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{\n\t\t\treturn continuationByte & 0x3F;\n\t\t}\n\n\t\t// If we end up here, it’s not a continuation byte\n\t\tthrow Error('Invalid continuation byte');\n\t}\n\n\tfunction decodeSymbol() {\n\t\tvar byte1;\n\t\tvar byte2;\n\t\tvar byte3;\n\t\tvar byte4;\n\t\tvar codePoint;\n\n\t\tif (byteIndex > byteCount) {\n\t\t\tthrow Error('Invalid byte index');\n\t\t}\n\n\t\tif (byteIndex == byteCount) {\n\t\t\treturn false;\n\t\t}\n\n\t\t// Read first byte\n\t\tbyte1 = byteArray[byteIndex] & 0xFF;\n\t\tbyteIndex++;\n\n\t\t// 1-byte sequence (no continuation bytes)\n\t\tif ((byte1 & 0x80) == 0) {\n\t\t\treturn byte1;\n\t\t}\n\n\t\t// 2-byte sequence\n\t\tif ((byte1 & 0xE0) == 0xC0) {\n\t\t\tbyte2 = readContinuationByte();\n\t\t\tcodePoint = ((byte1 & 0x1F) << 6) | byte2;\n\t\t\tif (codePoint >= 0x80) {\n\t\t\t\treturn codePoint;\n\t\t\t} else {\n\t\t\t\tthrow Error('Invalid continuation byte');\n\t\t\t}\n\t\t}\n\n\t\t// 3-byte sequence (may include unpaired surrogates)\n\t\tif ((byte1 & 0xF0) == 0xE0) {\n\t\t\tbyte2 = readContinuationByte();\n\t\t\tbyte3 = readContinuationByte();\n\t\t\tcodePoint = ((byte1 & 0x0F) << 12) | (byte2 << 6) | byte3;\n\t\t\tif (codePoint >= 0x0800) {\n\t\t\t\tcheckScalarValue(codePoint);\n\t\t\t\treturn codePoint;\n\t\t\t} else {\n\t\t\t\tthrow Error('Invalid continuation byte');\n\t\t\t}\n\t\t}\n\n\t\t// 4-byte sequence\n\t\tif ((byte1 & 0xF8) == 0xF0) {\n\t\t\tbyte2 = readContinuationByte();\n\t\t\tbyte3 = readContinuationByte();\n\t\t\tbyte4 = readContinuationByte();\n\t\t\tcodePoint = ((byte1 & 0x07) << 0x12) | (byte2 << 0x0C) |\n\t\t\t\t(byte3 << 0x06) | byte4;\n\t\t\tif (codePoint >= 0x010000 && codePoint <= 0x10FFFF) {\n\t\t\t\treturn codePoint;\n\t\t\t}\n\t\t}\n\n\t\tthrow Error('Invalid UTF-8 detected');\n\t}\n\n\tvar byteArray;\n\tvar byteCount;\n\tvar byteIndex;\n\tfunction utf8decode(byteString) {\n\t\tbyteArray = ucs2decode(byteString);\n\t\tbyteCount = byteArray.length;\n\t\tbyteIndex = 0;\n\t\tvar codePoints = [];\n\t\tvar tmp;\n\t\twhile ((tmp = decodeSymbol()) !== false) {\n\t\t\tcodePoints.push(tmp);\n\t\t}\n\t\treturn ucs2encode(codePoints);\n\t}\n\n\t/*--------------------------------------------------------------------------*/\n\n\troot.version = '3.0.0';\n\troot.encode = utf8encode;\n\troot.decode = utf8decode;\n\n}(typeof exports === 'undefined' ? this.utf8 = {} : exports));\n","import { decode, encode } from 'utf8';\nconst defaultByteLength = 1024 * 8;\nexport class IOBuffer {\n /**\n * @param data - The data to construct the IOBuffer with.\n * If data is a number, it will be the new buffer's length
\n * If data is `undefined`, the buffer will be initialized with a default length of 8Kb
\n * If data is an ArrayBuffer, SharedArrayBuffer, an ArrayBufferView (Typed Array), an IOBuffer instance,\n * or a Node.js Buffer, a view will be created over the underlying ArrayBuffer.\n * @param options\n */\n constructor(data = defaultByteLength, options = {}) {\n let dataIsGiven = false;\n if (typeof data === 'number') {\n data = new ArrayBuffer(data);\n }\n else {\n dataIsGiven = true;\n this.lastWrittenByte = data.byteLength;\n }\n const offset = options.offset ? options.offset >>> 0 : 0;\n const byteLength = data.byteLength - offset;\n let dvOffset = offset;\n if (ArrayBuffer.isView(data) || data instanceof IOBuffer) {\n if (data.byteLength !== data.buffer.byteLength) {\n dvOffset = data.byteOffset + offset;\n }\n data = data.buffer;\n }\n if (dataIsGiven) {\n this.lastWrittenByte = byteLength;\n }\n else {\n this.lastWrittenByte = 0;\n }\n this.buffer = data;\n this.length = byteLength;\n this.byteLength = byteLength;\n this.byteOffset = dvOffset;\n this.offset = 0;\n this.littleEndian = true;\n this._data = new DataView(this.buffer, dvOffset, byteLength);\n this._mark = 0;\n this._marks = [];\n }\n /**\n * Checks if the memory allocated to the buffer is sufficient to store more\n * bytes after the offset.\n * @param byteLength - The needed memory in bytes.\n * @returns `true` if there is sufficient space and `false` otherwise.\n */\n available(byteLength = 1) {\n return this.offset + byteLength <= this.length;\n }\n /**\n * Check if little-endian mode is used for reading and writing multi-byte\n * values.\n * @returns `true` if little-endian mode is used, `false` otherwise.\n */\n isLittleEndian() {\n return this.littleEndian;\n }\n /**\n * Set little-endian mode for reading and writing multi-byte values.\n */\n setLittleEndian() {\n this.littleEndian = true;\n return this;\n }\n /**\n * Check if big-endian mode is used for reading and writing multi-byte values.\n * @returns `true` if big-endian mode is used, `false` otherwise.\n */\n isBigEndian() {\n return !this.littleEndian;\n }\n /**\n * Switches to big-endian mode for reading and writing multi-byte values.\n */\n setBigEndian() {\n this.littleEndian = false;\n return this;\n }\n /**\n * Move the pointer n bytes forward.\n * @param n - Number of bytes to skip.\n */\n skip(n = 1) {\n this.offset += n;\n return this;\n }\n /**\n * Move the pointer to the given offset.\n * @param offset\n */\n seek(offset) {\n this.offset = offset;\n return this;\n }\n /**\n * Store the current pointer offset.\n * @see {@link IOBuffer#reset}\n */\n mark() {\n this._mark = this.offset;\n return this;\n }\n /**\n * Move the pointer back to the last pointer offset set by mark.\n * @see {@link IOBuffer#mark}\n */\n reset() {\n this.offset = this._mark;\n return this;\n }\n /**\n * Push the current pointer offset to the mark stack.\n * @see {@link IOBuffer#popMark}\n */\n pushMark() {\n this._marks.push(this.offset);\n return this;\n }\n /**\n * Pop the last pointer offset from the mark stack, and set the current\n * pointer offset to the popped value.\n * @see {@link IOBuffer#pushMark}\n */\n popMark() {\n const offset = this._marks.pop();\n if (offset === undefined) {\n throw new Error('Mark stack empty');\n }\n this.seek(offset);\n return this;\n }\n /**\n * Move the pointer offset back to 0.\n */\n rewind() {\n this.offset = 0;\n return this;\n }\n /**\n * Make sure the buffer has sufficient memory to write a given byteLength at\n * the current pointer offset.\n * If the buffer's memory is insufficient, this method will create a new\n * buffer (a copy) with a length that is twice (byteLength + current offset).\n * @param byteLength\n */\n ensureAvailable(byteLength = 1) {\n if (!this.available(byteLength)) {\n const lengthNeeded = this.offset + byteLength;\n const newLength = lengthNeeded * 2;\n const newArray = new Uint8Array(newLength);\n newArray.set(new Uint8Array(this.buffer));\n this.buffer = newArray.buffer;\n this.length = this.byteLength = newLength;\n this._data = new DataView(this.buffer);\n }\n return this;\n }\n /**\n * Read a byte and return false if the byte's value is 0, or true otherwise.\n * Moves pointer forward by one byte.\n */\n readBoolean() {\n return this.readUint8() !== 0;\n }\n /**\n * Read a signed 8-bit integer and move pointer forward by 1 byte.\n */\n readInt8() {\n return this._data.getInt8(this.offset++);\n }\n /**\n * Read an unsigned 8-bit integer and move pointer forward by 1 byte.\n */\n readUint8() {\n return this._data.getUint8(this.offset++);\n }\n /**\n * Alias for {@link IOBuffer#readUint8}.\n */\n readByte() {\n return this.readUint8();\n }\n /**\n * Read `n` bytes and move pointer forward by `n` bytes.\n */\n readBytes(n = 1) {\n const bytes = new Uint8Array(n);\n for (let i = 0; i < n; i++) {\n bytes[i] = this.readByte();\n }\n return bytes;\n }\n /**\n * Read a 16-bit signed integer and move pointer forward by 2 bytes.\n */\n readInt16() {\n const value = this._data.getInt16(this.offset, this.littleEndian);\n this.offset += 2;\n return value;\n }\n /**\n * Read a 16-bit unsigned integer and move pointer forward by 2 bytes.\n */\n readUint16() {\n const value = this._data.getUint16(this.offset, this.littleEndian);\n this.offset += 2;\n return value;\n }\n /**\n * Read a 32-bit signed integer and move pointer forward by 4 bytes.\n */\n readInt32() {\n const value = this._data.getInt32(this.offset, this.littleEndian);\n this.offset += 4;\n return value;\n }\n /**\n * Read a 32-bit unsigned integer and move pointer forward by 4 bytes.\n */\n readUint32() {\n const value = this._data.getUint32(this.offset, this.littleEndian);\n this.offset += 4;\n return value;\n }\n /**\n * Read a 32-bit floating number and move pointer forward by 4 bytes.\n */\n readFloat32() {\n const value = this._data.getFloat32(this.offset, this.littleEndian);\n this.offset += 4;\n return value;\n }\n /**\n * Read a 64-bit floating number and move pointer forward by 8 bytes.\n */\n readFloat64() {\n const value = this._data.getFloat64(this.offset, this.littleEndian);\n this.offset += 8;\n return value;\n }\n /**\n * Read a 1-byte ASCII character and move pointer forward by 1 byte.\n */\n readChar() {\n return String.fromCharCode(this.readInt8());\n }\n /**\n * Read `n` 1-byte ASCII characters and move pointer forward by `n` bytes.\n */\n readChars(n = 1) {\n let result = '';\n for (let i = 0; i < n; i++) {\n result += this.readChar();\n }\n return result;\n }\n /**\n * Read the next `n` bytes, return a UTF-8 decoded string and move pointer\n * forward by `n` bytes.\n */\n readUtf8(n = 1) {\n const bString = this.readChars(n);\n return decode(bString);\n }\n /**\n * Write 0xff if the passed value is truthy, 0x00 otherwise and move pointer\n * forward by 1 byte.\n */\n writeBoolean(value) {\n this.writeUint8(value ? 0xff : 0x00);\n return this;\n }\n /**\n * Write `value` as an 8-bit signed integer and move pointer forward by 1 byte.\n */\n writeInt8(value) {\n this.ensureAvailable(1);\n this._data.setInt8(this.offset++, value);\n this._updateLastWrittenByte();\n return this;\n }\n /**\n * Write `value` as an 8-bit unsigned integer and move pointer forward by 1\n * byte.\n */\n writeUint8(value) {\n this.ensureAvailable(1);\n this._data.setUint8(this.offset++, value);\n this._updateLastWrittenByte();\n return this;\n }\n /**\n * An alias for {@link IOBuffer#writeUint8}.\n */\n writeByte(value) {\n return this.writeUint8(value);\n }\n /**\n * Write all elements of `bytes` as uint8 values and move pointer forward by\n * `bytes.length` bytes.\n */\n writeBytes(bytes) {\n this.ensureAvailable(bytes.length);\n for (let i = 0; i < bytes.length; i++) {\n this._data.setUint8(this.offset++, bytes[i]);\n }\n this._updateLastWrittenByte();\n return this;\n }\n /**\n * Write `value` as a 16-bit signed integer and move pointer forward by 2\n * bytes.\n */\n writeInt16(value) {\n this.ensureAvailable(2);\n this._data.setInt16(this.offset, value, this.littleEndian);\n this.offset += 2;\n this._updateLastWrittenByte();\n return this;\n }\n /**\n * Write `value` as a 16-bit unsigned integer and move pointer forward by 2\n * bytes.\n */\n writeUint16(value) {\n this.ensureAvailable(2);\n this._data.setUint16(this.offset, value, this.littleEndian);\n this.offset += 2;\n this._updateLastWrittenByte();\n return this;\n }\n /**\n * Write `value` as a 32-bit signed integer and move pointer forward by 4\n * bytes.\n */\n writeInt32(value) {\n this.ensureAvailable(4);\n this._data.setInt32(this.offset, value, this.littleEndian);\n this.offset += 4;\n this._updateLastWrittenByte();\n return this;\n }\n /**\n * Write `value` as a 32-bit unsigned integer and move pointer forward by 4\n * bytes.\n */\n writeUint32(value) {\n this.ensureAvailable(4);\n this._data.setUint32(this.offset, value, this.littleEndian);\n this.offset += 4;\n this._updateLastWrittenByte();\n return this;\n }\n /**\n * Write `value` as a 32-bit floating number and move pointer forward by 4\n * bytes.\n */\n writeFloat32(value) {\n this.ensureAvailable(4);\n this._data.setFloat32(this.offset, value, this.littleEndian);\n this.offset += 4;\n this._updateLastWrittenByte();\n return this;\n }\n /**\n * Write `value` as a 64-bit floating number and move pointer forward by 8\n * bytes.\n */\n writeFloat64(value) {\n this.ensureAvailable(8);\n this._data.setFloat64(this.offset, value, this.littleEndian);\n this.offset += 8;\n this._updateLastWrittenByte();\n return this;\n }\n /**\n * Write the charCode of `str`'s first character as an 8-bit unsigned integer\n * and move pointer forward by 1 byte.\n */\n writeChar(str) {\n return this.writeUint8(str.charCodeAt(0));\n }\n /**\n * Write the charCodes of all `str`'s characters as 8-bit unsigned integers\n * and move pointer forward by `str.length` bytes.\n */\n writeChars(str) {\n for (let i = 0; i < str.length; i++) {\n this.writeUint8(str.charCodeAt(i));\n }\n return this;\n }\n /**\n * UTF-8 encode and write `str` to the current pointer offset and move pointer\n * forward according to the encoded length.\n */\n writeUtf8(str) {\n const bString = encode(str);\n return this.writeChars(bString);\n }\n /**\n * Export a Uint8Array view of the internal buffer.\n * The view starts at the byte offset and its length\n * is calculated to stop at the last written byte or the original length.\n */\n toArray() {\n return new Uint8Array(this.buffer, this.byteOffset, this.lastWrittenByte);\n }\n /**\n * Update the last written byte offset\n * @private\n */\n _updateLastWrittenByte() {\n if (this.offset > this.lastWrittenByte) {\n this.lastWrittenByte = this.offset;\n }\n }\n}\n//# sourceMappingURL=IOBuffer.js.map","'use strict';\n\n/**\n * Throws a non-valid NetCDF exception if the statement it's true\n * @ignore\n * @param {boolean} statement - Throws if true\n * @param {string} reason - Reason to throw\n */\nfunction notNetcdf(statement, reason) {\n if (statement) {\n throw new TypeError(`Not a valid NetCDF v3.x file: ${reason}`);\n }\n}\n\n/**\n * Moves 1, 2, or 3 bytes to next 4-byte boundary\n * @ignore\n * @param {IOBuffer} buffer - Buffer for the file data\n */\nfunction padding(buffer) {\n if ((buffer.offset % 4) !== 0) {\n buffer.skip(4 - (buffer.offset % 4));\n }\n}\n\n\n/**\n * Reads the name\n * @ignore\n * @param {IOBuffer} buffer - Buffer for the file data\n * @return {string} - Name\n */\nfunction readName(buffer) {\n // Read name\n var nameLength = buffer.readUint32();\n var name = buffer.readChars(nameLength);\n\n // validate name\n // TODO\n\n // Apply padding\n padding(buffer);\n return name;\n}\n\nmodule.exports.notNetcdf = notNetcdf;\nmodule.exports.padding = padding;\nmodule.exports.readName = readName;\n","'use strict';\n\nconst notNetcdf = require('./utils').notNetcdf;\n\nconst types = {\n BYTE: 1,\n CHAR: 2,\n SHORT: 3,\n INT: 4,\n FLOAT: 5,\n DOUBLE: 6\n};\n\n/**\n * Parse a number into their respective type\n * @ignore\n * @param {number} type - integer that represents the type\n * @return {string} - parsed value of the type\n */\nfunction num2str(type) {\n switch (Number(type)) {\n case types.BYTE:\n return 'byte';\n case types.CHAR:\n return 'char';\n case types.SHORT:\n return 'short';\n case types.INT:\n return 'int';\n case types.FLOAT:\n return 'float';\n case types.DOUBLE:\n return 'double';\n /* istanbul ignore next */\n default:\n return 'undefined';\n }\n}\n\n/**\n * Parse a number type identifier to his size in bytes\n * @ignore\n * @param {number} type - integer that represents the type\n * @return {number} -size of the type\n */\nfunction num2bytes(type) {\n switch (Number(type)) {\n case types.BYTE:\n return 1;\n case types.CHAR:\n return 1;\n case types.SHORT:\n return 2;\n case types.INT:\n return 4;\n case types.FLOAT:\n return 4;\n case types.DOUBLE:\n return 8;\n /* istanbul ignore next */\n default:\n return -1;\n }\n}\n\n/**\n * Reverse search of num2str\n * @ignore\n * @param {string} type - string that represents the type\n * @return {number} - parsed value of the type\n */\nfunction str2num(type) {\n switch (String(type)) {\n case 'byte':\n return types.BYTE;\n case 'char':\n return types.CHAR;\n case 'short':\n return types.SHORT;\n case 'int':\n return types.INT;\n case 'float':\n return types.FLOAT;\n case 'double':\n return types.DOUBLE;\n /* istanbul ignore next */\n default:\n return -1;\n }\n}\n\n/**\n * Auxiliary function to read numeric data\n * @ignore\n * @param {number} size - Size of the element to read\n * @param {function} bufferReader - Function to read next value\n * @return {Array|number}\n */\nfunction readNumber(size, bufferReader) {\n if (size !== 1) {\n var numbers = new Array(size);\n for (var i = 0; i < size; i++) {\n numbers[i] = bufferReader();\n }\n return numbers;\n } else {\n return bufferReader();\n }\n}\n\n/**\n * Given a type and a size reads the next element\n * @ignore\n * @param {IOBuffer} buffer - Buffer for the file data\n * @param {number} type - Type of the data to read\n * @param {number} size - Size of the element to read\n * @return {string|Array|number}\n */\nfunction readType(buffer, type, size) {\n switch (type) {\n case types.BYTE:\n return buffer.readBytes(size);\n case types.CHAR:\n return trimNull(buffer.readChars(size));\n case types.SHORT:\n return readNumber(size, buffer.readInt16.bind(buffer));\n case types.INT:\n return readNumber(size, buffer.readInt32.bind(buffer));\n case types.FLOAT:\n return readNumber(size, buffer.readFloat32.bind(buffer));\n case types.DOUBLE:\n return readNumber(size, buffer.readFloat64.bind(buffer));\n /* istanbul ignore next */\n default:\n notNetcdf(true, `non valid type ${type}`);\n return undefined;\n }\n}\n\n/**\n * Removes null terminate value\n * @ignore\n * @param {string} value - String to trim\n * @return {string} - Trimmed string\n */\nfunction trimNull(value) {\n if (value.charCodeAt(value.length - 1) === 0) {\n return value.substring(0, value.length - 1);\n }\n return value;\n}\n\nmodule.exports = types;\nmodule.exports.num2str = num2str;\nmodule.exports.num2bytes = num2bytes;\nmodule.exports.str2num = str2num;\nmodule.exports.readType = readType;\n","'use strict';\n\nconst types = require('./types');\n\n// const STREAMING = 4294967295;\n\n/**\n * Read data for the given non-record variable\n * @ignore\n * @param {IOBuffer} buffer - Buffer for the file data\n * @param {object} variable - Variable metadata\n * @return {Array} - Data of the element\n */\nfunction nonRecord(buffer, variable) {\n // variable type\n const type = types.str2num(variable.type);\n\n // size of the data\n var size = variable.size / types.num2bytes(type);\n\n // iterates over the data\n var data = new Array(size);\n for (var i = 0; i < size; i++) {\n data[i] = types.readType(buffer, type, 1);\n }\n\n return data;\n}\n\n/**\n * Read data for the given record variable\n * @ignore\n * @param {IOBuffer} buffer - Buffer for the file data\n * @param {object} variable - Variable metadata\n * @param {object} recordDimension - Record dimension metadata\n * @return {Array} - Data of the element\n */\nfunction record(buffer, variable, recordDimension) {\n // variable type\n const type = types.str2num(variable.type);\n const width = variable.size ? variable.size / types.num2bytes(type) : 1;\n\n // size of the data\n // TODO streaming data\n var size = recordDimension.length;\n\n // iterates over the data\n var data = new Array(size);\n const step = recordDimension.recordStep;\n\n for (var i = 0; i < size; i++) {\n var currentOffset = buffer.offset;\n data[i] = types.readType(buffer, type, width);\n buffer.seek(currentOffset + step);\n }\n\n return data;\n}\n\nmodule.exports.nonRecord = nonRecord;\nmodule.exports.record = record;\n","'use strict';\n\nconst utils = require('./utils');\nconst types = require('./types');\n\n// Grammar constants\nconst ZERO = 0;\nconst NC_DIMENSION = 10;\nconst NC_VARIABLE = 11;\nconst NC_ATTRIBUTE = 12;\n\n/**\n * Read the header of the file\n * @ignore\n * @param {IOBuffer} buffer - Buffer for the file data\n * @param {number} version - Version of the file\n * @return {object} - Object with the fields:\n * * `recordDimension`: Number with the length of record dimension\n * * `dimensions`: List of dimensions\n * * `globalAttributes`: List of global attributes\n * * `variables`: List of variables\n */\nfunction header(buffer, version) {\n // Length of record dimension\n // sum of the varSize's of all the record variables.\n var header = { recordDimension: { length: buffer.readUint32() } };\n\n // Version\n header.version = version;\n\n // List of dimensions\n var dimList = dimensionsList(buffer);\n header.recordDimension.id = dimList.recordId; // id of the unlimited dimension\n header.recordDimension.name = dimList.recordName; // name of the unlimited dimension\n header.dimensions = dimList.dimensions;\n\n // List of global attributes\n header.globalAttributes = attributesList(buffer);\n\n // List of variables\n var variables = variablesList(buffer, dimList.recordId, version);\n header.variables = variables.variables;\n header.recordDimension.recordStep = variables.recordStep;\n\n return header;\n}\n\nconst NC_UNLIMITED = 0;\n\n/**\n * List of dimensions\n * @ignore\n * @param {IOBuffer} buffer - Buffer for the file data\n * @return {object} - Ojbect containing the following properties:\n * * `dimensions` that is an array of dimension object:\n * * `name`: String with the name of the dimension\n * * `size`: Number with the size of the dimension dimensions: dimensions\n * * `recordId`: the id of the dimension that has unlimited size or undefined,\n * * `recordName`: name of the dimension that has unlimited size\n */\nfunction dimensionsList(buffer) {\n var recordId, recordName;\n const dimList = buffer.readUint32();\n if (dimList === ZERO) {\n utils.notNetcdf((buffer.readUint32() !== ZERO), 'wrong empty tag for list of dimensions');\n return [];\n } else {\n utils.notNetcdf((dimList !== NC_DIMENSION), 'wrong tag for list of dimensions');\n\n // Length of dimensions\n const dimensionSize = buffer.readUint32();\n var dimensions = new Array(dimensionSize);\n for (var dim = 0; dim < dimensionSize; dim++) {\n // Read name\n var name = utils.readName(buffer);\n\n // Read dimension size\n const size = buffer.readUint32();\n if (size === NC_UNLIMITED) { // in netcdf 3 one field can be of size unlimmited\n recordId = dim;\n recordName = name;\n }\n\n dimensions[dim] = {\n name: name,\n size: size\n };\n }\n }\n return {\n dimensions: dimensions,\n recordId: recordId,\n recordName: recordName\n };\n}\n\n/**\n * List of attributes\n * @ignore\n * @param {IOBuffer} buffer - Buffer for the file data\n * @return {Array} - List of attributes with:\n * * `name`: String with the name of the attribute\n * * `type`: String with the type of the attribute\n * * `value`: A number or string with the value of the attribute\n */\nfunction attributesList(buffer) {\n const gAttList = buffer.readUint32();\n if (gAttList === ZERO) {\n utils.notNetcdf((buffer.readUint32() !== ZERO), 'wrong empty tag for list of attributes');\n return [];\n } else {\n utils.notNetcdf((gAttList !== NC_ATTRIBUTE), 'wrong tag for list of attributes');\n\n // Length of attributes\n const attributeSize = buffer.readUint32();\n var attributes = new Array(attributeSize);\n for (var gAtt = 0; gAtt < attributeSize; gAtt++) {\n // Read name\n var name = utils.readName(buffer);\n\n // Read type\n var type = buffer.readUint32();\n utils.notNetcdf(((type < 1) || (type > 6)), `non valid type ${type}`);\n\n // Read attribute\n var size = buffer.readUint32();\n var value = types.readType(buffer, type, size);\n\n // Apply padding\n utils.padding(buffer);\n\n attributes[gAtt] = {\n name: name,\n type: types.num2str(type),\n value: value\n };\n }\n }\n return attributes;\n}\n\n/**\n * List of variables\n * @ignore\n * @param {IOBuffer} buffer - Buffer for the file data\n * @param {number} recordId - Id of the unlimited dimension (also called record dimension)\n * This value may be undefined if there is no unlimited dimension\n * @param {number} version - Version of the file\n * @return {object} - Number of recordStep and list of variables with:\n * * `name`: String with the name of the variable\n * * `dimensions`: Array with the dimension IDs of the variable\n * * `attributes`: Array with the attributes of the variable\n * * `type`: String with the type of the variable\n * * `size`: Number with the size of the variable\n * * `offset`: Number with the offset where of the variable begins\n * * `record`: True if is a record variable, false otherwise (unlimited size)\n */\n\nfunction variablesList(buffer, recordId, version) {\n const varList = buffer.readUint32();\n var recordStep = 0;\n if (varList === ZERO) {\n utils.notNetcdf((buffer.readUint32() !