lorentzianFct(i - center, fwhm) * height;\n        data[length - 1 - i] = data[i];\n    }\n    return data;\n};\n//# sourceMappingURL=Lorentzian.js.map","import { calculateLorentzianHeight, getLorentzianFactor, lorentzianFwhmToWidth, lorentzianWidthToFWHM, } from '../lorentzian/Lorentzian';\nexport class LorentzianDispersive {\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    //eslint-disable-next-line\n    getArea(_height) {\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}\nexport const lorentzianDispersiveFct = (x, fwhm) => {\n    return (2 * fwhm * x) / (4 * x ** 2 + fwhm ** 2);\n};\nexport const getLorentzianDispersiveData = (shape = {}, options = {}) => {\n    let { fwhm = 500 } = shape;\n    let { length, factor = getLorentzianFactor(), height = calculateLorentzianHeight({ fwhm, area: 1 }), } = options;\n    if (!length) {\n        length = Math.min(Math.ceil(fwhm * factor), Math.pow(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        data[i] = lorentzianDispersiveFct(i - center, fwhm) * height;\n        data[length - 1 - i] = -data[i];\n    }\n    return data;\n};\n//# sourceMappingURL=LorentzianDispersive.js.map","import { GAUSSIAN_EXP_FACTOR, ROOT_2LN2_MINUS_ONE, ROOT_PI_OVER_LN2, } from '../../../util/constants';\nimport { gaussianFct, getGaussianFactor } from '../gaussian/Gaussian';\nimport { lorentzianFct, getLorentzianFactor } from '../lorentzian/Lorentzian';\nexport class PseudoVoigt {\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);\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}\nexport const calculatePseudoVoigtHeight = (options = {}) => {\n    let { 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    return (1 - mu) * lorentzianFct(x, fwhm) + mu * gaussianFct(x, fwhm);\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 mu < 1 ? getLorentzianFactor(area) : getGaussianFactor(area);\n};\nexport const getPseudoVoigtData = (shape = {}, options = {}) => {\n    let { fwhm = 500, mu = 0.5 } = shape;\n    let { length, factor = getPseudoVoigtFactor(0.999, mu), 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), Math.pow(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        data[i] = pseudoVoigtFct(i - center, fwhm, mu) * height;\n        data[length - 1 - i] = data[i];\n    }\n    return data;\n};\n//# sourceMappingURL=PseudoVoigt.js.map","import { ROOT_THREE } from '../../../util/constants';\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 http://www.ebyte.it/stan/Talk_ML_UserMeeting_SMASH_2010_GeneralizedLorentzian.html\n */\nexport class GeneralizedLorentzian {\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}\nexport const calculateGeneralizedLorentzianHeight = ({ fwhm = 1, gamma = 1, area = 1, }) => {\n    return (area / fwhm / (3.14159 - 0.420894 * gamma)) * 2;\n};\n/**\n * expression of integral generated by Mathematica of the function\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};\nexport const generalizedLorentzianWidthToFWHM = (width) => {\n    return width * ROOT_THREE;\n};\nexport const generalizedLorentzianFwhmToWidth = (fwhm) => {\n    return fwhm / ROOT_THREE;\n};\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    const quantileFunction = (p) => Math.tan(Math.PI * (p - 0.5));\n    return ((quantileFunction(1 - halfResidual) - quantileFunction(halfResidual)) / 2);\n};\nexport const getGeneralizedLorentzianData = (shape = {}, options = {}) => {\n    let { fwhm = 500, gamma = 1 } = shape;\n    let { length, factor = getGeneralizedLorentzianFactor(), height = calculateGeneralizedLorentzianHeight({ fwhm, area: 1, gamma }), } = options;\n    if (!length) {\n        length = Math.min(Math.ceil(fwhm * factor), Math.pow(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        data[i] = generalizedLorentzianFct(i - center, fwhm, gamma) * height;\n        data[length - 1 - i] = data[i];\n    }\n    return data;\n};\n//# sourceMappingURL=GeneralizedLorentzian.js.map","import { Gaussian } from './gaussian/Gaussian';\nimport { GeneralizedLorentzian } from './generalizedLorentzian/GeneralizedLorentzian';\nimport { Lorentzian } from './lorentzian/Lorentzian';\nimport { LorentzianDispersive } from './lorentzianDispersive/LorentzianDispersive';\nimport { PseudoVoigt } from './pseudoVoigt/PseudoVoigt';\n/**\n * Generate a instance of a specific kind of shape.\n */\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 'lorentzianDispersive':\n            return new LorentzianDispersive(shape);\n        case 'generalizedLorentzian':\n            return new GeneralizedLorentzian(shape);\n        default: {\n            throw Error(`Unknown distribution ${kind}`);\n        }\n    }\n}\n//# sourceMappingURL=getShape1D.js.map","/**\n * Asserts that value is truthy.\n *\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 ? message : 'unreachable');\n    }\n}\n//# sourceMappingURL=assert.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    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 options\n * @returns\n */\nexport function getInternalPeaks(peaks, yScale, options = {}) {\n    let index = 0;\n    const internalPeaks = [];\n    const normalizedPeaks = peaks.map((peak) => {\n        return {\n            ...peak,\n            y: peak.y / yScale,\n        };\n    });\n    for (const peak of normalizedPeaks) {\n        const { id, shape = options.shape ? options.shape : { kind: 'gaussian' } } = peak;\n        const shapeFct = getShape1D(shape);\n        const parameters = ['x', 'y', ...shapeFct.getParameters()];\n        const propertiesValues = {\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                    propertiesValues[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                        propertiesValues[property].push(generalParameterValue);\n                        continue;\n                    }\n                    else {\n                        let value = generalParameterValue(peak);\n                        value = getNormalizedValue(value, parameter, property, yScale);\n                        propertiesValues[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                //@ts-expect-error should never happen\n                propertiesValues[property].push(defaultParameterValues(peak, shapeFct));\n            }\n        }\n        const fromIndex = index;\n        const toIndex = fromIndex + parameters.length - 1;\n        index += toIndex - fromIndex + 1;\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 { inverse, Matrix } from 'ml-matrix';\nimport gradientFunction from \"./gradient_function.js\";\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 */\nexport default function step(data, params, damping, gradientDifference, parameterizedFunction, centralDifference, weights) {\n    const identity = Matrix.eye(params.length, params.length, damping);\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 = gradientFunction(data, evaluatedData, params, gradientDifference, parameterizedFunction, centralDifference);\n    const residualError = matrixFunction(data, evaluatedData);\n    const inverseMatrix = inverse(identity.add(gradientFunc.mmul(gradientFunc.transpose().scale('row', { scale: weights }))));\n    const jacobianWeightResidualError = gradientFunc.mmul(residualError.scale('row', { scale: weights }));\n    const perturbations = inverseMatrix.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    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);\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';\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 } = result;\n    return {\n        parameterError: result.minFunctionValue,\n        iterations: result.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 { getFixedParametersResult } from \"./util/getFixedParametersResult.js\";\nimport { getGlobalParameterVectors } from \"./util/getGlobalParameterVectors.js\";\nimport { getInternalPeaks } from \"./util/internalPeaks/getInternalPeaks.js\";\nimport { selectMethod } from \"./util/selectMethod.js\";\n/**\n * Fits a set of points to the sum of a set of bell functions.