== ZERO), 'wrong empty tag for list of variables');\n return [];\n } else {\n utils.notNetcdf((varList !== NC_VARIABLE), 'wrong tag for list of variables');\n\n // Length of variables\n const variableSize = buffer.readUint32();\n var variables = new Array(variableSize);\n for (var v = 0; v < variableSize; v++) {\n // Read name\n var name = utils.readName(buffer);\n\n // Read dimensionality of the variable\n const dimensionality = buffer.readUint32();\n\n // Index into the list of dimensions\n var dimensionsIds = new Array(dimensionality);\n for (var dim = 0; dim < dimensionality; dim++) {\n dimensionsIds[dim] = buffer.readUint32();\n }\n\n // Read variables size\n var attributes = attributesList(buffer);\n\n // Read type\n var type = buffer.readUint32();\n utils.notNetcdf(((type < 1) && (type > 6)), `non valid type ${type}`);\n\n // Read variable size\n // The 32-bit varSize field is not large enough to contain the size of variables that require\n // more than 2^32 - 4 bytes, so 2^32 - 1 is used in the varSize field for such variables.\n const varSize = buffer.readUint32();\n\n // Read offset\n var offset = buffer.readUint32();\n if (version === 2) {\n utils.notNetcdf((offset > 0), 'offsets larger than 4GB not supported');\n offset = buffer.readUint32();\n }\n\n let record = false;\n // Count amount of record variables\n if ((typeof recordId !== 'undefined') && (dimensionsIds[0] === recordId)) {\n recordStep += varSize;\n record = true;\n }\n variables[v] = {\n name: name,\n dimensions: dimensionsIds,\n attributes,\n type: types.num2str(type),\n size: varSize,\n offset,\n record\n };\n }\n }\n\n return {\n variables: variables,\n recordStep: recordStep\n };\n}\n\nmodule.exports = header;\n","'use strict';\n\nfunction toString() {\n let result = [];\n\n result.push('DIMENSIONS');\n for (let dimension of this.dimensions) {\n result.push(` ${dimension.name.padEnd(30)} = size: ${dimension.size}`);\n }\n\n result.push('');\n result.push('GLOBAL ATTRIBUTES');\n for (let attribute of this.globalAttributes) {\n result.push(` ${attribute.name.padEnd(30)} = ${attribute.value}`);\n }\n\n let variables = JSON.parse(JSON.stringify(this.variables));\n result.push('');\n result.push('VARIABLES:');\n for (let variable of variables) {\n variable.value = this.getDataVariable(variable);\n let stringify = JSON.stringify(variable.value);\n if (stringify.length > 50) stringify = stringify.substring(0, 50);\n if (!isNaN(variable.value.length)) {\n stringify += ` (length: ${variable.value.length})`;\n }\n result.push(` ${variable.name.padEnd(30)} = ${stringify}`);\n }\n return result.join('\\n');\n}\n\nmodule.exports = toString;\n","'use strict';\n\nconst { IOBuffer } = require('iobuffer');\n\nconst utils = require('./utils');\nconst data = require('./data');\nconst readHeader = require('./header');\nconst toString = require('./toString');\n\n/**\n * Reads a NetCDF v3.x file\n * https://www.unidata.ucar.edu/software/netcdf/docs/file_format_specifications.html\n * @param {ArrayBuffer} data - ArrayBuffer or any Typed Array (including Node.js' Buffer from v4) with the data\n * @constructor\n */\nclass NetCDFReader {\n constructor(data) {\n const buffer = new IOBuffer(data);\n buffer.setBigEndian();\n\n // Validate that it's a NetCDF file\n utils.notNetcdf(buffer.readChars(3) !== 'CDF', 'should start with CDF');\n\n // Check the NetCDF format\n const version = buffer.readByte();\n utils.notNetcdf(version > 2, 'unknown version');\n\n // Read the header\n this.header = readHeader(buffer, version);\n this.buffer = buffer;\n }\n\n /**\n * @return {string} - Version for the NetCDF format\n */\n get version() {\n if (this.header.version === 1) {\n return 'classic format';\n } else {\n return '64-bit offset format';\n }\n }\n\n /**\n * @return {object} - Metadata for the record dimension\n * * `length`: Number of elements in the record dimension\n * * `id`: Id number in the list of dimensions for the record dimension\n * * `name`: String with the name of the record dimension\n * * `recordStep`: Number with the record variables step size\n */\n get recordDimension() {\n return this.header.recordDimension;\n }\n\n /**\n * @return {Array} - List of dimensions with:\n * * `name`: String with the name of the dimension\n * * `size`: Number with the size of the dimension\n */\n get dimensions() {\n return this.header.dimensions;\n }\n\n /**\n * @return {Array} - List of global attributes with:\n * * `name`: String with the name of the attribute\n * * `type`: String with the type of the attribute\n * * `value`: A number or string with the value of the attribute\n */\n get globalAttributes() {\n return this.header.globalAttributes;\n }\n\n /**\n * Returns the value of an attribute\n * @param {string} attributeName\n * @return {string} Value of the attributeName or null\n */\n getAttribute(attributeName) {\n const attribute = this.globalAttributes.find(\n (val) => val.name === attributeName\n );\n if (attribute) return attribute.value;\n return null;\n }\n\n /**\n * Returns the value of a variable as a string\n * @param {string} variableName\n * @return {string} Value of the variable as a string or null\n */\n getDataVariableAsString(variableName) {\n const variable = this.getDataVariable(variableName);\n if (variable) return variable.join('');\n return null;\n }\n\n /**\n * @return {Array} - List of variables with:\n * * `name`: String with the name of the variable\n * * `dimensions`: Array with the dimension IDs of the variable\n * * `attributes`: Array with the attributes of the variable\n * * `type`: String with the type of the variable\n * * `size`: Number with the size of the variable\n * * `offset`: Number with the offset where of the variable begins\n * * `record`: True if is a record variable, false otherwise\n */\n get variables() {\n return this.header.variables;\n }\n\n toString() {\n return toString.call(this);\n }\n\n /**\n * Retrieves the data for a given variable\n * @param {string|object} variableName - Name of the variable to search or variable object\n * @return {Array} - List with the variable values\n */\n getDataVariable(variableName) {\n let variable;\n if (typeof variableName === 'string') {\n // search the variable\n variable = this.header.variables.find(function (val) {\n return val.name === variableName;\n });\n } else {\n variable = variableName;\n }\n\n // throws if variable not found\n utils.notNetcdf(\n variable === undefined,\n `variable not found: ${variableName}`\n );\n\n // go to the offset position\n this.buffer.seek(variable.offset);\n\n if (variable.record) {\n // record variable case\n return data.record(this.buffer, variable, this.header.recordDimension);\n } else {\n // non-record variable case\n return data.nonRecord(this.buffer, variable);\n }\n }\n\n /**\n * Check if a dataVariable exists\n * @param {string} variableName - Name of the variable to find\n * @return {boolean}\n */\n dataVariableExists(variableName) {\n const variable = this.header.variables.find(function (val) {\n return val.name === variableName;\n });\n return variable !== undefined;\n }\n\n /**\n * Check if an attribute exists\n * @param {string} attributeName - Name of the attribute to find\n * @return {boolean}\n */\n attributeExists(attributeName) {\n const attribute = this.globalAttributes.find(\n (val) => val.name === attributeName\n );\n return attribute !== undefined;\n }\n}\n\nmodule.exports = NetCDFReader;\n","'use strict';\n\n/* reader.toString() provides the following information\n GLOBAL ATTRIBUTES\n dataset_completeness = C1+C2\n ms_template_revision = 1.0.1\n netcdf_revision = 2.3.2\n languages = English\n administrative_comments = 1% CH2Cl2\n dataset_origin = Santa Clara, CA\n netcdf_file_date_time_stamp = 20161012052159+0200\n experiment_title = P071 Essence super BP\n experiment_date_time_stamp = 20070923040800+0200\n operator_name = SC\n external_file_ref_0 = FIRE_RTL.M\n experiment_type = Centroided Mass Spectrum\n number_of_times_processed = 1\n number_of_times_calibrated = 0\n sample_state = Other State\n test_separation_type = No Chromatography\n test_ms_inlet = Capillary Direct\n test_ionization_mode = Electron Impact\n test_ionization_polarity = Positive Polarity\n test_detector_type = Electron Multiplier\n test_resolution_type = Constant Resolution\n test_scan_function = Mass Scan\n test_scan_direction = Up\n test_scan_law = Linear\n raw_data_mass_format = Float\n raw_data_time_format = Short\n raw_data_intensity_format = Float\n\n VARIABLES:\n error_log = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 64)\n a_d_sampling_rate = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 6401)\n a_d_coaddition_factor = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 6402)\n scan_acquisition_time = [5.25,5.84,6.428999999999999,7.019,7.609,8.199,8.7 (length: 6401)\n scan_duration = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 6401)\n inter_scan_time = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 6401)\n resolution = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 6401)\n actual_scan_number = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 (length: 6401)\n total_intensity = [3134,3157,3085,3134,3093,3113,3061,3057,3030,3166 (length: 6401)\n mass_range_min = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 6401)\n mass_range_max = [206.89999389648438,206.89999389648438,207,207.100 (length: 6401)\n time_range_min = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 6401)\n time_range_max = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 6401)\n scan_index = [0,11,22,33,44,55,66,76,88,99,111,122,134,145,156, (length: 6401)\n point_count = [11,11,11,11,11,11,10,12,11,12,11,12,11,11,11,11,1 (length: 6401)\n flag_count = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 6401)\n mass_values = [16,17,18.100000381469727,28,32,35,36,38,40,44.099 (length: 157201)\n time_values = [9.969209968386869e+36,9.969209968386869e+36,9.969 (length: 157201)\n intensity_values = [37,293,1243,737,420,45,196,72,22,35,34,28,299,123 (length: 157201)\n instrument_name = [\"G\",\"a\",\"s\",\" \",\"C\",\"h\",\"r\",\"o\",\"m\",\"a\",\"t\",\"o\",\" (length: 32)\n instrument_id = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_mfr = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_model = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_serial_no = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_sw_version = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_fw_version = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_os_version = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_app_version = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_comments = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n*/\n\nfunction agilentGCMS(reader) {\n const time = reader.getDataVariable('scan_acquisition_time');\n const tic = reader.getDataVariable('total_intensity');\n\n // variables to get the mass-intensity values\n const pointCount = reader.getDataVariable('point_count');\n const massValues = reader.getDataVariable('mass_values');\n const intensityValues = reader.getDataVariable('intensity_values');\n\n let ms = new Array(pointCount.length);\n let index = 0;\n for (let i = 0; i < ms.length; i++) {\n let size = pointCount[i];\n ms[i] = [new Array(size), new Array(size)];\n\n for (let j = 0; j < size; j++) {\n ms[i][0][j] = massValues[index];\n ms[i][1][j] = intensityValues[index++];\n }\n }\n\n return {\n times: time,\n series: [\n {\n name: 'tic',\n dimension: 1,\n data: tic,\n },\n {\n name: 'ms',\n dimension: 2,\n data: ms,\n },\n ],\n };\n}\n\nmodule.exports = agilentGCMS;\n","'use strict';\n\n/* reader.toString() provides the following information\n GLOBAL ATTRIBUTES\n dataset_completeness = C1+C2\n ms_template_revision = 1.0.1\n netcdf_revision = 2.3.2\n languages = English\n netcdf_file_date_time_stamp = 20170428032023+0000\n experiment_title = MS51762A16\n 11829FC03_3__60_40\n experiment_date_time_stamp = 20160930202145-0001\n operator_name = Begemann/Eikenberg/Roettger\n pre_experiment_program_name = otofControl 3.4.16.0\n post_experiment_program_name = Bruker Compass DataAnalysis 4.2\n source_file_reference = X:\\2016\\MS5\\1700\\MS51762A16.d\n source_file_format = Bruker Daltonics Data File\n experiment_type = Centroided Mass Spectrum\n sample_state = Other State\n test_separation_type = No Chromatography\n test_ms_inlet = Direct Inlet Probe\n test_ionization_mode = Electrospray Ionization\n test_ionization_polarity = Positive Polarity\n test_detector_type = Electron Multiplier\n test_resolution_type = Proportional Resolution\n test_scan_function = Mass Scan\n test_scan_direction = Up\n test_scan_law = Linear\n raw_data_mass_format = Double\n raw_data_time_format = Float\n raw_data_intensity_format = Float\n units = Seconds\n scale_factor = 1\n\n VARIABLES:\n error_log = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 64)\n a_d_sampling_rate = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 4513)\n a_d_coaddition_factor = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 4514)\n scan_acquisition_time = [0.329,0.73,1.132,1.534,1.936,2.337,2.739,3.14,3.5 (length: 4513)\n scan_duration = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 4513)\n inter_scan_time = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 4513)\n resolution = [106.6623112889557,110.7855343519544,104.407495112 (length: 4513)\n actual_scan_number = [0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34, (length: 4513)\n total_intensity = [5297.4945068359375,6172.123912811279,5934.7557412 (length: 4513)\n mass_range_min = [49.99999997418507,49.99999997418507,49.9999999741 (length: 4513)\n mass_range_max = [1599.9999564432276,1599.9999564432276,1599.999956 (length: 4513)\n time_range_min = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 4513)\n time_range_max = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 4513)\n scan_index = [0,60,128,195,265,324,399,472,542,596,671,738,803, (length: 4513)\n point_count = [60,68,67,70,59,75,73,70,54,75,67,65,64,73,56,69,6 (length: 4513)\n flag_count = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 4513)\n mass_values = [51.53516375878996,95.32974890044508,106.334477231 (length: 1176507)\n intensity_values = [76.99999237060547,80,90,78.99799346923828,80.9352 (length: 1176507)\n instrument_name = [\"m\",\"i\",\"c\",\"r\",\"O\",\"T\",\"O\",\"F\",\"\",\" \",\" \",\" \",\" (length: 32)\n instrument_id = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_mfr = [\"B\",\"r\",\"u\",\"k\",\"e\",\"r\",\" \",\"D\",\"a\",\"l\",\"t\",\"o\",\" (length: 32)\n instrument_model = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_serial_no = [\"2\",\"1\",\"3\",\"7\",\"5\",\"0\",\".\",\"1\",\"0\",\"3\",\"5\",\"9\",\" (length: 32)\n instrument_sw_version = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_fw_version = [\"\",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_os_version = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_app_version = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_comments = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n*/\n\nfunction finniganGCMS(reader) {\n const time = reader.getDataVariable('scan_acquisition_time');\n const tic = reader.getDataVariable('total_intensity');\n\n // variables to get the mass-intensity values\n let scanIndex = reader.getDataVariable('scan_index');\n const massValues = reader.getDataVariable('mass_values');\n const intensityValues = reader.getDataVariable('intensity_values');\n scanIndex.push(massValues.length);\n\n let ms = new Array(time.length);\n let index = 0;\n for (let i = 0; i < ms.length; i++) {\n let size = scanIndex[i + 1] - scanIndex[i];\n ms[i] = [new Array(size), new Array(size)];\n\n for (let j = 0; j < size; j++) {\n ms[i][0][j] = massValues[index];\n ms[i][1][j] = intensityValues[index++];\n }\n }\n\n return {\n times: time,\n series: [\n {\n name: 'tic',\n dimension: 1,\n data: tic,\n },\n {\n name: 'ms',\n dimension: 2,\n data: ms,\n },\n ],\n };\n}\n\nmodule.exports = finniganGCMS;\n","'use strict';\n\n/* reader.toString() provides the following information\n GLOBAL ATTRIBUTES\n dataset_completeness = C1+C2\n aia_template_revision = 1.0\n netcdf_revision = 2.3\n languages = English only\n injection_date_time_stamp = 20181030174305+0000\n HP_injection_time = 30-Oct-18, 17:43:05\n experiment_title = SequenceLine: 1 Inj: 1\n operator_name = SYSTEM\n separation_experiment_type = liquid chromatography\n source_file_reference = C:\\CHEM32\\1\\DATA\\MINGMING\\MW-1-MEO-I IC-90 2018-10-30 17-42-13\\MW-2-6-6 IC 90.D\n sample_name = MW-2-6-6 IC 90\n sample_id =\n detector_unit = mAU\n detection_method_name = POS 3 IC 90-10 31 MIN.M\n detector_name = DAD1 A, Sig=254,4 Ref=360,100\n retention_unit = seconds\n\n VARIABLES:\n detector_maximum_value = [130.9263458251953] (length: 1)\n detector_minimum_value = [-0.1758841574192047] (length: 1)\n actual_run_time_length = [1860] (length: 1)\n actual_delay_time = [0.012000000104308128] (length: 1)\n actual_sampling_interval = [0.4000000059604645] (length: 1)\n ordinate_values = [-0.07588416337966919,-0.07525086402893066,-0.0740 (length: 4651)\n peak_retention_time = [196.0651397705078,332.5663757324219,527.549865722 (length: 8)\n peak_start_time = [186.81199645996094,239.21200561523438,502.4119873 (length: 8)\n peak_end_time = [220.81201171875,471.5176696777344,572.47869873046 (length: 8)\n peak_width = [4.974428176879883,62.90694808959961,11.9328641891 (length: 8)\n peak_area = [556.7650146484375,419.825439453125,66.56610107421 (length: 8)\n peak_area_percent = [7.0321502685546875,5.302552223205566,0.8407546877 (length: 8)\n peak_height = [100.07515716552734,5.1860527992248535,4.827196121 (length: 8)\n peak_height_percent = [29.76352310180664,1.5423927307128906,1.4356645345 (length: 8)\n peak_asymmetry = [1.4555920362472534,0.8351489901542664,1.707817316 (length: 8)\n baseline_start_time = [186.81199645996094,239.21200561523438,502.4119873 (length: 8)\n baseline_start_value = [1.9561424255371094,0.9857341647148132,1.127734780 (length: 8)\n baseline_stop_time = [220.81201171875,471.5176696777344,572.47869873046 (length: 8)\n baseline_stop_value = [1.1907591819763184,1.10896897315979,1.18347382545 (length: 8)\n peak_start_detection_code = [\"B\",\"\",\"B\",\"\",\"B\",\"\",\"B\",\"\",\"V\",\"\",\"B\",\"\",\"B\",\"\", (length: 16)\n peak_stop_detection_code = [\"B\",\"\",\"B\",\"\",\"B\",\"\",\"V\",\"\",\"B\",\"\",\"B\",\"\",\"B\",\"\", (length: 16)\n migration_time = [196.0651397705078,332.5663757324219,527.549865722 (length: 8)\n peak_area_square_root = [23.595869064331055,20.489643096923828,8.158804893 (length: 8)\n manually_reintegrated_peaks = [0,0,0,0,0,0,0,0] (length: 8)\n*/\n\nfunction agilentHPLC(reader) {\n const intensities = reader.getDataVariable('ordinate_values');\n const numberPoints = intensities.length;\n const detector = reader.getAttribute('detector_name');\n let channel;\n if (detector.match(/dad/i)) {\n channel = `uv${Number(detector.replace(/.*Sig=([0-9]+).*/, '$1'))}`;\n } else if (detector.match(/tic/i)) {\n channel = 'tic';\n } else {\n channel = 'unknown';\n }\n const delayTime = reader.getDataVariable('actual_delay_time')[0];\n const runtimeLength = reader.getDataVariable('actual_run_time_length')[0];\n let samplingInterval;\n if (reader.dataVariableExists('actual_sampling_interval')) {\n samplingInterval = reader.getDataVariable('actual_sampling_interval')[0];\n\n if (\n Math.abs(delayTime + samplingInterval * numberPoints - runtimeLength) > 3\n ) {\n throw new Error(\n 'The expected last time does not correspond to the runtimeLength',\n );\n }\n } else {\n samplingInterval = (runtimeLength - delayTime) / numberPoints;\n }\n\n let times = [];\n let time = delayTime;\n for (let i = 0; i < numberPoints; i++) {\n times.push(time);\n time += samplingInterval;\n }\n\n return {\n times,\n series: [\n {\n name: channel,\n dimension: 1,\n data: intensities,\n },\n ],\n };\n}\n\nmodule.exports = agilentHPLC;\n","'use strict';\n\n/* reader.toString() provides the following information\n GLOBAL ATTRIBUTES\n dataset_completeness = C1+C2\n ms_template_revision = 1.0.1\n administrative_comments =\n dataset_owner =\n experiment_title =\n experiment_date_time_stamp = 20150902041002+0100\n netcdf_file_date_time_stamp = 20151026063419+0000\n experiment_type = Centroided Mass Spectrum\n netcdf_revision = 2.3.2\n operator_name = DSQ\n source_file_reference = G:\\FCO\\FCO_CIO\\K2\\MP2013\\T1 IL database 2013\\9. IL Data Entry\\12_HU_HIFS\\IL database\\RAW files\\Lukoil-Disel-150901.RAW\n source_file_date_time_stamp = 20150902041002+0100\n source_file_format = Finnigan\n languages = English\n external_file_ref_0 =\n instrument_number = 1\n sample_prep_comments =\n sample_comments = Lukoil Disel 0,5 % CS2 1 % inkt\n test_separation_type =\n test_ms_inlet =\n test_ionization_mode =\n test_ionization_polarity = Positive Polarity\n test_detector_type = Conversion Dynode Electron Multiplier\n test_scan_function = Mass Scan\n test_scan_direction =\n test_scan_law = Linear\n number_of_scans = 11832\n raw_data_mass_format = Double\n raw_data_intensity_format = Long\n actual_run_time = 3519.6410000000005\n actual_delay_time = 82.328\n global_mass_min = 0\n global_mass_max = 450\n calibrated_mass_min = 0\n calibrated_mass_max = 0\n mass_axis_label = M/Z\n intensity_axis_label = Abundance\n\n VARIABLES:\n error_log = [\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\" (length: 64)\n instrument_name = [\"L\",\"C\",\"Q\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\" (length: 32)\n instrument_id = [\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\" (length: 32)\n instrument_mfr = [\"F\",\"i\",\"n\",\"n\",\"i\",\"g\",\"a\",\"n\",\"-\",\"M\",\"A\",\"T\",\" (length: 32)\n instrument_model = [\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\" (length: 32)\n instrument_sw_version = [\"3\",\".\",\"1\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\" (length: 32)\n instrument_os_version = [\"W\",\"i\",\"n\",\"d\",\"o\",\"w\",\"s\",\" \",\"V\",\"i\",\"s\",\"t\",\" (length: 32)\n scan_index = [0,34,74,113,145,177,211,239,267,299,341,374,400,4 (length: 11832)\n point_count = [34,40,39,32,32,34,28,28,32,42,33,26,29,34,31,28,2 (length: 11832)\n flag_count = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 11832)\n a_d_sampling_rate = [1000,1000,1000,1000,1000,1000,1000,1000,1000,1000 (length: 11832)\n scan_acquisition_time = [82.328,82.625,82.76599999999999,83.063,83.188,83. (length: 11832)\n scan_duration = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 11832)\n mass_range_min = [35,16,35,16,35,16,35,16,35,16,35,16,35,16,35,16,3 (length: 11832)\n mass_range_max = [450,150,450,150,450,150,450,150,450,150,450,150,4 (length: 11832)\n scan_type = [65537,65537,65537,65537,65537,65537,65537,65537,6 (length: 11832)\n resolution = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 11832)\n total_intensity = [375220,1054339,228245,576718,58280,288629,29815,1 (length: 11832)\n mass_values = [36.3023681640625,36.98402404785156,38.08326721191 (length: 1366002)\n intensity_values = [335,287,331,266,2423,448,9009,833,261,661,4003,21 (length: 1366002)\n\n*/\n\nfunction finniganGCMS(reader) {\n const time = reader.getDataVariable('scan_acquisition_time');\n const tic = reader.getDataVariable('total_intensity');\n\n // variables to get the mass-intensity values\n let scanIndex = reader.getDataVariable('scan_index');\n const massValues = reader.getDataVariable('mass_values');\n const intensityValues = reader.getDataVariable('intensity_values');\n scanIndex.push(massValues.length);\n\n let ms = new Array(time.length);\n let index = 0;\n for (let i = 0; i < ms.length; i++) {\n let size = scanIndex[i + 1] - scanIndex[i];\n ms[i] = [new Array(size), new Array(size)];\n\n for (let j = 0; j < size; j++) {\n ms[i][0][j] = massValues[index];\n ms[i][1][j] = intensityValues[index++];\n }\n }\n\n return {\n times: time,\n series: [\n {\n name: 'tic',\n dimension: 1,\n data: tic,\n },\n {\n name: 'ms',\n dimension: 2,\n data: ms,\n },\n ],\n };\n}\n\nmodule.exports = finniganGCMS;\n","'use strict';\n\n/* reader.toString() provides the following information\n GLOBAL ATTRIBUTES\n dataset_completeness = C1+C2\n ms_template_revision = 1.0.1\n netcdf_revision = 2.3.2\n languages = English\n administrative_comments =\n netcdf_file_date_time_stamp = 20180913165502+0000\n experiment_title =\n experiment_date_time_stamp = 20180910165319+0000\n operator_name = Admin\n source_file_reference = D:\\GCMSsolution\\Data\\Chromatograms\\Cato\\bormann_CB000_Test2.qgd\n source_file_format = Shimadzu GCMSsolution\n source_file_date_time_stamp = 20180910165319+0000\n experiment_type = Centroided Mass Spectrum\n sample_internal_id =\n sample_comments =\n sample_state = Other State\n test_separation_type = Gas-Solid Chromatography\n test_ms_inlet = Other Probe\n test_ionization_mode = Electron Impact\n test_ionization_polarity = Positive Polarity\n test_electron_energy = 70\n test_detector_type = Electron Multiplier\n test_resolution_type = Constant Resolution\n test_scan_function = Mass Scan\n test_scan_direction = Up\n test_scan_law = Quadratic\n test_scan_time = 0\n raw_data_mass_format = Double\n raw_data_time_format = Long\n raw_data_intensity_format = Long\n units = Seconds\n scale_factor = 1\n long_name = Seconds\n starting_scan_number = 0\n actual_run_time_length = 1289\n actual_delay_time = 0\n raw_data_uniform_sampling_flag = 1\n\n VARIABLES:\n error_log = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 64)\n a_d_sampling_rate = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 3820)\n a_d_coaddition_factor = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 3820)\n scan_acquisition_time = [144,144.3,144.6,144.9,145.2,145.5,145.8,146.1,146 (length: 3820)\n scan_duration = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 3820)\n inter_scan_time = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 3820)\n resolution = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 3820)\n actual_scan_number = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,2 (length: 3820)\n total_intensity = [63566,61702,61873,59738,58321,59001,59364,59871,6 (length: 3820)\n mass_range_min = [35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,3 (length: 3820)\n mass_range_max = [500,500,500,500,500,500,500,500,500,500,500,500,5 (length: 3820)\n time_range_min = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 3820)\n time_range_max = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 3820)\n scan_index = [0,466,932,1398,1863,2329,2795,3260,3726,4192,4658 (length: 3820)\n point_count = [466,466,466,465,466,466,465,466,466,466,466,466,4 (length: 3820)\n flag_count = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 3820)\n mass_values = [35,36,37.1,38.1,39.1,40.15,41.1,42.1,43.15,44.1,4 (length: 1779397)\n intensity_values = [26,111,412,785,3098,485,5772,7391,11213,711,687,1 (length: 1779397)\n instrument_name = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_id = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n instrument_mfr = [\"S\",\"h\",\"i\",\"m\",\"a\",\"d\",\"z\",\"u\",\" \",\"C\",\"o\",\"r\",\" (length: 32)\n instrument_model = [\"G\",\"C\",\"M\",\"S\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\", (length: 32)\n instrument_serial_no = [\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\" (length: 32)\n instrument_sw_version = [\"4\",\".\",\"2\",\"0\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\", (length: 32)\n instrument_fw_version = [\"G\",\"C\",\"M\",\"S\",\"-\",\"Q\",\"P\",\"2\",\"0\",\"1\",\"0\",\"1\",\" (length: 32)\n instrument_os_version = [\"W\",\"i\",\"n\",\"d\",\"o\",\"w\",\"s\",\"\",\"\",\"\",\"\",\"\",\"\",\"\", (length: 32)\n instrument_app_version = [\"G\",\"C\",\"M\",\"S\",\"s\",\"o\",\"l\",\"u\",\"t\",\"i\",\"o\",\"n\",\" (length: 32)\n instrument_comments = [\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" (length: 32)\n*/\n\nfunction shimadzuGCMS(reader) {\n const time = reader.getDataVariable('scan_acquisition_time');\n const tic = reader.getDataVariable('total_intensity');\n\n // variables to get the mass-intensity values\n let scanIndex = reader.getDataVariable('scan_index');\n const massValues = reader.getDataVariable('mass_values');\n const intensityValues = reader.getDataVariable('intensity_values');\n scanIndex.push(massValues.length);\n\n let ms = new Array(time.length);\n let index = 0;\n for (let i = 0; i < ms.length; i++) {\n let size = scanIndex[i + 1] - scanIndex[i];\n ms[i] = [new Array(size), new Array(size)];\n\n for (let j = 0; j < size; j++) {\n ms[i][0][j] = massValues[index];\n ms[i][1][j] = intensityValues[index++];\n }\n }\n\n return {\n times: time,\n series: [\n {\n name: 'tic',\n dimension: 1,\n data: tic,\n },\n {\n name: 'ms',\n dimension: 2,\n data: ms,\n },\n ],\n };\n}\n\nmodule.exports = shimadzuGCMS;\n","'use strict';\n\n/* reader.toString() provides the following information\n DIMENSIONS\n point_number = size: 0\n scan_number = size: 60\n error_number = size: 1\n _64_byte_string = size: 64\n\n GLOBAL ATTRIBUTES\n dataset_completeness = C1\n ms_template_revision = 1.0.1\n netcdf_revision = 4.2\n languages = English\n administrative_comments =\n netcdf_file_date_time_stamp = 202003031432433600000\n experiment_date_time_stamp = 202003031432433600000\n source_file_reference = JC-012_cleavage test_Scan1_is1.datx 2020.03.03 14:32:43\n source_file_format = Advion ExpressIon Compact Mass Spectrometer Data System\n source_file_date_time_stamp = 202003031432433600000\n experiment_title =\n experiment_type = Continuum Mass Spectrum\n test_ionization_mode = Electrospray Ionization\n test_ionization_polarity = Positive Polarity\n sample_state = Other State\n test_separation_type = No Chromatography\n test_ms_inlet = Direct Inlet Probe\n test_detector_type = Electron Multiplier\n test_resolution_type = Constant Resolution\n test_scan_function = Mass Scan\n test_scan_direction = Up\n test_scan_law = Linear\n raw_data_mass_format = Float\n raw_data_time_format = Double\n raw_data_intensity_format = Float\n units = Seconds\n global_mass_min = 9.949999809265137\n global_mass_max = 1199.75\n actual_run_time_length = 133.46099853515625\n starting_scan_number = 1\n actual_delay_time = 0\n raw_data_uniform_sampling_flag = 0\n\n VARIABLES:\n error_log = [\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\" (length: 64)\n scan_index = [0,3096,6282,9865,13409,16765,20281,23603,27099,30 (length: 60)\n point_count = [3096,3186,3583,3544,3356,3516,3322,3496,3351,3031 (length: 60)\n flag_count = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (length: 60)\n actual_scan_number = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 (length: 60)\n a_d_coaddition_factor = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 60)\n a_d_sampling_rate = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 60)\n inter_scan_time = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 60)\n mass_range_min = [9.949999809265137,9.949999809265137,9.94999980926 (length: 60)\n mass_range_max = [164.6999969482422,169.1999969482422,189.050003051 (length: 60)\n scan_acquisition_time = [0.08100000023841858,2.3420000076293945,4.60300016 (length: 60)\n scan_duration = [2.261000007390976,2.261000156402588,2.25999975204 (length: 60)\n resolution = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 60)\n time_range_min = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 60)\n time_range_max = [-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999,- (length: 60)\n total_intensity = [4498210816,4468554240,5001547264,5405233152,50000 (length: 60)\n mass_values = [9.949999809265137,83.5,83.55000305175781,83.59999 (length: 199393)\n intensity_values = [0,818716,462148,0,735558,952901,0,165241,421829,0 (length: 199393)\n*/\n\nfunction advionGCMS(reader) {\n const time = reader.getDataVariable('scan_acquisition_time');\n const tic = reader.getDataVariable('total_intensity');\n\n // variables to get the mass-intensity values\n let scanIndex = reader.getDataVariable('scan_index');\n const massValues = reader.getDataVariable('mass_values');\n const intensityValues = reader.getDataVariable('intensity_values');\n scanIndex.push(massValues.length);\n\n let ms = new Array(time.length);\n let index = 0;\n for (let i = 0; i < ms.length; i++) {\n let size = scanIndex[i + 1] - scanIndex[i];\n ms[i] = [new Array(size), new Array(size)];\n\n for (let j = 0; j < size; j++) {\n ms[i][0][j] = massValues[index];\n ms[i][1][j] = intensityValues[index++];\n }\n }\n\n return {\n times: time,\n series: [\n {\n name: 'tic',\n dimension: 1,\n data: tic,\n },\n {\n name: 'ms',\n dimension: 2,\n data: ms,\n },\n ],\n };\n}\n\nmodule.exports = advionGCMS;\n","'use strict';\n\nfunction aiaTemplate(reader) {\n let time = [];\n const tic = reader.getDataVariable('ordinate_values');\n\n // variables to get the time\n const delayTime = Number(reader.getDataVariable('actual_delay_time'));\n const interval = Number(reader.getDataVariable('actual_sampling_interval'));\n\n let currentTime = delayTime;\n for (let i = 0; i < tic.length; i++) {\n time.push(currentTime);\n currentTime += interval;\n }\n\n return {\n times: time,\n series: [\n {\n name: 'tic',\n dimension: 1,\n data: tic,\n },\n ],\n };\n}\n\nmodule.exports = aiaTemplate;\n","'use strict';\n\nconst NetCDFReader = require('netcdfjs');\n\nconst agilentGCMS = require('./agilentGCMS');\nconst brukerGCMS = require('./brukerGCMS');\nconst agilentHPLC = require('./agilentHPLC');\nconst finniganGCMS = require('./finniganGCMS');\nconst shimadzuGCMS = require('./shimadzuGCMS');\nconst advionGCMS = require('./advionGCMS');\nconst aiaTemplate = require('./aiaTemplate');\n\n/**\n * Reads a NetCDF file and returns a formatted JSON with the data from it\n * @param {ArrayBuffer} data - ArrayBuffer or any Typed Array (including Node.js' Buffer from v4) with the data\n * @param {object} [options={}]\n * @param {boolean} [options.meta] - add meta information\n * @param {boolean} [options.variables] -add variables information\n * @return {{times, series}} - JSON with the time, TIC and mass spectra values\n */\nfunction netcdfGcms(data, options = {}) {\n let reader = new NetCDFReader(data);\n const globalAttributes = reader.globalAttributes;\n\n let instrument_mfr =\n reader.dataVariableExists('instrument_mfr') &&\n reader.getDataVariableAsString('instrument_mfr');\n let dataset_origin = reader.attributeExists('dataset_origin');\n let mass_values = reader.dataVariableExists('mass_values');\n let detector_name = reader.getAttribute('detector_name');\n let aia_template_revision = reader.attributeExists('aia_template_revision');\n let source_file_format = reader.getAttribute('source_file_format');\n\n let ans;\n\n if (mass_values && dataset_origin) {\n ans = agilentGCMS(reader);\n } else if (\n mass_values &&\n instrument_mfr &&\n instrument_mfr.match(/finnigan/i)\n ) {\n ans = finniganGCMS(reader);\n } else if (mass_values && instrument_mfr && instrument_mfr.match(/bruker/i)) {\n ans = brukerGCMS(reader);\n } else if (mass_values && instrument_mfr && instrument_mfr.match(/bruker/i)) {\n ans = brukerGCMS(reader);\n } else if (\n mass_values &&\n source_file_format &&\n source_file_format.match(/shimadzu/i)\n ) {\n ans = shimadzuGCMS(reader);\n } else if (\n mass_values &&\n source_file_format &&\n source_file_format.match(/advion/i)\n ) {\n ans = advionGCMS(reader);\n } else if (detector_name && detector_name.match(/(dad|tic)/i)) {\n // diode array agilent HPLC\n ans = agilentHPLC(reader);\n } else if (aia_template_revision) {\n ans = aiaTemplate(reader);\n } else {\n throw new TypeError('Unknown file format');\n }\n\n if (options.meta) {\n ans.meta = addMeta(globalAttributes);\n }\n\n if (options.variables) {\n ans.variables = addVariables(reader);\n }\n\n return ans;\n}\n\n/**\n * Reads a NetCDF file with Agilent GCMS format and returns a formatted JSON with the data from it\n * @param {ArrayBuffer} data - ArrayBuffer or any Typed Array (including Node.js' Buffer from v4) with the data\n * @return {{times, series}} - JSON with the time, TIC and mass spectra values\n */\nfunction fromAgilentGCMS(data) {\n return agilentGCMS(new NetCDFReader(data));\n}\n\n/**\n * Reads a NetCDF file with Agilent HPLC format and returns a formatted JSON with the data from it\n * @param {ArrayBuffer} data - ArrayBuffer or any Typed Array (including Node.js' Buffer from v4) with the data\n * @return {{times, series}} - JSON with the time, TIC and mass spectra values\n */\nfunction fromAgilentHPLC(data) {\n return agilentHPLC(new NetCDFReader(data));\n}\n\n/**\n * Reads a NetCDF file with Finnigan format and returns a formatted JSON with the data from it\n * @param {ArrayBuffer} data - ArrayBuffer or any Typed Array (including Node.js' Buffer from v4) with the data\n * @return {{times, series}} - JSON with the time, TIC and mass spectra values\n */\nfunction fromFinniganGCMS(data) {\n return finniganGCMS(new NetCDFReader(data));\n}\n\nfunction fromAiaTemplate(data) {\n return aiaTemplate(new NetCDFReader(data));\n}\n\nfunction addMeta(globalAttributes) {\n let ans = {};\n for (const item of globalAttributes) {\n ans[item.name] = item.value;\n }\n return ans;\n}\n\nfunction addVariables(reader) {\n for (let variable of reader.variables) {\n variable.value = reader.getDataVariable(variable);\n }\n return reader.variables;\n}\n\nmodule.exports = netcdfGcms;\nmodule.exports.fromAgilentGCMS = fromAgilentGCMS;\nmodule.exports.fromAgilentHPLC = fromAgilentHPLC;\nmodule.exports.fromFinniganGCMS = fromFinniganGCMS;\nmodule.exports.fromAiaTemplate = fromAiaTemplate;\n","'use strict'\n\nexports.byteLength = byteLength\nexports.toByteArray = toByteArray\nexports.fromByteArray = fromByteArray\n\nvar lookup = []\nvar revLookup = []\nvar Arr = typeof Uint8Array !== 'undefined' ? Uint8Array : Array\n\nvar code = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'\nfor (var i = 0, len = code.length; i < len; ++i) {\n lookup[i] = code[i]\n revLookup[code.charCodeAt(i)] = i\n}\n\n// Support decoding URL-safe base64 strings, as Node.js does.\n// See: https://en.wikipedia.org/wiki/Base64#URL_applications\nrevLookup['-'.charCodeAt(0)] = 62\nrevLookup['_'.charCodeAt(0)] = 63\n\nfunction getLens (b64) {\n var len = b64.length\n\n if (len % 4 > 0) {\n throw new Error('Invalid string. Length must be a multiple of 4')\n }\n\n // Trim off extra bytes after placeholder bytes are found\n // See: https://github.com/beatgammit/base64-js/issues/42\n var validLen = b64.indexOf('=')\n if (validLen === -1) validLen = len\n\n var placeHoldersLen = validLen === len\n ? 0\n : 4 - (validLen % 4)\n\n return [validLen, placeHoldersLen]\n}\n\n// base64 is 4/3 + up to two characters of the original data\nfunction byteLength (b64) {\n var lens = getLens(b64)\n var validLen = lens[0]\n var placeHoldersLen = lens[1]\n return ((validLen + placeHoldersLen) * 3 / 4) - placeHoldersLen\n}\n\nfunction _byteLength (b64, validLen, placeHoldersLen) {\n return ((validLen + placeHoldersLen) * 3 / 4) - placeHoldersLen\n}\n\nfunction toByteArray (b64) {\n var tmp\n var lens = getLens(b64)\n var validLen = lens[0]\n var placeHoldersLen = lens[1]\n\n var arr = new Arr(_byteLength(b64, validLen, placeHoldersLen))\n\n var curByte = 0\n\n // if there are placeholders, only get up to the last complete 4 chars\n var len = placeHoldersLen > 0\n ? validLen - 4\n : validLen\n\n var i\n for (i = 0; i < len; i += 4) {\n tmp =\n (revLookup[b64.charCodeAt(i)] << 18) |\n (revLookup[b64.charCodeAt(i + 1)] << 12) |\n (revLookup[b64.charCodeAt(i + 2)] << 6) |\n revLookup[b64.charCodeAt(i + 3)]\n arr[curByte++] = (tmp >> 16) & 0xFF\n arr[curByte++] = (tmp >> 8) & 0xFF\n arr[curByte++] = tmp & 0xFF\n }\n\n if (placeHoldersLen === 2) {\n tmp =\n (revLookup[b64.charCodeAt(i)] << 2) |\n (revLookup[b64.charCodeAt(i + 1)] >> 4)\n arr[curByte++] = tmp & 0xFF\n }\n\n if (placeHoldersLen === 1) {\n tmp =\n (revLookup[b64.charCodeAt(i)] << 10) |\n (revLookup[b64.charCodeAt(i + 1)] << 4) |\n (revLookup[b64.charCodeAt(i + 2)] >> 2)\n arr[curByte++] = (tmp >> 8) & 0xFF\n arr[curByte++] = tmp & 0xFF\n }\n\n return arr\n}\n\nfunction tripletToBase64 (num) {\n return lookup[num >> 18 & 0x3F] +\n lookup[num >> 12 & 0x3F] +\n lookup[num >> 6 & 0x3F] +\n lookup[num & 0x3F]\n}\n\nfunction encodeChunk (uint8, start, end) {\n var tmp\n var output = []\n for (var i = start; i < end; i += 3) {\n tmp =\n ((uint8[i] << 16) & 0xFF0000) +\n ((uint8[i + 1] << 8) & 0xFF00) +\n (uint8[i + 2] & 0xFF)\n output.push(tripletToBase64(tmp))\n }\n return output.join('')\n}\n\nfunction fromByteArray (uint8) {\n var tmp\n var len = uint8.length\n var extraBytes = len % 3 // if we have 1 byte left, pad 2 bytes\n var parts = []\n var maxChunkLength = 16383 // must be multiple of 3\n\n // go through the array every three bytes, we'll deal with trailing stuff later\n for (var i = 0, len2 = len - extraBytes; i < len2; i += maxChunkLength) {\n parts.push(encodeChunk(uint8, i, (i + maxChunkLength) > len2 ? len2 : (i + maxChunkLength)))\n }\n\n // pad the end with zeros, but make sure to not forget the extra bytes\n if (extraBytes === 1) {\n tmp = uint8[len - 1]\n parts.push(\n lookup[tmp >> 2] +\n lookup[(tmp << 4) & 0x3F] +\n '=='\n )\n } else if (extraBytes === 2) {\n tmp = (uint8[len - 2] << 8) + uint8[len - 1]\n parts.push(\n lookup[tmp >> 10] +\n lookup[(tmp >> 4) & 0x3F] +\n lookup[(tmp << 2) & 0x3F] +\n '='\n )\n }\n\n return parts.join('')\n}\n","/**\n * Ensure that the data is string. If it is an ArrayBuffer it will be converted to string using TextDecoder.\n * @param blob\n * @param options\n * @returns\n */\nexport function ensureString(blob, options = {}) {\n if (typeof blob === 'string') {\n return blob;\n }\n if (ArrayBuffer.isView(blob) || blob instanceof ArrayBuffer) {\n if (options.encoding) {\n return new TextDecoder(options.encoding).decode(blob);\n }\n else {\n return decodeText(blob);\n }\n }\n throw new TypeError(`blob must be a string, ArrayBuffer or ArrayBufferView`);\n}\nfunction decodeText(blob) {\n const uint8 = ArrayBuffer.isView(blob)\n ? new Uint8Array(blob.buffer, blob.byteOffset, blob.byteLength)\n : new Uint8Array(blob);\n if (uint8.length >= 2) {\n if (uint8[0] === 0xfe && uint8[1] === 0xff) {\n return new TextDecoder('utf-16be').decode(uint8);\n }\n if (uint8[0] === 0xff && uint8[1] === 0xfe) {\n return new TextDecoder('utf-16le').decode(uint8);\n }\n }\n try {\n return new TextDecoder('utf-8', { fatal: true }).decode(uint8);\n }\n catch {\n return new TextDecoder('latin1').decode(uint8);\n }\n}\n//# sourceMappingURL=index.js.map","export function parseString(value) {\n if (value.length === 4 || value.length === 5) {\n const lowercase = value.toLowerCase();\n if (lowercase === 'true')\n return true;\n if (lowercase === 'false')\n return false;\n }\n const number = Number(value);\n if (number === 0 && !value.includes('0')) {\n return value;\n }\n if (!Number.isNaN(number))\n return number;\n return value;\n}\n//# sourceMappingURL=parseString.js.map","export const gyromagneticRatioMHzT = {\n '1H': 42.577478615342585,\n '2H': 6.5359028540009305,\n '3H': 45.41483815473964,\n '3He': -32.43604456417949,\n '6Li': 6.266099405837534,\n '7Li': 16.548177299618295,\n '9Be': -5.983379963834242,\n '10B': 4.57473388220653,\n '11B': 13.66160796005943,\n '13C': 10.707746367473973,\n '14N': 3.076272817251739,\n '15N': -4.3152552187859134,\n '17O': -5.7734832203316975,\n '19F': 40.06924371705693,\n '21Ne': -3.362579959801532,\n '22Na': 4.436349259342206,\n '23Na': 11.268733657034753,\n '25Mg': -2.607933066661972,\n '26Al': 4.273225764239245,\n '27Al': 11.100630067688776,\n '29Si': -8.461871234008282,\n '31P': 17.241162495263175,\n '33S': 3.2688220630834754,\n '35Cl': 4.175656570906633,\n '36Cl': 4.897135020041924,\n '37Cl': 3.4759025124743057,\n '39Ar': -3.462835209795831,\n '39K': 1.9893443809332112,\n '40K': -2.4734743333970806,\n '41K': 1.091921234883595,\n '41Ca': -3.4724832349401047,\n '43Ca': -2.8689916395725414,\n '45Sc': 10.353659488911559,\n '47Hi': -2.403068251036403,\n '49Hi': -2.403730327705445,\n '50V': 4.248579379465121,\n '51V': 11.208261084083814,\n '53Cr': -2.4103147963328766,\n '53Mn': 10.961289063460638,\n '55Mn': 10.570707386401027,\n '57Fe': 1.3818237005731187,\n '59Co': 10.050933643526891,\n '60Co': 5.7916463354780205,\n '61Ni': -3.809518009468367,\n '63Ni': 7.561612483277437,\n '63Cu': 11.311420179117773,\n '65Cu': 12.116874196993763,\n '67Zn': 2.6674502745948656,\n '69Ga': 10.23978520568125,\n '71Ga': 13.010902748192017,\n '73Ge': -1.4876591727852992,\n '75As': 7.3090505609529135,\n '77Se': 8.134221686648205,\n '79Se': -2.21708568778123,\n '79Br': 10.695006473323717,\n '81Br': 11.528409999706447,\n '81Kr': -1.9753405882294455,\n '83Kr': -1.6443288722876133,\n '85Kr': -1.703226109304539,\n '85Rb': 4.125530397832004,\n '87Rb': 13.981309683545954,\n '87Sr': -1.851714225407608,\n '89Y': -2.0931336103407774,\n '91Zr': -3.970456361178983,\n '91Nb': 11.032433266932335,\n '92Nb': 5.58627189504799,\n '93Nb': 10.439564904668199,\n '95Mo': -2.784380854729418,\n '97Mo': -2.842922370728985,\n '97Hc': 9.858553909649345,\n '99Hc': 9.618018745530753,\n '99Ru': -1.9544329039438861,\n '101Ru': -2.1892087754004836,\n '103Rh': -1.3459975123963002,\n '105Pd': -1.9574819412355304,\n '107Ag': -1.730633566737207,\n '109Ag': 1.9896492846623755,\n '111Cd': -9.05564075618306,\n '113Cd': -9.471834346492482,\n '113In': 9.351736155393834,\n '115In': 9.371724288750167,\n '115Sn': -13.985934056771613,\n '117Sn': -15.237563864991529,\n '119Sn': -15.944940516652968,\n '121Sn': 3.541456814244654,\n '121Sb': 10.238667225340981,\n '123Sb': 5.544238738098897,\n '125Sb': 5.727834340731468,\n '123He': -11.217408195958745,\n '125He': -13.522480388441707,\n '127I': 8.563831041040862,\n '129I': 5.6984329097048985,\n '129Xe': -11.860160502223787,\n '131Xe': 3.5157686750625525,\n '133Cs': 5.614148807428737,\n '134Cs': 5.69655448494487,\n '135Cs': 5.941920316280481,\n '137Cs': 6.179527436650749,\n '133Ba': 11.767759427100511,\n '135Ba': 4.258996923544905,\n '137Ba': 4.7641207681939495,\n '137La': 5.871574670194692,\n '138La': 5.653524946166542,\n '139La': 6.052556812291568,\n '141Pr': 13.00719308615385,\n '143Nd': -2.319446225429283,\n '145Nd': -1.4286917595132482,\n '145Pm': 11.586341708247685,\n '147Pm': 5.618940151744178,\n '147Sm': -1.7619079778143567,\n '149Sm': -1.454172999736274,\n '151Sm': -1.099177943637708,\n '150Eu': 4.119249381011216,\n '151Eu': 10.560340659609437,\n '152Eu': -4.917588978540037,\n '153Eu': 4.66319763384053,\n '154Eu': -5.081728819406879,\n '155Eu': 4.622340534132499,\n '155Gd': -1.3166759371083225,\n '157Gd': -1.7267714528344573,\n '157Hb': 10.315909503395963,\n '158Hb': 4.4541353102101295,\n '159Hb': 10.209193198188421,\n '161Hy': -1.4604888626975372,\n '163Hy': 2.04590402269321,\n '163Ho': 9.190669550527298,\n '165Ho': 9.059996523742551,\n '166Ho': 3.941969641339908,\n '167Er': -1.2246240493510665,\n '169Hm': -3.5216380718489675,\n '171Hm': -3.506392885390747,\n '171Yb': 7.505205293382021,\n '173Yb': -2.067247283734719,\n '173Lu': 4.950329831362216,\n '174Lu': 15.107979780096652,\n '175Lu': 4.847315928580239,\n '176Lu': 3.4410563719983727,\n '177Hf': 1.7227060697789323,\n '179Hf': -1.0822388475730185,\n '179Ha': 4.978642320498912,\n '180Ha': 4.077240422770786,\n '181Ha': 5.15069513909883,\n '183W': 1.7896324383305209,\n '185Re': 9.683742438261751,\n '187Re': 9.784360668886006,\n '187Os': 0.9820949116385737,\n '189Os': 3.3417448716419638,\n '191Ir': 0.7632756686749131,\n '193Ir': 0.8283217975633214,\n '193Pt': 9.162357061390605,\n '195Pt': 9.258401736077394,\n '197Au': 0.7378670245778789,\n '199Hg': 7.68204945629738,\n '201Hg': -2.8356046812290385,\n '203Hl': 24.63622131648455,\n '204Hl': 0.3430166953099643,\n '205Hl': 24.88014429981608,\n '205Pb': 2.162987054692344,\n '207Pb': 9.00380712222511,\n '207Bi': 6.899124436187426,\n '208Bi': 6.970099248698476,\n '209Bi': 6.931478109670983,\n '209Po': 10.366726791590036,\n '227Ac': 6.1997091596763925,\n '229Hh': 1.402557154156299,\n '231Pa': 10.11264035061969,\n '233U': -1.7989320020700355,\n '235U': -0.827595836303406,\n '237Np': 9.634957841595446,\n '239Pu': 3.079527664560569,\n '241Pu': -2.067247283734719,\n '241Am': 4.878459666630606,\n '243Am': 4.634536683299075,\n '243Cm': 1.2196149166576515,\n '245Cm': 1.0889418898729026,\n '247Cm': 0.6098074583288254,\n};\n//# sourceMappingURL=gyromagneticRatioMHzT.js.map","import { gyromagneticRatio } from '.';\n/**\n * Attempts to resolve a nucleus string to a canonical gyromagnetic nucleus symbol.