\n *\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 { freeIndices, globalMin, globalMax, globalInit, globalGrad } = getGlobalParameterVectors(internalPeaks, peaks, options);\n    const nbParams = globalInit.length;\n    const { algorithm, optimizationOptions } = selectMethod(options.optimization);\n    const baseSumOfShapes = getSumOfShapes(internalPeaks);\n    if (freeIndices.length === 0) {\n        return getFixedParametersResult(internalPeaks, normalizedY, data.x, globalInit, baseSumOfShapes, yScale);\n    }\n    // wrapper that maps reduced (free) parameters into the full parameter vector\n    const sumOfShapesForReduced = (reducedParameters) => {\n        const full = new Float64Array(nbParams);\n        full.set(globalInit);\n        for (let k = 0; k < freeIndices.length; k++) {\n            full[freeIndices[k]] = reducedParameters[k];\n        }\n        return baseSumOfShapes(Array.from(full));\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 = baseSumOfShapes;\n    if (freeIndices.length === nbParams) {\n        // nothing to reduce\n        minValues = globalMin;\n        maxValues = globalMax;\n        initialValues = globalInit;\n        gradientDifferences = globalGrad;\n    }\n    else {\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] = globalMin[i];\n            maxValues[j] = globalMax[i];\n            initialValues[j] = globalInit[i];\n            gradientDifferences[j] = globalGrad[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    // reconstruct full parameter vector\n    let fittedValues;\n    if (freeIndices.length === nbParams) {\n        fittedValues = fitted.parameterValues;\n    }\n    else {\n        const full = Array.from(globalInit);\n        for (let k = 0; k < freeIndices.length; k++) {\n            full[freeIndices[k]] = fitted.parameterValues[k];\n        }\n        fittedValues = full;\n    }\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 = fittedValues[fromIndex];\n        newPeak.y = fittedValues[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]] = fittedValues[fromIndex + i];\n        }\n        newPeaks.push(newPeak);\n    }\n    return {\n        error: fitted.parameterError,\n        iterations: fitted.iterations,\n        peaks: newPeaks,\n    };\n}\n//# sourceMappingURL=index.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 = 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","export function getGlobalParameterVectors(internalPeaks, peaks, options) {\n    const nbParams = internalPeaks[internalPeaks.length - 1].toIndex + 1;\n    const globalMin = new Float64Array(nbParams);\n    const globalMax = new Float64Array(nbParams);\n    const globalInit = new Float64Array(nbParams);\n    const globalGrad = new Float64Array(nbParams);\n    const isOptimizable = new Array(nbParams);\n    let index = 0;\n    for (let pIndex = 0; pIndex < internalPeaks.length; pIndex++) {\n        const peak = internalPeaks[pIndex];\n        for (let i = 0; i < peak.parameters.length; i++) {\n            const paramName = peak.parameters[i];\n            globalMin[index] = peak.propertiesValues.min[i];\n            globalMax[index] = peak.propertiesValues.max[i];\n            globalInit[index] = peak.propertiesValues.init[i];\n            globalGrad[index] = peak.propertiesValues.gradientDifference[i];\n            let optimizeFlag = true;\n            const perPeakParam = peaks[pIndex]?.parameters?.[paramName];\n            const globalParam = options.parameters?.[paramName];\n            if (perPeakParam?.optimize !== undefined) {\n                if (typeof perPeakParam.optimize === 'function') {\n                    optimizeFlag = perPeakParam.optimize(peaks[pIndex]);\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(peaks[pIndex]);\n                }\n                else {\n                    const { optimize = true } = globalParam;\n                    optimizeFlag = optimize;\n                }\n            }\n            isOptimizable[index] = optimizeFlag;\n            index++;\n        }\n    }\n    const freeIndices = [];\n    for (let i = 0; i < nbParams; i++) {\n        if (isOptimizable[i])\n            freeIndices.push(i);\n    }\n    return { freeIndices, globalMin, globalMax, globalInit, globalGrad };\n}\n//# sourceMappingURL=getGlobalParameterVectors.js.map","import { levenbergMarquardt } from 'ml-levenberg-marquardt';\nimport { directOptimization } from \"./wrappers/directOptimization.js\";\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                    iterations: 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        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                for (let i = 2; i < parameters.length; i++) {\n                    const shapeFctKey = peak.parameters[i];\n                    peak.shapeFct[shapeFctKey] = parameters[peak.fromIndex + i];\n                }\n                totalY += y * peak.shapeFct.fct(x - peakX);\n            }\n            return totalY;\n        };\n    };\n}\n//# sourceMappingURL=getSumOfShapes.js.map","export function getFixedParametersResult(internalPeaks, normalizedY, x, globalInit, baseSumOfShapes, yScale) {\n    const fittedValues = Array.from(globalInit);\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            //@ts-expect-error it is right step\n            newPeak = { ...newPeak, id };\n        }\n        newPeak.x = fittedValues[fromIndex];\n        newPeak.y = fittedValues[fromIndex + 1] * yScale;\n        for (let i = 2; i < parameters.length; i++) {\n            //@ts-expect-error it is right step\n            newPeak.shape[parameters[i]] = fittedValues[fromIndex + i];\n        }\n        newPeaks.push(newPeak);\n    }\n    const fct = baseSumOfShapes(fittedValues);\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: newPeaks,\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","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' }, groupingFactor = 1, 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, { factor: groupingFactor });\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 * 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 { factor = 1 } = 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            factor) {\n            currentGroup.push(peak);\n        }\n        else {\n            currentGroup = [peak];\n            groups.push(currentGroup);\n        }\n        previousPeak = peak;\n    }\n    return groups;\n}\n//# sourceMappingURL=groupPeaks.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, 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, minMaxRatio = 0.00025, realTopDetection = false, peakDetectionAlgorithm = 'second', } = options;\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] *= -1;\n        }\n    }\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 = Math.max(noiseLevel, minY + (maxY - minY) * minMaxRatio);\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\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","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","fromIndex","toIndex","from","to","Matrix","matrix","inverse","xMaxValue","maxValue","xMinValue","minValue","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_THREE","ROOT_2LN2","ROOT_2LN2_MINUS_ONE","Gaussian","constructor","fwhm","this","gaussianWidthToFWHM","fwhmToWidth","gaussianFwhmToWidth","widthToFWHM","fct","gaussianFct","getArea","height","calculateGaussianHeight","getGaussianArea","getFactor","area","getGaussianFactor","getData","shape","factor","min","ceil","pow","data","getGaussianData","calculateHeight","getParameters","exp","ln1MinusXSqrd","log","lnEtcBy2Plus2","firstSqrt","erfinv","Lorentzian","lorentzianFwhmToWidth","lorentzianWidthToFWHM","lorentzianFct","getLorentzianArea","getLorentzianFactor","getLorentzianData","calculateLorentzianHeight","halfResidual","quantileFunction","p","tan","LorentzianDispersive","lorentzianDispersiveFct","_height","getLorentzianDispersiveData","PseudoVoigt","mu","pseudoVoigtFwhmToWidth","pseudoVoigtWidthToFWHM","pseudoVoigtFct","getPseudoVoigtArea","getPseudoVoigtFactor","calculatePseudoVoigtHeight","getPseudoVoigtData","GeneralizedLorentzian","gamma","generalizedLorentzianFwhmToWidth","generalizedLorentzianWidthToFWHM","generalizedLorentzianFct","getGeneralizedLorentzianArea","getGeneralizedLorentzianFactor","getGeneralizedLorentzianData","calculateGeneralizedLorentzianHeight","u","getShape1D","kind","assert","message","DefaultParameters","init","peak","peakShape","max","gradientDifference","properties","getNormalizedValue","parameter","property","yScale","checkOptions","timeout","initialValues","damping","dampingStepUp","dampingStepDown","maxIterations","errorTolerance","centralDifference","improvementThreshold","minValues","maxValues","parameters","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","identity","eye","evaluatedData","gradientFunc","paramFunction","nbParams","nbPoints","zeros","rowIndex","param","delta","auxParams","funcParam","funcParam2","point","set","gradientFunction","residualError","matrixFunction","inverseMatrix","add","mmul","transpose","scale","jacobianWeightResidualError","perturbations","levenbergMarquardt","checkedOptions","optimalError","optimalParameters","converged","iteration","previousError","get","isNaN","mul","parameterValues","parameterError","iterations","antiLowerConvexHull","currentPoint","moveOn","c","moveBack","map","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","e","f","a1","b1","a2","slope","constant","S2","Uint32Array","xHull","yHull","lowerIndexHull","largerSide","largeSidesIndex","bestFunctionValues","r","firstMiddleCenter","secondMiddleCenter","firstMiddleValue","secondMiddleValue","firstMinValue","secondMinValue","sort","ix1","ix2","Set","minIndex","minFunctionValue","pair","finalState","minimizer","optima","direct","optimize","xMaxAbsoluteValue","internalPeaks","normalizedPeaks","shapeFct","propertiesValues","propertyValue","generalParameterValue","defaultParameterValues","getInternalPeaks","normalizedY","freeIndices","globalMin","globalMax","globalInit","globalGrad","isOptimizable","pIndex","paramName","optimizeFlag","perPeakParam","globalParam","getGlobalParameterVectors","algorithm","optimizationOptions","selectMethod","optimization","baseSumOfShapes","totalY","peakX","shapeFctKey","getSumOfShapes","fittedValues","newPeaks","newPeak","getFixedParametersResult","sumOfShapesForReduced","reducedParameters","full","gradientDifferences","sumOfShapesToUse","fitted","addMissingShape","output","structuredClone","defaultShapeInstance","hasShape","optimizePeaksWithLogs","peakList","fromTo","groupingFactor","factorLimits","groups","sortedPeaks","toSorted","previousPeak","currentGroup","groupPeaks","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","minMaxRatio","realTopDetection","peakDetectionAlgorithm","xIsMonotonic","isEquallySpaced","maxDx","minDx","absoluteDifference","xIsEquallySpaced","noiseInfo","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","candidates","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,CCLM,SAAUqD,EACdC,EACAlD,EAA0B,IAE1B,IAAKT,EAAW2D,GACd,MAAM,IAAI3C,UAAU,0BAGtB,GAAqB,IAAjB2C,EAAMzC,OACR,MAAM,IAAIF,UAAU,2BAGtB,MAAM4C,MAAEA,GAAQ,GAAUnD,GAAW,CAAA,EAC/BoD,EAAQF,EAAMG,QAEdC,EAAcC,EAAW,EAAGH,EAAM3C,OAAS,GAE3C+C,EAASC,EAAYL,EAAOE,GAClC,GAAIF,EAAM3C,OAAS,GAAM,IAAM0C,EAC7B,OAAOK,EAGT,OAAQA,EADWC,EAAYL,EAAOE,EAAc,IACrB,CACjC,CAEA,SAASG,EAAYL,EAAoBE,GACvC,IAAII,EAAM,EACNC,EAAOP,EAAM3C,OAAS,EACtBmD,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,EAAoBvB,EAAWL,GAC3C,MAAMwC,EAAOZ,EAAM5B,GACnB4B,EAAM5B,GAAK4B,EAAMvB,GACjBuB,EAAMvB,GAAKmC,CACb,CAEA,SAAST,EAAW1B,EAAWL,GAC7B,OAAOX,KAAKC,OAAOe,EAAIL,GAAK,EAC9B,CC1FM,SAAUyC,EACdf,EACAlD,EAAyB,IAEzB,MAAMkE,UAAEA,GAAclE,EACtB,IAAKT,EAAW2D,GACd,MAAM,IAAI3C,UAAU,0BAEtB,GAAqB,IAAjB2C,EAAMzC,OACR,MAAM,IAAIF,UAAU,2BAEtB,GAAwB,iBAAb2C,EAAM,GACf,MAAM,IAAI3C,UAAU,8BAEtB,GAAI2D,GAAahB,EAAMzC,OAASyD,EAC9B,MAAM,IAAIC,MAAM,wCAAwCD,IAE5D,CCjBM,SAAUE,EACdhB,EACAiB,EACArE,EAAoC,CAAA,GAEpC,MAAMsE,OAAEA,GAAS,GAAStE,EAC1B,GAAIsE,EAAQ,CACV,IAAIZ,EAAM,EACNC,EAAOP,EAAM3C,OAAS,EACtBmD,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,EAAM3C,OAAS,EACnBI,KAAK0D,IAAIF,EAASjB,EAAMM,IAAQ7C,KAAK0D,IAAInB,EAAMM,EAAM,GAAKW,GACrDX,EAEAA,EAAM,EAGRA,CAEX,CAAO,CACL,IAAIc,EAAQ,EACRC,EAAOrE,OAAOsE,kBAClB,IAAK,IAAI7C,EAAI,EAAGA,EAAIuB,EAAM3C,OAAQoB,IAAK,CACrC,MAAM8C,EAAc9D,KAAK0D,IAAInB,EAAMvB,GAAKwC,GACpCM,EAAcF,IAChBA,EAAOE,EACPH,EAAQ3C,EAEZ,CACA,OAAO2C,CACT,CACF,CC1BM,SAAUI,EACdC,EACA7E,EAAkC,IAElC,IAAI8E,UAAEA,EAASC,QAAEA,GAAY/E,EAC7B,MAAMgF,KAAEA,EAAIC,GAAEA,GAAOjF,EAsBrB,YApBkBQ,IAAdsE,IAEAA,OADWtE,IAATwE,EACUZ,EAAkBS,EAAGG,GAErB,QAGAxE,IAAZuE,IAEAA,OADSvE,IAAPyE,EACQb,EAAkBS,EAAGI,GAErBJ,EAAEpE,OAAS,GAGrBqE,EAAY,IAAGA,EAAY,GAC3BC,EAAU,IAAGA,EAAU,GACvBD,GAAaD,EAAEpE,SAAQqE,EAAYD,EAAEpE,OAAS,GAC9CsE,GAAWF,EAAEpE,SAAQsE,EAAUF,EAAEpE,OAAS,GAE1CqE,EAAYC,KAAUD,EAAWC,GAAW,CAACA,EAASD,IACnD,CAAEA,YAAWC,UACtB,6FCtDA,SAAAxF,EAAAC,gtkECGO,MAAM0F,GAASC,GAqBPA,GAAeD,QAASC,GAAeD,OAE/C,MAAME,GAAUD,GCtBjB,SAAUE,GACdjC,EACApD,EAAkC,IAElCiE,EAAOb,GACP,MAAM0B,UAAEA,EAASC,QAAEA,GAAYH,EAAgBxB,EAAOpD,GACtD,IAAIsF,EAAWlC,EAAM0B,GAErB,IAAK,IAAIjD,EAAIiD,EAAY,EAAGjD,GAAKkD,EAASlD,IACpCuB,EAAMvB,GAAKyD,IACbA,EAAWlC,EAAMvB,IAGrB,OAAOyD,CACT,CCdM,SAAUC,GACdnC,EACApD,EAAkC,IAElCiE,EAAOb,GACP,MAAM0B,UAAEA,EAASC,QAAEA,GAAYH,EAAgBxB,EAAOpD,GACtD,IAAIwF,EAAWpC,EAAM0B,GACrB,IAAK,IAAIjD,EAAIiD,EAAY,EAAGjD,GAAKkD,EAASlD,IACpCuB,EAAMvB,GAAK2D,IACbA,EAAWpC,EAAMvB,IAGrB,OAAO2D,CACT,CCIM,SAAUC,GACdrC,GAEA,MAAMsC,IAAEA,EAAGlC,OAAEA,GCVT,SACJJ,GAEA,MAAMI,EAASP,EAAQG,GACjBuC,EAAoB,IAAI1E,aAAamC,EAAM3C,QACjD,IAAK,IAAIoB,EAAI,EAAGA,EAAIuB,EAAM3C,OAAQoB,IAChC8D,EAAkB9D,GAAKhB,KAAK0D,IAAInB,EAAMvB,GAAK2B,GAE7C,MAAO,CACLA,SACAkC,IAAKzC,EAAQ0C,GAEjB,CDF0BC,CAAyBxC,GACjD,MAAO,CAAEyC,GAAIH,EAAM,kBAAoBA,MAAKlC,SAC9C,CEzBM,SAAUsC,GAAM1C,GACpB,IAAI2C,EAAS,EACb,IAAK,MAAMC,KAAW5C,EACpB2C,GAAUC,GAAW,EAEvB,OAAOnF,KAAKoF,KAAKF,EACnB,CCDM,SAAUG,GACdC,EACAtB,EACAuB,EACAC,GAEA,IAAIC,EAAyB,KACzBC,EAAyB,KAC7B,MAAMC,EAAsB,GACtBC,EAAsB,GAC5B,IAAK,IAAI5E,EAAI,EAAGA,EAAIsE,EAAE1F,OAAS,IAAKoB,GAE/BuE,EAAGvE,GAAKuE,EAAGvE,EAAI,IAAMuE,EAAGvE,IAAMuE,EAAGvE,EAAI,IACrCuE,EAAGvE,IAAMuE,EAAGvE,EAAI,IAAMuE,EAAGvE,GAAKuE,EAAGvE,EAAI,MAEtC0E,EAAU,CACR1B,EAAGA,EAAEhD,GACL2C,MAAO3C,GAELwE,EAAK,GAAiB,OAAZC,IACZE,EAAUE,KAAKJ,GACfG,EAAUC,KAAKH,MAMhBH,EAAGvE,IAAMuE,EAAGvE,EAAI,IAAMuE,EAAGvE,GAAKuE,EAAGvE,EAAI,IACrCuE,EAAGvE,GAAKuE,EAAGvE,EAAI,IAAMuE,EAAGvE,IAAMuE,EAAGvE,EAAI,MAEtCyE,EAAU,CACRzB,EAAGA,EAAEhD,GACL2C,MAAO3C,GAELwE,EAAK,GAAiB,OAAZE,IACZC,EAAUE,KAAKJ,GACfG,EAAUC,KAAKH,KAKrB,MAAO,CAAEC,YAAWC,YACtB,CCrCM,SAAUE,GACd3G,GAEA,MAAM6E,EACJA,EAAC+B,MACDA,EAAKC,QACLA,EAAOC,WACPA,EAAUC,cACVA,EAAaC,eACbA,EAAcC,MACdA,GACEjH,EAEJ,IAAIkH,EAAc9G,OAAOsE,kBACrByC,GAAW,EACXC,EAAeR,EACnB,IAAK,IAAIlE,EAAI0E,EAAe,EAAG1E,EAAImE,EAAQpG,OAAQiC,IAAK,CACtD,MAAM2E,EAAcR,EAAQnE,GAC5B,GAAIuE,EAAMI,IAAgBP,EACxB,SAGF,MAAMQ,EAASzC,EAAEwC,GACXE,EAAkB1G,KAAK0D,IAAI+C,EAASN,GAS1C,GAPIO,EAAkBR,IAChBQ,EAAkBL,IACpBC,EAAWzE,GAEb0E,EAAe1E,GAGb6E,GAAmBL,EAAa,MACpCA,EAAcK,CAChB,CAEA,MAAO,CAAEC,UAAWJ,EAAcD,WACpC,CC/BM,SAAUM,GAAqBzH,GACnC,IAAI4G,GAAQ,EACZ,MAAMc,EAAqB,IACrB7C,EAAEA,EAAC8C,IAAEA,EAAGV,MAAEA,EAAKH,WAAEA,EAAUL,UAAEA,EAASD,UAAEA,EAASK,QAAEA,GAAY7G,EAErE,IAAK,IAAI6B,EAAI,EAAGA,EAAI2E,EAAU/F,OAAQoB,IAAK,CACzC,MAAMkF,GAAiBN,EAAU5E,GAAGgD,EAAI2B,EAAU3E,GAAGgD,GAAK,EACpDmC,GAAkBP,EAAU5E,GAAGgD,EAAI2B,EAAU3E,GAAGgD,GAAK,GACrDsC,SAAEA,EAAQK,UAAEA,GAAcb,GAA+B,CAC7D9B,IACA+B,QACAC,UACAC,aACAC,gBACAC,iBACAC,UAGF,IAAiB,IAAbE,EAAiB,CACnB,MAAME,EAAcR,EAAQM,GACtBS,EAAQ/G,KAAK0D,IAAIkC,EAAU5E,GAAGgD,EAAI2B,EAAU3E,GAAGgD,GACrD6C,EAAMhB,KAAK,CACTmB,GAAIC,OAAOC,aACXlD,EAAGA,EAAEwC,GACLlB,EAAGc,EAAMI,GACTO,QACApD,MAAO6C,EACPM,IAAKA,EAAIN,GACTW,iBAAkB,CAChBhD,KAAMwB,EAAU3E,GAChBoD,GAAIwB,EAAU5E,KAGpB,CACA+E,EAAQY,CACV,CAEA,OAAOE,CACT,CC/BA,SAASO,GAAyBC,EAAcC,GAC9C,OAAQD,EAAO,GAAKC,EAAO,GAAOD,EAAO,GAAKC,EAAO,CACvD,CCvBM,SAAUC,GAAgBlF,GAC9B,MAAMiD,EAAEA,EAACtB,EAAEA,EAACuB,GAAEA,EAAEC,GAAEA,EAAEY,MAAEA,EAAKH,WAAEA,EAAUa,IAAEA,GAAQzE,EAC3CmF,EDDF,SAA8BnF,GAClC,MAAMiD,EAAEA,EAACC,GAAEA,GAAOlD,EAEZmF,EAAoB,GAE1B,IAAK,IAAIxG,EAAI,EAAGA,EAAIsE,EAAE1F,OAAS,IAAKoB,EAC9BoG,GAAyB7B,EAAGvE,GAAIuE,EAAGvE,EAAI,IAEzCwG,EAAQ3B,KAAK7F,KAAK0D,IAAI6B,EAAGvE,IAAMhB,KAAK0D,IAAI6B,EAAGvE,EAAI,IAAMA,EAAIA,EAAI,GAGnD,IAAVuE,EAAGvE,IACHoG,GAAyB7B,EAAGvE,EAAI,GAAIuE,EAAGvE,EAAI,KAE3CwG,EAAQ3B,KAAK7E,GAGjB,OAAOwG,CACT,CCjBkBC,CAAoBpF,IAC9BsD,UAAEA,EAASC,UAAEA,GAAcP,GAAqBC,EAAGtB,EAAGuB,EAAIC,GAEhE,OAAOoB,GAAqB,CAC1BZ,QAASwB,EACT7B,YACAC,YACA5B,IACAoC,QACAH,aACAa,OAEJ,CCxBO,MAAMY,IAAsB,EAAK1H,KAAK2H,IAChCC,GAAmB5H,KAAKoF,KAAKpF,KAAK6H,GAAK7H,KAAK2H,KAC5CG,GAAa9H,KAAKoF,KAAK,GACvB2C,GAAY/H,KAAKoF,KAAK,EAAIpF,KAAK2H,KAC/BK,GAAsBhI,KAAKoF,KAAK,EAAIpF,KAAK2H,KAAO,ECiDvD,MAAOM,GAOXC,WAAAA,CAAmB/I,EAAgC,IACjD,MAAMgJ,KAAEA,EAAO,IAAGnD,GAAEA,GAAO7F,EAE3BiJ,KAAKD,KAAOnD,EAAKqD,GAAoB,EAAIrD,GAAMmD,CACjD,CAEOG,WAAAA,CAAYH,EAAOC,KAAKD,MAC7B,OAyDE,SAA8BA,GAClC,OAAOA,EAAOJ,EAChB,CA3DWQ,CAAoBJ,EAC7B,CAEOK,WAAAA,CAAYzB,GACjB,OAAOsB,GAAoBtB,EAC7B,CAEO0B,GAAAA,CAAIzE,GACT,OAAO0E,GAAY1E,EAAGoE,KAAKD,KAC7B,CAEOQ,OAAAA,CAAQC,EAASC,GAAwB,CAAEV,KAAMC,KAAKD,QAC3D,OAiDE,SAA0BhJ,GAC9B,IAAIgJ,KAAEA,EAAO,IAAGnD,GAAEA,EAAE4D,OAAEA,EAAS,GAAMzJ,EAEjC6F,IAAImD,EAAOE,GAAoB,EAAIrD,IAEvC,OAAQ4D,EAAShB,GAAmBO,EAAQ,CAC9C,CAvDWW,CAAgB,CAAEX,KAAMC,KAAKD,KAAMS,UAC5C,CAEOG,SAAAA,CAAUC,GACf,OAAOC,GAAkBD,EAC3B,CAEOE,OAAAA,CAAQ/J,EAA4B,IACzC,OAqDE,SACJgK,EAA8B,GAC9BhK,EAA4B,CAAA,GAE5B,IAAIgJ,KAAEA,EAAO,IAAGnD,GAAEA,GAAOmE,EACrBnE,IAAImD,EAAOE,GAAoB,EAAIrD,IAEvC,IAAIpF,OACFA,EAAMwJ,OACNA,EAASH,KAAmBL,OAC5BA,EAASC,GAAwB,CAAEV,UACjChJ,EAECS,IACHA,EAASI,KAAKqJ,IAAIrJ,KAAKsJ,KAAKnB,EAAOiB,GAASpJ,KAAKuJ,IAAI,EAAG,IAAM,GAC1D3J,EAAS,GAAM,GAAGA,KAGxB,MAAM8B,GAAU9B,EAAS,GAAK,EACxB4J,EAAO,IAAIpJ,aAAaR,GAC9B,IAAK,IAAIoB,EAAI,EAAGA,GAAKU,EAAQV,IAC3BwI,EAAKxI,GAAK0H,GAAY1H,EAAIU,EAAQyG,GAAQS,EAC1CY,EAAK5J,EAAS,EAAIoB,GAAKwI,EAAKxI,GAG9B,OAAOwI,CACT,CA/EWC,CAAgBrB,KAAMjJ,EAC/B,CAEOuK,eAAAA,CAAgBV,EAAO,GAC5B,OAAOH,GAAwB,CAAEV,KAAMC,KAAKD,KAAMa,QACpD,CAEOW,aAAAA,GACL,MAAO,CAAC,OACV,EAGI,SAAUd,GACd1J,GAEA,IAAIgJ,KAAEA,EAAO,IAAGa,KAAEA,EAAO,EAAChE,GAAEA,GAAO7F,EAInC,OAFI6F,IAAImD,EAAOE,GAAoB,EAAIrD,IAE/B,EAAIgE,EAAQpB,GAAmBO,CACzC,CASM,SAAUO,GAAY1E,EAAWmE,GACrC,OAAOnI,KAAK4J,IAAIlC,GAAsB1H,KAAKuJ,IAAIvF,EAAImE,EAAM,GAC3D,CAEM,SAAUE,GAAoBtB,GAClC,OAAOA,EAAQgB,EACjB,CAcM,SAAUkB,GAAkBD,EAAO,OACvC,OAAOhJ,KAAKoF,KAAK,GCpIL,SAAiBpB,GAE7B,GAAU,IAANA,EAAS,OAAO,EACpB,IAAI6F,EAAgB7J,KAAK8J,IAAI,EAAI9F,EAAIA,GACjC+F,EAAgBF,EAAgB,EAAI,GAHhC,KAGqC7J,KAAK6H,IAC9CmC,EAAYhK,KAAKoF,KAAK2E,GAAiB,EAAIF,EAJvC,MAMR,OADiB7J,KAAKoF,KAAK4E,EAAYD,IAClB/F,EAAI,EAAI,GAAI,EACnC,CD4HwBiG,CAAOjB,EAC/B,CEjHM,MAAOkB,GAOXhC,WAAAA,CAAmB/I,EAAkC,IACnD,MAAMgJ,KAAEA,EAAO,KAAQhJ,EAEvBiJ,KAAKD,KAAOA,CACd,CAEOG,WAAAA,CAAYH,EAAOC,KAAKD,MAC7B,OAAOgC,GAAsBhC,EAC/B,CAEOK,WAAAA,CAAYzB,GACjB,OAAOqD,GAAsBrD,EAC/B,CAEO0B,GAAAA,CAAIzE,GACT,OAAOqG,GAAcrG,EAAGoE,KAAKD,KAC/B,CAEOQ,OAAAA,CAAQC,EAAS,GACtB,OAAO0B,GAAkB,CAAEnC,KAAMC,KAAKD,KAAMS,UAC9C,CAEOG,SAAAA,CAAUC,GACf,OAAOuB,GAAoBvB,EAC7B,CAEOE,OAAAA,CAAQ/J,EAA4B,IACzC,OAAOqL,GAAkBpC,KAAMjJ,EACjC,CAEOuK,eAAAA,CAAgBV,EAAO,GAC5B,OAAOyB,GAA0B,CAAEtC,KAAMC,KAAKD,KAAMa,QACtD,CAEOW,aAAAA,GACL,MAAO,CAAC,OACV,EAGK,MAAMc,GAA4BA,EAAGtC,OAAO,EAAGa,OAAO,KACnD,EAAIA,EAAQhJ,KAAK6H,GAAKM,EAGnBmC,GAAqBnL,IAChC,MAAMgJ,KAAEA,EAAO,IAAGS,OAAEA,EAAS,GAAMzJ,EACnC,OAAQyJ,EAAS5I,KAAK6H,GAAKM,EAAQ,GAGxBkC,GAAgBA,CAACrG,EAAWmE,IAChCA,GAAQ,GAAK,EAAInE,GAAK,EAAImE,GAAQ,GAG9BiC,GAAyBrD,GAC7BA,EAAQe,GAGJqC,GAAyBhC,GAC7BA,EAAOL,GAGHyC,GAAsBA,CAACvB,EAAO,SACzC,GAAIA,GAAQ,EACV,MAAM,IAAI1F,MAAM,0BAElB,MAAMoH,EAA4B,IAAZ,EAAI1B,GACpB2B,EAAoBC,GAAc5K,KAAK6K,IAAI7K,KAAK6H,IAAM+C,EAAI,KAChE,OACGD,EAAiB,EAAID,GAAgBC,EAAiBD,IAAiB,GAI/DF,GAAoBA,CAC/BrB,EAAgC,GAChChK,EAA4B,CAAA,KAE5B,IAAIgJ,KAAEA,EAAO,KAAQgB,GACjBvJ,OACFA,EAAMwJ,OACNA,EAASmB,KAAqB3B,OAC9BA,EAAS6B,GAA0B,CAAEtC,OAAMa,KAAM,KAC/C7J,EAECS,IACHA,EAASI,KAAKqJ,IAAIrJ,KAAKsJ,KAAKnB,EAAOiB,GAASpJ,KAAKuJ,IAAI,EAAG,IAAM,GAC1D3J,EAAS,GAAM,GAAGA,KAGxB,MAAM8B,GAAU9B,EAAS,GAAK,EACxB4J,EAAO,IAAIpJ,aAAaR,GAC9B,IAAK,IAAIoB,EAAI,EAAGA,GAAKU,EAAQV,IAC3BwI,EAAKxI,GAAKqJ,GAAcrJ,EAAIU,EAAQyG,GAAQS,EAC5CY,EAAK5J,EAAS,EAAIoB,GAAKwI,EAAKxI,GAG9B,OAAOwI,GCpHH,MAAOsB,GAOX5C,WAAAA,CAAmB/I,EAAkC,IACnD,MAAMgJ,KAAEA,EAAO,KAAQhJ,EAEvBiJ,KAAKD,KAAOA,CACd,CAEOG,WAAAA,CAAYH,EAAOC,KAAKD,MAC7B,OAAOgC,GAAsBhC,EAC/B,CAEOK,WAAAA,CAAYzB,GACjB,OAAOqD,GAAsBrD,EAC/B,CAEO0B,GAAAA,CAAIzE,GACT,OAAO+G,GAAwB/G,EAAGoE,KAAKD,KACzC,CAGOQ,OAAAA,CAAQqC,GACb,OAAO,CACT,CAEOjC,SAAAA,CAAUC,GACf,OAAOuB,GAAoBvB,EAC7B,CAEOE,OAAAA,CAAQ/J,EAA4B,IACzC,OAAO8L,GAA4B7C,KAAMjJ,EAC3C,CAEOuK,eAAAA,CAAgBV,EAAO,GAC5B,OAAOyB,GAA0B,CAAEtC,KAAMC,KAAKD,KAAMa,QACtD,CAEOW,aAAAA,GACL,MAAO,CAAC,OACV,EAGK,MAAMoB,GAA0BA,CAAC/G,EAAWmE,IACzC,EAAIA,EAAOnE,GAAM,EAAIA,GAAK,EAAImE,GAAQ,GAGnC8C,GAA8BA,CACzC9B,EAAgC,GAChChK,EAA4B,CAAA,KAE5B,IAAIgJ,KAAEA,EAAO,KAAQgB,GACjBvJ,OACFA,EAAMwJ,OACNA,EAASmB,KAAqB3B,OAC9BA,EAAS6B,GAA0B,CAAEtC,OAAMa,KAAM,KAC/C7J,EAECS,IACHA,EAASI,KAAKqJ,IAAIrJ,KAAKsJ,KAAKnB,EAAOiB,GAASpJ,KAAKuJ,IAAI,EAAG,IAAM,GAC1D3J,EAAS,GAAM,GAAGA,KAGxB,MAAM8B,GAAU9B,EAAS,GAAK,EACxB4J,EAAO,IAAIpJ,aAAaR,GAC9B,IAAK,IAAIoB,EAAI,EAAGA,GAAKU,EAAQV,IAC3BwI,EAAKxI,GAAK+J,GAAwB/J,EAAIU,EAAQyG,GAAQS,EACtDY,EAAK5J,EAAS,EAAIoB,IAAMwI,EAAKxI,GAG/B,OAAOwI,GC5BH,MAAO0B,GAQXhD,WAAAA,CAAmB/I,EAAmC,IACpD,MAAMgJ,KAAEA,EAAO,IAAGgD,GAAEA,EAAK,IAAQhM,EAEjCiJ,KAAK+C,GAAKA,EACV/C,KAAKD,KAAOA,CACd,CAEOG,WAAAA,CAAYH,EAAOC,KAAKD,KAAMgD,EAAK/C,KAAK+C,IAC7C,OAAOC,GAAuBjD,EAAMgD,EACtC,CAEO3C,WAAAA,CAAYzB,EAAeoE,EAAa/C,KAAK+C,IAClD,OAAOE,GAAuBtE,EAAOoE,EACvC,CAEO1C,GAAAA,CAAIzE,GACT,OAAOsH,GAAetH,EAAGoE,KAAKD,KAAMC,KAAK+C,GAC3C,CAEOxC,OAAAA,CAAQC,EAAS,GACtB,OAAO2C,GAAmB,CAAEpD,KAAMC,KAAKD,KAAMS,SAAQuC,GAAI/C,KAAK+C,IAChE,CAEOpC,SAAAA,CAAUC,GACf,OAAOwC,GAAqBxC,EAC9B,CAEOE,OAAAA,CAAQ/J,EAA4B,IACzC,MAAMS,OACJA,EAAMwJ,OACNA,EAAMR,OACNA,EAAS6C,GAA2B,CAClCtD,KAAMC,KAAKD,KACXgD,GAAI/C,KAAK+C,GACTnC,KAAM,KAEN7J,EACJ,OAAOuM,GAAmBtD,KAAM,CAAEgB,SAAQxJ,SAAQgJ,UACpD,CAEOc,eAAAA,CAAgBV,EAAO,GAC5B,OAAOyC,GAA2B,CAAEtD,KAAMC,KAAKD,KAAMgD,GAAI/C,KAAK+C,GAAInC,QACpE,CAEOW,aAAAA,GACL,MAAO,CAAC,OAAQ,KAClB,EAGK,MAAM8B,GAA6BA,CACxCtM,EAA8C,MAE9C,IAAIgJ,KAAEA,EAAO,EAACgD,GAAEA,EAAK,GAAGnC,KAAEA,EAAO,GAAM7J,EACvC,OAAQ,EAAI6J,GAASb,GAAQgD,EAAKvD,IAAoB,EAAIuD,GAAMnL,KAAK6H,MAG1DyD,GAAiBA,CAACtH,EAAWmE,EAAcgD,KAC9C,EAAIA,GAAMd,GAAcrG,EAAGmE,GAAQgD,EAAKzC,GAAY1E,EAAGmE,GAGpDkD,GAAyBA,CAACtE,EAAeoE,EAAK,KAClDpE,GAASoE,EAAKnD,GAAsB,GAGhCoD,GAAyBA,CAACjD,EAAcgD,EAAK,KACjDhD,GAAQgD,EAAKnD,GAAsB,GAG/BuD,GAAsBpM,IACjC,MAAMgJ,KAAEA,EAAO,IAAGS,OAAEA,EAAS,EAACuC,GAAEA,EAAK,IAAQhM,EAC7C,OAAQgJ,EAAOS,GAAUuC,EAAKvD,IAAoB,EAAIuD,GAAMnL,KAAK6H,IAAO,GAG7D2D,GAAuBA,CAACxC,EAAO,MAAQmC,EAAK,KAChDA,EAAK,EAAIZ,GAAoBvB,GAAQC,GAAkBD,GAGnD0C,GAAqBA,CAChCvC,EAAiC,GACjChK,EAA4B,CAAA,KAE5B,IAAIgJ,KAAEA,EAAO,IAAGgD,GAAEA,EAAK,IAAQhC,GAC3BvJ,OACFA,EAAMwJ,OACNA,EAASoC,GAAqB,KAAOL,GAAGvC,OACxCA,EAAS6C,GAA2B,CAAEtD,OAAMgD,KAAInC,KAAM,KACpD7J,EAECyJ,IACHA,EACE,GACEuC,EAAKnL,KAAKoF,MAAMsC,GAAsB1H,KAAK6H,IAAOM,GAChD,EAAIgD,GAAMhD,EAAOnI,KAAK6H,GAAM,IAG/BjI,IACHA,EAASI,KAAKqJ,IAAIrJ,KAAKsJ,KAAKnB,EAAOiB,GAASpJ,KAAKuJ,IAAI,EAAG,IAAM,GAC1D3J,EAAS,GAAM,GAAGA,KAGxB,MAAM8B,GAAU9B,EAAS,GAAK,EACxB4J,EAAO,IAAIpJ,aAAaR,GAC9B,IAAK,IAAIoB,EAAI,EAAGA,GAAKU,EAAQV,IAC3BwI,EAAKxI,GAAKsK,GAAetK,EAAIU,EAAQyG,EAAMgD,GAAMvC,EACjDY,EAAK5J,EAAS,EAAIoB,GAAKwI,EAAKxI,GAG9B,OAAOwI,GCrIH,MAAOmC,GAYXzD,WAAAA,CAAmB/I,EAA6C,IAC9D,MAAMgJ,KAAEA,EAAO,IAAGyD,MAAEA,EAAQ,IAAQzM,EAEpCiJ,KAAKD,KAAOA,EACZC,KAAKwD,MAAQA,CACf,CAEOtD,WAAAA,CAAYH,EAAOC,KAAKD,MAC7B,OAAO0D,GAAiC1D,EAC1C,CAEOK,WAAAA,CAAYzB,GACjB,OAAO+E,GAAiC/E,EAC1C,CAEO0B,GAAAA,CAAIzE,GACT,OAAO+H,GAAyB/H,EAAGoE,KAAKD,KAAMC,KAAKwD,MACrD,CAEOjD,OAAAA,CAAQC,EAAS,GACtB,OAAOoD,GAA6B,CAClC7D,KAAMC,KAAKD,KACXS,SACAgD,MAAOxD