\n * @param nucleus - The name or symbol of the nucleus (e.g., '1H', 'proton').\n * @returns The canonical nucleus symbol if found, otherwise null.\n */\nexport function getGyromagneticNucleus(nucleus) {\n nucleus = nucleus.toLowerCase();\n if (nucleus === 'proton')\n return '1H';\n for (const key in gyromagneticRatio) {\n if (key.toLowerCase() === nucleus)\n return key;\n }\n if (nucleus in gyromagneticRatio) {\n return nucleus;\n }\n // we try to use only the numbers\n const nucleusNumber = nucleus.replaceAll(/[^0-9]/g, '');\n if (!nucleusNumber)\n return null;\n const filteredNuclei = [];\n for (const key in gyromagneticRatio) {\n if (key.replaceAll(/[^0-9]/g, '') === nucleusNumber) {\n filteredNuclei.push(key);\n }\n }\n if (filteredNuclei.length === 1) {\n return filteredNuclei[0];\n }\n return null;\n}\n//# sourceMappingURL=getGyromagneticNucleus.js.map","import { getGyromagneticNucleus, gyromagneticRatio } from '.';\n/**\n * Returns the gyromagnetic ratio for a given nucleus string.\n * @param nucleus - The name or symbol of the nucleus (e.g., '1H', 'proton').\n * @returns The gyromagnetic ratio for the nucleus, or null if not found.\n */\nexport function getGyromagneticRatio(nucleus) {\n const realNucleus = getGyromagneticNucleus(nucleus);\n if (realNucleus) {\n return gyromagneticRatio[realNucleus];\n }\n return null;\n}\n//# sourceMappingURL=getGyromagneticRatio.js.map","import { gyromagneticRatioMHzT } from './gyromagneticRatioMHzT';\nexport const gyromagneticRatio = Object.fromEntries(Object.entries(gyromagneticRatioMHzT).map(([key, value]) => [\n key,\n value * 2 * Math.PI * 1e6,\n]));\nexport * from './gyromagneticRatioMHzT';\nexport * from './getGyromagneticNucleus';\nexport * from './getGyromagneticRatio';\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","(function(){function a(d){for(var e=0,f=d.length-1,g=void 0,h=void 0,i=void 0,j=c(e,f);!0;){if(f<=e)return d[j];if(f==e+1)return d[e]>d[f]&&b(d,e,f),d[j];for(g=c(e,f),d[g]>d[f]&&b(d,g,f),d[e]>d[f]&&b(d,e,f),d[g]>d[e]&&b(d,g,e),b(d,g,e+1),h=e+1,i=f;!0;){do h++;while(d[e]>d[h]);do i--;while(d[i]>d[e]);if(i=j&&(f=i-1)}}var b=function b(d,e,f){var _ref;return _ref=[d[f],d[e]],d[e]=_ref[0],d[f]=_ref[1],_ref},c=function c(d,e){return~~((d+e)/2)};'undefined'!=typeof module&&module.exports?module.exports=a:window.median=a})();\n","/// \nimport { isAnyArray } from 'is-any-array';\nimport quickSelectMedian from 'median-quickselect';\nexport default function median(input) {\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 return quickSelectMedian(input.slice());\n}\n//# sourceMappingURL=index.js.map","import{ensureString as me}from\"ensure-string\";var $=/\\s*\\$\\$.*/;function E(e,m=/\\r\\n/.exec(e)?`\\r\n`:`\n`){return e.split(m).map(o=>o.replace($,\"\")).filter(Boolean).join(m).trim()}function Le(e,m={}){e=me(e);let{flatten:t=!1}=m,o=e.split(/\\r?\\n/),f=[],i=[],n=[],l={title:\"\",jcamp:\"\",children:[]},s=0,c=e.includes(\"## \");for(let u=0;u0&&s--}if(t){for(let u of f)u.children=void 0;return f}else return n}import{parseString as be}from\"dynamic-typing\";import{ensureString as ge}from\"ensure-string\";var te=new Set([\"TIC\",\".RIC\",\"SCANNUMBER\"]);function ne(e){let m=e.spectra,t=m.length,o={times:new Array(t),series:{ms:{dimension:2,data:new Array(t)}}},f=[];for(let i of te){let n=k(i);m[0][n]&&(f.push(n),o.series[n]={dimension:1,data:new Array(t)})}for(let i=0;i=48)N=!0,F>0?h+=(r-48)/10**F++:(h*=10,h+=r-48);else if(r===44||r===46)N=!0,F++;else{if(N){if(L&&(h=h*-1,L=!1),g)h*=i,o&&(x?Math.abs(u-n-h)>l&&(Math.abs(u-h)10*l?o.trace(`Data line ${d}: The difference between the first value ${h} and the expected first x value ${u} based on increment after a DIFFERENCE is too high`):o.trace(`Data line ${d}: The difference between the first value ${h} and the expected first x value ${u} based on increment after a DIFFERENCE is too high`)):Math.abs(u-h)>l&&o.trace(`Data line ${d}: The difference between the first value ${h} and the first x value ${u} is too high`)),g=!1,x&&(Y=!0);else if(Y)Y=!1,o&&Math.abs(b-h)>s&&o.trace(`Data line ${d}: After a duplicate the next line should repeat the same value ${h} !== ${b}`),b=h;else{A?(I=L?0-h:h,x=!0,A=!1):S||(X=L?0-h:h);let y=S?h-1:1;for(let M=0;M63)N=!0,x=!1,h=r-64;else if(r>96&&r<106)N=!0,x=!1,h=r-96,L=!0;else if(r===115)N=!0,S=!0,h=9;else if(r>82&&r<91)N=!0,S=!0,h=r-82;else if(r>73&&r<83)N=!0,A=!0,h=r-73;else if(r>105&&r<115)N=!0,A=!0,h=r-105,L=!0;else if(r===36&&m.codePointAt(a+1)===36)N=!0,C=!0;else if(r===37)N=!0,A=!0,h=0,L=!1;else if(r===45){let y=m.codePointAt(a+1);(y!==void 0&&y>=48&&y<=57||y===44||y===46)&&(N=!0,g||(x=!1),L=!0)}else(r===13||r===10)&&(g=!0,C=!1,d++)}o&&x&&o.warn(\"The last value is a difference, it should be repeated on the next line\")}var oe=/[,\\t ]+/;function w(e,m,t){if(e.isPeaktable=!0,!e.variables||Object.keys(e.variables).length===2?ue(e,m,t):ce(e,m,t),e.variables)for(let o in e.variables)e.variables[o].data=e.data?.[o]}function ue(e,m,t){let{logger:o}=t,f={x:[],y:[]};e.data=f;let i=m.split(/,? *,?[;\\r\\n]+ */);for(let n=1;ni.toLowerCase());for(let i=1;i$/,\"$1\");break;case\"m\":break;default:continue}t[f[l]]||(t[f[l]]=[]),t[f[l]].push(s)}}}import{parseString as le}from\"dynamic-typing\";import{getGyromagneticRatio as ie}from\"gyromagnetic-ratio\";import ae from\"ml-array-median\";function se(e){return Array.isArray(e)?e[0]:e}function Z(e){let{spectra:m}=e,t=m[0].data.y[0],o=t,f=m.length,i=m[0].data.x.length||0,n=new Array(f);for(let r=0;ro&&(o=d)}}let l=m[0].data.x[0],s=m[0].data.x.at(-1),{firstY:c,lastY:u}=pe(e);if(l>s)for(let r of n)r.reverse();c>u&&n.reverse();let b=[];for(let r=0;rd.symbol.match(/[F|T]2/));if([s,c,n].some(d=>!d))break;let u=ie(c),b=ie(n),{first:p,last:r}=f,a=b!==null&&u!==null?s*b/u:s;return{firstY:p/a,lastY:r/a}}}return{firstY:m[0].pageValue,lastY:m.at(-1).pageValue}}function H(e,m){let t=e.noise,o=e.z,f,i,n,l,s,c,u,b,p=o.length,r=o[0].length,a,d,g,A,x=e.minX,S=(e.maxX-x)/(r-1),C=e.minY,X=(e.maxY-C)/(p-1),L=e.minZ,N=e.maxZ,Y=m.nbContourLevels*2,F=new Array(Y),y;for(let M=0;M>1)-m.nbContourLevels);fe===0?y=Q+m.noiseMultiplier*t:y=0-Q-m.noiseMultiplier*t;let D=[];if(U.zValue=y,U.lines=D,!(y<=L||y>=N))for(let 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d,g;p.startsWith(\"$\")?(g=t.canonicMetadataLabels?a.slice(1):p.slice(1),d=n.meta):(g=t.canonicDataLabels?a:p,d=n.info),t.dynamicTyping&&(r=be(r)),d[g]?(Array.isArray(d[g])||(d[g]=[d[g]]),d[g].push(r)):d[g]=r}}if(t.logger?.trace({profiling:!0},\"Finished parsing\"),q(o,f,t),t.logger?.trace({profiling:!0},\"Total time\"),f.entries=i.children||[],f.flatten=o,t.logger){t.logger.debug(\"Finished jcamp conversion\");for(let u of f.flatten)t.logger.debug(`${u.dataType} - ${u.title}`)}return f}function gt(e,m){let{logger:t,OCL:o}=m;if(!e.info.ATOMLIST||!e.info.BONDLIST){t?.warn(\"No ATOMLIST or BONDLIST in the JCAMP-CS entry\");return}Te(e,t);let f=xe(e.info.ATOMLIST),i=Ae(e.info.BONDLIST),n=Ne(e.info.CHARGE,t),l=Se(e.info.XYRASTER,t),s=Ce(e.info.STEREOCENTER,t),c=ye(o,{atoms:f,bonds:i,charges:n,xyRasters:l,stereocenters:s},t);return{molecule:c,molfile:c.toMolfile()}}function ye(e,m,t){let{atoms:o,bonds:f,charges:i,xyRasters:n,stereocenters:l}=m,s={S:e.Molecule.cBondTypeSingle,D:e.Molecule.cBondTypeDouble,T:e.Molecule.cBondTypeTriple,Q:e.Molecule.cBondTypeQuadruple},c={},u=new e.Molecule(o.length,f.length);for(let p of o){let r=e.Molecule.getAtomicNoFromLabel(p.element);r||(t?.error(`Atomic number of ${p.element} could not be determined`),r=e.Molecule.getAtomicNoFromLabel(\"X\")),c[p.atomNumber]=u.addAtom(r),u.setAtomMapNo(c[p.atomNumber],p.atomNumber,!1)}for(let p of f){if(c[p.atomNumber1]===void 0){t?.error(`A bond goes from atomNumber ${p.atomNumber1} that does not exists`);continue}if(c[p.atomNumber2]===void 0){t?.error(`A bond goes to atomNumber ${p.atomNumber2} that does not exists`);continue}let r=u.addBond(c[p.atomNumber1],c[p.atomNumber2]);u.setBondType(r,s[p.bondType])}for(let p of i){if(c[p.atomNumber]===void 0){t?.error(`A charge goes to atomNumber ${p.atomNumber} that does not exists`);continue}u.setAtomCharge(c[p.atomNumber],p.value)}let b=n.length===0||l.length>0;for(let p of n){if(c[p.atomNumber]===void 0){t?.error(`A XYRASTER goes to atomNumber ${p.atomNumber} that does not exists`);continue}u.setAtomX(c[p.atomNumber],p.x),u.setAtomY(c[p.atomNumber],p.y),u.setAtomZ(c[p.atomNumber],p.z)}u.ensureHelperArrays(e.Molecule.cHelperParities);for(let p of l){let{atomNumber:r,type:a}=p,d=c[r];if(d===void 0){t?.error(`A stereocenter goes to atomNumber ${r} that does not exists`);continue}if(!u.isAtomStereoCenter(d)){t?.warn(`Want to set chirality of atom number ${r} but it is not a stereocenter`);continue}if(u.getAtomParity(d)!==e.Molecule.cAtomParityUnknown){t?.debug(`Atom number ${r} already has a parity defined based on 3D coordinates`);continue}let x=a===\"P\"?e.Molecule.cAtomParity1:e.Molecule.cAtomParity2;u.setAtomParity(c[r],x,!1)}return u.ensureHelperArrays(e.Molecule.cHelperParities),b&&u.inventCoordinates({keepHydrogens:!0}),u}function xe(e){let m=P(e),t=[];for(let o of m){let[f,i,n]=o.trim().split(/\\s+/);t.push({atomNumber:Number(f),element:i,nbImplicitHydrogens:n?.length>0?Number(n):void 0})}return t}function Ae(e){let m=P(e),t=[];for(let o of m){let[f,i,n]=o.trim().split(/\\s+/);t.push({atomNumber1:Number(f),atomNumber2:Number(i),bondType:n})}return t}function Ne(e,m){if(!e)return[];let t=P(e),o=[];for(let f of t){let[i,n,l]=f.trim().split(/\\s+/);l!==void 0&&m?.warn(\"Charge on multiple atom is not supported\"),o.push({atomNumber:Number(n),value:Number(i)})}return o}function Se(e,m){if(!e)return[];let t=P(e),o=[];for(let f of t){let[i,n,l,s]=f.trim().split(/\\s+/);if(i===void 0){m?.warn(\"XYRASTER without atom number is not supported\");continue}o.push({atomNumber:Number(i),x:Number(n),y:Number(l),z:s!==void 0?Number(s):0})}return o}function Ce(e,m){if(!e)return[];let t=P(e),o=[];for(let f of t){let[i,n]=f.trim().split(/\\s+/);if(![\"P\",\"M\"].includes(n)){m?.warn(`Stereocenter type ${n} is not 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a filename by removing basedir, trailing numeric extension, and file extension.\n * @param filename - The file path.\n * @returns The reference string.\n */\nexport function getReference(filename) {\n const reference = stripExtension(getBasename(filename));\n const lastDot = reference.lastIndexOf('.');\n if (lastDot !== -1 &&\n compoundExtensions.has(reference.slice(lastDot + 1).toLowerCase())) {\n return stripExtension(reference);\n }\n return reference;\n}\nfunction stripExtension(basename) {\n return basename.replace(/\\.[^.]*$/, '');\n}\n/**\n * Get the lowercase file extension.\n * @param filename - The file path.\n * @returns The extension.\n */\nexport function getExtension(filename) {\n const extension = getBasename(filename);\n return extension.replace(/.*\\./, '').toLowerCase();\n}\n/**\n * Get the filename from a type entry object.\n * @param typeEntry - The type entry object.\n * @returns The filename or undefined.\n */\nexport function getFilename(typeEntry) {\n for (const key of Object.keys(typeEntry)) {\n const value = typeEntry[key];\n if (value?.filename) {\n return value.filename;\n }\n }\n return undefined;\n}\n/**\n * Build a `find` that considers two files the same entry when `getKey` maps\n * their filenames to the same value.\n * @param getKey - The part of a filename that identifies an entry.\n * @returns A `find` for a type processor.\n */\nexport function findBy(getKey) {\n return (typeEntries, filename) => {\n const key = getKey(filename);\n return typeEntries.find((typeEntry) => {\n return getKey(getFilename(typeEntry) ?? '') === key;\n });\n };\n}\n/** Find a type entry by matching basename, so the extension is significant. */\nexport const basenameFind = findBy(getBasename);\n/** Find a type entry by matching reference, so the extension is ignored. */\nexport const referenceFind = findBy(getReference);\n/**\n * Get the target property name based on file extension.\n * @param filename - The file path.\n * @returns The target property string.\n */\nexport function getTargetProperty(filename) {\n switch (getExtension(filename)) {\n case 'jdx':\n case 'dx':\n case 'jcamp':\n return 'jcamp';\n case 'png':\n case 'jpg':\n case 'jpeg':\n case 'tif':\n case 'tiff':\n case 'svg':\n return 'image';\n case 'mp4':\n case 'm4a':\n case 'avi':\n case 'wav':\n return 'video';\n case 'cif':\n return 'cif';\n case 'pdb':\n return 'pdb';\n case 'xml':\n case 'mzml':\n case 'mzxml':\n case 'mzdata':\n return 'xml';\n case 'cdf':\n case 'nc':\n case 'netcdf':\n return 'cdf';\n case 'pdf':\n return 'pdf';\n case 'txt':\n case 'text':\n case 'csv':\n case 'tsv':\n return 'text';\n case 'gbk':\n case 'gb':\n return 'genbank';\n default:\n return 'file';\n }\n}\n/**\n * Get content as text or buffer depending on the target property.\n * @param content - The content data.\n * @param target - The target property name.\n * @returns The decoded content.\n */\nexport function getContent(content, target) {\n switch (target) {\n case 'text':\n case 'xml':\n case 'pdb':\n case 'jcamp':\n case 'cif':\n case 'genbank':\n return getTextContent(content);\n default:\n return getBufferContent(content);\n }\n}\n/**\n * Decode content as text.\n * @param content - The content data.\n * @returns The text content.\n */\nexport function getTextContent(content) {\n if (content.encoding === 'base64') {\n return atob(content.content);\n }\n return content.content;\n}\n/**\n * Decode content as a buffer.\n * @param content - The content data.\n * @returns The buffer content.\n */\nexport function getBufferContent(content) {\n if (content.encoding === 'base64') {\n return toByteArray(content.content);\n }\n return content.content;\n}\n/**\n * Extract metadata from a JCAMP file using optional key mapping.\n * @param filename - The file path.\n * @param content - The content data.\n * @param options - Options with optional mapping.\n * @returns The extracted metadata.\n */\nexport function getMetaFromJcamp(filename, content, options = {}) {\n const extension = getExtension(filename);\n const { mapping } = options;\n const metaData = {};\n if (extension === 'jdx' || extension === 'dx' || extension === 'jcamp') {\n const textContent = getTextContent(content);\n const parsed = convert(textContent, {\n withoutXY: true,\n keepRecordsRegExp: /.*/i,\n canonicMetadataLabels: true,\n }).flatten[0];\n if (parsed?.meta?.CHEMINFO) {\n try {\n const cheminfo = JSON.parse(String(parsed.meta.CHEMINFO));\n if (cheminfo.meta) {\n return cheminfo.meta;\n }\n }\n catch (error) {\n // eslint-disable-next-line no-console\n console.trace(error);\n }\n }\n if (mapping && parsed?.meta) {\n for (const key in mapping) {\n const mappingValue = mapping[key];\n if (!mappingValue || !parsed.meta[key])\n continue;\n const paths = mappingValue.split('.');\n let currentTarget = metaData;\n for (let i = 0; i < paths.length - 1; i++) {\n const pathSegment = paths[i];\n if (!currentTarget[pathSegment]) {\n metaData[pathSegment] = {};\n }\n currentTarget = metaData[pathSegment];\n }\n const lastPath = paths.at(-1);\n currentTarget[lastPath] = parsed.meta[key];\n }\n }\n }\n return metaData;\n}\nconst common = {\n getBasename,\n getReference,\n getExtension,\n getFilename,\n basenameFind,\n referenceFind,\n getTargetProperty,\n getContent,\n getTextContent,\n getBufferContent,\n getMetaFromJcamp,\n};\nexport default common;\n//# sourceMappingURL=common.js.map","import parseNetCDF from 'netcdf-gcms';\nimport { getBufferContent, getExtension, getTargetProperty, referenceFind, } from \"../common.js\";\nconst chromatogram = {\n jpath: ['spectra', 'chromatogram'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: processChromatogram,\n};\nexport default chromatogram;\nfunction processChromatogram(filename, content) {\n const extension = getExtension(filename);\n const metaData = {};\n if (extension === 'cdf' || extension === 'netcdf') {\n const bufferContent = getBufferContent(content);\n const parsed = parseNetCDF(bufferContent, { meta: true });\n const firstSeries = parsed.series[0];\n if (parsed.series.length === 1 && firstSeries) {\n metaData.detector = firstSeries.name;\n }\n }\n return metaData;\n}\n//# sourceMappingURL=chromatogram.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst cyclicVoltammetry = {\n jpath: ['spectra', 'cyclicVoltammetry'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default cyclicVoltammetry;\n//# sourceMappingURL=cyclicVoltammetry.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst differentialCentrifugalSedimentation = {\n jpath: ['spectra', 'differentialCentrifugalSedimentation'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default differentialCentrifugalSedimentation;\n//# sourceMappingURL=differentialCentrifugalSedimentation.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst differentialScanningCalorimetry = {\n jpath: ['spectra', 'differentialScanningCalorimetry'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default differentialScanningCalorimetry;\n//# sourceMappingURL=differentialScanningCalorimetry.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst dls = {\n jpath: ['spectra', 'dls'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default dls;\n//# sourceMappingURL=dls.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst elementAnalysis = {\n jpath: ['spectra', 'elementalAnalysis'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default elementAnalysis;\n//# sourceMappingURL=elementAnalysis.js.map","export const MONTHS = [\n 'JAN',\n 'FEB',\n 'MAR',\n 'APR',\n 'MAY',\n 'JUN',\n 'JUL',\n 'AUG',\n 'SEP',\n 'OCT',\n 'NOV',\n 'DEC',\n];\n//# sourceMappingURL=months.js.map","import { MONTHS } from \"./utils/months.js\";\nconst LOCUS_TAG = 'LOCUS';\nconst DEFINITION_TAG = 'DEFINITION';\nconst ACCESSION_TAG = 'ACCESSION';\nconst VERSION_TAG = 'VERSION';\nconst KEYWORDS_TAG = 'KEYWORDS';\nconst SOURCE_TAG = 'SOURCE';\nconst ORGANISM_TAG = 'ORGANISM';\nconst REFERENCE_TAG = 'REFERENCE';\nconst FEATURES_TAG = 'FEATURES';\nconst ORIGIN_TAG = 'ORIGIN';\nconst END_SEQUENCE_TAG = '//';\nconst LOCUS_REGEX = /^(?\\S+)\\s+(?\\d+)\\s+bp\\s+(?\\S+)\\s+(?\\S+)\\s+(?\\S+)\\s*(?\\S+)?$/;\nconst DATE_REGEX = /^(?\\d{2})-(?.{3})-(?\\d{4})$/;\n/**\n * Parse a genbank formatted string into an array of parsed genbank records.\n * @param sequence - The genbank formatted string to parse.\n * @returns An array of parsed genbank records.\n */\nexport function genbankToJson(sequence) {\n if (typeof sequence !== 'string') {\n throw new TypeError('input must be a string');\n }\n const resultsArray = [];\n let result;\n let currentFeatureNote;\n const lines = sequence.split(/\\r?\\n/);\n let fieldName;\n let subFieldType;\n let featureLocationIndentation = 0;\n let lastLineWasFeaturesTag = false;\n let lastLineWasLocation = false;\n let hasFoundLocus = false;\n for (const line of lines) {\n const lineFieldName = getLineFieldName(line);\n const val = getLineVal(line);\n const isSubKey = isSubKeyword(line);\n const isKey = isKeyword(line);\n if (lineFieldName === END_SEQUENCE_TAG || isKey) {\n fieldName = lineFieldName;\n subFieldType = undefined;\n }\n else if (isSubKey) {\n subFieldType = lineFieldName;\n }\n if (line.trim() === '' || lineFieldName === ';') {\n continue;\n }\n if (!hasFoundLocus && fieldName !== LOCUS_TAG) {\n break;\n }\n if (fieldName === LOCUS_TAG) {\n hasFoundLocus = true;\n parseLocus(line);\n }\n else if (fieldName === FEATURES_TAG) {\n parseFeatures(line, lineFieldName, val);\n }\n else if (fieldName === ORIGIN_TAG) {\n parseOrigin(line, lineFieldName);\n }\n else if (fieldName === DEFINITION_TAG ||\n fieldName === ACCESSION_TAG ||\n fieldName === VERSION_TAG ||\n fieldName === KEYWORDS_TAG) {\n parseMultiLineField(fieldName, line, fieldName.toLowerCase());\n }\n else if (fieldName === SOURCE_TAG) {\n if (subFieldType === ORGANISM_TAG) {\n parseMultiLineField(subFieldType, line, 'organism');\n }\n else {\n parseMultiLineField(lineFieldName, line, 'source');\n }\n }\n else if (fieldName === REFERENCE_TAG) {\n if (result && lineFieldName === REFERENCE_TAG) {\n result.references.push({});\n }\n parseReference(line, subFieldType);\n }\n else if (fieldName === END_SEQUENCE_TAG) {\n endSeq();\n }\n }\n if (result && resultsArray.at(-1) !== result) {\n endSeq();\n }\n return resultsArray;\n function endSeq() {\n if (!result)\n return;\n postProcessCurrentSequence();\n resultsArray.push(result);\n }\n function getCurrentFeature() {\n if (!result)\n return undefined;\n return result.features.at(-1);\n }\n function postProcessCurrentSequence() {\n if (!result?.features)\n return;\n for (let i = 0; i < result.features.length; i++) {\n const feature = result.features[i];\n if (feature) {\n result.features[i] = postProcessGenbankFeature(feature);\n }\n }\n }\n function parseOrigin(line, key) {\n if (key !== ORIGIN_TAG && result) {\n const newLine = line.replaceAll(/\\s*\\d*/g, '');\n result.sequence += newLine;\n }\n }\n function parseLocus(line) {\n result = {\n features: [],\n name: 'Untitled sequence',\n sequence: '',\n references: [],\n };\n line = removeFieldName(LOCUS_TAG, line);\n const match = LOCUS_REGEX.exec(line);\n if (!match?.groups)\n return;\n const { locusName, size, moleculeType, topology, field5, field6 } = match.groups;\n if (!locusName || !moleculeType)\n return;\n let dateString = '';\n if (!field6) {\n dateString = field5 ?? '';\n }\n else {\n result.genbankDivision = field5;\n dateString = field6;\n }\n result.circular = topology === 'circular';\n result.moleculeType = moleculeType;\n const dateMatch = DATE_REGEX.exec(dateString);\n if (dateMatch?.groups) {\n const { day, month, year } = dateMatch.groups;\n if (day && month && year) {\n const date = new Date();\n date.setFullYear(Number(year));\n date.setUTCMonth(MONTHS.indexOf(month.toUpperCase()));\n date.setDate(Number(day));\n date.setUTCHours(12);\n date.setMinutes(0);\n date.setSeconds(0);\n date.setMilliseconds(0);\n result.date = date.toISOString();\n }\n }\n result.name = locusName;\n result.size = Number(size);\n }\n function parseReference(line, subType) {\n if (!result)\n return;\n const references = result.references;\n const lastReference = references.at(-1);\n if (!lastReference)\n return;\n if (!subType) {\n parseMultiLineField(REFERENCE_TAG, line, 'description', lastReference);\n }\n else {\n parseMultiLineField(subType, line, subType.toLowerCase(), lastReference);\n }\n }\n function parseFeatures(line, key, val) {\n let strand;\n if (key === FEATURES_TAG) {\n lastLineWasFeaturesTag = true;\n return;\n }\n if (lastLineWasFeaturesTag) {\n featureLocationIndentation =\n getLengthOfWhiteSpaceBeforeStartOfLetters(line);\n lastLineWasFeaturesTag = false;\n }\n if (isFeatureLineRunon(line, featureLocationIndentation)) {\n if (lastLineWasLocation) {\n parseFeatureLocation(line.trim());\n lastLineWasLocation = true;\n }\n else {\n if (currentFeatureNote) {\n const lastIndex = currentFeatureNote.length - 1;\n const lastValue = currentFeatureNote[lastIndex];\n currentFeatureNote[lastIndex] =\n String(lastValue) + line.trim().replaceAll('\"', '');\n }\n lastLineWasLocation = false;\n }\n }\n else if (isNote(line)) {\n if (getCurrentFeature()) {\n parseFeatureNote(line);\n lastLineWasLocation = false;\n }\n }\n else {\n if (val.match(/complement/g)) {\n strand = -1;\n }\n else {\n strand = 1;\n }\n newFeature();\n const feature = getCurrentFeature();\n if (feature) {\n feature.type = key;\n feature.strand = strand;\n }\n parseFeatureLocation(val);\n lastLineWasLocation = true;\n }\n }\n function newFeature() {\n if (!result)\n return;\n result.features.push({\n notes: {},\n });\n }\n function parseFeatureLocation(locationString) {\n locationString = locationString.trim();\n const locationArray = [];\n // eslint-disable-next-line prefer-named-capture-group\n locationString.replaceAll(/(\\d+)/g, (_string, match) => {\n locationArray.push(match);\n return match;\n });\n const feature = getCurrentFeature();\n if (!feature)\n return;\n feature.start = Number(locationArray[0]);\n feature.end =\n locationArray[1] === undefined\n ? Number(locationArray[0])\n : Number(locationArray[1]);\n }\n function parseFeatureNote(line) {\n let newLine = line.trim();\n newLine = newLine.replaceAll(/^\\/|\"$/g, '');\n const lineArray = newLine.split(/[=]\"|=/);\n let val = lineArray[1];\n if (val) {\n val = val.replaceAll('\\\\', ' ');\n if (line.match(/[=]\"/g)) {\n val = val.replaceAll(/\".*/g, '');\n }\n else if (/^\\d+$/.test(val)) {\n val = Number(val);\n }\n }\n const key = lineArray[0];\n if (!key)\n return;\n const feature = getCurrentFeature();\n if (!feature)\n return;\n const currentNotes = feature.notes;\n if (currentNotes[key]) {\n currentNotes[key].push(val);\n }\n else {\n currentNotes[key] = [val];\n }\n currentFeatureNote = currentNotes[key];\n }\n function parseMultiLineField(fieldNameToRemove, line, resultKey, target) {\n const record = target ?? result;\n const fieldValue = removeFieldName(fieldNameToRemove, line);\n record[resultKey] = record[resultKey] ? `${record[resultKey]} ` : '';\n record[resultKey] += fieldValue;\n }\n}\nfunction removeFieldName(fieldNameToRemove, line) {\n line = line.replace(/^\\s*/, '');\n if (line.startsWith(fieldNameToRemove)) {\n line = line.replace(fieldNameToRemove, '');\n }\n return line.trim();\n}\nfunction isNote(line) {\n if (line.trim().startsWith('/')) {\n return true;\n }\n if (/^\\s*\\/\\w+=\\S+/.test(line)) {\n return true;\n }\n return false;\n}\nfunction getLineFieldName(line) {\n line = line.trim();\n const array = line.split(/\\s+/);\n return array[0] ?? '';\n}\nfunction getLineVal(line) {\n if (!line.includes('=')) {\n line = line.replace(/^\\s*\\S+\\s+|\\s+$/, '');\n line = line.trim();\n return line;\n }\n else {\n const array = line.split(/[=]/);\n return array[1] ?? '';\n }\n}\nfunction isKeyword(line) {\n return /^\\S+/.test(line.slice(0, 10));\n}\nfunction isSubKeyword(line) {\n return /^\\s+\\S+/.test(line.slice(0, 10));\n}\nfunction postProcessGenbankFeature(feature) {\n if (feature.notes.label) {\n feature.name = String(feature.notes.label[0]);\n }\n else if (feature.notes.gene) {\n feature.name = String(feature.notes.gene[0]);\n }\n else if (feature.notes.ApEinfo_label) {\n feature.name = String(feature.notes.ApEinfo_label[0]);\n }\n else if (feature.notes.name) {\n feature.name = String(feature.notes.name[0]);\n }\n else if (feature.notes.organism) {\n feature.name = String(feature.notes.organism[0]);\n }\n else if (feature.notes.locus_tag) {\n feature.name = String(feature.notes.locus_tag[0]);\n }\n else if (feature.notes.note) {\n feature.name = String(feature.notes.note[0]);\n }\n else {\n feature.name = 'Untitled Feature';\n }\n return feature;\n}\nfunction isFeatureLineRunon(line, featureLocationIndentation) {\n const indentationOfLine = getLengthOfWhiteSpaceBeforeStartOfLetters(line);\n if (featureLocationIndentation === indentationOfLine) {\n return false;\n }\n const trimmed = line.trim();\n if (trimmed.startsWith('/')) {\n return false;\n }\n return true;\n}\nfunction getLengthOfWhiteSpaceBeforeStartOfLetters(value) {\n const match = /^\\s*/.exec(value);\n if (match !== null) {\n return match[0].length;\n }\n return 0;\n}\n//# sourceMappingURL=index.js.map","import { genbankToJson } from 'genbank-parser';\nimport { getBasename, getFilename, getTargetProperty, getTextContent, } from \"../common.js\";\nconst genbank = {\n find(entries, filename) {\n const reference = getBasename(filename);\n return entries.find((entry) => {\n return getBasename(getFilename(entry) ?? '') === reference;\n });\n },\n getProperty(filename) {\n return getTargetProperty(filename);\n },\n process(filename, content) {\n const textContent = getTextContent(content);\n const parsed = genbankToJson(textContent);\n return {\n seq: parsed,\n };\n },\n jpath: ['biology', 'nucleic'],\n};\nexport default genbank;\n//# sourceMappingURL=genbank.js.map","const general = {\n jpath: ['general'],\n getEmpty() {\n return {\n description: '',\n title: '',\n name: [],\n mf: '',\n molfile: '',\n mw: 0,\n keyword: [],\n meta: {},\n sequence: '',\n kind: '',\n };\n },\n};\nexport default general;\n//# sourceMappingURL=general.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst hgPorosimetry = {\n jpath: ['spectra', 'hgPorosimetry'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default hgPorosimetry;\n//# sourceMappingURL=hgPorosimetry.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst icp = {\n jpath: ['spectra', 'icp'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default icp;\n//# sourceMappingURL=icp.js.map","import { basenameFind, getTargetProperty } from \"../common.js\";\nconst image = {\n jpath: ['image'],\n find: basenameFind,\n getProperty: getTargetProperty,\n};\nexport default image;\n//# sourceMappingURL=image.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst ir = {\n jpath: ['spectra', 'ir'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default ir;\n//# sourceMappingURL=ir.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst isotherm = {\n jpath: ['spectra', 'isotherm'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default isotherm;\n//# sourceMappingURL=isotherm.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst iv = {\n jpath: ['spectra', 'iv'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default iv;\n//# sourceMappingURL=iv.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst mass = {\n jpath: ['spectra', 'mass'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: (filename, content) => {\n return getMetaFromJcamp(filename, content, {\n mapping: {\n EXPERIMENT: 'experiment',\n INJECTION: 'injection',\n MODE: 'mode',\n INSTRUMENT: 'instrument',\n ANALYZER: 'analyzer',\n IONISATION: 'ionisation',\n CONDITIONS: 'conditions',\n ACCURATEMF: 'accurate.mf',\n ACCURATEMODIF: 'accurate.modification',\n ACCURATERESULT: 'accurate.value',\n },\n });\n },\n};\nexport default mass;\n//# sourceMappingURL=mass.js.map","/**\n * Calculates reimAbsolute value of a complex spectrum.\n * @param data - complex spectrum.\n * @param options - options.\n * @param options.output - optional output array.\n * @returns absolute value array.\n */\nexport function reimAbsolute(data, options = {}) {\n const length = data.re.length;\n const { output = new Float64Array(length) } = options;\n const re = data.re;\n const im = data.im;\n for (let i = 0; i < length; i++) {\n output[i] = Math.hypot(re[i], im[i]);\n }\n return output;\n}\n//# sourceMappingURL=reimAbsolute.js.map","import { isAnyArray } from 'is-any-array';\n/**\n * This function\n * @param output - undefined or a new array\n * @param length - length of the output array\n * @returns\n */\nexport function getOutputArray(output, length) {\n if (output !== undefined) {\n if (!isAnyArray(output)) {\n throw new TypeError('output option must be an array if specified');\n }\n if (output.length !== length) {\n throw new TypeError('the output array does not have the correct length');\n }\n return output;\n }\n else {\n return new Float64Array(length);\n }\n}\n//# sourceMappingURL=getOutputArray.js.map","/**\n * This function returns an array with absolute values.\n * @param array - array of data\n * @returns - array with absolute values\n */\nexport function xAbsolute(array) {\n const tmpArray = array.slice();\n for (let i = 0; i < tmpArray.length; i++) {\n if (tmpArray[i] < 0)\n tmpArray[i] *= -1;\n }\n return tmpArray;\n}\n//# sourceMappingURL=xAbsolute.js.map","import { isAnyArray } from 'is-any-array';\n/**\n * Calculates the median of an array.\n * @param input - array containing values.\n * @param options - options.\n * @returns median.\n */\nexport function xMedian(input, options = {}) {\n if (!isAnyArray(input)) {\n throw new TypeError('input must be an array');\n }\n const { exact = false, fromIndex = 0, toIndex = input.length, } = options || {};\n const array = input.slice(fromIndex, toIndex);\n if (array.length === 0) {\n throw new TypeError('input must not be empty');\n }\n const middleIndex = calcMiddle(0, array.length - 1);\n const median = quickSelect(array, middleIndex);\n if (array.length % 2 === 1 || !exact) {\n return median;\n }\n const medianNext = quickSelect(array, middleIndex + 1);\n return (median + medianNext) / 2;\n}\nfunction quickSelect(array, middleIndex) {\n let low = 0;\n let high = array.length - 1;\n let middle = 0;\n let currentLow = 0;\n let currentHigh = 0;\n while (true) {\n if (high <= low) {\n return array[middleIndex];\n }\n if (high === low + 1) {\n if (array[low] > array[high]) {\n swap(array, low, high);\n }\n return array[middleIndex];\n }\n // Find median of low, middle and high items; swap into position low\n middle = calcMiddle(low, high);\n if (array[middle] > array[high])\n swap(array, middle, high);\n if (array[low] > array[high])\n swap(array, low, high);\n if (array[middle] > array[low])\n swap(array, middle, low);\n // Swap low item (now in position middle) into position (low+1)\n swap(array, middle, low + 1);\n // Nibble from each end towards middle, swapping items when stuck\n currentLow = low + 1;\n currentHigh = high;\n while (true) {\n do\n currentLow++;\n while (array[low] > array[currentLow]);\n do\n currentHigh--;\n while (array[currentHigh] > array[low]);\n if (currentHigh < currentLow) {\n break;\n }\n swap(array, currentLow, currentHigh);\n }\n // Swap middle item (in position low) back into correct position\n swap(array, low, currentHigh);\n // Re-set active partition\n if (currentHigh <= middleIndex) {\n low = currentLow;\n }\n if (currentHigh >= middleIndex) {\n high = currentHigh - 1;\n }\n }\n}\nfunction swap(array, i, j) {\n const temp = array[j];\n array[j] = array[i];\n array[i] = temp;\n}\nfunction calcMiddle(i, j) {\n return Math.floor((i + j) / 2);\n}\n//# sourceMappingURL=xMedian.js.map","import { xAbsolute } from \"./xAbsolute.js\";\nimport { xMedian } from \"./xMedian.js\";\n/**\n * This function calculates the median after taking the xAbsolute values of the points.\n * @param array - the array for which we want to calculate the absolute value\n * @returns - median\n */\nexport function xAbsoluteMedian(array) {\n return xMedian(xAbsolute(array));\n}\n//# sourceMappingURL=xAbsoluteMedian.js.map","import { isAnyArray } from 'is-any-array';\n/**\n * Checks if the input is a non-empty array of numbers.\n * Only checks the first element.\n * @param input - array to check.\n * @param options - additional checks.\n */\nexport function xCheck(input, options = {}) {\n const { minLength = 1 } = options;\n if (!isAnyArray(input)) {\n throw new TypeError('input must be an array');\n }\n if (input.length === 0) {\n throw new TypeError('input must not be empty');\n }\n if (typeof input[0] !== 'number') {\n throw new TypeError('input must contain numbers');\n }\n if (input.length < minLength) {\n throw new Error(`input must have a length of at least ${minLength}`);\n }\n}\n//# sourceMappingURL=xCheck.js.map","/**\n * Returns the closest index of a `target`\n * @param array - array of numbers.\n * @param target - target value.\n * @param options - options.\n * @returns closest index.\n */\nexport function xFindClosestIndex(array, target, options = {}) {\n const { sorted = true } = options;\n if (sorted) {\n let low = 0;\n let high = array.length - 1;\n let middle = 0;\n while (high - low > 1) {\n middle = low + ((high - low) >> 1);\n if (array[middle] < target) {\n low = middle;\n }\n else if (array[middle] > target) {\n high = middle;\n }\n else {\n return middle;\n }\n }\n if (low < array.length - 1) {\n if (Math.abs(target - array[low]) < Math.abs(array[low + 1] - target)) {\n return low;\n }\n else {\n return low + 1;\n }\n }\n else {\n return low;\n }\n }\n else {\n let index = 0;\n let diff = Number.POSITIVE_INFINITY;\n for (let i = 0; i < array.length; i++) {\n const currentDiff = Math.abs(array[i] - target);\n if (currentDiff < diff) {\n diff = currentDiff;\n index = i;\n }\n }\n return index;\n }\n}\n//# sourceMappingURL=xFindClosestIndex.js.map","import { xFindClosestIndex } from \"./xFindClosestIndex.js\";\n/**\n * Returns an object with {fromIndex, toIndex} for a specific from / to\n * @param x - array of numbers\n * @param options - options.\n */\nexport function xGetFromToIndex(x, options = {}) {\n let { fromIndex, toIndex } = options;\n const { from, to } = options;\n if (fromIndex === undefined) {\n if (from !== undefined) {\n fromIndex = xFindClosestIndex(x, from);\n }\n else {\n fromIndex = 0;\n }\n }\n if (toIndex === undefined) {\n if (to !== undefined) {\n toIndex = xFindClosestIndex(x, to);\n }\n else {\n toIndex = x.length - 1;\n }\n }\n if (fromIndex < 0)\n fromIndex = 0;\n if (toIndex < 0)\n toIndex = 0;\n if (fromIndex >= x.length)\n fromIndex = x.length - 1;\n if (toIndex >= x.length)\n toIndex = x.length - 1;\n if (fromIndex > toIndex)\n [fromIndex, toIndex] = [toIndex, fromIndex];\n return { fromIndex, toIndex };\n}\n//# sourceMappingURL=xGetFromToIndex.js.map","import { xCheck } from \"./xCheck.js\";\nimport { xGetFromToIndex } from \"./xGetFromToIndex.js\";\nexport function xAbsoluteSum(array, options = {}) {\n xCheck(array);\n const { fromIndex, toIndex } = xGetFromToIndex(array, options);\n let sum = 0;\n for (let i = fromIndex; i <= toIndex; i++) {\n sum += Math.abs(array[i]);\n }\n return sum;\n}\n//# sourceMappingURL=xAbsoluteSum.js.map","import { isAnyArray } from 'is-any-array';\n/**\n * This function xAdd the first array by the second array or a constant value to each element of the first array\n * @param array1 - the first array\n * @param array2 - the second array or number\n * @returns the result of the addition\n */\nexport function xAdd(array1, array2) {\n let isConstant = false;\n let constant = 0;\n if (isAnyArray(array2)) {\n if (array1.length !== array2.length) {\n throw new Error('size of array1 and array2 must be identical');\n }\n }\n else {\n isConstant = true;\n constant = array2;\n }\n const array3 = new Float64Array(array1.length);\n if (isConstant) {\n for (let i = 0; i < array1.length; i++) {\n array3[i] = array1[i] + constant;\n }\n }\n else {\n for (let i = 0; i < array1.length; i++) {\n array3[i] = array1[i] + array2[i];\n }\n }\n return array3;\n}\n//# sourceMappingURL=xAdd.js.map","/**\n * Will apply a function on each element of the array described as a string\n * By default we will use as variable 'x'\n * In front of sequence of lowercase we will add 'Math.'. This allows to write\n * `sin(x) + cos(x)` and it will be replace internally by (x) => (Math.sin(x) + Math.cos(x))\n * @param array\n * @param options\n * @returns\n */\nexport function xApplyFunctionStr(array, options = {}) {\n const { variableLabel = 'x', fctString = variableLabel } = options;\n const fct = new Function(variableLabel, `return Number(${fctString\n .replaceAll(/(?^|\\W)(?[\\da-z]{2,}\\()/g, '$Math.$')\n .replaceAll('Math.Math', 'Math')})`);\n const toReturn = new Float64Array(array.length);\n for (let i = 0; i < array.length; i++) {\n toReturn[i] = fct(array[i]);\n if (Number.isNaN(toReturn[i])) {\n throw new Error(`The callback ${fctString} does not return a number: ${array[i]}`);\n }\n }\n return toReturn;\n}\n//# sourceMappingURL=xApplyFunctionStr.js.map","import { xCheck } from \"./xCheck.js\";\n/**\n * Downsample an array by averaging consecutive non-overlapping points (binning).\n * Either specify a fixed `binSize` or a target `numberOfPoints`.\n * @param array - input array.\n * @param options - options.\n * @returns downsampled array.\n */\nexport function xBinning(array, options = {}) {\n xCheck(array);\n const { binSize, numberOfPoints, keepFirstAndLast = true } = options;\n const { length } = array;\n if (numberOfPoints !== undefined) {\n if (binSize !== undefined) {\n throw new RangeError('binSize and numberOfPoints are mutually exclusive');\n }\n if (!Number.isInteger(numberOfPoints) || numberOfPoints <= 0) {\n throw new RangeError('numberOfPoints must be a positive integer');\n }\n if (numberOfPoints > length) {\n throw new RangeError('numberOfPoints must be <= array.length');\n }\n return binByNumberOfPoints(array, numberOfPoints, keepFirstAndLast);\n }\n const effectiveBinSize = binSize ?? 10;\n if (!Number.isInteger(effectiveBinSize) || effectiveBinSize <= 0) {\n throw new RangeError('binSize must be a positive integer');\n }\n if (effectiveBinSize === 1) {\n return Float64Array.from(array);\n }\n if (keepFirstAndLast) {\n if (length <= 2) {\n return Float64Array.from(array);\n }\n const innerLength = length - 2;\n const innerBinCount = Math.ceil(innerLength / effectiveBinSize);\n const output = new Float64Array(innerBinCount + 2);\n output[0] = array[0];\n output[output.length - 1] = array[length - 1];\n for (let i = 0, j = 1; i < innerLength; i += effectiveBinSize, j++) {\n const start = i + 1;\n const end = Math.min(start + effectiveBinSize, length - 1);\n let sum = 0;\n for (let k = start; k < end; k++) {\n sum += array[k];\n }\n output[j] = sum / (end - start);\n }\n return output;\n }\n const outputLength = Math.ceil(length / effectiveBinSize);\n const output = new Float64Array(outputLength);\n for (let i = 0, j = 0; i < length; i += effectiveBinSize, j++) {\n const end = Math.min(i + effectiveBinSize, length);\n let sum = 0;\n for (let k = i; k < end; k++) {\n sum += array[k];\n }\n output[j] = sum / (end - i);\n }\n return output;\n}\nfunction binByNumberOfPoints(array, numberOfPoints, keepFirstAndLast) {\n const { length } = array;\n const output = new Float64Array(numberOfPoints);\n if (keepFirstAndLast) {\n if (numberOfPoints < 2) {\n throw new RangeError('numberOfPoints must be >= 2 when keepFirstAndLast is true');\n }\n output[0] = array[0];\n output[numberOfPoints - 1] = array[length - 1];\n if (numberOfPoints === 2) {\n return output;\n }\n const innerLength = length - 2;\n const innerBins = numberOfPoints - 2;\n for (let j = 0; j < innerBins; j++) {\n const start = 1 + Math.floor((j * innerLength) / innerBins);\n const end = 1 + Math.floor(((j + 1) * innerLength) / innerBins);\n let sum = 0;\n for (let k = start; k < end; k++) {\n sum += array[k];\n }\n output[j + 1] = sum / (end - start);\n }\n return output;\n }\n for (let j = 0; j < numberOfPoints; j++) {\n const start = Math.floor((j * length) / numberOfPoints);\n const end = Math.floor(((j + 1) * length) / numberOfPoints);\n let sum = 0;\n for (let k = start; k < end; k++) {\n sum += array[k];\n }\n output[j] = sum / (end - start);\n }\n return output;\n}\n//# sourceMappingURL=xBinning.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 applyAlongAxis(callback, by) {\n if (typeof callback !== 'function') {\n throw new TypeError('callback must be a function');\n }\n const result = [];\n switch (by) {\n case 'row': {\n for (let i = 0; i < this.rows; i++) {\n result.push(callback.call(this, this.getRow(i), i));\n }\n break;\n }\n case 'column': {\n for (let i = 0; i < this.columns; i++) {\n result.push(callback.call(this, this.getColumn(i), i));\n }\n break;\n }\n default:\n throw new Error(`invalid option: ${by}`);\n }\n return result;\n }\n\n to1DArray() {\n let array = [];\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n array.push(this.get(i, j));\n }\n }\n return array;\n }\n\n to2DArray() {\n let copy = [];\n for (let i = 0; i < this.rows; i++) {\n copy.push([]);\n for (let j = 0; j < this.columns; j++) {\n copy[i].push(this.get(i, j));\n }\n }\n return copy;\n }\n\n toJSON() {\n return this.to2DArray();\n }\n\n isRowVector() {\n return this.rows === 1;\n }\n\n isColumnVector() {\n return this.columns === 1;\n }\n\n isVector() {\n return this.rows === 1 || this.columns === 1;\n }\n\n isSquare() {\n return this.rows === this.columns;\n }\n\n isEmpty() {\n return this.rows === 0 || this.columns === 0;\n }\n\n isSymmetric() {\n if (this.isSquare()) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j <= i; j++) {\n if (this.get(i, j) !== this.get(j, i)) {\n return false;\n }\n }\n }\n return true;\n }\n return false;\n }\n\n isDistance() {\n if (!this.isSymmetric()) return false;\n\n for (let i = 0; i < this.rows; i++) {\n if (this.get(i, i) !== 0) return false;\n }\n\n return true;\n }\n\n isEchelonForm() {\n let i = 0;\n let j = 0;\n let previousColumn = -1;\n let isEchelonForm = true;\n let checked = false;\n while (i < this.rows && isEchelonForm) {\n j = 0;\n checked = false;\n while (j < this.columns && checked === false) {\n if (this.get(i, j) === 0) {\n j++;\n } else if (this.get(i, j) === 1 && j > previousColumn) {\n checked = true;\n previousColumn = j;\n } else {\n isEchelonForm = false;\n checked = true;\n }\n }\n i++;\n }\n return isEchelonForm;\n }\n\n isReducedEchelonForm() {\n let i = 0;\n let j = 0;\n let previousColumn = -1;\n let isReducedEchelonForm = true;\n let checked = false;\n while (i < this.rows && isReducedEchelonForm) {\n j = 0;\n checked = false;\n while (j < this.columns && checked === false) {\n if (this.get(i, j) === 0) {\n j++;\n } else if (this.get(i, j) === 1 && j > previousColumn) {\n checked = true;\n previousColumn = j;\n } else {\n isReducedEchelonForm = false;\n checked = true;\n }\n }\n for (let k = j + 1; k < this.rows; k++) {\n if (this.get(i, k) !== 0) {\n isReducedEchelonForm = false;\n }\n }\n i++;\n }\n return isReducedEchelonForm;\n }\n\n echelonForm() {\n let result = this.clone();\n let h = 0;\n let k = 0;\n while (h < result.rows && k < result.columns) {\n let iMax = h;\n for (let i = h; i < result.rows; i++) {\n if (result.get(i, k) > result.get(iMax, k)) {\n iMax = i;\n }\n }\n if (result.get(iMax, k) === 0) {\n k++;\n } else {\n result.swapRows(h, iMax);\n let tmp = result.get(h, k);\n for (let j = k; j < result.columns; j++) {\n result.set(h, j, result.get(h, j) / tmp);\n }\n for (let i = h + 1; i < result.rows; i++) {\n let factor = result.get(i, k) / result.get(h, k);\n result.set(i, k, 0);\n for (let j = k + 1; j < result.columns; j++) {\n result.set(i, j, result.get(i, j) - result.get(h, j) * factor);\n }\n }\n h++;\n k++;\n }\n }\n return result;\n }\n\n reducedEchelonForm() {\n let result = this.echelonForm();\n let m = result.columns;\n let n = result.rows;\n let h = n - 1;\n while (h >= 0) {\n if (result.maxRow(h) === 0) {\n h--;\n } else {\n let p = 0;\n let pivot = false;\n while (p < n && pivot === false) {\n if (result.get(h, p) === 1) {\n pivot = true;\n } else {\n p++;\n }\n }\n for (let i = 0; i < h; i++) {\n let factor = result.get(i, p);\n for (let j = p; j < m; j++) {\n let tmp = result.get(i, j) - factor * result.get(h, j);\n result.set(i, j, tmp);\n }\n }\n h--;\n }\n }\n return result;\n }\n\n set() {\n throw new Error('set method is unimplemented');\n }\n\n get() {\n throw new Error('get method is unimplemented');\n }\n\n repeat(options = {}) {\n if (typeof options !== 'object') {\n throw new TypeError('options must be an object');\n }\n const { rows = 1, columns = 1 } = options;\n if (!Number.isInteger(rows) || rows <= 0) {\n throw new TypeError('rows must be a positive integer');\n }\n if (!Number.isInteger(columns) || columns <= 0) {\n throw new TypeError('columns must be a positive integer');\n }\n let matrix = new Matrix(this.rows * rows, this.columns * columns);\n for (let i = 0; i < rows; i++) {\n for (let j = 0; j < columns; j++) {\n matrix.setSubMatrix(this, this.rows * i, this.columns * j);\n }\n }\n return matrix;\n }\n\n fill(value) {\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, value);\n }\n }\n return this;\n }\n\n neg() {\n return this.mulS(-1);\n }\n\n getRow(index) {\n checkRowIndex(this, index);\n let row = [];\n for (let i = 0; i < this.columns; i++) {\n row.push(this.get(index, i));\n }\n return row;\n }\n\n getRowVector(index) {\n return Matrix.rowVector(this.getRow(index));\n }\n\n setRow(index, array) {\n checkRowIndex(this, index);\n array = checkRowVector(this, array);\n for (let i = 0; i < this.columns; i++) {\n this.set(index, i, array[i]);\n }\n return this;\n }\n\n swapRows(row1, row2) {\n checkRowIndex(this, row1);\n checkRowIndex(this, row2);\n for (let i = 0; i < this.columns; i++) {\n let temp = this.get(row1, i);\n this.set(row1, i, this.get(row2, i));\n this.set(row2, i, temp);\n }\n return this;\n }\n\n getColumn(index) {\n checkColumnIndex(this, index);\n let column = [];\n for (let i = 0; i < this.rows; i++) {\n column.push(this.get(i, index));\n }\n return column;\n }\n\n getColumnVector(index) {\n return Matrix.columnVector(this.getColumn(index));\n }\n\n setColumn(index, array) {\n checkColumnIndex(this, index);\n array = checkColumnVector(this, array);\n for (let i = 0; i < this.rows; i++) {\n this.set(i, index, array[i]);\n }\n return this;\n }\n\n swapColumns(column1, column2) {\n checkColumnIndex(this, column1);\n checkColumnIndex(this, column2);\n for (let i = 0; i < this.rows; i++) {\n let temp = this.get(i, column1);\n this.set(i, column1, this.get(i, column2));\n this.set(i, column2, temp);\n }\n return this;\n }\n\n addRowVector(vector) {\n vector = checkRowVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) + vector[j]);\n }\n }\n return this;\n }\n\n subRowVector(vector) {\n vector = checkRowVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) - vector[j]);\n }\n }\n return this;\n }\n\n mulRowVector(vector) {\n vector = checkRowVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) * vector[j]);\n }\n }\n return this;\n }\n\n divRowVector(vector) {\n vector = checkRowVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) / vector[j]);\n }\n }\n return this;\n }\n\n addColumnVector(vector) {\n vector = checkColumnVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) + vector[i]);\n }\n }\n return this;\n }\n\n subColumnVector(vector) {\n vector = checkColumnVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) - vector[i]);\n }\n }\n return this;\n }\n\n mulColumnVector(vector) {\n vector = checkColumnVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) * vector[i]);\n }\n }\n return this;\n }\n\n divColumnVector(vector) {\n vector = checkColumnVector(this, vector);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n this.set(i, j, this.get(i, j) / vector[i]);\n }\n }\n return this;\n }\n\n mulRow(index, value) {\n checkRowIndex(this, index);\n for (let i = 0; i < this.columns; i++) {\n this.set(index, i, this.get(index, i) * value);\n }\n return this;\n }\n\n mulColumn(index, value) {\n checkColumnIndex(this, index);\n for (let i = 0; i < this.rows; i++) {\n this.set(i, index, this.get(i, index) * value);\n }\n return this;\n }\n\n max(by) {\n if (this.isEmpty()) {\n return NaN;\n }\n switch (by) {\n case 'row': {\n const max = new Array(this.rows).fill(Number.NEGATIVE_INFINITY);\n for (let row = 0; row < this.rows; row++) {\n for (let column = 0; column < this.columns; column++) {\n if (this.get(row, column) > max[row]) {\n max[row] = this.get(row, column);\n }\n }\n }\n return max;\n }\n case 'column': {\n const max = new Array(this.columns).fill(Number.NEGATIVE_INFINITY);\n for (let row = 0; row < this.rows; row++) {\n for (let column = 0; column < this.columns; column++) {\n if (this.get(row, column) > max[column]) {\n max[column] = this.get(row, column);\n }\n }\n }\n return max;\n }\n case undefined: {\n let max = this.get(0, 0);\n for (let row = 0; row < this.rows; row++) {\n for (let column = 0; column < this.columns; column++) {\n if (this.get(row, column) > max) {\n max = this.get(row, column);\n }\n }\n }\n return max;\n }\n default:\n throw new Error(`invalid option: ${by}`);\n }\n }\n\n maxIndex() {\n checkNonEmpty(this);\n let v = this.get(0, 0);\n let idx = [0, 0];\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n if (this.get(i, j) > v) {\n v = this.get(i, j);\n idx[0] = i;\n idx[1] = j;\n }\n }\n }\n return idx;\n }\n\n min(by) {\n if (this.isEmpty()) {\n return NaN;\n }\n\n switch (by) {\n case 'row': {\n const min = new Array(this.rows).fill(Number.POSITIVE_INFINITY);\n for (let row = 0; row < this.rows; row++) {\n for (let column = 0; column < this.columns; column++) {\n if (this.get(row, column) < min[row]) {\n min[row] = this.get(row, column);\n }\n }\n }\n return min;\n }\n case 'column': {\n const min = new Array(this.columns).fill(Number.POSITIVE_INFINITY);\n for (let row = 0; row < this.rows; row++) {\n for (let column = 0; column < this.columns; column++) {\n if (this.get(row, column) < min[column]) {\n min[column] = this.get(row, column);\n }\n }\n }\n return min;\n }\n case undefined: {\n let min = this.get(0, 0);\n for (let row = 0; row < this.rows; row++) {\n for (let column = 0; column < this.columns; column++) {\n if (this.get(row, column) < min) {\n min = this.get(row, column);\n }\n }\n }\n return min;\n }\n default:\n throw new Error(`invalid option: ${by}`);\n }\n }\n\n minIndex() {\n checkNonEmpty(this);\n let v = this.get(0, 0);\n let idx = [0, 0];\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n if (this.get(i, j) < v) {\n v = this.get(i, j);\n idx[0] = i;\n idx[1] = j;\n }\n }\n }\n return idx;\n }\n\n maxRow(row) {\n checkRowIndex(this, row);\n if (this.isEmpty()) {\n return NaN;\n }\n let v = this.get(row, 0);\n for (let i = 1; i < this.columns; i++) {\n if (this.get(row, i) > v) {\n v = this.get(row, i);\n }\n }\n return v;\n }\n\n maxRowIndex(row) {\n checkRowIndex(this, row);\n checkNonEmpty(this);\n let v = this.get(row, 0);\n let idx = [row, 0];\n for (let i = 1; i < this.columns; i++) {\n if (this.get(row, i) > v) {\n v = this.get(row, i);\n idx[1] = i;\n }\n }\n return idx;\n }\n\n minRow(row) {\n checkRowIndex(this, row);\n if (this.isEmpty()) {\n return NaN;\n }\n let v = this.get(row, 0);\n for (let i = 1; i < this.columns; i++) {\n if (this.get(row, i) < v) {\n v = this.get(row, i);\n }\n }\n return v;\n }\n\n minRowIndex(row) {\n checkRowIndex(this, row);\n checkNonEmpty(this);\n let v = this.get(row, 0);\n let idx = [row, 0];\n for (let i = 1; i < this.columns; i++) {\n if (this.get(row, i) < v) {\n v = this.get(row, i);\n idx[1] = i;\n }\n }\n return idx;\n }\n\n maxColumn(column) {\n checkColumnIndex(this, column);\n if (this.isEmpty()) {\n return NaN;\n }\n let v = this.get(0, column);\n for (let i = 1; i < this.rows; i++) {\n if (this.get(i, column) > v) {\n v = this.get(i, column);\n }\n }\n return v;\n }\n\n maxColumnIndex(column) {\n checkColumnIndex(this, column);\n checkNonEmpty(this);\n let v = this.get(0, column);\n let idx = [0, column];\n for (let i = 1; i < this.rows; i++) {\n if (this.get(i, column) > v) {\n v = this.get(i, column);\n idx[0] = i;\n }\n }\n return idx;\n }\n\n minColumn(column) {\n checkColumnIndex(this, column);\n if (this.isEmpty()) {\n return NaN;\n }\n let v = this.get(0, column);\n for (let i = 1; i < this.rows; i++) {\n if (this.get(i, column) < v) {\n v = this.get(i, column);\n }\n }\n return v;\n }\n\n minColumnIndex(column) {\n checkColumnIndex(this, column);\n checkNonEmpty(this);\n let v = this.get(0, column);\n let idx = [0, column];\n for (let i = 1; i < this.rows; i++) {\n if (this.get(i, column) < v) {\n v = this.get(i, column);\n idx[0] = i;\n }\n }\n return idx;\n }\n\n diag() {\n let min = Math.min(this.rows, this.columns);\n let diag = [];\n for (let i = 0; i < min; i++) {\n diag.push(this.get(i, i));\n }\n return diag;\n }\n\n norm(type = 'frobenius') {\n switch (type) {\n case 'max':\n return this.max();\n case 'frobenius':\n return Math.sqrt(this.dot(this));\n default:\n throw new RangeError(`unknown norm type: ${type}`);\n }\n }\n\n cumulativeSum() {\n let sum = 0;\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n sum += this.get(i, j);\n this.set(i, j, sum);\n }\n }\n return this;\n }\n\n dot(vector2) {\n if (AbstractMatrix.isMatrix(vector2)) vector2 = vector2.to1DArray();\n let vector1 = this.to1DArray();\n if (vector1.length !== vector2.length) {\n throw new RangeError('vectors do not have the same size');\n }\n let dot = 0;\n for (let i = 0; i < vector1.length; i++) {\n dot += vector1[i] * vector2[i];\n }\n return dot;\n }\n\n mmul(other) {\n other = Matrix.checkMatrix(other);\n\n let m = this.rows;\n let n = this.columns;\n let p = other.columns;\n\n let result = new Matrix(m, p);\n\n let Bcolj = new Float64Array(n);\n for (let j = 0; j < p; j++) {\n for (let k = 0; k < n; k++) {\n Bcolj[k] = other.get(k, j);\n }\n\n for (let i = 0; i < m; i++) {\n let s = 0;\n for (let k = 0; k < n; k++) {\n s += this.get(i, k) * Bcolj[k];\n }\n\n result.set(i, j, s);\n }\n }\n return result;\n }\n\n gram() {\n const rows = this.rows;\n const n = this.columns;\n\n // The Gram matrix `thisᵀ · this` is symmetric, so only its upper triangle is\n // accumulated (then mirrored) and the transpose is never materialized.\n // Row-streaming rank-1 updates read each row of `this` contiguously and skip\n // zero entries, so the cost scales with the number of non-zeros: it is as\n // fast as the dense version on dense matrices (the skip never fires) and far\n // faster on sparse ones.\n const gramData = new Float64Array(n * n);\n for (let r = 0; r < rows; r++) {\n for (let i = 0; i < n; i++) {\n const value = this.get(r, i);\n if (value === 0) continue;\n const offset = i * n;\n for (let j = i; j < n; j++) {\n gramData[offset + j] += value * this.get(r, j);\n }\n }\n }\n\n const result = new Matrix(n, n);\n for (let i = 0; i < n; i++) {\n const offset = i * n;\n for (let j = i; j < n; j++) {\n const value = gramData[offset + j];\n result.set(i, j, value);\n result.set(j, i, value);\n }\n }\n return result;\n }\n\n transposeMultiply(other) {\n other = Matrix.checkMatrix(other);\n if (this.rows !== other.rows) {\n throw new RangeError(\n 'the number of rows of the two matrices must be equal',\n );\n }\n const n = this.columns;\n const p = other.columns;\n\n const result = new Matrix(n, p);\n const otherRow = new Float64Array(p);\n for (let r = 0; r < this.rows; r++) {\n for (let j = 0; j < p; j++) {\n otherRow[j] = other.get(r, j);\n }\n for (let i = 0; i < n; i++) {\n const value = this.get(r, i);\n if (value === 0) continue;\n const resultRow = result.data[i];\n for (let j = 0; j < p; j++) {\n resultRow[j] += value * otherRow[j];\n }\n }\n }\n return result;\n }\n\n mmulByTranspose(scale) {\n let m = this.rows;\n let n = this.columns;\n\n if (scale !== undefined && scale.length !== n) {\n throw new RangeError('scale must have one value per column');\n }\n\n let result = new Matrix(m, m);\n\n // result = this · diag(scale) · thisᵀ is symmetric, so only the upper\n // triangle is computed and mirrored, and the transpose is never\n // materialized. `scale` (one factor per column) is folded into one operand.\n let rowj = new Float64Array(n);\n for (let j = 0; j < m; j++) {\n if (scale === undefined) {\n for (let k = 0; k < n; k++) {\n rowj[k] = this.get(j, k);\n }\n } else {\n for (let k = 0; k < n; k++) {\n rowj[k] = scale[k] * this.get(j, k);\n }\n }\n\n for (let i = j; i < m; i++) {\n let s = 0;\n for (let k = 0; k < n; k++) {\n s += this.get(i, k) * rowj[k];\n }\n\n result.set(i, j, s);\n result.set(j, i, s);\n }\n }\n return result;\n }\n\n mpow(scalar) {\n if (!this.isSquare()) {\n throw new RangeError('Matrix must be square');\n }\n if (!Number.isInteger(scalar) || scalar < 0) {\n throw new RangeError('Exponent must be a non-negative integer');\n }\n // Russian Peasant exponentiation, i.e. exponentiation by squaring\n let result = Matrix.eye(this.rows);\n let bb = this;\n // Note: Don't bit shift. In JS, that would truncate at 32 bits\n for (let e = scalar; e >= 1; e /= 2) {\n if ((e & 1) !== 0) {\n result = result.mmul(bb);\n }\n bb = bb.mmul(bb);\n }\n return result;\n }\n\n strassen2x2(other) {\n other = Matrix.checkMatrix(other);\n let result = new Matrix(2, 2);\n const a11 = this.get(0, 0);\n const b11 = other.get(0, 0);\n const a12 = this.get(0, 1);\n const b12 = other.get(0, 1);\n const a21 = this.get(1, 0);\n const b21 = other.get(1, 0);\n const a22 = this.get(1, 1);\n const b22 = other.get(1, 1);\n\n // Compute intermediate values.\n const m1 = (a11 + a22) * (b11 + b22);\n const m2 = (a21 + a22) * b11;\n const m3 = a11 * (b12 - b22);\n const m4 = a22 * (b21 - b11);\n const m5 = (a11 + a12) * b22;\n const m6 = (a21 - a11) * (b11 + b12);\n const m7 = (a12 - a22) * (b21 + b22);\n\n // Combine intermediate values into the output.\n const c00 = m1 + m4 - m5 + m7;\n const c01 = m3 + m5;\n const c10 = m2 + m4;\n const c11 = m1 - m2 + m3 + m6;\n\n result.set(0, 0, c00);\n result.set(0, 1, c01);\n result.set(1, 0, c10);\n result.set(1, 1, c11);\n return result;\n }\n\n strassen3x3(other) {\n other = Matrix.checkMatrix(other);\n let result = new Matrix(3, 3);\n\n const a00 = this.get(0, 0);\n const a01 = this.get(0, 1);\n const a02 = this.get(0, 2);\n const a10 = this.get(1, 0);\n const a11 = this.get(1, 1);\n const a12 = this.get(1, 2);\n const a20 = this.get(2, 0);\n const a21 = this.get(2, 1);\n const a22 = this.get(2, 2);\n\n const b00 = other.get(0, 0);\n const b01 = other.get(0, 1);\n const b02 = other.get(0, 2);\n const b10 = other.get(1, 0);\n const b11 = other.get(1, 1);\n const b12 = other.get(1, 2);\n const b20 = other.get(2, 0);\n const b21 = other.get(2, 1);\n const b22 = other.get(2, 2);\n\n const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;\n const m2 = (a00 - a10) * (-b01 + b11);\n const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);\n const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);\n const m5 = (a10 + a11) * (-b00 + b01);\n const m6 = a00 * b00;\n const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);\n const m8 = (-a00 + a20) * (b02 - b12);\n const m9 = (a20 + a21) * (-b00 + b02);\n const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;\n const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);\n const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);\n const m13 = (a02 - a22) * (b11 - b21);\n const m14 = a02 * b20;\n const m15 = (a21 + a22) * (-b20 + b21);\n const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);\n const m17 = (a02 - a12) * (b12 - b22);\n const m18 = (a11 + a12) * (-b20 + b22);\n const m19 = a01 * b10;\n const m20 = a12 * b21;\n const m21 = a10 * b02;\n const m22 = a20 * b01;\n const m23 = a22 * b22;\n\n const c00 = m6 + m14 + m19;\n const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;\n const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;\n const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;\n const c11 = m2 + m4 + m5 + m6 + m20;\n const c12 = m14 + m16 + m17 + m18 + m21;\n const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;\n const c21 = m12 + m13 + m14 + m15 + m22;\n const c22 = m6 + m7 + m8 + m9 + m23;\n\n result.set(0, 0, c00);\n result.set(0, 1, c01);\n result.set(0, 2, c02);\n result.set(1, 0, c10);\n result.set(1, 1, c11);\n result.set(1, 2, c12);\n result.set(2, 0, c20);\n result.set(2, 1, c21);\n result.set(2, 2, c22);\n return result;\n }\n\n mmulStrassen(y) {\n y = Matrix.checkMatrix(y);\n let x = this.clone();\n let r1 = x.rows;\n let c1 = x.columns;\n let r2 = y.rows;\n let c2 = y.columns;\n if (c1 !== r2) {\n // eslint-disable-next-line no-console\n console.warn(\n `Multiplying ${r1} x ${c1} and ${r2} x ${c2} matrix: dimensions do not match.`,\n );\n }\n\n // Put a matrix into the top left of a matrix of zeros.\n // `rows` and `cols` are the dimensions of the output matrix.\n function embed(mat, rows, cols) {\n let r = mat.rows;\n let c = mat.columns;\n if (r === rows && c === cols) {\n return mat;\n } else {\n let resultat = AbstractMatrix.zeros(rows, cols);\n resultat = resultat.setSubMatrix(mat, 0, 0);\n return resultat;\n }\n }\n\n // Make sure both matrices are the same size.\n // This is exclusively for simplicity:\n // this algorithm can be implemented with matrices of different sizes.\n\n let r = Math.max(r1, r2);\n let c = Math.max(c1, c2);\n x = embed(x, r, c);\n y = embed(y, r, c);\n\n // Our recursive multiplication function.\n function blockMult(a, b, rows, cols) {\n // For small matrices, resort to naive multiplication.\n if (rows <= 512 || cols <= 512) {\n return a.mmul(b); // a is equivalent to this\n }\n\n // Apply dynamic padding.\n if (rows % 2 === 1 && cols % 2 === 1) {\n a = embed(a, rows + 1, cols + 1);\n b = embed(b, rows + 1, cols + 1);\n } else if (rows % 2 === 1) {\n a = embed(a, rows + 1, cols);\n b = embed(b, rows + 1, cols);\n } else if (cols % 2 === 1) {\n a = embed(a, rows, cols + 1);\n b = embed(b, rows, cols + 1);\n }\n\n let halfRows = parseInt(a.rows / 2, 10);\n let halfCols = parseInt(a.columns / 2, 10);\n // Subdivide input matrices.\n let a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n let b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n\n let a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);\n let b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);\n\n let a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);\n let b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);\n\n let a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);\n let b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1);\n\n // Compute intermediate values.\n let m1 = blockMult(\n AbstractMatrix.add(a11, a22),\n AbstractMatrix.add(b11, b22),\n halfRows,\n halfCols,\n );\n let m2 = blockMult(AbstractMatrix.add(a21, a22), b11, halfRows, halfCols);\n let m3 = blockMult(a11, AbstractMatrix.sub(b12, b22), halfRows, halfCols);\n let m4 = blockMult(a22, AbstractMatrix.sub(b21, b11), halfRows, halfCols);\n let m5 = blockMult(AbstractMatrix.add(a11, a12), b22, halfRows, halfCols);\n let m6 = blockMult(\n AbstractMatrix.sub(a21, a11),\n AbstractMatrix.add(b11, b12),\n halfRows,\n halfCols,\n );\n let m7 = blockMult(\n AbstractMatrix.sub(a12, a22),\n AbstractMatrix.add(b21, b22),\n halfRows,\n halfCols,\n );\n\n // Combine intermediate values into the output.\n let c11 = AbstractMatrix.add(m1, m4);\n c11.sub(m5);\n c11.add(m7);\n let c12 = AbstractMatrix.add(m3, m5);\n let c21 = AbstractMatrix.add(m2, m4);\n let c22 = AbstractMatrix.sub(m1, m2);\n c22.add(m3);\n c22.add(m6);\n\n // Crop output to the desired size (undo dynamic padding).\n let result = AbstractMatrix.zeros(2 * c11.rows, 2 * c11.columns);\n result = result.setSubMatrix(c11, 0, 0);\n result = result.setSubMatrix(c12, c11.rows, 0);\n result = result.setSubMatrix(c21, 0, c11.columns);\n result = result.setSubMatrix(c22, c11.rows, c11.columns);\n return result.subMatrix(0, rows - 1, 0, cols - 1);\n }\n\n return blockMult(x, y, r, c);\n }\n\n scaleRows(options = {}) {\n if (typeof options !== 'object') {\n throw new TypeError('options must be an object');\n }\n const { min = 0, max = 1 } = options;\n if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n if (min >= max) throw new RangeError('min must be smaller than max');\n let newMatrix = new Matrix(this.rows, this.columns);\n for (let i = 0; i < this.rows; i++) {\n const row = this.getRow(i);\n if (row.length > 0) {\n rescale(row, { min, max, output: row });\n }\n newMatrix.setRow(i, row);\n }\n return newMatrix;\n }\n\n scaleColumns(options = {}) {\n if (typeof options !== 'object') {\n throw new TypeError('options must be an object');\n }\n const { min = 0, max = 1 } = options;\n if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n if (min >= max) throw new RangeError('min must be smaller than max');\n let newMatrix = new Matrix(this.rows, this.columns);\n for (let i = 0; i < this.columns; i++) {\n const column = this.getColumn(i);\n if (column.length) {\n rescale(column, {\n min,\n max,\n output: column,\n });\n }\n newMatrix.setColumn(i, column);\n }\n return newMatrix;\n }\n\n flipRows() {\n const middle = Math.ceil(this.columns / 2);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < middle; j++) {\n let first = this.get(i, j);\n let last = this.get(i, this.columns - 1 - j);\n this.set(i, j, last);\n this.set(i, this.columns - 1 - j, first);\n }\n }\n return this;\n }\n\n flipColumns() {\n const middle = Math.ceil(this.rows / 2);\n for (let j = 0; j < this.columns; j++) {\n for (let i = 0; i < middle; i++) {\n let first = this.get(i, j);\n let last = this.get(this.rows - 1 - i, j);\n this.set(i, j, last);\n this.set(this.rows - 1 - i, j, first);\n }\n }\n return this;\n }\n\n kroneckerProduct(other) {\n other = Matrix.checkMatrix(other);\n\n let m = this.rows;\n let n = this.columns;\n let p = other.rows;\n let q = other.columns;\n\n let result = new Matrix(m * p, n * q);\n for (let i = 0; i < m; i++) {\n for (let j = 0; j < n; j++) {\n for (let k = 0; k < p; k++) {\n for (let l = 0; l < q; l++) {\n result.set(p * i + k, q * j + l, this.get(i, j) * other.get(k, l));\n }\n }\n }\n }\n return result;\n }\n\n kroneckerSum(other) {\n other = Matrix.checkMatrix(other);\n if (!this.isSquare() || !other.isSquare()) {\n throw new Error('Kronecker Sum needs two Square Matrices');\n }\n let m = this.rows;\n let n = other.rows;\n let AxI = this.kroneckerProduct(Matrix.eye(n, n));\n let IxB = Matrix.eye(m, m).kroneckerProduct(other);\n return AxI.add(IxB);\n }\n\n transpose() {\n let result = new Matrix(this.columns, this.rows);\n for (let i = 0; i < this.rows; i++) {\n for (let j = 0; j < this.columns; j++) {\n result.set(j, i, this.get(i, j));\n }\n }\n return result;\n }\n\n sortRows(compareFunction = compareNumbers) {\n for (let i = 0; i < this.rows; i++) {\n this.setRow(i, this.getRow(i).sort(compareFunction));\n }\n return this;\n }\n\n sortColumns(compareFunction = compareNumbers) {\n for (let i = 0; i < this.columns; i++) {\n this.setColumn(i, this.getColumn(i).sort(compareFunction));\n }\n return this;\n }\n\n subMatrix(startRow, endRow, startColumn, endColumn) {\n checkRange(this, startRow, endRow, startColumn, endColumn);\n let newMatrix = new Matrix(\n endRow - startRow + 1,\n endColumn - startColumn + 1,\n );\n for (let i = startRow; i <= endRow; i++) {\n for (let j = startColumn; j <= endColumn; j++) {\n newMatrix.set(i - startRow, j - startColumn, this.get(i, j));\n }\n }\n return newMatrix;\n }\n\n subMatrixRow(indices, startColumn, endColumn) {\n if (startColumn === undefined) startColumn = 0;\n if (endColumn === undefined) endColumn = this.columns - 1;\n if (\n startColumn > endColumn ||\n startColumn < 0 ||\n startColumn >= this.columns ||\n endColumn < 0 ||\n endColumn >= this.columns\n ) {\n throw new RangeError('Argument out of range');\n }\n\n let newMatrix = new Matrix(indices.length, endColumn - startColumn + 1);\n for (let i = 0; i < indices.length; i++) {\n for (let j = startColumn; j <= endColumn; j++) {\n if (indices[i] < 0 || indices[i] >= this.rows) {\n throw new RangeError(`Row index out of range: ${indices[i]}`);\n }\n newMatrix.set(i, j - startColumn, this.get(indices[i], j));\n }\n }\n return newMatrix;\n }\n\n subMatrixColumn(indices, startRow, endRow) {\n if (startRow === undefined) startRow = 0;\n if (endRow === undefined) endRow = this.rows - 1;\n if (\n startRow > endRow ||\n startRow < 0 ||\n startRow >= this.rows ||\n endRow < 0 ||\n endRow >= this.rows\n ) {\n throw new RangeError('Argument out of range');\n }\n\n let newMatrix = new Matrix(endRow - startRow + 1, indices.length);\n for (let i = 0; i < indices.length; i++) {\n for (let j = startRow; j <= endRow; j++) {\n if (indices[i] < 0 || indices[i] >= this.columns) {\n throw new RangeError(`Column index out of range: ${indices[i]}`);\n }\n newMatrix.set(j - startRow, i, this.get(j, indices[i]));\n }\n }\n return newMatrix;\n }\n\n setSubMatrix(matrix, startRow, startColumn) {\n matrix = Matrix.checkMatrix(matrix);\n if (matrix.isEmpty()) {\n return this;\n }\n let endRow = startRow + matrix.rows - 1;\n let endColumn = startColumn + matrix.columns - 1;\n checkRange(this, startRow, endRow, startColumn, endColumn);\n for (let i = 0; i < matrix.rows; i++) {\n for (let j = 0; j < matrix.columns; j++) {\n this.set(startRow + i, startColumn + j, matrix.get(i, j));\n }\n }\n return this;\n }\n\n concat(other, by = 'row') {\n other = Matrix.checkMatrix(other);\n switch (by) {\n case 'row': {\n if (this.columns !