,KAAKwD,OAEhB,CAEO7C,SAAAA,CAAUC,GACf,OAAOiD,GAA+BjD,EACxC,CAEOE,OAAAA,CAAQ/J,EAA4B,IACzC,OAAO+M,GAA6B9D,KAAMjJ,EAC5C,CAEOuK,eAAAA,CAAgBV,EAAO,GAC5B,MAAM4C,MAAEA,EAAKzD,KAAEA,GAASC,KACxB,OAAO+D,GAAqC,CAAEhE,OAAMa,OAAM4C,SAC5D,CAEOjC,aAAAA,GACL,MAAO,CAAC,OAAQ,QAClB,EAGK,MAAMwC,GAAuCA,EAClDhE,OAAO,EACPyD,QAAQ,EACR5C,OAAO,KAECA,EAAOb,GAAQ,QAAU,QAAWyD,GAAU,EAM3CI,GACX7M,IAEA,MAAMgJ,KAAEA,EAAO,IAAGS,OAAEA,EAAS,EAACgD,MAAEA,EAAQ,GAAMzM,EAC9C,OAAQyJ,EAAST,GAAQ,QAAU,QAAWyD,GAAU,GAG7CG,GAA2BA,CACtC/H,EACAmE,EACAyD,KAEA,MAAMQ,GAAM,EAAIpI,EAAKmE,IAAS,EAC9B,OAAQ,EAAIyD,IAAU,EAAIQ,GAAMR,GAAS,EAAIQ,EAAI,IAAO,EAAIA,EAAIA,GAAK,IAG1DN,GAAoC/E,GACxCA,EAAQe,GAGJ+D,GAAoC1D,GACxCA,EAAOL,GAGHmE,GAAiCA,CAACjD,EAAO,SACpD,GAAIA,GAAQ,EACV,MAAM,IAAI1F,MAAM,0BAElB,MAAMoH,EAA4B,IAAZ,EAAI1B,GACpB2B,EAAoBC,GAAc5K,KAAK6K,IAAI7K,KAAK6H,IAAM+C,EAAI,KAChE,OACGD,EAAiB,EAAID,GAAgBC,EAAiBD,IAAiB,GAQ/DwB,GAA+BA,CAC1C/C,EAA2C,GAC3ChK,EAAwC,CAAA,KAExC,IAAIgJ,KAAEA,EAAO,IAAGyD,MAAEA,EAAQ,GAAMzC,GAC5BvJ,OACFA,EAAMwJ,OACNA,EAAS6C,KAAgCrD,OACzCA,EAASuD,GAAqC,CAAEhE,OAAMa,KAAM,EAAG4C,WAC7DzM,EAECS,IACHA,EAASI,KAAKqJ,IAAIrJ,KAAKsJ,KAAKnB,EAAOiB,GAASpJ,KAAKuJ,IAAI,EAAG,IAAM,GAC1D3J,EAAS,GAAM,GAAGA,KAGxB,MAAM8B,GAAU9B,EAAS,GAAK,EACxB4J,EAAO,IAAIpJ,aAAaR,GAC9B,IAAK,IAAIoB,EAAI,EAAGA,GAAKU,EAAQV,IAC3BwI,EAAKxI,GAAK+K,GAAyB/K,EAAIU,EAAQyG,EAAMyD,GAAShD,EAC9DY,EAAK5J,EAAS,EAAIoB,GAAKwI,EAAKxI,GAG9B,OAAOwI,GC7JH,SAAU6C,GAAWlD,GACzB,MAAMmD,KAAEA,GAASnD,EAEjB,OAAQmD,GACN,IAAK,WACH,OAAO,IAAIrE,GAASkB,GACtB,IAAK,aACH,OAAO,IAAIe,GAAWf,GACxB,IAAK,cACH,OAAO,IAAI+B,GAAY/B,GACzB,IAAK,uBACH,OAAO,IAAI2B,GAAqB3B,GAClC,IAAK,wBACH,OAAO,IAAIwC,GAAsBxC,GACnC,QACE,MAAM7F,MAAM,wBAAwBgJ,KAG1C,CCvBM,SAAUC,GAAO5N,EAAgB6N,GACrC,IAAK7N,EACH,MAAM,IAAI2E,MAAMkJ,GAAoB,cAExC,CCFO,MAAMC,GAAoB,CAC/BzI,EAAG,CACD0I,KAAOC,GAAeA,EAAK3I,EAC3BqF,IAAKA,CAACsD,EAAYC,IAChBD,EAAK3I,EAAqB,EAAjB4I,EAAUzE,KACrB0E,IAAKA,CAACF,EAAYC,IAChBD,EAAK3I,EAAqB,EAAjB4I,EAAUzE,KACrB2E,mBAAoBA,CAACH,EAAYC,IACd,KAAjBA,EAAUzE,MAEd7C,EAAG,CACDoH,KAAOC,GAAeA,EAAKrH,EAC3B+D,IAAMsD,GAAgBA,EAAKrH,EAAI,GAAI,IAAO,EAC1CuH,IAAMF,GAAgBA,EAAKrH,EAAI,EAAI,EAAI,IACvCwH,mBAAoBA,IAAM,MAE5B3E,KAAM,CACJuE,KAAMA,CAACC,EAAYC,IAA+BA,EAAUzE,KAC5DkB,IAAKA,CAACsD,EAAYC,IAAgD,IAAjBA,EAAUzE,KAC3D0E,IAAKA,CAACF,EAAYC,IAAgD,EAAjBA,EAAUzE,KAC3D2E,mBAAoBA,CAACH,EAAYC,IACd,KAAjBA,EAAUzE,MAEdgD,GAAI,CACFuB,KAAMA,CAACC,EAAYC,IAA2BA,EAAUzB,GACxD9B,IAAKA,IAAM,EACXwD,IAAKA,IAAM,EACXC,mBAAoBA,IAAM,KAE5BlB,MAAO,CACLc,KAAMA,CAACC,EAAYC,IACjBA,EAAUhB,OAAS,GACrBvC,IAAKA,KAAM,EACXwD,IAAKA,IAAM,EACXC,mBAAoBA,IAAM,MC/BxBC,GAAyB,CAAC,OAAQ,MAAO,MAAO,sBAiHtD,SAASC,GACPrO,EACAsO,EACAC,EACAC,GAEA,MAAkB,MAAdF,EACe,uBAAbC,EACKvO,EAEAA,EAAQwO,EAGZxO,CACT,CCzIA,MAAMJ,GAAWC,OAAOC,UAAUF,SAmB5B,SAAUG,GAAWC,GACzB,MAAMC,EAAML,GAASM,KAAKF,GAC1B,OAAOC,EAAIE,SAAS,YAAcF,EAAIG,SAAS,MACjD,CCHc,SAAUqO,GACtB5D,EACArK,GAEA,MAAMkO,QACJA,EAAOC,cACPA,EAAajN,QACbA,EAAU,EAACkN,QACXA,EAAU,IAAIC,cACdA,EAAgB,GAAEC,gBAClBA,EAAkB,EAACC,cACnBA,EAAgB,IAAGC,eACnBA,EAAiB,KAAIC,kBACrBA,GAAoB,EAAKd,mBACzBA,EAAqB,GAAKe,qBAC1BA,EAAuB,MACrB1O,EACJ,IAAI2O,UAAEA,EAASC,UAAEA,GAAc5O,EAE/B,GAAIoO,GAAW,EACb,MAAM,IAAIjK,MAAM,gDACX,IAAKkG,EAAKxF,IAAMwF,EAAKlE,EAC1B,MAAM,IAAIhC,MAAM,iDACX,IACJ5E,GAAW8K,EAAKxF,IACjBwF,EAAKxF,EAAEpE,OAAS,IACflB,GAAW8K,EAAKlE,IACjBkE,EAAKlE,EAAE1F,OAAS,EAEhB,MAAM,IAAI0D,MACR,wEAEG,GAAIkG,EAAKxF,EAAEpE,SAAW4J,EAAKlE,EAAE1F,OAClC,MAAM,IAAI0D,MAAM,uDAGlB,KAAMgK,GAAiBA,EAAc1N,OAAS,GAC5C,MAAM,IAAI0D,MACR,8DAGJ,MAAM0K,EAAavN,MAAM0D,KAAKmJ,GAExBW,EAASD,EAAWpO,OAI1B,GAHAmO,EAAYA,GAAa,IAAItN,MAAMwN,GAAQC,KAAK3O,OAAO4O,kBACvDL,EAAYA,GAAa,IAAIrN,MAAMwN,GAAQC,KAAK3O,OAAO6O,kBAEnDL,EAAUnO,SAAWkO,EAAUlO,OACjC,MAAM,IAAI0D,MAAM,iDAGlB,MAAM+K,EA6BR,SACEvB,EACAkB,GAEA,GAAkC,iBAAvBlB,EACT,OAAO,IAAIrM,MAAMuN,EAAWpO,QAAQsO,KAAKpB,GACpC,GAAIpO,GAAWoO,GAAqB,CACzC,MAAMmB,EAASD,EAAWpO,OAC1B,OAAIkN,EAAmBlN,SAAWqO,EACzB,IAAIxN,MAAMwN,GAAQC,KAAKpB,EAAmB,IAE5CrM,MAAM0D,KAAK2I,EACpB,CAEA,MAAM,IAAIxJ,MACR,+FAEJ,CA9CkCgL,CAC9BxB,EACAkB,GAGIO,EA2CR,SACElO,EACAmO,GAEA,GAAuB,iBAAZnO,EAAsB,CAC/B,MAAM1B,EAAQ,EAAI0B,GAAW,EAC7B,MAAO,IAAM1B,CACf,CAAO,GAAID,GAAW2B,GAAU,CAC9B,GAAIA,EAAQT,OAAS4O,EAAY,CAC/B,MAAM7P,EAAQ,EAAI0B,EAAQ,IAAM,EAChC,MAAO,IAAM1B,CACf,CAEA,OAAQqC,GAAc,EAAIX,EAAQW,IAAM,CAC1C,CAEA,MAAM,IAAIsC,MACR,qFAEJ,CA9DiBmL,CAAUpO,EAASmJ,EAAKxF,EAAEpE,QACnC8O,EA+DR,SAAyBrB,GACvB,QAAgB1N,IAAZ0N,EAAuB,CACzB,GAAuB,iBAAZA,EACT,MAAM,IAAI/J,MAAM,8BAElB,MAAMqL,EAAUC,KAAKC,MAAkB,IAAVxB,EAC7B,MAAO,IAAMuB,KAAKC,MAAQF,CAC5B,CACE,MAAO,KAAM,CAEjB,CAzEuBG,CAAgBzB,GAMrC,MAAO,CACLqB,eACAZ,YACAC,YACAC,aACAe,aATmBtO,MAAM0D,KAAK,CAAEvE,OAAQ4J,EAAKxF,EAAEpE,QAAU,CAACoP,EAAGhO,IAC7DuN,EAAOvN,IASPuM,UACAC,gBACAC,kBACAC,gBACAC,iBACAC,oBACAd,mBAAoBuB,EACpBR,uBAEJ,CCvFc,SAAUoB,GACtBzF,EACAwE,EACAkB,EACAH,GAEA,IAAII,EAAQ,EACZ,MAAMC,EAAOF,EAAsBlB,GACnC,IAAK,IAAIhN,EAAI,EAAGA,EAAIwI,EAAKxF,EAAEpE,OAAQoB,IACjCmO,IAAU3F,EAAKlE,EAAEtE,GAAKoO,EAAK5F,EAAKxF,EAAEhD,MAAQ,EAAI+N,EAAa/N,GAG7D,OAAOmO,CACT,CCSc,SAAUE,GACtB7F,EACA8F,EACA/B,EACAT,EACAoC,EACAtB,EACAvN,GAEA,MAAMkP,EAAWlL,GAAOmL,IAAIF,EAAO1P,OAAQ0P,EAAO1P,OAAQ2N,GAEpD6B,EAAOF,EAAsBI,GAE7BG,EAAgB,IAAIrP,aAAaoJ,EAAKxF,EAAEpE,QAC9C,IAAK,IAAIoB,EAAI,EAAGA,EAAIwI,EAAKxF,EAAEpE,OAAQoB,IACjCyO,EAAczO,GAAKoO,EAAK5F,EAAKxF,EAAEhD,IAGjC,MAAM0O,ECtCM,SACZlG,EACAiG,EACAH,EACAxC,EACA6C,EACA/B,GAEA,MAAMgC,EAAWN,EAAO1P,OAClBiQ,EAAWrG,EAAKxF,EAAEpE,OAClBO,EAAMkE,GAAOyL,MAAMF,EAAUC,GAEnC,IAAIE,EAAW,EACf,IAAK,IAAIC,EAAQ,EAAGA,EAAQJ,EAAUI,IAAS,CAC7C,GAAkC,IAA9BlD,EAAmBkD,GAAc,SACrC,IAAIC,EAAQnD,EAAmBkD,GAC3BE,EAAYZ,EAAO9M,QACvB0N,EAAUF,IAAUC,EACpB,MAAME,EAAYR,EAAcO,GAChC,GAAKtC,EAQE,CACLsC,EAAYZ,EAAO9M,QACnB0N,EAAUF,IAAUC,EACpBA,GAAS,EACT,MAAMG,EAAaT,EAAcO,GACjC,IAAK,IAAIG,EAAQ,EAAGA,EAAQR,EAAUQ,IACpClQ,EAAImQ,IACFP,EACAM,GACCD,EAAW5G,EAAKxF,EAAEqM,IAAUF,EAAU3G,EAAKxF,EAAEqM,KAAWJ,EAG/D,MAnBE,IAAK,IAAII,EAAQ,EAAGA,EAAQR,EAAUQ,IACpClQ,EAAImQ,IACFP,EACAM,GACCZ,EAAcY,GAASF,EAAU3G,EAAKxF,EAAEqM,KAAWJ,GAgB1DF,GACF,CAEA,OAAO5P,CACT,CDNuBoQ,CACnB/G,EACAiG,EACAH,EACAxC,EACAoC,EACAtB,GAEI4C,EAhDR,SAAwBhH,EAAciG,GACpC,MAAMnP,EAAIkJ,EAAKxF,EAAEpE,OAEXO,EAAM,IAAIkE,GAAO/D,EAAG,GAE1B,IAAK,IAAI+P,EAAQ,EAAGA,EAAQ/P,EAAG+P,IAC7BlQ,EAAImQ,IAAID,EAAO,EAAG7G,EAAKlE,EAAE+K,GAASZ,EAAcY,IAElD,OAAOlQ,CACT,CAuCwBsQ,CAAejH,EAAMiG,GAErCiB,EAAgBnM,GACpBgL,EAASoB,IACPjB,EAAakB,KACXlB,EAAamB,YAAYC,MAAM,MAAO,CAAEA,MAAOzQ,OAK/C0Q,EAA8BrB,EAAakB,KAC/CJ,EAAcM,MAAM,MAAO,CAAEA,MAAOzQ,KAKtC,MAAO,CACL2Q,cAHoBN,EAAcE,KAAKG,GAIvCA,8BAEJ,CE/DM,SAAUE,GACdzH,EACA0F,EACA/P,GAEA,MAAM+R,EAAiB9D,GAAa5D,EAAMrK,IACpCuP,aACJA,EAAYZ,UACZA,EAASC,UACTA,EAASC,WACTA,EAAUe,aACVA,EAAYvB,cACZA,EAAaC,gBACbA,EAAeC,cACfA,EAAaC,eACbA,EAAcC,kBACdA,EAAiBd,mBACjBA,EAAkBe,qBAClBA,GACEqD,EACJ,IAAI3D,EAAU2D,EAAe3D,QAEzB4B,EAAQF,GACVzF,EACAwE,EACAkB,EACAH,GAEEoC,EAAehC,EACfiC,EAAoBpD,EAAWxL,QAE/B6O,EAAYlC,GAASxB,EAErB2D,EAAY,EAChB,KAAOA,EAAY5D,IAAkB2D,EAAWC,IAAa,CAC3D,MAAMC,EAAgBpC,GAEhB6B,cAAEA,EAAaD,4BAAEA,GAAgC1B,GACrD7F,EACAwE,EACAT,EACAT,EACAoC,EACAtB,EACAmB,GAGF,IAAK,IAAIlN,EAAI,EAAGA,EAAImM,EAAWpO,OAAQiC,IACrCmM,EAAWnM,GAAK7B,KAAKqJ,IACnBrJ,KAAK6M,IAAIiB,EAAUjM,GAAImM,EAAWnM,GAAKmP,EAAcQ,IAAI3P,EAAG,IAC5DkM,EAAUlM,IAWd,GAPAsN,EAAQF,GACNzF,EACAwE,EACAkB,EACAH,GAGE0C,MAAMtC,GAAQ,MAEdA,EAAQgC,EAAexD,IACzBwD,EAAehC,EACfiC,EAAoBpD,EAAWxL,SAgBjC,GALE+K,GAPCgE,EAAgBpC,GACjB6B,EACGH,YACAD,KAAKI,EAAcU,IAAInE,GAASoD,IAAII,IACpCS,IAAI,EAAG,GAEY3D,EACZ7N,KAAK6M,IAAIU,EAAUE,EAAiB,MAEpCzN,KAAKqJ,IAAIkE,EAAUC,EAAe,KAG1CkB,IACF,MAAM,IAAIpL,MACR,iCAAiCnE,EAAQkO,mBAI7CgE,EAAYlC,GAASxB,CACvB,CAEA,MAAO,CACLgE,gBAAiBP,EACjBQ,eAAgBT,EAChBU,WAAYP,EAEhB,CCxGe,SAASQ,GAAoB9N,EAAGsB,GAC7C,GAAItB,EAAEpE,SAAW0F,EAAE1F,OACjB,MAAM,IAAIH,WAAW,4CAGvB,MAAMoQ,EAAW7L,EAAEpE,OAAS,EAC5B,GAAiB,IAAbiQ,EAAgB,MAAO,CAAC,GAC5B,GAAiB,IAAbA,EAAgB,MAAO,CAAC,EAAG,GAE/B,IAAIkC,EAAe,EACf7M,EAAS,IAAIzE,MAAMuD,EAAEpE,QAAQsO,MAAK,GACtC,OAAa,CACX,MAAMlM,EAAI+P,EACJ9P,EAAI+P,GAAOD,EAAclC,EAAU3K,GACnC+M,EAAID,GAAOA,GAAOD,EAAclC,EAAU3K,GAAS2K,EAAU3K,GAanE,GAVElB,EAAEiO,IAAM3M,EAAEtD,GAAKsD,EAAErD,IAAM+B,EAAEhC,IAAMsD,EAAErD,GAAKqD,EAAE2M,IAAMjO,EAAE/B,IAAMqD,EAAE2M,GAAK3M,EAAEtD,KAEzC,EAGtB+P,EAAe9P,GAEfiD,EAAOjD,IAAK,EACZ8P,EAAeG,GAASH,EAAclC,EAAU3K,IAE9C+M,IAAMpC,EAAU,KACtB,CAEA,OAAO3K,EACJiN,IAAI,CAACC,EAAMzO,KAAoB,IAATyO,GAAyBzO,GAC/C0O,OAAQD,IAAkB,IAATA,EACtB,CAUA,SAASF,GAASH,EAAclC,EAAUyC,GACxC,IAAIC,EAAUR,EAAe,EAC7B,MAA2B,IAApBO,EAAOC,IAAoBA,IAClC,OAAwB,IAAjBR,EAAqBlC,EAAW0C,CACzC,CAEA,SAASP,GAAOD,EAAclC,EAAUyC,GACtC,IAAIC,EAAUR,EAAe,EAC7B,MAA2B,IAApBO,EAAOC,IAAoBA,IAClC,OAAOR,IAAiBlC,EAAW,EAAI0C,CACzC,CCmQA,SAASC,GACPC,EACAC,EACAC,EACAC,GAEA,IAAIR,EAAO,GACX,IAAK,IAAIpR,EAAI,EAAGA,EAAIyR,EAAe7S,OAAQoB,IACzCoR,EAAKpR,GACHhB,KAAK0D,IAAI+O,EAAezR,IAAM4R,EAAmBD,IACjDD,EAAkB1R,GAEtB,MAAMqI,EAAM3E,GAAU0N,GAEtB,OADaA,EAAKS,UAAW7O,GAAMA,IAAMqF,EAE3C,CCjUM,SAAUyJ,GACdtJ,EACAuJ,EACA5T,GAEA,MAAM2O,UACJA,EAASC,UACTA,EAASL,cACTA,EAAasF,QACbA,EAAOC,UACPA,EAASC,WACTA,EAAUC,aACVA,GACEhU,EACEiU,EAyBR,SACE5J,EACAuJ,GAEA,MAAM/O,EAAEA,EAACsB,EAAEA,GAAMkE,EACXqG,EAAW7L,EAAEpE,OACnB,OAAQoO,IACN,MAAMvF,EAAMsK,EAAY/E,GACxB,IAAImB,EAAQ,EACZ,IAAK,IAAInO,EAAI,EAAGA,EAAI6O,EAAU7O,IAC5BmO,IAAU7J,EAAEtE,GAAKyH,EAAIzE,EAAEhD,MAAQ,EAEjC,OAAOmO,EAEX,CAvC4BkE,CAAqB7J,EAAMuJ,GAC/C7N,EDLO,SACbkO,EACAE,EACAC,EACApU,EAAU,CAAA,GAEV,MAAM0S,WACJA,EAAa,GAAEmB,QACfA,EAAU,KAAIC,UACdA,EAAY,MAAKC,WACjBA,EAAa,MAAKC,aAClBA,EAAe,CAAA,GACbhU,EAEJ,QACwBQ,IAAtByT,QACoBzT,IAApB2T,QACoB3T,IAApB4T,EAEA,MAAM,IAAI9T,WAAW,gCAMvB,GAHA6T,EAAkB,IAAIlT,aAAakT,GACnCC,EAAkB,IAAInT,aAAamT,GAE/BD,EAAgB1T,SAAW2T,EAAgB3T,OAC7C,MAAM,IAAI0D,MACR,kEAOJ,IAAI/C,EAAI+S,EAAgB1T,OACpB4T,EAAcD,EAAgBpB,IAAI,CAACnO,EAAGhD,IAAMgD,EAAIsP,EAAgBtS,KAEhEyS,mBACFA,EAAqB,EAACC,gBACtBA,EAAkB,EAACC,mBACnBA,EAAqB,CAAC,IAAIvT,aAAaG,GAAG2N,KAAK,KAAK0F,YACpDA,EAAc,IAAIxT,aAAaG,GAAG4R,IAAI,CAACxT,EAAOgF,IAE1C2P,EAAgB3P,GAChBgQ,EAAmB,GAAGhQ,GAAS6P,EAAY7P,IAE7CiP,iBACFA,EAAmBQ,EAAkBQ,GAAYC,OACjDA,EAAS,EAACC,gBACVA,EAAkB,EAACC,UACnBA,EAAY,CAAC,IAAI3T,aAAaG,GAAG2N,KAAK,KAAKwE,kBAC3CA,EAAoB,CAAC1S,KAAKoF,KAAS,IAAJ7E,IAAckS,eAC7CA,EAAiB,CAACG,GAAiBoB,mBACnCA,EAAqBtB,EAAiBuB,wBACtCA,EAA0B,CAACrB,GAAiBD,YAC5CA,GACEQ,EACJ,GACEA,EAAae,qBACbf,EAAae,oBAAoBtU,OAAS,EAC1C,CACAgT,EAAmBlO,GAAU+N,GAC7BE,EACEK,EAAUhT,KAAK0D,IAAIkP,GAAoB,KACnCI,EAAUhT,KAAK0D,IAAIkP,GACnB,KAENkB,EAAkBtB,GAChBC,EACAC,EACAC,EACAC,GAGFe,EAAqBR,EAAae,oBAAoB1R,QACtD,IAAK,IAAI7B,EAAI,EAAGA,EAAIgT,EAAmB/T,OAAQe,IAC7C,IAAK,IAAIK,EAAI,EAAGA,EAAIsS,EAAgB1T,OAAQoB,IAC1C2S,EAAmBhT,GAAGK,IACnB2S,EAAmBhT,GAAGK,GAAKsS,EAAgBtS,IAAMwS,EAAYxS,EAGtE,CAEA,IAAIsQ,EAAY,EAKhB,KAAOA,EAAYO,GAAY,CAK7B,IAiBIsC,EAAoBC,EAjBpBC,EAAK,GACLC,EAAMN,EAAmBnB,UAE1B0B,GAAMA,IAAM7B,EAAkBoB,IAE7BvB,EAAU,EACd,IAAK,IAAIvR,EAAIsT,EAAKtT,EAAIgT,EAAmBpU,OAAQoB,IAC/C,IAAK,IAAIwT,EAAI,EAAGA,EAAI/B,EAAe7S,OAAQ4U,IAEtC/B,EAAe+B,KAAOP,EAAwBjT,GAC9C0R,EAAkB8B,KAAOR,EAAmBhT,KAE7CqT,EAAG9B,KAAaiC,GAMtB,GAAIR,EAAmBpU,OAAS0U,EAAM,EAAG,CACvC,IAAIG,EAAK/B,EAAkBoB,GACvBY,EAAKjC,EAAeqB,GACpBa,EAAKX,EAAmBA,EAAmBpU,OAAS,GAEpDgV,GADKX,EAAwBD,EAAmBpU,OAAS,GAC3C8U,IAAOC,EAAKF,GAC1BI,EAAWH,EAAKE,EAAQH,EACxBK,EAAK,IAAIC,YAAYxC,GACzBA,EAAU,EACV,IAAK,IAAIvR,EAAI,EAAGA,EAAI8T,EAAGlV,OAAQoB,IAAK,CAClC,IAAIL,EAAI0T,EAAGrT,GAETyR,EAAe9R,IACfiU,EAAQlC,EAAkB/R,GAAKkU,EAAW3B,IAE1C4B,EAAGvC,KAAa5R,EAEpB,CAEA,IAAIqU,EAAQ,GACRC,EAAQ,GACZ,IAAK,IAAIjU,EAAI,EAAGA,EAAIuR,EAASvR,IAC3BgU,EAAMnP,KAAK6M,EAAkBoC,EAAG9T,KAChCiU,EAAMpP,KAAK4M,EAAeqC,EAAG9T,KAG/B,IAAIkU,EAAiBpD,GAAoBkD,EAAOC,GAEhDb,EAAK,GACL,IAAK,IAAIpT,EAAI,EAAGA,EAAIkU,EAAetV,OAAQoB,IACzCoT,EAAGvO,KAAKiP,EAAGI,EAAelU,IAE9B,MACEoT,EAAKC,EAAG7R,MAAM,EAAG+P,GAEnB4B,EAAqBC,EAIrB,IAAK,IAAIvS,EAAI,EAAGA,EAAIsS,EAAmBvU,OAAQiC,IAAK,CAClD,IAAIlB,EAAIwT,EAAmBtS,GACvBsT,EAAa3Q,GAAUuP,EAAUpT,IACjCyU,EAAkB,IAAIL,YAAYhB,EAAUpT,GAAGf,QACnD2S,EAAU,EACV,IAAK,IAAIvR,EAAI,EAAGA,EAAI+S,EAAUpT,GAAGf,OAAQoB,IACnChB,KAAK0D,IAAIqQ,EAAUpT,GAAGK,GAAKmU,GAAclC,IAC3CmC,EAAgB7C,KAAavR,GAGjC,IAAIiP,EAAS,EAAIkF,EAAc,EAC3BE,EAAqB,GACzB,IAAK,IAAIC,EAAI,EAAGA,EAAI/C,EAAS+C,IAAK,CAChC,IAAItU,EAAIoU,EAAgBE,GACpBC,EAAoB5B,EAAmBhT,GAAG6B,QAC1CgT,EAAqB7B,EAAmBhT,GAAG6B,QAC/C+S,EAAkBvU,IAAMiP,EACxBuF,EAAmBxU,IAAMiP,EACzB,IAAIwF,EAAmB,IAAIrV,aAAamV,EAAkB3V,QACtD8V,EAAoB,IAAItV,aAAaoV,EAAmB5V,QAC5D,IAAK,IAAIoB,EAAI,EAAGA,EAAIuU,EAAkB3V,OAAQoB,IAC5CyU,EAAiBzU,GACfsS,EAAgBtS,GAAKuU,EAAkBvU,GAAKwS,EAAYxS,GAC1D0U,EAAkB1U,GAChBsS,EAAgBtS,GAAKwU,EAAmBxU,GAAKwS,EAAYxS,GAE7D,IAAI2U,EAAgBvC,EAAkBqC,GAClCG,EAAiBxC,EAAkBsC,GACvC7B,GAAU,EACVwB,EAAmBxP,KAAK,CACtBlB,SAAU3E,KAAKqJ,IAAIsM,EAAeC,GAClCjS,MAAO2R,IAGT3B,EAAmB9N,KAAK0P,EAAmBC,GAC3C/C,EAAe5M,KAAK8P,EAAeC,EACrC,CAEA,IAAI3T,EAAIoT,EAAmBQ,KAAK,CAAC7T,EAAGC,IAAMD,EAAE2C,SAAW1C,EAAE0C,UACzD,IAAK,IAAI2Q,EAAI,EAAGA,EAAI/C,EAAS+C,IAAK,CAChC,IAAIlJ,EAAIgJ,EAAgBnT,EAAEqT,GAAG3R,OACzBmS,EAAMrC,EAAqB,GAAKxR,EAAEqT,GAAG3R,MAAQ,GAAK,EAClDoS,EAAMtC,EAAqB,GAAKxR,EAAEqT,GAAG3R,MAAQ,GACjDoQ,EAAUpT,GAAGyL,GAAK6D,EAAQ,EAC1B8D,EAAU+B,GAAO/B,EAAUpT,GAAG6B,QAC9BuR,EAAUgC,GAAOhC,EAAUpT,GAAG6B,QAC9BkQ,EAAkB/R,GAAKsE,GAAM8O,EAAUpT,IACvC+R,EAAkBoD,GAAOpD,EAAkB/R,GAC3C+R,EAAkBqD,GAAOrD,EAAkB/R,EAC7C,CACA8S,GAAsB,EAAIlB,CAC5B,CAMAK,EAAmBlO,GAAU+N,GAE7BE,EACEK,EAAUhT,KAAK0D,IAAIkP,GAAoB,KACnCI,EAAUhT,KAAK0D,IAAIkP,GACnB,KAENkB,EAAkBtB,GAChBC,EACAC,EACAC,EACAC,GAIFoB,EAAqBvT,MAAM0D,KAAK,IAAI6R,IAAItD,IACxCsB,EAAqBA,EAAmB6B,KAAK,CAAC7T,EAAGC,IAAMD,EAAIC,GAE3DgS,EAA0B,GAC1B,IAAK,IAAIjT,EAAI,EAAGA,EAAIgT,EAAmBpU,OAAQoB,IAAK,CAClD,IAAIiV,EACAtR,EAAWpF,OAAOsE,kBACtB,IAAK,IAAIhC,EAAI,EAAGA,EAAI6Q,EAAkB9S,OAAQiC,IACxC6Q,EAAkB7Q,KAAOmS,EAAmBhT,IAC1CyR,EAAe5Q,GAAK8C,IACtBA,EAAW8N,EAAe5Q,GAC1BoU,EAAWpU,GAIjBoS,EAAwBpO,KAAK4M,EAAewD,GAC9C,CAGA,IAAK,IAAItV,EAAI,EAAGA,EAAI8R,EAAe7S,OAAQe,IACzC,GAAI8R,EAAe9R,KAAOiS,EAAkB,CAC1C,IAAIzP,EAAO,GACX,IAAK,IAAInC,EAAI,EAAGA,EAAIsS,EAAgB1T,OAAQoB,IAC1CmC,EAAK0C,KACHyN,EAAgBtS,GAAK2S,EAAmBhT,GAAGK,GAAKwS,EAAYxS,GAIlE,CAEFsQ,GAAa,CACf,CAKA,IAAIpM,EAAS,CAAA,EACbA,EAAOgR,iBAAmBtD,EAC1B1N,EAAO2M,WAAaP,EACpB,IAAI4C,EAAsB,GAC1B,IAAK,IAAIvT,EAAI,EAAGA,EAAI8S,EAAqB,EAAG9S,IAAK,CAC/C,IAAIwV,EAAO,GACX,IAAK,IAAInV,EAAI,EAAGA,EAAIsS,EAAgB1T,OAAQoB,IAC1CmV,EAAKtQ,KAAKyN,EAAgBtS,GAAK2S,EAAmBhT,GAAGK,GAAKwS,EAAYxS,IAExEkT,EAAoBrO,KAAKsQ,EAC3B,CAEAjR,EAAOkR,WAAa,CAClB3C,qBACAC,gBAAkBA,GAAmB7B,EACrCqC,sBACAN,cACAC,SACAC,kBACAC,YACArB,oBACAD,iBACAuB,qBACAC,0BACAtB,eAGF,IAAI0D,EAAY,GAChB,IAAK,IAAIrV,EAAI,EAAGA,EAAIyR,EAAe7S,OAAQoB,IACrCyR,EAAezR,KAAO4R,GACxByD,EAAUxQ,KAAKqO,EAAoBlT,IAKvC,OADAkE,EAAOoR,OAASD,EACTnR,CACT,CCjSiBqR,CACbnD,EAGAtF,EACAC,EACA,CACE8D,WAAYnE,EACZsF,UACAC,YACAC,aACAC,kBAIEmD,OAAEA,GAAWpR,EAEnB,MAAO,CACL0M,eAAgB1M,EAAOgR,iBACvBrE,WAAY3M,EAAO2M,WACnBF,gBAAiB2E,EAAO,GAE5B,CCiEM,SAAUE,GACdhN,EACA3C,EACA1H,EAA2B,CAAA,GAO3B,MAAM0N,EClHF,SACJtK,EACApD,EAAkC,IAElCiE,EAAOb,GACP,MAAM0B,UAAEA,EAASC,QAAEA,GAAYH,EAAgBxB,EAAOpD,GACtD,IAAIsF,EAAWlC,EAAM0B,GAErB,IAAK,IAAIjD,EAAIiD,EAAY,EAAGjD,GAAKkD,EAASlD,IACpCuB,EAAMvB,IAAM,EACVuB,EAAMvB,GAAKyD,IACbA,EAAWlC,EAAMvB,KAETuB,EAAMvB,GAAKyD,IACrBA,GAAYlC,EAAMvB,IAGtB,OAAOyD,CACT,CDgGcgS,CAAkBjN,EAAKlE,GAC7B6H,EAAiB,IAARN,EAAY,EAAIA,EAEzB6J,EVnGF,SACJ7P,EACAsG,EACAhO,EAA2B,CAAA,GAE3B,IAAIwE,EAAQ,EACZ,MAAM+S,EAAgC,GAEhCC,EAAkB9P,EAAMsL,IAAKxF,IAC1B,IACFA,EACHrH,EAAGqH,EAAKrH,EAAI6H,KAIhB,IAAK,MAAMR,KAAQgK,EAAiB,CAClC,MAAM3P,GAAEA,EAAEmC,MAAEA,GAAQhK,EAAQgK,MAAQhK,EAAQgK,MAAQ,CAAEmD,KAAM,cAC1DK,EAEIiK,EAA4BvK,GAAWlD,GAEvC6E,EAA0B,CAAC,IAAK,OAAQ4I,EAASjN,iBAEjDkN,EAA+C,CACnDxN,IAAK,GACLwD,IAAK,GACLH,KAAM,GACNI,mBAAoB,IAGtB,IAAK,MAAMG,KAAae,EACtB,IAAK,MAAMd,KAAYH,GAAY,CAEjC,IAAI+J,EAAgBnK,GAAMqB,aAAaf,KAAaC,GACpD,QAAsBvN,IAAlBmX,EAA6B,CAC/BA,EAAgB9J,GACd8J,EACA7J,EACAC,EACAC,GAGF0J,EAAiB3J,GAAUrH,KAAKiR,GAChC,QACF,CAGA,IAAIC,EACF5X,GAAS6O,aAAaf,KAAaC,GACrC,QAA8BvN,IAA1BoX,EAAqC,CACvC,GAAqC,iBAA1BA,EAAoC,CAC7CA,EAAwB/J,GACtB+J,EACA9J,EACAC,EACAC,GAEF0J,EAAiB3J,GAAUrH,KAAKkR,GAChC,QACF,CAAO,CACL,IAAIpY,EAAQoY,EAAsBpK,GAClChO,EAAQqO,GAAmBrO,EAAOsO,EAAWC,EAAUC,GACvD0J,EAAiB3J,GAAUrH,KAAKlH,GAChC,QACF,CACF,CAGA4N,GACEE,GAAkBQ,GAClB,4BAA4BA,KAE9B,MAAM+J,EAAyBvK,GAAkBQ,GAAWC,GAE5D2J,EAAiB3J,GAAUrH,KAAKmR,EAAuBrK,EAAMiK,GAC/D,CAGF,MAAM3S,EAAYN,EACZO,EAAUD,EAAY+J,EAAWpO,OAAS,EAChD+D,GAASO,EAAUD,EAAY,EAE/ByS,EAAc7Q,KAAK,CACjBmB,KACAmC,QACAyN,WACA5I,aACA6I,mBACA5S,YACAC,WAEJ,CACA,OAAOwS,CACT,CUMwBO,CAAiBpQ,EAAOsG,EAAQhO,GAGhD+X,EAAc,IAAI9W,aAAaoJ,EAAKlE,EAAE1F,QAC5C,IAAK,IAAIoB,EAAI,EAAGA,EAAIwI,EAAKlE,EAAE1F,OAAQoB,IACjCkW,EAAYlW,GAAKwI,EAAKlE,EAAEtE,GAAKmM,EAG/B,MAAMgK,YAAEA,EAAWC,UAAEA,EAASC,UAAEA,EAASC,WAAEA,EAAUC,WAAEA,GE5HnD,SACJb,EACA7P,EACA1H,GAEA,MAAMyQ,EAAW8G,EAAcA,EAAc9W,OAAS,GAAGsE,QAAU,EAC7DkT,EAAY,IAAIhX,aAAawP,GAC7ByH,EAAY,IAAIjX,aAAawP,GAC7B0H,EAAa,IAAIlX,aAAawP,GAC9B2H,EAAa,IAAInX,aAAawP,GAC9B4H,EAAgB,IAAI/W,MAAemP,GAEzC,IAAIjM,EAAQ,EACZ,IAAK,IAAI8T,EAAS,EAAGA,EAASf,EAAc9W,OAAQ6X,IAAU,CAC5D,MAAM9K,EAAO+J,EAAce,GAC3B,IAAK,IAAIzW,EAAI,EAAGA,EAAI2L,EAAKqB,WAAWpO,OAAQoB,IAAK,CAC/C,MAAM0W,EAAY/K,EAAKqB,WAAWhN,GAClCoW,EAAUzT,GAASgJ,EAAKkK,iBAAiBxN,IAAIrI,GAC7CqW,EAAU1T,GAASgJ,EAAKkK,iBAAiBhK,IAAI7L,GAC7CsW,EAAW3T,GAASgJ,EAAKkK,iBAAiBnK,KAAK1L,GAC/CuW,EAAW5T,GAASgJ,EAAKkK,iBAAiB/J,mBAAmB9L,GAE7D,IAAI2W,GAAe,EACnB,MAAMC,EAAe/Q,EAAM4Q,IAASzJ,aAAa0J,GAC3CG,EAAc1Y,EAAQ6O,aAAa0J,GAEzC,QAA+B/X,IAA3BiY,GAAcpB,SAChB,GAAqC,mBAA1BoB,EAAapB,SACtBmB,EAAeC,EAAapB,SAAS3P,EAAM4Q,QACtC,CACL,MAAMjB,SAAEA,GAAW,GAASoB,EAC5BD,EAAenB,CACjB,MACK,QAA8B7W,IAA1BkY,GAAarB,SACtB,GAAoC,mBAAzBqB,EAAYrB,SACrBmB,EAAeE,EAAYrB,SAAS3P,EAAM4Q,QACrC,CACL,MAAMjB,SAAEA,GAAW,GAASqB,EAC5BF,EAAenB,CACjB,CAGFgB,EAAc7T,GAASgU,EACvBhU,GACF,CACF,CAEA,MAAMwT,EAAwB,GAC9B,IAAK,IAAInW,EAAI,EAAGA,EAAI4O,EAAU5O,IACxBwW,EAAcxW,IAAImW,EAAYtR,KAAK7E,GAGzC,MAAO,CAAEmW,cAAaC,YAAWC,YAAWC,aAAYC,aAC1D,CFwEIO,CAA0BpB,EAAe7P,EAAO1H,GAC5CyQ,EAAW0H,EAAW1X,QAEtBmY,UAAEA,EAASC,oBAAEA,GGlIf,SAAuBA,EAA2C,IACtE,MAAM1L,KAAEA,EAAO,KAAInN,QAAEA,GAAY6Y,EAEjC,OAAQ1L,GACN,IAAK,KACL,IAAK,qBACH,MAAO,CACLyL,UAAW9G,GACX+G,oBAAqB,CACnBzK,QAAS,IACTG,cAAe,IACfC,eAAgB,QACbxO,IAGT,IAAK,SACH,MAAO,CACL4Y,UAAWjF,GACXkF,oBAAqB,CACnBnG,WAAY,GACZmB,QAAS,KACTC,UAAW,MACXC,WAAY,MACZC,aAAc,CAAA,KACXhU,IAIT,QACE,MAAM,IAAImE,MAAM,6BAEtB,CHmG6C2U,CAAa9Y,EAAQ+Y,cAE1DC,EItIF,SAAyBzB,GAC7B,OAAO,SAAqB1I,GAC1B,OAAQhK,IACN,IAAIoU,EAAS,EACb,IAAK,MAAMzL,KAAQ+J,EAAe,CAChC,MAAM2B,EAAQrK,EAAWrB,EAAK1I,WACxBqB,EAAI0I,EAAWrB,EAAK1I,UAAY,GACtC,IAAK,IAAIjD,EAAI,EAAGA,EAAIgN,EAAWpO,OAAQoB,IAAK,CAE1C,MAAMsX,EAAc3L,EAAKqB,WAAWhN,GAIpC2L,EAAKiK,SAAS0B,GAAetK,EAAWrB,EAAK1I,UAAYjD,EAC3D,CACAoX,GAAU9S,EAAIqH,EAAKiK,SAASnO,IAAIzE,EAAIqU,EACtC,CACA,OAAOD,EAEX,CACF,CJkH0BG,CAAe7B,GAEvC,GAA2B,IAAvBS,EAAYvX,OACd,OK3IE,SACJ8W,EACAQ,EACAlT,EACAsT,EACAa,EACAhL,GAMA,MAAMqL,EAAe/X,MAAM0D,KAAKmT,GAC1BmB,EAAmB,GACzB,IAAK,MAAM9L,KAAQ+J,EAAe,CAChC,MAAM1P,GAAEA,EAAEmC,MAAEA,EAAK6E,WAAEA,EAAU/J,UAAEA,GAAc0I,EAC7C,IAAI+L,EAAU,CAAE1U,EAAG,EAAGsB,EAAG,EAAG6D,SACxBnC,IAEF0R,EAAU,IAAKA,EAAS1R,OAE1B0R,EAAQ1U,EAAIwU,EAAavU,GACzByU,EAAQpT,EAAIkT,EAAavU,EAAY,GAAKkJ,EAC1C,IAAK,IAAInM,EAAI,EAAGA,EAAIgN,EAAWpO,OAAQoB,IAErC0X,EAAQvP,MAAM6E,EAAWhN,IAAMwX,EAAavU,EAAYjD,GAE1DyX,EAAS5S,KAAK6S,EAChB,CAEA,MAAMjQ,EAAM0P,EAAgBK,GAC5B,IAAIrJ,EAAQ,EACZ,IAAK,IAAInO,EAAI,EAAGA,EAAIkW,EAAYtX,OAAQoB,IACtCmO,IAAU+H,EAAYlW,GAAKyH,EAAIzE,EAAEhD,MAAQ,EAG3C,MAAO,CACLmO,QACA0C,WAAY,EACZhL,MAAO4R,EAEX,CLkGWE,CACLjC,EACAQ,EACA1N,EAAKxF,EACLsT,EACAa,EACAhL,GAKJ,MAAMyL,EAAyBC,IAC7B,MAAMC,EAAO,IAAI1Y,aAAawP,GAC9BkJ,EAAKxI,IAAIgH,GACT,IAAK,IAAIzV,EAAI,EAAGA,EAAIsV,EAAYvX,OAAQiC,IACtCiX,EAAK3B,EAAYtV,IAAMgX,EAAkBhX,GAE3C,OAAOsW,EAAgB1X,MAAM0D,KAAK2U,KAIpC,IAAIhL,EACAC,EACAT,EACAyL,EACAC,EAAmBb,EAEvB,GAAIhB,EAAYvX,SAAWgQ,EAEzB9B,EAAYsJ,EACZrJ,EAAYsJ,EACZ/J,EAAgBgK,EAChByB,EAAsBxB,MACjB,CACLzJ,EAAY,IAAI1N,aAAa+W,EAAYvX,QACzCmO,EAAY,IAAI3N,aAAa+W,EAAYvX,QACzC0N,EAAgB,IAAIlN,aAAa+W,EAAYvX,QAC7CmZ,EAAsB,IAAI3Y,aAAa+W,EAAYvX,QACnD,IAAK,IAAIe,EAAI,EAAGA,EAAIwW,EAAYvX,OAAQe,IAAK,CAC3C,MAAMK,EAAImW,EAAYxW,GACtBmN,EAAUnN,GAAKyW,EAAUpW,GACzB+M,EAAUpN,GAAK0W,EAAUrW,GACzBsM,EAAc3M,GAAK2W,EAAWtW,GAC9B+X,EAAoBpY,GAAK4W,EAAWvW,EACtC,CACAgY,EAAmBJ,CACrB,CAEA,MAAMK,EAASlB,EAAU,CAAE/T,EAAGwF,EAAKxF,EAAGsB,EAAG4R,GAAe8B,EAAkB,CACxElL,YACAC,YACAT,gBACAR,mBAAoBiM,KACjBf,IAIL,IAAIQ,EACJ,GAAIrB,EAAYvX,SAAWgQ,EACzB4I,EAAeS,EAAOtH,oBACjB,CACL,MAAMmH,EAAOrY,MAAM0D,KAAKmT,GACxB,IAAK,IAAIzV,EAAI,EAAGA,EAAIsV,EAAYvX,OAAQiC,IACtCiX,EAAK3B,EAAYtV,IAAMoX,EAAOtH,gBAAgB9P,GAEhD2W,EAAeM,CACjB,CAEA,MAAML,EAAW,GACjB,IAAK,MAAM9L,KAAQ+J,EAAe,CAChC,MAAM1P,GAAEA,EAAEmC,MAAEA,EAAK6E,WAAEA,EAAU/J,UAAEA,GAAc0I,EAE7C,IAAI+L,EAAU,CAAE1U,EAAG,EAAGsB,EAAG,EAAG6D,SAExBnC,IACF0R,EAAU,IAAKA,EAAS1R,OAG1B0R,EAAQ1U,EAAIwU,EAAavU,GACzByU,EAAQpT,EAAIkT,EAAavU,EAAY,GAAKkJ,EAC1C,IAAK,IAAInM,EAAI,EAAGA,EAAIgN,EAAWpO,OAAQoB,IAErC0X,EAAQvP,MAAM6E,EAAWhN,IAAMwX,EAAavU,EAAYjD,GAE1DyX,EAAS5S,KAAK6S,EAChB,CAEA,MAAO,CACLvJ,MAAO8J,EAAOrH,eACdC,WAAYoH,EAAOpH,WACnBhL,MAAO4R,EAEX,CMtNM,SAAUS,GACdrS,EACA1H,EAAqC,IAErC,MAAMgK,MAAEA,EAAQ,CAAEmD,KAAM,YAAY6M,OAAEA,EAASC,gBAAgBvS,IAC7D1H,EACIka,EAAuBhN,GAAWlD,GACxC,OAAOgQ,EAAOhH,IAAKxF,GAiBrB,SACEA,GAEA,MAAO,UAAWA,CACpB,CApBQ2M,CAAS3M,IACL,SAAUA,EAAKxD,QACnBwD,EAAKxD,MAAMhB,KAAOkE,GAAWM,EAAKxD,OAAOX,YAAYmE,EAAK5F,QAErD4F,GAEF,IACFA,EACHxD,MAAO,CACLhB,KAAMkR,EAAqB7Q,YAAYmE,EAAK5F,UACzCoC,IAIX,CCUM,SAAUoQ,GACd/P,EACAgQ,EACAra,EAAgC,CAAA,GAKhC,MAAMsa,OACJA,EAAS,CAAA,EAAEtQ,MACXA,EAAQ,CAAEmD,KAAM,YAAYoN,eAC5BA,EAAiB,EAACC,aAClBA,EAAe,EAAC3L,WAChBA,EAAUkK,aACVA,EAAe,CACb5L,KAAM,KACNnN,QAAS,CACPkO,QAAS,MAGXlO,EAIEya,EC9DF,SACJ/S,EACA1H,EAA6B,IAE7B,GAAqB,IAAjB0H,EAAMjH,OAAc,MAAO,GAE/B,MAAMwJ,OAAEA,EAAS,GAAMjK,EAEjB0a,EAAchT,EAAMiT,SAAS,CAAC9X,EAAGC,IAAMD,EAAEgC,EAAI/B,EAAE+B,GAErD,IAAI+V,EAAeF,EAAY,GAC3BG,EAAoB,CAACD,GACzB,MAAMH,EAAgB,CAACI,GAEvB,IAAK,IAAIhZ,EAAI,EAAGA,EAAI6Y,EAAYja,OAAQoB,IAAK,CAC3C,MAAM2L,EAAOkN,EAAY7Y,IAEtB2L,EAAK3I,EAAI+V,EAAa/V,KAAO2I,EAAK5F,MAAQgT,EAAahT,OAAS,IACjEqC,EAEA4Q,EAAanU,KAAK8G,IAElBqN,EAAe,CAACrN,GAChBiN,EAAO/T,KAAKmU,IAEdD,EAAepN,CACjB,CAEA,OAAOiN,CACT,CDiCiBK,CAAWT,EAAU,CAAEpQ,OAAQsQ,IACxCQ,EAA2B,GAC3BC,EAA6C,GACnD,IAAK,MAAMC,KAAaR,EAAQ,CAC9B,MAAMS,EAAQzL,KAAKC,MACbhI,EAAQqS,GAAgBkB,EAAW,CAAEjR,UAErCmR,EAAYzT,EAAM,GAClB0T,EAAW1T,EAAM2T,QAEjBrW,KACJA,EAAOmW,EAAUtW,EAAIsW,EAAUvT,MAAQ4S,EAAYvV,GACnDA,EAAKmW,EAASvW,EAAIuW,EAASxT,MAAQ4S,GACjCF,GACExV,UAAEA,EAASC,QAAEA,GAAYH,EAAgByF,EAAKxF,EAAG,CAAEG,OAAMC,OAEzDJ,EACJwF,EAAKxF,aAAa5D,aACdoJ,EAAKxF,EAAEyW,SAASxW,EAAWC,GAC3BsF,EAAKxF,EAAExB,MAAMyB,EAAWC,GACxBoB,EACJkE,EAAKlE,aAAalF,aACdoJ,EAAKlE,EAAEmV,SAASxW,EAAWC,GAC3BsF,EAAKlE,EAAE9C,MAAMyB,EAAWC,GAExB4F,EAAM,CACV4Q,MAAO,CAAEvW,OAAMC,MACf4J,WAAY,CAAEkK,eAAclK,cAC5B2M,UAAWP,EAAUxa,OACrBgb,KAAMhM,KAAKC,MAAQwL,GAGrB,GAAIrW,EAAEpE,OAAS,EAAG,CAChB,MAAMiS,WACJA,EAAU1C,MACVA,EACAtI,MAAOgU,GACLrE,GAAS,CAAExS,IAAGsB,KAAKuB,EAAO,CAC5BsC,QACA6E,aACAkK,iBAGF,IAAK,IAAIlX,EAAI,EAAGA,EAAI6F,EAAMjH,OAAQoB,IAChCmZ,EAAQtU,KAAK,IACRgV,EAAe7Z,GAClB+F,MAAOsF,GAAWxF,EAAM7F,GAAGmI,OAAOb,YAChCuS,EAAe7Z,GAAGmI,MAAMhB,QAI9B+R,EAAKrU,KAAK,IACLiE,EACH+H,aACA1C,QACA3C,QAAS,2BAEb,MACE2N,EAAQtU,QAASgB,GACjBqT,EAAKrU,KAAK,IACLiE,EACH+H,WAAY,EACZrF,QAAS,uCAGf,CAEA,MAAO,CAAE0N,OAAMW,eAAgBV,EACjC,CE5IM,SAAUW,GACdjU,EACA1H,EAA4B,IAE5B,MAAMga,OAAEA,EAASC,gBAAgBvS,IAAW1H,EAC5C,IAAK,MAAMwN,KAAQwM,EACX,OAAQxM,IACZA,EAAK3F,GAAKC,OAAOC,cAIrB,OAAOiS,CACT,CC4HA,SAAS4B,GACPC,EACAC,EACApU,GAEA,IAAK,MAAMlD,KAASsX,EAClBpU,EAAMhB,KAAKqV,GAA6BF,EAAWrX,IAEvD,CACA,SAASuX,GAA6BvO,GACpC,MAAM3F,GAAEA,EAAEmC,MAAEA,EAAKnF,EAAEA,EAACsB,EAAEA,EAACyB,MAAEA,GAAU4F,EAE7B+L,EAAU,CACd1U,IACAsB,IACAyB,QACAoC,SAKF,OAFInC,IAAI0R,EAAQ1R,GAAKA,GAEd0R,CACT,qCC5HM,SACJc,EACAra,EAA+B,IAE/B,MAAMiK,OAAEA,EAAS,EAAC+R,QAAEA,GAAU,GAAUhc,EAElC0H,EA8BR,SACEA,EACAuC,GAEA,OAAOvC,EAAMsL,IAAKxF,IAChB,MAAM3F,GAAEA,EAAEmC,MAAEA,EAAKnF,EAAEA,EAACsB,EAAEA,EAAC3B,MAAEA,EAAKwD,iBAAEA,GAAqBwF,EAC/CyO,EAAQpX,GAAKA,EAAImD,EAAiBhD,KAAKH,GAAKoF,EAC5CiS,EAAMrX,GAAKmD,EAAiB/C,GAAGJ,EAAIA,GAAKoF,EAE9C,IAAIlE,EAAS,CACXlB,IACAsB,IACA3B,QACAoD,MAAOsU,EAAMD,EACbjX,KAAM,CAAEH,EAAGoX,GACXhX,GAAI,CAAEJ,EAAGqX,IAWX,OARIrU,IACF9B,EAAS,IAAKA,EAAQ8B,OAGpBmC,IACFjE,EAAS,IAAKA,EAAQiE,UAGjBjE,GAEX,CA1DgBoW,CAAS9B,EAAUpQ,GAEjC,IAAK+R,EACH,IAAK,IAAIna,EAAI,EAAGA,EAAI6F,EAAMjH,OAAS,EAAGoB,IAAK,CACzC,MAAM2L,EAAO9F,EAAM7F,GACbua,EAAW1U,EAAM7F,EAAI,GACvB2L,EAAKvI,GAAGJ,EAAIuX,EAASpX,KAAKH,IAE5B2I,EAAKvI,GAAGJ,EACL2I,EAAK5F,OAASwU,EAASxU,MAAQ4F,EAAK5F,QAAWwU,EAASvX,EAAI2I,EAAK3I,GAClE2I,EAAK3I,EACPuX,EAASpX,KAAKH,EAAI2I,EAAKvI,GAAGJ,EAE9B,CAGF,IAAK,MAAM2I,KAAQ9F,EAEjB,GADA8F,EAAK5F,MAAQ4F,EAAKvI,GAAGJ,EAAI2I,EAAKxI,KAAKH,EAC/B2I,EAAKxD,MAAO,CACd,MAAMA,MAAEA,EAAKpC,MAAEA,GAAU4F,EACzB,QAAmBhN,IAAfwJ,EAAMhB,KAAoB,CAC5B,MAAMyO,EAAWvK,GAAWlD,GAC5BA,EAAMhB,KAAOyO,EAASpO,YAAYzB,EACpC,CACF,CAGF,OAAOF,CACT,QCFM,SAAc2C,EAAcrK,EAAsB,IACtD,IAAIqc,WAAEA,GAAerc,EACrB,MAAMsc,UACJA,EAAY,CACVrc,WAAY,EACZE,WAAY,GACboc,QACDA,GAAU,EAAKC,YACfA,GAAc,EAAIC,YAClBA,EAAc,MAAOC,iBACrBA,GAAmB,EAAKC,uBACxBA,EAAyB,UACvB3c,GACE6E,EAAEA,GAAMwF,EACd,IAAIlE,EAAEA,GAAMkE,EACZ,GAAwB,ICjFpB,SAAuBjH,GAC3B,GAAIA,EAAM3C,QAAU,EAClB,OAAO,EAET,GAAI2C,EAAM,KAAOA,EAAM,GAAI,CAEzB,IAAK,IAAIvB,EAAI,EAAGA,EAAIuB,EAAM3C,OAAS,EAAGoB,IACpC,GAAIuB,EAAMvB,KAAOuB,EAAMvB,EAAI,GAAI,OAAO,EAExC,OAAO,CACT,CAEA,GAAIuB,EAAM,GAAMA,EAAMiY,IAAG,GAAgB,CACvC,IAAK,IAAIxZ,EAAI,EAAGA,EAAIuB,EAAM3C,OAAS,EAAGoB,IACpC,GAAIuB,EAAMvB,IAAMuB,EAAMvB,EAAI,GAAI,OAAO,EAEvC,OAAO,CACT,CACE,IAAK,IAAIA,EAAI,EAAGA,EAAIuB,EAAM3C,OAAS,EAAGoB,IACpC,GAAIuB,EAAMvB,IAAMuB,EAAMvB,EAAI,GAAI,OAAO,EAEvC,OAAO,CAEX,CD0DM+a,CAAa/X,GACf,MAAM,IAAIV,MAAM,iDAGlBgC,EAAIA,EAAE9C,QAIN,MAAMwZ,EEjFF,SACJzZ,EACApD,EAAmC,IAEnC,GAAIoD,EAAM3C,OAAS,EAAG,OAAO,EAC7B,MAAMqT,UAAEA,EAAY,KAAS9T,EAC7B,IAAI8c,EAAQ,EACRC,EAAQ3c,OAAO4O,iBACnB,IAAK,IAAInN,EAAI,EAAGA,EAAIuB,EAAM3C,OAAS,IAAKoB,EAAG,CACzC,MAAMmb,EAAqB5Z,EAAMvB,EAAI,GAAKuB,EAAMvB,GAC5Cmb,EAAqBD,IACvBA,EAAQC,GAENA,EAAqBF,IACvBA,EAAQE,EAEZ,CACA,OAAQF,EAAQC,GAASD,EAAQhJ,CACnC,CF+D0BmJ,CAAiBpY,GAEzC,QAAmBrE,IAAf6b,EACF,GAAIQ,EAAiB,CACnB,MAAMK,EAAYzX,GAAwBU,GAExCkW,EADEG,EACWU,EAAU1Z,OAAS,IAAM0Z,EAAUrX,IAElCqX,EAAU1Z,OAAS,IAAM0Z,EAAUrX,EAErD,MACEwW,EAAa,OAELG,IACVH,IAAc,GAGhB,IAAKG,EACH,IAAK,IAAI3a,EAAI,EAAGA,EAAIsE,EAAE1F,OAAQoB,IAC5BsE,EAAEtE,KAAM,EAGZ,QAAmBrB,IAAf6b,EACF,IAAK,IAAIxa,EAAI,EAAGA,EAAIsE,EAAE1F,OAAQoB,IACxBsE,EAAEtE,GAAKwa,IACTlW,EAAEtE,GAAKwa,GAKb,MAAMc,EAASN,EAAkBhY,EAAE,GAAKA,EAAE,GAAKA,EAEzCoC,EAAQsV,EACV1c,EAAIsG,EAAGgX,EAAQ,IACVb,EACHpc,WAAY,IAEdiG,GAEI+D,IAAKkT,EAAM1P,IAAK2P,GG9HpB,SAAwBja,GAI5Ba,EAAOb,GAEP,IAAI8G,EAAM9G,EAAM,GACZsK,EAAMtK,EAAM,GAEhB,IAAK,MAAM5D,KAAS4D,EACd5D,EAAQ0K,IAAKA,EAAM1K,GACnBA,EAAQkO,IAAKA,EAAMlO,GAGzB,MAAO,CAAE0K,MAAKwD,MAChB,CH+GmC4P,CAAcrW,GAC/C,GAAImW,EAAOC,GAAQD,IAASC,EAAM,MAAO,GAEzC,MAAMjX,EAAKvG,EAAIsG,EAAGgX,EAAQ,IACrBb,EACHpc,WAAY,IAGRyH,EAAM9H,EAAIsG,EAAGgX,EAAQ,IACtBb,EACHpc,WAAY,IAGR4G,EAAajG,KAAK6M,IAAI2O,EAAYe,GAAQC,EAAOD,GAAQX,GAIzDc,EAAW,CAAE1Y,IAAGsB,IAAGc,QAAOb,KAAIuB,MAAKtB,GAF9BxB,EAAE,GAAKA,EAAE,GAEyBiC,cAC7C,IAAIY,EAAqB,GAEvBA,EAD6B,UAA3BiV,EACMvU,GAAgBmV,GACY,WAA3BZ,EInJP,SAA2BzZ,GAC/B,MAAM2B,EAAEA,EAACsB,EAAEA,EAACc,MAAEA,EAAKb,GAAEA,EAAEuB,IAAEA,EAAGtB,GAAEA,EAAES,WAAEA,GAAe5D,EAE3Csa,EAAmB,IACnBhX,UAAEA,EAASC,UAAEA,GAAcP,GAAqBC,EAAGtB,EAAGuB,EAAIC,GAGhE,IAAK,IAAIxE,EAAI,EAAGA,EAAIsE,EAAE1F,OAAS,IAAKoB,EAE9B8F,EAAI9F,GAAK8F,EAAI9F,EAAI,IAAM8F,EAAI9F,GAAK8F,EAAI9F,EAAI,IAC1C2b,EAAO9W,KAAK7E,GAIhB,OAAO4F,GAAqB,CAC1BZ,QAAS2W,EACThX,YACAC,YACA5B,IACAoC,QACAH,aACAa,OAEJ,CJ6HY8V,CAAiBF,GKjJvB,SAAwBra,GAC5B,MAAM2B,EAAEA,EAACsB,EAAEA,EAACc,MAAEA,EAAKb,GAAEA,EAAEuB,IAAEA,EAAGtB,GAAEA,EAAES,WAAEA,GAAe5D,EAE3Csa,EAAmB,GACnBnV,EAAoB,IACpB7B,UAAEA,EAASC,UAAEA,GAAcP,GAAqBC,EAAGtB,EAAGuB,EAAIC,GAEhE,IAAK,IAAIxE,EAAI,EAAGA,EAAIsE,EAAE1F,OAAS,IAAKoB,GAC7BuE,EAAGvE,GAAK,GAAKuE,EAAGvE,EAAI,GAAK,GAAOuE,EAAGvE,GAAK,GAAKuE,EAAGvE,EAAI,GAAK,IAE5DwG,EAAQ3B,KAAK7F,KAAK0D,IAAI6B,EAAGvE,IAAMhB,KAAK0D,IAAI6B,EAAGvE,EAAI,IAAMA,EAAIA,EAAI,GAInD,IAAVuE,EAAGvE,IACHuE,EAAGvE,GAAKhB,KAAK0D,IAAI6B,EAAGvE,EAAI,KACxBuE,EAAGvE,GAAKhB,KAAK0D,IAAI6B,EAAGvE,EAAI,KAExBwG,EAAQ3B,KAAK7E,GAIX8F,EAAI9F,GAAK8F,EAAI9F,EAAI,IAAM8F,EAAI9F,GAAK8F,EAAI9F,EAAI,IAC1C2b,EAAO9W,KAAK7E,GAIhB,MAAM6F,EAAqB,GAC3B,IAAId,GAAQ,EACR8W,GAAQ,EACZ,IAAK,IAAI7b,EAAI,EAAGA,EAAI2E,EAAU/F,OAAQoB,IAAK,CACzC,MAAMkF,GAAiBN,EAAU5E,GAAGgD,EAAI2B,EAAU3E,GAAGgD,GAAK,EACpDmC,GAAkBP,EAAU5E,GAAGgD,EAAI2B,EAAU3E,GAAGgD,GAAK,EAE3D,IAAI8Y,GAAS,EACTC,EAAQjX,GAA+B,CACzC9B,IACAoC,QACAL,QACAE,aACAC,gBACAC,iBACAH,QAASwB,IAqBX,GAnBAzB,EAAQgX,EAAMpW,eACVoW,EAAMzW,SACRwW,EAAStV,EAAQuV,EAAMzW,WAEvByW,EAAQjX,GAA+B,CACrC9B,IACAoC,QACAH,aACAF,MAAO8W,EACP3W,gBACAC,iBACAH,QAAS2W,SAEPI,EAAMzW,WACRwW,EAASH,EAAOI,EAAMzW,WAExBuW,EAAQE,EAAMpW,YAGD,IAAXmW,EAAe,CACjB,MAAM/V,EAAQ/G,KAAK0D,IAAIkC,EAAU5E,GAAGgD,EAAI2B,EAAU3E,GAAGgD,GACrD6C,EAAMhB,KAAK,CACTmB,GAAIC,OAAOC,aACXlD,EAAGA,EAAE8Y,GACLxX,EAAGA,EAAEwX,GACL/V,QACApD,MAAOmZ,EACPhW,IAAKA,EAAIgW,GACT3V,iBAAkB,CAChBhD,KAAMwB,EAAU3E,GAChBoD,GAAIwB,EAAU5E,KAGpB,CACF,CAEA,OAAO6F,CACT,CLkEYmW,CAAcN,GAGpBb,GMzJA,SACJrS,EACA3C,GAEA,MAAM7C,EAAEA,EAACsB,EAAEA,GAAMkE,EAEjB,IAAK,MAAMmD,KAAQ9F,EAAO,CACxB,IAAIoW,EAAetQ,EAAKhJ,MAwBxB,GArBE2B,EAAE2X,EAAe,IAAM3X,EAAE2X,EAAe,IACxC3X,EAAE2X,EAAe,IAAM3X,EAAE2X,GAEzBA,IAEA3X,EAAE2X,EAAe,IAAM3X,EAAE2X,IACzB3X,EAAE2X,EAAe,IAAM3X,EAAE2X,EAAe,GAExCA,IAEA3X,EAAE2X,EAAe,IAAM3X,EAAE2X,EAAe,IACxC3X,EAAE2X,EAAe,IAAM3X,EAAE2X,EAAe,GAExCA,GAAgB,EAEhB3X,EAAE2X,EAAe,IAAM3X,EAAE2X,EAAe,IACxC3X,EAAE2X,EAAe,IAAM3X,EAAE2X,EAAe,KAExCA,GAAgB,GAIhB3X,EAAE2X,EAAe,GAAK,GACtB3X,EAAE2X,EAAe,GAAK,GACtB3X,EAAE2X,IAAiB3X,EAAE2X,EAAe,IACpC3X,EAAE2X,IAAiB3X,EAAE2X,EAAe,KACnC3X,EAAE2X,KAAkB3X,EAAE2X,EAAe,IACpC3X,EAAE2X,KAAkB3X,EAAE2X,EAAe,IACvC,CACA,MAAMC,EAAQld,KAAKmd,MAAM7X,EAAE2X,EAAe,IACpCG,EAAOpd,KAAKmd,MAAM7X,EAAE2X,IACpBrR,EAAQ5L,KAAKmd,MAAM7X,EAAE2X,EAAe,IACpCrS,EAAK,IAAOsS,EAAQtR,IAAWsR,EAAQ,EAAIE,EAAOxR,GAClDyR,EAAmBrZ,EAAEiZ,GACrBK,EAAoBtZ,EAAEiZ,EAAe,GAC3CtQ,EAAK3I,EAAIqZ,GAAYA,EAAWC,GAAa1S,EAC7C+B,EAAKrH,EACHA,EAAE2X,GACF,KAAQ3X,EAAE2X,EAAe,GAAK3X,EAAE2X,EAAe,IAAMrS,CACzD,CACF,CACF,CNuGI2S,CAAY,CAAEvZ,IAAGsB,EAAGc,GAASS,GAG/B,IAAK,MAAM8F,KAAQ9F,EACZ8U,IACHhP,EAAKrH,IAAK,EACVqH,EAAK7F,KAAiB,EAAX6F,EAAK7F,KAQpB,OAJAD,EAAMgP,KAAK,CAAC7T,EAAGC,IACND,EAAEgC,EAAI/B,EAAE+B,GAGV6C,CACT,mBFtIM,SACJ2S,EACAra,EAAiC,IAEjC,MAAMgK,MACJA,EAAQ,CAAEmD,KAAM,YAAY4L,aAC5BA,EAAe,CAAE5L,KAAM,KAAMnN,QAAS,CAAEkO,QAAS,KAAMmQ,WACvDA,EAAa,IAAIC,WACjBA,EAAa,OACXte,EAEJ,IAAI0N,EAAM,EACN6Q,EAAO,EACP/b,EAAQ,EACZ,MAAMqZ,EAAqC,GAE3C,GAAIxB,EAAS5Z,OAAS,EACpB,OAAOkb,GACL5B,GAAgBM,EAASrH,IAAI+I,IAA+B,CAAE/R,WAIlE,IAAIwU,EAAS3d,KAAK0D,IAAI8V,EAAS,GAAG1S,KAClC,IAAK,IAAI9F,EAAI,EAAGA,EAAIwY,EAAS5Z,OAAQoB,IAAK,CACxC,MAAM4c,EAAS5d,KAAK0D,IAAI8V,EAASxY,GAAG8F,KAChC8W,EAASD,IAAQA,EAASC,EAChC,CAEA,MAAMnF,EAA+B,GACrC,IAAK,MAAM9L,KAAQ6M,EACbxZ,KAAK0D,IAAIiJ,EAAK7F,MAAQ2W,EAAaE,EACrC3C,EAAWnV,KAAK8G,GAEhB8L,EAAS5S,KAAKqV,GAA6BvO,IAM/CqO,EAAWnV,KAAK,CAAE7B,EAAGzE,OAAOse,UAAWvY,EAAG,IAC1C,IAAIwY,EAA2C,CAC7C9Z,EAAG,CAACgX,EAAW,GAAGhX,GAClBsB,EAAG,CAAC0V,EAAW,GAAG1V,IAEhB2V,EAAoB,CAAC,GACzB,IAAK,IAAIja,EAAI,EAAGA,EAAIga,EAAWpb,OAAQoB,IACrC,GAAIhB,KAAK0D,IAAIsX,EAAWha,EAAI,GAAGgD,EAAIgX,EAAWha,GAAGgD,GAAKwZ,EACpDM,EAAW9Z,EAAE6B,KAAKmV,EAAWha,GAAGgD,GAChC8Z,EAAWxY,EAAEO,KAAKmV,EAAWha,GAAGsE,GAC5B0V,EAAWha,GAAGsE,EAAIuH,IACpBA,EAAMmO,EAAWha,GAAGsE,EACpBoY,EAAO1c,GAETia,EAAQpV,KAAK7E,GACbW,QACK,CACL,GAAIA,EAAQ,EAAG,CACb,MAAMoc,EAAe/d,KAAK0D,IACvBoa,EAAW9Z,EAAEwW,IAAG,GAAiBsD,EAAW9Z,EAAE,KAE3CkW,KAAEA,EAAIW,eAAEA,GAAmBtB,GAC/BuE,EACA,CACE,CACE9W,GAAIC,OAAOC,aACXlD,EAAGgX,EAAW0C,GAAM1Z,EACpBsB,EAAGuH,EACH9F,MAAOgX,EACP/P,WAAY,CACVjH,MAAO,CAAE8F,IAAoB,EAAfkR,EAAkB1U,IAAoB,GAAf0U,MAI3C,CAAE5U,MAAO,CAAEmD,KAAM,eAAiB4L,iBAEpCrL,EAAM,EACN6Q,EAAO,EACP,MAAM5T,EAAMoQ,EAAK8D,KAAM3c,GAAoB,4BAAdA,EAAEmL,cACZ7M,IAAfmK,GAAKqF,OAAuBrF,EAAIqF,MAAQ,GAC1CsJ,EAAS5S,KAAKgV,EAAe,IAE7BE,GAAcC,EAAYC,EAASxC,EAEvC,MACEsC,GAAcC,EAAYC,EAASxC,GAGrCqF,EAAa,CAAE9Z,EAAG,CAACgX,EAAWha,GAAGgD,GAAIsB,EAAG,CAAC0V,EAAWha,GAAGsE,IACvD2V,EAAU,CAACja,GACX6L,EAAMmO,EAAWha,GAAGsE,EACpBoY,EAAO1c,EACPW,EAAQ,CACV,CAIF,OAFA8W,EAAS5C,KAAK,CAAC7T,EAAGC,IAAMD,EAAEgC,EAAI/B,EAAE+B,GAEzB8W,GAAcrC,EAAU,CAAEU,OAAQV,GAC3C,kBSvFM,SACJjP,EACAgQ,EACAra,EAAgC,CAAA,GAEhC,OAAOoa,GAAsB/P,EAAMgQ,EAAUra,GAAS0b,cACxD,wCCtCM,SACJhU,EACA1H,EAA8B,IAE9B,MAAMgK,MAAEA,EAAQ,CAAEmD,KAAM,YAAY6M,OAAEA,EAASC,gBAAgBvS,IAC7D1H,EACI8e,EAAgB5R,GAAWlD,GACjC,OAAOgQ,EAAOhH,IAAKxF,IAAI,IAClBA,EACHxD,MAAO,IAAKA,EAAOhB,KAAM8V,EAAczV,YAAYmE,EAAK5F,UAE5D","x_google_ignoreList":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,52,53,54]}