== other.columns) {\n throw new RangeError(\n 'both matrices must have the same number of columns',\n );\n }\n const result = new Matrix(this.rows + other.rows, this.columns);\n result.setSubMatrix(this, 0, 0);\n result.setSubMatrix(other, this.rows, 0);\n return result;\n }\n case 'column': {\n if (this.rows !== other.rows) {\n throw new RangeError(\n 'both matrices must have the same number of rows',\n );\n }\n const result = new Matrix(this.rows, this.columns + other.columns);\n result.setSubMatrix(this, 0, 0);\n result.setSubMatrix(other, 0, this.columns);\n return result;\n }\n default:\n throw new Error(`invalid option: ${by}`);\n }\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}\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>} diagonalSize\n * @return {this}\n */\n constructor(diagonalSize) {\n super();\n\n if (Matrix.isMatrix(diagonalSize)) {\n if (!diagonalSize.isSymmetric()) {\n throw new TypeError('not symmetric data');\n }\n\n this.#matrix = Matrix.copy(\n diagonalSize,\n new Matrix(diagonalSize.rows, diagonalSize.rows),\n );\n } else if (Number.isInteger(diagonalSize) && diagonalSize >= 0) {\n this.#matrix = new Matrix(diagonalSize, diagonalSize);\n } else {\n this.#matrix = new Matrix(diagonalSize);\n\n if (!this.isSymmetric()) {\n throw new TypeError('not symmetric data');\n }\n }\n }\n\n clone() {\n const matrix = new SymmetricMatrix(this.diagonalSize);\n\n for (const [row, col, value] of this.upperRightEntries()) {\n matrix.set(row, col, value);\n }\n\n return matrix;\n }\n\n toMatrix() {\n return new Matrix(this);\n }\n\n get(rowIndex, columnIndex) {\n return this.#matrix.get(rowIndex, columnIndex);\n }\n set(rowIndex, columnIndex, value) {\n // symmetric set\n this.#matrix.set(rowIndex, columnIndex, value);\n this.#matrix.set(columnIndex, rowIndex, value);\n\n return this;\n }\n\n removeCross(index) {\n // symmetric remove side\n this.#matrix.removeRow(index);\n this.#matrix.removeColumn(index);\n\n return this;\n }\n\n addCross(index, array) {\n if (array === undefined) {\n array = index;\n index = this.diagonalSize;\n }\n\n const row = array.slice();\n row.splice(index, 1);\n\n this.#matrix.addRow(index, row);\n this.#matrix.addColumn(index, array);\n\n return this;\n }\n\n /**\n * @param {Mask[]} mask\n */\n applyMask(mask) {\n if (mask.length !== this.diagonalSize) {\n throw new RangeError('Mask size do not match with matrix size');\n }\n\n // prepare sides to remove from matrix from mask\n /** @type {number[]} */\n const sidesToRemove = [];\n for (const [index, passthroughs] of mask.entries()) {\n if (passthroughs) continue;\n sidesToRemove.push(index);\n }\n // to remove from highest to lowest for no mutation shifting\n sidesToRemove.reverse();\n\n // remove sides\n for (const sideIndex of sidesToRemove) {\n this.removeCross(sideIndex);\n }\n\n return this;\n }\n\n /**\n * Compact format upper-right corner of matrix\n * iterate from left to right, from top to bottom.\n *\n * ```\n * A B C D\n * A 1 2 3 4\n * B 2 5 6 7\n * C 3 6 8 9\n * D 4 7 9 10\n * ```\n *\n * will return compact 1D array `[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]`\n *\n * length is S(i=0, n=sideSize) => 10 for a 4 sideSized matrix\n *\n * @returns {number[]}\n */\n toCompact() {\n const { diagonalSize } = this;\n\n /** @type {number[]} */\n const compact = new Array((diagonalSize * (diagonalSize + 1)) / 2);\n for (let col = 0, row = 0, index = 0; index < compact.length; index++) {\n compact[index] = this.get(row, col);\n\n if (++col >= diagonalSize) col = ++row;\n }\n\n return compact;\n }\n\n /**\n * @param {number[]} compact\n * @return {SymmetricMatrix}\n */\n static fromCompact(compact) {\n const compactSize = compact.length;\n // compactSize = (sideSize * (sideSize + 1)) / 2\n // https://mathsolver.microsoft.com/fr/solve-problem/y%20%3D%20%20x%20%60cdot%20%20%20%60frac%7B%20%20%60left(%20x%2B1%20%20%60right)%20%20%20%20%7D%7B%202%20%20%7D\n // sideSize = (Sqrt(8 × compactSize + 1) - 1) / 2\n const diagonalSize = (Math.sqrt(8 * compactSize + 1) - 1) / 2;\n\n if (!Number.isInteger(diagonalSize)) {\n throw new TypeError(\n `This array is not a compact representation of a Symmetric Matrix, ${JSON.stringify(\n compact,\n )}`,\n );\n }\n\n const matrix = new SymmetricMatrix(diagonalSize);\n for (let col = 0, row = 0, index = 0; index < compactSize; index++) {\n matrix.set(col, row, compact[index]);\n if (++col >= diagonalSize) col = ++row;\n }\n\n return matrix;\n }\n\n /**\n * half iterator upper-right-corner from left to right, from top to bottom\n * yield [row, column, value]\n *\n * @returns {Generator<[number, number, number], void, void>}\n */\n *upperRightEntries() {\n for (let row = 0, col = 0; row < this.diagonalSize; void 0) {\n const value = this.get(row, col);\n\n yield [row, col, value];\n\n // at the end of row, move cursor to next row at diagonal position\n if (++col >= this.diagonalSize) col = ++row;\n }\n }\n\n /**\n * half iterator upper-right-corner from left to right, from top to bottom\n * yield value\n *\n * @returns {Generator<[number, number, number], void, void>}\n */\n *upperRightValues() {\n for (let row = 0, col = 0; row < this.diagonalSize; void 0) {\n const value = this.get(row, col);\n\n yield value;\n\n // at the end of row, move cursor to next row at diagonal position\n if (++col >= this.diagonalSize) col = ++row;\n }\n }\n}\nSymmetricMatrix.prototype.klassType = 'SymmetricMatrix';\n\nclass DistanceMatrix extends SymmetricMatrix {\n /**\n * not the same as matrix.isSymmetric()\n * Here is to check if it's instanceof SymmetricMatrix without bundling issues\n *\n * @param value\n * @returns {boolean}\n */\n static isDistanceMatrix(value) {\n return (\n SymmetricMatrix.isSymmetricMatrix(value) &&\n value.klassSubType === 'DistanceMatrix'\n );\n }\n\n constructor(sideSize) {\n super(sideSize);\n\n if (!this.isDistance()) {\n throw new TypeError('Provided arguments do no produce a distance matrix');\n }\n }\n\n set(rowIndex, columnIndex, value) {\n // distance matrix diagonal is 0\n if (rowIndex === columnIndex) value = 0;\n\n return super.set(rowIndex, columnIndex, value);\n }\n\n addCross(index, array) {\n if (array === undefined) {\n array = index;\n index = this.diagonalSize;\n }\n\n // ensure distance\n array = array.slice();\n array[index] = 0;\n\n return super.addCross(index, array);\n }\n\n toSymmetricMatrix() {\n return new SymmetricMatrix(this);\n }\n\n clone() {\n const matrix = new DistanceMatrix(this.diagonalSize);\n\n for (const [row, col, value] of this.upperRightEntries()) {\n if (row === col) continue;\n matrix.set(row, col, value);\n }\n\n return matrix;\n }\n\n /**\n * Compact format upper-right corner of matrix\n * no diagonal (only zeros)\n * iterable from left to right, from top to bottom.\n *\n * ```\n * A B C D\n * A 0 1 2 3\n * B 1 0 4 5\n * C 2 4 0 6\n * D 3 5 6 0\n * ```\n *\n * will return compact 1D array `[1, 2, 3, 4, 5, 6]`\n *\n * length is S(i=0, n=sideSize-1) => 6 for a 4 side sized matrix\n *\n * @returns {number[]}\n */\n toCompact() {\n const { diagonalSize } = this;\n const compactLength = ((diagonalSize - 1) * diagonalSize) / 2;\n\n /** @type {number[]} */\n const compact = new Array(compactLength);\n for (let col = 1, row = 0, index = 0; index < compact.length; index++) {\n compact[index] = this.get(row, col);\n\n if (++col >= diagonalSize) col = ++row + 1;\n }\n\n return compact;\n }\n\n /**\n * @param {number[]} compact\n */\n static fromCompact(compact) {\n const compactSize = compact.length;\n\n if (compactSize === 0) {\n return new this(0);\n }\n\n // compactSize in Natural integer range ]0;∞]\n // compactSize = (sideSize * (sideSize - 1)) / 2\n // sideSize = (Sqrt(8 × compactSize + 1) + 1) / 2\n const diagonalSize = (Math.sqrt(8 * compactSize + 1) + 1) / 2;\n\n if (!Number.isInteger(diagonalSize)) {\n throw new TypeError(\n `This array is not a compact representation of a DistanceMatrix, ${JSON.stringify(\n compact,\n )}`,\n );\n }\n\n const matrix = new this(diagonalSize);\n for (let col = 1, row = 0, index = 0; index < compactSize; index++) {\n matrix.set(col, row, compact[index]);\n if (++col >= diagonalSize) col = ++row + 1;\n }\n\n return matrix;\n }\n}\nDistanceMatrix.prototype.klassSubType = 'DistanceMatrix';\n\nclass BaseView extends AbstractMatrix {\n constructor(matrix, rows, columns) {\n super();\n this.matrix = matrix;\n this.rows = rows;\n this.columns = columns;\n }\n}\n\nclass MatrixColumnView extends BaseView {\n constructor(matrix, column) {\n checkColumnIndex(matrix, column);\n super(matrix, matrix.rows, 1);\n this.column = column;\n }\n\n set(rowIndex, columnIndex, value) {\n this.matrix.set(rowIndex, this.column, value);\n return this;\n }\n\n get(rowIndex) {\n return this.matrix.get(rowIndex, this.column);\n }\n}\n\nclass MatrixColumnSelectionView extends BaseView {\n constructor(matrix, columnIndices) {\n checkColumnIndices(matrix, columnIndices);\n super(matrix, matrix.rows, columnIndices.length);\n this.columnIndices = columnIndices;\n }\n\n set(rowIndex, columnIndex, value) {\n this.matrix.set(rowIndex, this.columnIndices[columnIndex], value);\n return this;\n }\n\n get(rowIndex, columnIndex) {\n return this.matrix.get(rowIndex, this.columnIndices[columnIndex]);\n }\n}\n\nclass MatrixFlipColumnView extends BaseView {\n constructor(matrix) {\n super(matrix, matrix.rows, matrix.columns);\n }\n\n set(rowIndex, columnIndex, value) {\n this.matrix.set(rowIndex, this.columns - columnIndex - 1, value);\n return this;\n }\n\n get(rowIndex, columnIndex) {\n return this.matrix.get(rowIndex, this.columns - columnIndex - 1);\n }\n}\n\nclass MatrixFlipRowView extends BaseView {\n constructor(matrix) {\n super(matrix, matrix.rows, matrix.columns);\n }\n\n set(rowIndex, columnIndex, value) {\n this.matrix.set(this.rows - rowIndex - 1, columnIndex, value);\n return this;\n }\n\n get(rowIndex, columnIndex) {\n return this.matrix.get(this.rows - rowIndex - 1, columnIndex);\n }\n}\n\nclass MatrixRowView extends BaseView {\n constructor(matrix, row) {\n checkRowIndex(matrix, row);\n super(matrix, 1, matrix.columns);\n this.row = row;\n }\n\n set(rowIndex, columnIndex, value) {\n this.matrix.set(this.row, columnIndex, value);\n return this;\n }\n\n get(rowIndex, columnIndex) {\n return this.matrix.get(this.row, columnIndex);\n }\n}\n\nclass MatrixRowSelectionView extends BaseView {\n constructor(matrix, rowIndices) {\n checkRowIndices(matrix, rowIndices);\n super(matrix, rowIndices.length, matrix.columns);\n this.rowIndices = rowIndices;\n }\n\n set(rowIndex, columnIndex, value) {\n this.matrix.set(this.rowIndices[rowIndex], columnIndex, value);\n return this;\n }\n\n get(rowIndex, columnIndex) {\n return this.matrix.get(this.rowIndices[rowIndex], columnIndex);\n }\n}\n\nclass MatrixSelectionView extends BaseView {\n constructor(matrix, rowIndices, columnIndices) {\n checkRowIndices(matrix, rowIndices);\n checkColumnIndices(matrix, columnIndices);\n super(matrix, rowIndices.length, columnIndices.length);\n this.rowIndices = rowIndices;\n this.columnIndices = columnIndices;\n }\n\n set(rowIndex, columnIndex, value) {\n this.matrix.set(\n this.rowIndices[rowIndex],\n this.columnIndices[columnIndex],\n value,\n );\n return this;\n }\n\n get(rowIndex, columnIndex) {\n return this.matrix.get(\n this.rowIndices[rowIndex],\n this.columnIndices[columnIndex],\n );\n }\n}\n\nclass MatrixSubView extends BaseView {\n constructor(matrix, startRow, endRow, startColumn, endColumn) {\n checkRange(matrix, startRow, endRow, startColumn, endColumn);\n super(matrix, endRow - startRow + 1, endColumn - startColumn + 1);\n this.startRow = startRow;\n this.startColumn = startColumn;\n }\n\n set(rowIndex, columnIndex, value) {\n this.matrix.set(\n this.startRow + rowIndex,\n this.startColumn + columnIndex,\n value,\n );\n return this;\n }\n\n get(rowIndex, columnIndex) {\n return this.matrix.get(\n this.startRow + rowIndex,\n this.startColumn + columnIndex,\n );\n }\n}\n\nclass MatrixTransposeView extends BaseView {\n constructor(matrix) {\n super(matrix, matrix.columns, matrix.rows);\n }\n\n set(rowIndex, columnIndex, value) {\n this.matrix.set(columnIndex, rowIndex, value);\n return this;\n }\n\n get(rowIndex, columnIndex) {\n return this.matrix.get(columnIndex, rowIndex);\n }\n}\n\nclass WrapperMatrix1D extends AbstractMatrix {\n constructor(data, options = {}) {\n const { rows = 1 } = options;\n\n if (data.length % rows !== 0) {\n throw new Error('the data length is not divisible by the number of rows');\n }\n super();\n this.rows = rows;\n this.columns = data.length / rows;\n this.data = data;\n }\n\n set(rowIndex, columnIndex, value) {\n let index = this._calculateIndex(rowIndex, columnIndex);\n this.data[index] = value;\n return this;\n }\n\n get(rowIndex, columnIndex) {\n let index = this._calculateIndex(rowIndex, columnIndex);\n return this.data[index];\n }\n\n _calculateIndex(row, column) {\n return row * this.columns + column;\n }\n}\n\nclass WrapperMatrix2D extends AbstractMatrix {\n constructor(data) {\n super();\n this.data = data;\n this.rows = data.length;\n this.columns = data[0].length;\n }\n\n set(rowIndex, columnIndex, value) {\n this.data[rowIndex][columnIndex] = value;\n return this;\n }\n\n get(rowIndex, columnIndex) {\n return this.data[rowIndex][columnIndex];\n }\n}\n\nfunction wrap(array, options) {\n if (isAnyArray(array)) {\n if (array[0] && isAnyArray(array[0])) {\n return new WrapperMatrix2D(array);\n } else {\n return new WrapperMatrix1D(array, options);\n }\n } else {\n throw new Error('the argument is not an array');\n }\n}\n\nclass LuDecomposition {\n constructor(matrix) {\n matrix = WrapperMatrix2D.checkMatrix(matrix);\n\n let lu = matrix.clone();\n let rows = lu.rows;\n let columns = lu.columns;\n let pivotVector = new Float64Array(rows);\n let pivotSign = 1;\n let i, j, k, p, s, t, v;\n let LUcolj, kmax;\n\n for (i = 0; i < rows; i++) {\n pivotVector[i] = i;\n }\n\n LUcolj = new Float64Array(rows);\n\n for (j = 0; j < columns; j++) {\n for (i = 0; i < rows; i++) {\n LUcolj[i] = lu.get(i, j);\n }\n\n for (i = 0; i < rows; i++) {\n kmax = Math.min(i, j);\n s = 0;\n for (k = 0; k < kmax; k++) {\n s += lu.get(i, k) * LUcolj[k];\n }\n LUcolj[i] -= s;\n lu.set(i, j, LUcolj[i]);\n }\n\n p = j;\n for (i = j + 1; i < rows; i++) {\n if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {\n p = i;\n }\n }\n\n if (p !== j) {\n for (k = 0; k < columns; k++) {\n t = lu.get(p, k);\n lu.set(p, k, lu.get(j, k));\n lu.set(j, k, t);\n }\n\n v = pivotVector[p];\n pivotVector[p] = pivotVector[j];\n pivotVector[j] = v;\n\n pivotSign = -pivotSign;\n }\n\n if (j < rows && lu.get(j, j) !== 0) {\n for (i = j + 1; i < rows; i++) {\n lu.set(i, j, lu.get(i, j) / lu.get(j, j));\n }\n }\n }\n\n this.LU = lu;\n this.pivotVector = pivotVector;\n this.pivotSign = pivotSign;\n }\n\n isSingular() {\n let data = this.LU;\n let col = data.columns;\n for (let j = 0; j < col; j++) {\n if (data.get(j, j) === 0) {\n return true;\n }\n }\n return false;\n }\n\n solve(value) {\n value = Matrix.checkMatrix(value);\n\n let lu = this.LU;\n let rows = lu.rows;\n\n if (rows !== value.rows) {\n throw new Error('Invalid matrix dimensions');\n }\n if (this.isSingular()) {\n throw new Error('LU matrix is singular');\n }\n\n let count = value.columns;\n let X = value.subMatrixRow(this.pivotVector, 0, count - 1);\n let columns = lu.columns;\n let i, j, k;\n\n for (k = 0; k < columns; k++) {\n for (i = k + 1; i < columns; i++) {\n for (j = 0; j < count; j++) {\n X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n }\n }\n }\n for (k = columns - 1; k >= 0; k--) {\n for (j = 0; j < count; j++) {\n X.set(k, j, X.get(k, j) / lu.get(k, k));\n }\n for (i = 0; i < k; i++) {\n for (j = 0; j < count; j++) {\n X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n }\n }\n }\n return X;\n }\n\n get determinant() {\n let data = this.LU;\n if (!data.isSquare()) {\n throw new Error('Matrix must be square');\n }\n let determinant = this.pivotSign;\n let col = data.columns;\n for (let j = 0; j < col; j++) {\n determinant *= data.get(j, j);\n }\n return determinant;\n }\n\n get lowerTriangularMatrix() {\n let data = this.LU;\n let rows = data.rows;\n let columns = data.columns;\n let X = new Matrix(rows, columns);\n for (let i = 0; i < rows; i++) {\n for (let j = 0; j < columns; j++) {\n if (i > j) {\n X.set(i, j, data.get(i, j));\n } else if (i === j) {\n X.set(i, j, 1);\n } else {\n X.set(i, j, 0);\n }\n }\n }\n return X;\n }\n\n get upperTriangularMatrix() {\n let data = this.LU;\n let rows = data.rows;\n let columns = data.columns;\n let X = new Matrix(rows, columns);\n for (let i = 0; i < rows; i++) {\n for (let j = 0; j < columns; j++) {\n if (i <= j) {\n X.set(i, j, data.get(i, j));\n } else {\n X.set(i, j, 0);\n }\n }\n }\n return X;\n }\n\n get pivotPermutationVector() {\n return Array.from(this.pivotVector);\n }\n}\n\n/**\n * Transpose a square matrix in place, without allocating a copy.\n * Used to restore the logical layout of decomposition outputs that were\n * accumulated in transposed storage for cache-sequential inner loops.\n * @param {import('../matrix').default} matrix - square matrix, mutated in place\n * @returns {import('../matrix').default} the same matrix\n */\nfunction transposeSquareInPlace(matrix) {\n const data = matrix.data;\n const n = matrix.rows;\n for (let i = 0; i < n; i++) {\n const rowI = data[i];\n for (let j = i + 1; j < n; j++) {\n const tmp = rowI[j];\n rowI[j] = data[j][i];\n data[j][i] = tmp;\n }\n }\n return matrix;\n}\n\nfunction hypotenuse(a, b) {\n let r = 0;\n if (Math.abs(a) > Math.abs(b)) {\n r = b / a;\n return Math.abs(a) * Math.sqrt(1 + r * r);\n }\n if (b !== 0) {\n r = a / b;\n return Math.abs(b) * Math.sqrt(1 + r * r);\n }\n return 0;\n}\n\nclass QrDecomposition {\n constructor(value) {\n value = WrapperMatrix2D.checkMatrix(value);\n\n let qr = value.clone();\n let m = value.rows;\n let n = value.columns;\n let rdiag = new Float64Array(n);\n let i, j, k, s;\n\n for (k = 0; k < n; k++) {\n let nrm = 0;\n for (i = k; i < m; i++) {\n nrm = hypotenuse(nrm, qr.get(i, k));\n }\n if (nrm !== 0) {\n if (qr.get(k, k) < 0) {\n nrm = -nrm;\n }\n for (i = k; i < m; i++) {\n qr.set(i, k, qr.get(i, k) / nrm);\n }\n qr.set(k, k, qr.get(k, k) + 1);\n for (j = k + 1; j < n; j++) {\n s = 0;\n for (i = k; i < m; i++) {\n s += qr.get(i, k) * qr.get(i, j);\n }\n s = -s / qr.get(k, k);\n for (i = k; i < m; i++) {\n qr.set(i, j, qr.get(i, j) + s * qr.get(i, k));\n }\n }\n }\n rdiag[k] = -nrm;\n }\n\n this.QR = qr;\n this.Rdiag = rdiag;\n }\n\n solve(value) {\n value = Matrix.checkMatrix(value);\n\n let qr = this.QR;\n let m = qr.rows;\n\n if (value.rows !== m) {\n throw new Error('Matrix row dimensions must agree');\n }\n if (!this.isFullRank()) {\n throw new Error('Matrix is rank deficient');\n }\n\n let count = value.columns;\n let X = value.clone();\n let n = qr.columns;\n let i, j, k, s;\n\n for (k = 0; k < n; k++) {\n for (j = 0; j < count; j++) {\n s = 0;\n for (i = k; i < m; i++) {\n s += qr.get(i, k) * X.get(i, j);\n }\n s = -s / qr.get(k, k);\n for (i = k; i < m; i++) {\n X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n }\n }\n }\n for (k = n - 1; k >= 0; k--) {\n for (j = 0; j < count; j++) {\n X.set(k, j, X.get(k, j) / this.Rdiag[k]);\n }\n for (i = 0; i < k; i++) {\n for (j = 0; j < count; j++) {\n X.set(i, j, X.get(i, j) - X.get(k, j) * qr.get(i, k));\n }\n }\n }\n\n return X.subMatrix(0, n - 1, 0, count - 1);\n }\n\n isFullRank() {\n let columns = this.QR.columns;\n for (let i = 0; i < columns; i++) {\n if (this.Rdiag[i] === 0) {\n return false;\n }\n }\n return true;\n }\n\n get upperTriangularMatrix() {\n let qr = this.QR;\n let n = qr.columns;\n let X = new Matrix(n, n);\n let i, j;\n for (i = 0; i < n; i++) {\n for (j = 0; j < n; j++) {\n if (i < j) {\n X.set(i, j, qr.get(i, j));\n } else if (i === j) {\n X.set(i, j, this.Rdiag[i]);\n } else {\n X.set(i, j, 0);\n }\n }\n }\n return X;\n }\n\n get orthogonalMatrix() {\n let qr = this.QR;\n let rows = qr.rows;\n let columns = qr.columns;\n let X = new Matrix(rows, columns);\n let i, j, k, s;\n\n for (k = columns - 1; k >= 0; k--) {\n for (i = 0; i < rows; i++) {\n X.set(i, k, 0);\n }\n X.set(k, k, 1);\n for (j = k; j < columns; j++) {\n if (qr.get(k, k) !== 0) {\n s = 0;\n for (i = k; i < rows; i++) {\n s += qr.get(i, k) * X.get(i, j);\n }\n\n s = -s / qr.get(k, k);\n\n for (i = k; i < rows; i++) {\n X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n }\n }\n }\n }\n return X;\n }\n}\n\nclass SingularValueDecomposition {\n constructor(value, options = {}) {\n value = WrapperMatrix2D.checkMatrix(value);\n\n if (value.isEmpty()) {\n throw new Error('Matrix must be non-empty');\n }\n\n let m = value.rows;\n let n = value.columns;\n\n const {\n computeLeftSingularVectors = true,\n computeRightSingularVectors = true,\n autoTranspose = false,\n } = options;\n\n let wantu = Boolean(computeLeftSingularVectors);\n let wantv = Boolean(computeRightSingularVectors);\n\n // Work on the transpose of the input so the hot inner loops (which iterate\n // over rows for a fixed column) scan memory sequentially in the row-major\n // backing store. `at` holds the transpose: at.get(j, i) === a.get(i, j)\n // where `a` is the logical m x n working matrix.\n let swapped = false;\n let at;\n if (m < n) {\n if (!autoTranspose) {\n // eslint-disable-next-line no-console\n console.warn(\n 'Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose',\n );\n at = value.transpose();\n } else {\n at = value.clone();\n m = value.columns;\n n = value.rows;\n swapped = true;\n let aux = wantu;\n wantu = wantv;\n wantv = aux;\n }\n } else {\n at = value.transpose();\n }\n\n let nu = Math.min(m, n);\n let ni = Math.min(m + 1, n);\n let s = new Float64Array(ni);\n // U and V are stored transposed during the computation so the inner loops\n // (which always vary the row index) scan memory sequentially. They are\n // transposed back to their logical layout before being returned.\n // Ut.get(j, i) === U.get(i, j) and Vt.get(j, i) === V.get(i, j).\n let U = new Matrix(nu, m);\n let V = new Matrix(n, n);\n\n let e = new Float64Array(n);\n let work = new Float64Array(m);\n\n let si = new Float64Array(ni);\n for (let i = 0; i < ni; i++) si[i] = i;\n\n let nct = Math.min(m - 1, n);\n let nrt = Math.max(0, Math.min(n - 2, m));\n let mrc = Math.max(nct, nrt);\n\n for (let k = 0; k < mrc; k++) {\n if (k < nct) {\n s[k] = 0;\n for (let i = k; i < m; i++) {\n s[k] = hypotenuse(s[k], at.get(k, i));\n }\n if (s[k] !== 0) {\n if (at.get(k, k) < 0) {\n s[k] = -s[k];\n }\n for (let i = k; i < m; i++) {\n at.set(k, i, at.get(k, i) / s[k]);\n }\n at.set(k, k, at.get(k, k) + 1);\n }\n s[k] = -s[k];\n }\n\n for (let j = k + 1; j < n; j++) {\n if (k < nct && s[k] !== 0) {\n let t = 0;\n for (let i = k; i < m; i++) {\n t += at.get(k, i) * at.get(j, i);\n }\n t = -t / at.get(k, k);\n for (let i = k; i < m; i++) {\n at.set(j, i, at.get(j, i) + t * at.get(k, i));\n }\n }\n e[j] = at.get(j, k);\n }\n\n if (wantu && k < nct) {\n for (let i = k; i < m; i++) {\n U.set(k, i, at.get(k, i));\n }\n }\n\n if (k < nrt) {\n e[k] = 0;\n for (let i = k + 1; i < n; i++) {\n e[k] = hypotenuse(e[k], e[i]);\n }\n if (e[k] !== 0) {\n if (e[k + 1] < 0) {\n e[k] = 0 - e[k];\n }\n for (let i = k + 1; i < n; i++) {\n e[i] /= e[k];\n }\n e[k + 1] += 1;\n }\n e[k] = -e[k];\n if (k + 1 < m && e[k] !== 0) {\n for (let i = k + 1; i < m; i++) {\n work[i] = 0;\n }\n for (let i = k + 1; i < m; i++) {\n for (let j = k + 1; j < n; j++) {\n work[i] += e[j] * at.get(j, i);\n }\n }\n for (let j = k + 1; j < n; j++) {\n let t = -e[j] / e[k + 1];\n for (let i = k + 1; i < m; i++) {\n at.set(j, i, at.get(j, i) + t * work[i]);\n }\n }\n }\n if (wantv) {\n for (let i = k + 1; i < n; i++) {\n V.set(k, i, e[i]);\n }\n }\n }\n }\n\n let p = Math.min(n, m + 1);\n if (nct < n) {\n s[nct] = at.get(nct, nct);\n }\n if (m < p) {\n s[p - 1] = 0;\n }\n if (nrt + 1 < p) {\n e[nrt] = at.get(p - 1, nrt);\n }\n e[p - 1] = 0;\n\n if (wantu) {\n for (let j = nct; j < nu; j++) {\n for (let i = 0; i < m; i++) {\n U.set(j, i, 0);\n }\n U.set(j, j, 1);\n }\n for (let k = nct - 1; k >= 0; k--) {\n if (s[k] !== 0) {\n for (let j = k + 1; j < nu; j++) {\n let t = 0;\n for (let i = k; i < m; i++) {\n t += U.get(k, i) * U.get(j, i);\n }\n t = -t / U.get(k, k);\n for (let i = k; i < m; i++) {\n U.set(j, i, U.get(j, i) + t * U.get(k, i));\n }\n }\n for (let i = k; i < m; i++) {\n U.set(k, i, -U.get(k, i));\n }\n U.set(k, k, 1 + U.get(k, k));\n for (let i = 0; i < k - 1; i++) {\n U.set(k, i, 0);\n }\n } else {\n for (let i = 0; i < m; i++) {\n U.set(k, i, 0);\n }\n U.set(k, k, 1);\n }\n }\n }\n\n if (wantv) {\n for (let k = n - 1; k >= 0; k--) {\n if (k < nrt && e[k] !== 0) {\n for (let j = k + 1; j < n; j++) {\n let t = 0;\n for (let i = k + 1; i < n; i++) {\n t += V.get(k, i) * V.get(j, i);\n }\n t = -t / V.get(k, k + 1);\n for (let i = k + 1; i < n; i++) {\n V.set(j, i, V.get(j, i) + t * V.get(k, i));\n }\n }\n }\n for (let i = 0; i < n; i++) {\n V.set(k, i, 0);\n }\n V.set(k, k, 1);\n }\n }\n\n let pp = p - 1;\n let eps = Number.EPSILON;\n while (p > 0) {\n let k, kase;\n for (k = p - 2; k >= -1; k--) {\n if (k === -1) {\n break;\n }\n const alpha =\n Number.MIN_VALUE + eps * Math.abs(s[k] + Math.abs(s[k + 1]));\n if (Math.abs(e[k]) <= alpha || Number.isNaN(e[k])) {\n e[k] = 0;\n break;\n }\n }\n if (k === p - 2) {\n kase = 4;\n } else {\n let ks;\n for (ks = p - 1; ks >= k; ks--) {\n if (ks === k) {\n break;\n }\n let t =\n (ks !== p ? Math.abs(e[ks]) : 0) +\n (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);\n if (Math.abs(s[ks]) <= eps * t) {\n s[ks] = 0;\n break;\n }\n }\n if (ks === k) {\n kase = 3;\n } else if (ks === p - 1) {\n kase = 1;\n } else {\n kase = 2;\n k = ks;\n }\n }\n\n k++;\n\n switch (kase) {\n case 1: {\n let f = e[p - 2];\n e[p - 2] = 0;\n for (let j = p - 2; j >= k; j--) {\n let t = hypotenuse(s[j], f);\n let cs = s[j] / t;\n let sn = f / t;\n s[j] = t;\n if (j !== k) {\n f = -sn * e[j - 1];\n e[j - 1] = cs * e[j - 1];\n }\n if (wantv) {\n for (let i = 0; i < n; i++) {\n t = cs * V.get(j, i) + sn * V.get(p - 1, i);\n V.set(p - 1, i, -sn * V.get(j, i) + cs * V.get(p - 1, i));\n V.set(j, i, t);\n }\n }\n }\n break;\n }\n case 2: {\n let f = e[k - 1];\n e[k - 1] = 0;\n for (let j = k; j < p; j++) {\n let t = hypotenuse(s[j], f);\n let cs = s[j] / t;\n let sn = f / t;\n s[j] = t;\n f = -sn * e[j];\n e[j] = cs * e[j];\n if (wantu) {\n for (let i = 0; i < m; i++) {\n t = cs * U.get(j, i) + sn * U.get(k - 1, i);\n U.set(k - 1, i, -sn * U.get(j, i) + cs * U.get(k - 1, i));\n U.set(j, i, t);\n }\n }\n }\n break;\n }\n case 3: {\n const scale = Math.max(\n Math.abs(s[p - 1]),\n Math.abs(s[p - 2]),\n Math.abs(e[p - 2]),\n Math.abs(s[k]),\n Math.abs(e[k]),\n );\n const sp = s[p - 1] / scale;\n const spm1 = s[p - 2] / scale;\n const epm1 = e[p - 2] / scale;\n const sk = s[k] / scale;\n const ek = e[k] / scale;\n const b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;\n const c = sp * epm1 * (sp * epm1);\n let shift = 0;\n if (b !== 0 || c !== 0) {\n if (b < 0) {\n shift = 0 - Math.sqrt(b * b + c);\n } else {\n shift = Math.sqrt(b * b + c);\n }\n shift = c / (b + shift);\n }\n let f = (sk + sp) * (sk - sp) + shift;\n let g = sk * ek;\n for (let j = k; j < p - 1; j++) {\n let t = hypotenuse(f, g);\n if (t === 0) t = Number.MIN_VALUE;\n let cs = f / t;\n let sn = g / t;\n if (j !== k) {\n e[j - 1] = t;\n }\n f = cs * s[j] + sn * e[j];\n e[j] = cs * e[j] - sn * s[j];\n g = sn * s[j + 1];\n s[j + 1] = cs * s[j + 1];\n if (wantv) {\n for (let i = 0; i < n; i++) {\n t = cs * V.get(j, i) + sn * V.get(j + 1, i);\n V.set(j + 1, i, -sn * V.get(j, i) + cs * V.get(j + 1, i));\n V.set(j, i, t);\n }\n }\n t = hypotenuse(f, g);\n if (t === 0) t = Number.MIN_VALUE;\n cs = f / t;\n sn = g / t;\n s[j] = t;\n f = cs * e[j] + sn * s[j + 1];\n s[j + 1] = -sn * e[j] + cs * s[j + 1];\n g = sn * e[j + 1];\n e[j + 1] = cs * e[j + 1];\n if (wantu && j < m - 1) {\n for (let i = 0; i < m; i++) {\n t = cs * U.get(j, i) + sn * U.get(j + 1, i);\n U.set(j + 1, i, -sn * U.get(j, i) + cs * U.get(j + 1, i));\n U.set(j, i, t);\n }\n }\n }\n e[p - 2] = f;\n break;\n }\n case 4: {\n if (s[k] <= 0) {\n s[k] = s[k] < 0 ? -s[k] : 0;\n if (wantv) {\n for (let i = 0; i <= pp; i++) {\n V.set(k, i, -V.get(k, i));\n }\n }\n }\n while (k < pp) {\n if (s[k] >= s[k + 1]) {\n break;\n }\n let t = s[k];\n s[k] = s[k + 1];\n s[k + 1] = t;\n if (wantv && k < n - 1) {\n for (let i = 0; i < n; i++) {\n t = V.get(k + 1, i);\n V.set(k + 1, i, V.get(k, i));\n V.set(k, i, t);\n }\n }\n if (wantu && k < m - 1) {\n for (let i = 0; i < m; i++) {\n t = U.get(k + 1, i);\n U.set(k + 1, i, U.get(k, i));\n U.set(k, i, t);\n }\n }\n k++;\n }\n p--;\n break;\n }\n // no default\n }\n }\n\n // Restore the logical (row-major) layout of the singular vectors, which were\n // accumulated in transposed storage for cache-sequential inner loops. V is\n // always square and U is square whenever the input is, so this is done in\n // place (no allocation) in the common case.\n U = U.isSquare() ? transposeSquareInPlace(U) : U.transpose();\n V = transposeSquareInPlace(V);\n\n if (swapped) {\n let tmp = V;\n V = U;\n U = tmp;\n }\n\n this.m = m;\n this.n = n;\n this.s = s;\n this.U = U;\n this.V = V;\n }\n\n solve(value) {\n let Y = value;\n let e = this.threshold;\n let scols = this.s.length;\n let Ls = Matrix.zeros(scols, scols);\n\n for (let i = 0; i < scols; i++) {\n if (Math.abs(this.s[i]) <= e) {\n Ls.set(i, i, 0);\n } else {\n Ls.set(i, i, 1 / this.s[i]);\n }\n }\n\n let U = this.U;\n let V = this.rightSingularVectors;\n\n let VL = V.mmul(Ls);\n let vrows = V.rows;\n let urows = U.rows;\n let VLU = Matrix.zeros(vrows, urows);\n\n for (let i = 0; i < vrows; i++) {\n for (let j = 0; j < urows; j++) {\n let sum = 0;\n for (let k = 0; k < scols; k++) {\n sum += VL.get(i, k) * U.get(j, k);\n }\n VLU.set(i, j, sum);\n }\n }\n\n return VLU.mmul(Y);\n }\n\n solveForDiagonal(value) {\n return this.solve(Matrix.diag(value));\n }\n\n inverse() {\n let V = this.V;\n let e = this.threshold;\n let vrows = V.rows;\n let vcols = V.columns;\n let X = new Matrix(vrows, this.s.length);\n\n for (let i = 0; i < vrows; i++) {\n for (let j = 0; j < vcols; j++) {\n if (Math.abs(this.s[j]) > e) {\n X.set(i, j, V.get(i, j) / this.s[j]);\n }\n }\n }\n\n let U = this.U;\n\n let urows = U.rows;\n let ucols = U.columns;\n let Y = new Matrix(vrows, urows);\n\n for (let i = 0; i < vrows; i++) {\n for (let j = 0; j < urows; j++) {\n let sum = 0;\n for (let k = 0; k < ucols; k++) {\n sum += X.get(i, k) * U.get(j, k);\n }\n Y.set(i, j, sum);\n }\n }\n\n return Y;\n }\n\n get condition() {\n return this.s[0] / this.s[Math.min(this.m, this.n) - 1];\n }\n\n get norm2() {\n return this.s[0];\n }\n\n get rank() {\n let tol = Math.max(this.m, this.n) * this.s[0] * Number.EPSILON;\n let r = 0;\n let s = this.s;\n for (let i = 0, ii = s.length; i < ii; i++) {\n if (s[i] > tol) {\n r++;\n }\n }\n return r;\n }\n\n get diagonal() {\n return Array.from(this.s);\n }\n\n get threshold() {\n return (Number.EPSILON / 2) * Math.max(this.m, this.n) * this.s[0];\n }\n\n get leftSingularVectors() {\n return this.U;\n }\n\n get rightSingularVectors() {\n return this.V;\n }\n\n get diagonalMatrix() {\n return Matrix.diag(this.s);\n }\n}\n\nfunction inverse(matrix, useSVD = false) {\n matrix = WrapperMatrix2D.checkMatrix(matrix);\n if (useSVD) {\n return new SingularValueDecomposition(matrix).inverse();\n } else {\n return solve(matrix, Matrix.eye(matrix.rows));\n }\n}\n\nfunction solve(leftHandSide, rightHandSide, useSVD = false) {\n leftHandSide = WrapperMatrix2D.checkMatrix(leftHandSide);\n rightHandSide = WrapperMatrix2D.checkMatrix(rightHandSide);\n if (useSVD) {\n return new SingularValueDecomposition(leftHandSide).solve(rightHandSide);\n } else {\n return leftHandSide.isSquare()\n ? new LuDecomposition(leftHandSide).solve(rightHandSide)\n : new QrDecomposition(leftHandSide).solve(rightHandSide);\n }\n}\n\nfunction determinant(matrix) {\n matrix = Matrix.checkMatrix(matrix);\n if (matrix.isSquare()) {\n if (matrix.columns === 0) {\n return 1;\n }\n\n let a, b, c, d;\n if (matrix.columns === 2) {\n // 2 x 2 matrix\n a = matrix.get(0, 0);\n b = matrix.get(0, 1);\n c = matrix.get(1, 0);\n d = matrix.get(1, 1);\n\n return a * d - b * c;\n } else if (matrix.columns === 3) {\n // 3 x 3 matrix\n let subMatrix0, subMatrix1, subMatrix2;\n subMatrix0 = new MatrixSelectionView(matrix, [1, 2], [1, 2]);\n subMatrix1 = new MatrixSelectionView(matrix, [1, 2], [0, 2]);\n subMatrix2 = new MatrixSelectionView(matrix, [1, 2], [0, 1]);\n a = matrix.get(0, 0);\n b = matrix.get(0, 1);\n c = matrix.get(0, 2);\n\n return (\n a * determinant(subMatrix0) -\n b * determinant(subMatrix1) +\n c * determinant(subMatrix2)\n );\n } else {\n // general purpose determinant using the LU decomposition\n return new LuDecomposition(matrix).determinant;\n }\n } else {\n throw Error('determinant can only be calculated for a square matrix');\n }\n}\n\nfunction xrange(n, exception) {\n let range = [];\n for (let i = 0; i < n; i++) {\n if (i !== exception) {\n range.push(i);\n }\n }\n return range;\n}\n\nfunction dependenciesOneRow(\n error,\n matrix,\n index,\n thresholdValue = 10e-10,\n thresholdError = 10e-10,\n) {\n if (error > thresholdError) {\n return new Array(matrix.rows + 1).fill(0);\n } else {\n let returnArray = matrix.addRow(index, [0]);\n for (let i = 0; i < returnArray.rows; i++) {\n if (Math.abs(returnArray.get(i, 0)) < thresholdValue) {\n returnArray.set(i, 0, 0);\n }\n }\n return returnArray.to1DArray();\n }\n}\n\nfunction linearDependencies(matrix, options = {}) {\n const { thresholdValue = 10e-10, thresholdError = 10e-10 } = options;\n matrix = Matrix.checkMatrix(matrix);\n\n let n = matrix.rows;\n let results = new Matrix(n, n);\n\n for (let i = 0; i < n; i++) {\n let b = Matrix.columnVector(matrix.getRow(i));\n let Abis = matrix.subMatrixRow(xrange(n, i)).transpose();\n let svd = new SingularValueDecomposition(Abis);\n let x = svd.solve(b);\n // The residual scales with the row it explains; the coefficients don't.\n let scale = Matrix.abs(b).max() || 1;\n let error = Matrix.sub(b, Abis.mmul(x)).abs().max() / scale;\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 // Singular values scale with the matrix, so the cutoff must too. Same\n // tolerance as SVD.rank, and the `rcond * max(s)` rule used by LAPACK.\n const cutoff = threshold * Math.max(matrix.rows, matrix.columns) * s[0];\n\n for (let i = 0; i < s.length; i++) {\n if (Math.abs(s[i]) > cutoff) {\n s[i] = 1.0 / s[i];\n } else {\n s[i] = 0.0;\n }\n }\n\n return V.mmul(Matrix.diag(s).mmul(U.transpose()));\n}\n\nfunction covariance(xMatrix, yMatrix = xMatrix, options = {}) {\n xMatrix = new Matrix(xMatrix);\n let yIsSame = false;\n if (\n typeof yMatrix === 'object' &&\n !Matrix.isMatrix(yMatrix) &&\n !isAnyArray(yMatrix)\n ) {\n options = yMatrix;\n yMatrix = xMatrix;\n yIsSame = true;\n } else {\n yMatrix = new Matrix(yMatrix);\n }\n if (xMatrix.rows !== yMatrix.rows) {\n throw new TypeError('Both matrices must have the same number of rows');\n }\n const { center = true } = options;\n if (center) {\n xMatrix = xMatrix.center('column');\n if (!yIsSame) {\n yMatrix = yMatrix.center('column');\n }\n }\n const cov = xMatrix.transposeMultiply(yMatrix);\n for (let i = 0; i < cov.rows; i++) {\n for (let j = 0; j < cov.columns; j++) {\n cov.set(i, j, cov.get(i, j) * (1 / (xMatrix.rows - 1)));\n }\n }\n return cov;\n}\n\nfunction correlation(xMatrix, yMatrix = xMatrix, options = {}) {\n xMatrix = new Matrix(xMatrix);\n let yIsSame = false;\n if (\n typeof yMatrix === 'object' &&\n !Matrix.isMatrix(yMatrix) &&\n !isAnyArray(yMatrix)\n ) {\n options = yMatrix;\n yMatrix = xMatrix;\n yIsSame = true;\n } else {\n yMatrix = new Matrix(yMatrix);\n }\n if (xMatrix.rows !== yMatrix.rows) {\n throw new TypeError('Both matrices must have the same number of rows');\n }\n\n const { center = true, scale = true } = options;\n if (center) {\n xMatrix.center('column');\n if (!yIsSame) {\n yMatrix.center('column');\n }\n }\n if (scale) {\n xMatrix.scale('column');\n if (!yIsSame) {\n yMatrix.scale('column');\n }\n }\n\n const sdx = xMatrix.standardDeviation('column', { unbiased: true });\n const sdy = yIsSame\n ? sdx\n : yMatrix.standardDeviation('column', { unbiased: true });\n\n const corr = xMatrix.transposeMultiply(yMatrix);\n for (let i = 0; i < corr.rows; i++) {\n for (let j = 0; j < corr.columns; j++) {\n corr.set(\n i,\n j,\n corr.get(i, j) * (1 / (sdx[i] * sdy[j])) * (1 / (xMatrix.rows - 1)),\n );\n }\n }\n return corr;\n}\n\nclass EigenvalueDecomposition {\n constructor(matrix, options = {}) {\n const { assumeSymmetric = false } = options;\n\n matrix = WrapperMatrix2D.checkMatrix(matrix);\n if (!matrix.isSquare()) {\n throw new Error('Matrix is not a square matrix');\n }\n\n if (matrix.isEmpty()) {\n throw new Error('Matrix must be non-empty');\n }\n\n let n = matrix.columns;\n let V = new Matrix(n, n);\n let d = new Float64Array(n);\n let e = new Float64Array(n);\n let value = matrix;\n let i, j;\n\n let isSymmetric = false;\n if (assumeSymmetric) {\n isSymmetric = true;\n } else {\n isSymmetric = matrix.isSymmetric();\n }\n\n if (isSymmetric) {\n // tred2/tql2 access V almost exclusively down columns (the row index\n // varies in the hot loops). Storing V transposed turns those into\n // sequential row scans of the row-major backing store; we transpose it\n // back to the logical layout before returning. V.get(j, i) holds the\n // logical V(i, j).\n for (i = 0; i < n; i++) {\n for (j = 0; j < n; j++) {\n V.set(j, i, value.get(i, j));\n }\n }\n tred2(n, e, d, V);\n tql2(n, e, d, V);\n // V is square; restore the logical layout in place (no allocation).\n transposeSquareInPlace(V);\n } else {\n // The non-symmetric path (orthes/hqr2) has two O(n^3) phases with opposite\n // memory-layout preferences (the QR sweep favours column-major eigenvectors\n // while the back-transform favours row-major), so a single transposed\n // storage cannot help both. It is left in the original row-major layout.\n let H = new Matrix(n, n);\n let ort = new Float64Array(n);\n for (j = 0; j < n; j++) {\n for (i = 0; i < n; i++) {\n H.set(i, j, value.get(i, j));\n }\n }\n orthes(n, H, ort, V);\n hqr2(n, e, d, V, H);\n }\n\n this.n = n;\n this.e = e;\n this.d = d;\n this.V = V;\n }\n\n get realEigenvalues() {\n return Array.from(this.d);\n }\n\n get imaginaryEigenvalues() {\n return Array.from(this.e);\n }\n\n get eigenvectorMatrix() {\n return this.V;\n }\n\n get diagonalMatrix() {\n let n = this.n;\n let e = this.e;\n let d = this.d;\n let X = new Matrix(n, n);\n let i, j;\n for (i = 0; i < n; i++) {\n for (j = 0; j < n; j++) {\n X.set(i, j, 0);\n }\n X.set(i, i, d[i]);\n if (e[i] > 0) {\n X.set(i, i + 1, e[i]);\n } else if (e[i] < 0) {\n X.set(i, i - 1, e[i]);\n }\n }\n return X;\n }\n}\n\nfunction tred2(n, e, d, V) {\n let f, g, h, i, j, k, hh, scale;\n\n for (j = 0; j < n; j++) {\n d[j] = V.get(j, n - 1);\n }\n\n for (i = n - 1; i > 0; i--) {\n scale = 0;\n h = 0;\n for (k = 0; k < i; k++) {\n scale = scale + Math.abs(d[k]);\n }\n\n if (scale === 0) {\n e[i] = d[i - 1];\n for (j = 0; j < i; j++) {\n d[j] = V.get(j, i - 1);\n V.set(j, i, 0);\n V.set(i, j, 0);\n }\n } else {\n for (k = 0; k < i; k++) {\n d[k] /= scale;\n h += d[k] * d[k];\n }\n\n f = d[i - 1];\n g = Math.sqrt(h);\n if (f > 0) {\n g = -g;\n }\n\n e[i] = scale * g;\n h = h - f * g;\n d[i - 1] = f - g;\n for (j = 0; j < i; j++) {\n e[j] = 0;\n }\n\n for (j = 0; j < i; j++) {\n f = d[j];\n V.set(i, j, f);\n g = e[j] + V.get(j, j) * f;\n for (k = j + 1; k <= i - 1; k++) {\n g += V.get(j, k) * d[k];\n e[k] += V.get(j, k) * f;\n }\n e[j] = g;\n }\n\n f = 0;\n for (j = 0; j < i; j++) {\n e[j] /= h;\n f += e[j] * d[j];\n }\n\n hh = f / (h + h);\n for (j = 0; j < i; j++) {\n e[j] -= hh * d[j];\n }\n\n for (j = 0; j < i; j++) {\n f = d[j];\n g = e[j];\n for (k = j; k <= i - 1; k++) {\n V.set(j, k, V.get(j, k) - (f * e[k] + g * d[k]));\n }\n d[j] = V.get(j, i - 1);\n V.set(j, i, 0);\n }\n }\n d[i] = h;\n }\n\n for (i = 0; i < n - 1; i++) {\n V.set(i, n - 1, V.get(i, i));\n V.set(i, i, 1);\n h = d[i + 1];\n if (h !== 0) {\n for (k = 0; k <= i; k++) {\n d[k] = V.get(i + 1, k) / h;\n }\n\n for (j = 0; j <= i; j++) {\n g = 0;\n for (k = 0; k <= i; k++) {\n g += V.get(i + 1, k) * V.get(j, k);\n }\n for (k = 0; k <= i; k++) {\n V.set(j, k, V.get(j, k) - g * d[k]);\n }\n }\n }\n\n for (k = 0; k <= i; k++) {\n V.set(i + 1, k, 0);\n }\n }\n\n for (j = 0; j < n; j++) {\n d[j] = V.get(j, n - 1);\n V.set(j, n - 1, 0);\n }\n\n V.set(n - 1, n - 1, 1);\n e[0] = 0;\n}\n\nfunction tql2(n, e, d, V) {\n let g, h, i, j, k, l, m, p, r, dl1, c, c2, c3, el1, s, s2;\n\n for (i = 1; i < n; i++) {\n e[i - 1] = e[i];\n }\n\n e[n - 1] = 0;\n\n let f = 0;\n let tst1 = 0;\n let eps = Number.EPSILON;\n\n for (l = 0; l < n; l++) {\n tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l]));\n m = l;\n while (m < n) {\n if (Math.abs(e[m]) <= eps * tst1) {\n break;\n }\n m++;\n }\n\n if (m > l) {\n do {\n\n g = d[l];\n p = (d[l + 1] - g) / (2 * e[l]);\n r = hypotenuse(p, 1);\n if (p < 0) {\n r = -r;\n }\n\n d[l] = e[l] / (p + r);\n d[l + 1] = e[l] * (p + r);\n dl1 = d[l + 1];\n h = g - d[l];\n for (i = l + 2; i < n; i++) {\n d[i] -= h;\n }\n\n f = f + h;\n\n p = d[m];\n c = 1;\n c2 = c;\n c3 = c;\n el1 = e[l + 1];\n s = 0;\n s2 = 0;\n for (i = m - 1; i >= l; i--) {\n c3 = c2;\n c2 = c;\n s2 = s;\n g = c * e[i];\n h = c * p;\n r = hypotenuse(p, e[i]);\n e[i + 1] = s * r;\n s = e[i] / r;\n c = p / r;\n p = c * d[i] - s * g;\n d[i + 1] = h + s * (c * g + s * d[i]);\n\n for (k = 0; k < n; k++) {\n h = V.get(i + 1, k);\n V.set(i + 1, k, s * V.get(i, k) + c * h);\n V.set(i, k, c * V.get(i, k) - s * h);\n }\n }\n\n p = (-s * s2 * c3 * el1 * e[l]) / dl1;\n e[l] = s * p;\n d[l] = c * p;\n } while (Math.abs(e[l]) > eps * tst1);\n }\n d[l] = d[l] + f;\n e[l] = 0;\n }\n\n for (i = 0; i < n - 1; i++) {\n k = i;\n p = d[i];\n for (j = i + 1; j < n; j++) {\n if (d[j] < p) {\n k = j;\n p = d[j];\n }\n }\n\n if (k !== i) {\n d[k] = d[i];\n d[i] = p;\n for (j = 0; j < n; j++) {\n p = V.get(i, j);\n V.set(i, j, V.get(k, j));\n V.set(k, j, p);\n }\n }\n }\n}\n\nfunction orthes(n, H, ort, V) {\n let low = 0;\n let high = n - 1;\n let f, g, h, i, j, m;\n let scale;\n\n for (m = low + 1; m <= high - 1; m++) {\n scale = 0;\n for (i = m; i <= high; i++) {\n scale = scale + Math.abs(H.get(i, m - 1));\n }\n\n if (scale !== 0) {\n h = 0;\n for (i = high; i >= m; i--) {\n ort[i] = H.get(i, m - 1) / scale;\n h += ort[i] * ort[i];\n }\n\n g = Math.sqrt(h);\n if (ort[m] > 0) {\n g = -g;\n }\n\n h = h - ort[m] * g;\n ort[m] = ort[m] - g;\n\n for (j = m; j < n; j++) {\n f = 0;\n for (i = high; i >= m; i--) {\n f += ort[i] * H.get(i, j);\n }\n\n f = f / h;\n for (i = m; i <= high; i++) {\n H.set(i, j, H.get(i, j) - f * ort[i]);\n }\n }\n\n for (i = 0; i <= high; i++) {\n f = 0;\n for (j = high; j >= m; j--) {\n f += ort[j] * H.get(i, j);\n }\n\n f = f / h;\n for (j = m; j <= high; j++) {\n H.set(i, j, H.get(i, j) - f * ort[j]);\n }\n }\n\n ort[m] = scale * ort[m];\n H.set(m, m - 1, scale * g);\n }\n }\n\n for (i = 0; i < n; i++) {\n for (j = 0; j < n; j++) {\n V.set(i, j, i === j ? 1 : 0);\n }\n }\n\n for (m = high - 1; m >= low + 1; m--) {\n if (H.get(m, m - 1) !== 0) {\n for (i = m + 1; i <= high; i++) {\n ort[i] = H.get(i, m - 1);\n }\n\n for (j = m; j <= high; j++) {\n g = 0;\n for (i = m; i <= high; i++) {\n g += ort[i] * V.get(i, j);\n }\n\n g = g / ort[m] / H.get(m, m - 1);\n for (i = m; i <= high; i++) {\n V.set(i, j, V.get(i, j) + g * ort[i]);\n }\n }\n }\n }\n}\n\nfunction hqr2(nn, e, d, V, H) {\n let n = nn - 1;\n let low = 0;\n let high = nn - 1;\n let eps = Number.EPSILON;\n let exshift = 0;\n let norm = 0;\n let p = 0;\n let q = 0;\n let r = 0;\n let s = 0;\n let z = 0;\n let iter = 0;\n let i, j, k, l, m, t, w, x, y;\n let ra, sa, vr, vi;\n let notlast, cdivres;\n\n for (i = 0; i < nn; i++) {\n if (i < low || i > high) {\n d[i] = H.get(i, i);\n e[i] = 0;\n }\n\n for (j = Math.max(i - 1, 0); j < nn; j++) {\n norm = norm + Math.abs(H.get(i, j));\n }\n }\n\n while (n >= low) {\n l = n;\n while (l > low) {\n s = Math.abs(H.get(l - 1, l - 1)) + Math.abs(H.get(l, l));\n if (s === 0) {\n s = norm;\n }\n if (Math.abs(H.get(l, l - 1)) < eps * s) {\n break;\n }\n l--;\n }\n\n if (l === n) {\n H.set(n, n, H.get(n, n) + exshift);\n d[n] = H.get(n, n);\n e[n] = 0;\n n--;\n iter = 0;\n } else if (l === n - 1) {\n w = H.get(n, n - 1) * H.get(n - 1, n);\n p = (H.get(n - 1, n - 1) - H.get(n, n)) / 2;\n q = p * p + w;\n z = Math.sqrt(Math.abs(q));\n H.set(n, n, H.get(n, n) + exshift);\n H.set(n - 1, n - 1, H.get(n - 1, n - 1) + exshift);\n x = H.get(n, n);\n\n if (q >= 0) {\n z = p >= 0 ? p + z : p - z;\n d[n - 1] = x + z;\n d[n] = d[n - 1];\n if (z !== 0) {\n d[n] = x - w / z;\n }\n e[n - 1] = 0;\n e[n] = 0;\n x = H.get(n, n - 1);\n s = Math.abs(x) + Math.abs(z);\n p = x / s;\n q = z / s;\n r = Math.sqrt(p * p + q * q);\n p = p / r;\n q = q / r;\n\n for (j = n - 1; j < nn; j++) {\n z = H.get(n - 1, j);\n H.set(n - 1, j, q * z + p * H.get(n, j));\n H.set(n, j, q * H.get(n, j) - p * z);\n }\n\n for (i = 0; i <= n; i++) {\n z = H.get(i, n - 1);\n H.set(i, n - 1, q * z + p * H.get(i, n));\n H.set(i, n, q * H.get(i, n) - p * z);\n }\n\n for (i = low; i <= high; i++) {\n z = V.get(i, n - 1);\n V.set(i, n - 1, q * z + p * V.get(i, n));\n V.set(i, n, q * V.get(i, n) - p * z);\n }\n } else {\n d[n - 1] = x + p;\n d[n] = x + p;\n e[n - 1] = z;\n e[n] = -z;\n }\n\n n = n - 2;\n iter = 0;\n } else {\n x = H.get(n, n);\n y = 0;\n w = 0;\n if (l < n) {\n y = H.get(n - 1, n - 1);\n w = H.get(n, n - 1) * H.get(n - 1, n);\n }\n\n if (iter === 10) {\n exshift += x;\n for (i = low; i <= n; i++) {\n H.set(i, i, H.get(i, i) - x);\n }\n s = Math.abs(H.get(n, n - 1)) + Math.abs(H.get(n - 1, n - 2));\n // eslint-disable-next-line no-multi-assign\n x = y = 0.75 * s;\n w = -0.4375 * s * s;\n }\n\n if (iter === 30) {\n s = (y - x) / 2;\n s = s * s + w;\n if (s > 0) {\n s = Math.sqrt(s);\n if (y < x) {\n s = -s;\n }\n s = x - w / ((y - x) / 2 + s);\n for (i = low; i <= n; i++) {\n H.set(i, i, H.get(i, i) - s);\n }\n exshift += s;\n // eslint-disable-next-line no-multi-assign\n x = y = w = 0.964;\n }\n }\n\n iter = iter + 1;\n\n m = n - 2;\n while (m >= l) {\n z = H.get(m, m);\n r = x - z;\n s = y - z;\n p = (r * s - w) / H.get(m + 1, m) + H.get(m, m + 1);\n q = H.get(m + 1, m + 1) - z - r - s;\n r = H.get(m + 2, m + 1);\n s = Math.abs(p) + Math.abs(q) + Math.abs(r);\n p = p / s;\n q = q / s;\n r = r / s;\n if (m === l) {\n break;\n }\n if (\n Math.abs(H.get(m, m - 1)) * (Math.abs(q) + Math.abs(r)) <\n eps *\n (Math.abs(p) *\n (Math.abs(H.get(m - 1, m - 1)) +\n Math.abs(z) +\n Math.abs(H.get(m + 1, m + 1))))\n ) {\n break;\n }\n m--;\n }\n\n for (i = m + 2; i <= n; i++) {\n H.set(i, i - 2, 0);\n if (i > m + 2) {\n H.set(i, i - 3, 0);\n }\n }\n\n for (k = m; k <= n - 1; k++) {\n notlast = k !== n - 1;\n if (k !== m) {\n p = H.get(k, k - 1);\n q = H.get(k + 1, k - 1);\n r = notlast ? H.get(k + 2, k - 1) : 0;\n x = Math.abs(p) + Math.abs(q) + Math.abs(r);\n if (x !== 0) {\n p = p / x;\n q = q / x;\n r = r / x;\n }\n }\n\n if (x === 0) {\n break;\n }\n\n s = Math.sqrt(p * p + q * q + r * r);\n if (p < 0) {\n s = -s;\n }\n\n if (s !== 0) {\n if (k !== m) {\n H.set(k, k - 1, -s * x);\n } else if (l !== m) {\n H.set(k, k - 1, -H.get(k, k - 1));\n }\n\n p = p + s;\n x = p / s;\n y = q / s;\n z = r / s;\n q = q / p;\n r = r / p;\n\n for (j = k; j < nn; j++) {\n p = H.get(k, j) + q * H.get(k + 1, j);\n if (notlast) {\n p = p + r * H.get(k + 2, j);\n H.set(k + 2, j, H.get(k + 2, j) - p * z);\n }\n\n H.set(k, j, H.get(k, j) - p * x);\n H.set(k + 1, j, H.get(k + 1, j) - p * y);\n }\n\n for (i = 0; i <= Math.min(n, k + 3); i++) {\n p = x * H.get(i, k) + y * H.get(i, k + 1);\n if (notlast) {\n p = p + z * H.get(i, k + 2);\n H.set(i, k + 2, H.get(i, k + 2) - p * r);\n }\n\n H.set(i, k, H.get(i, k) - p);\n H.set(i, k + 1, H.get(i, k + 1) - p * q);\n }\n\n for (i = low; i <= high; i++) {\n p = x * V.get(i, k) + y * V.get(i, k + 1);\n if (notlast) {\n p = p + z * V.get(i, k + 2);\n V.set(i, k + 2, V.get(i, k + 2) - p * r);\n }\n\n V.set(i, k, V.get(i, k) - p);\n V.set(i, k + 1, V.get(i, k + 1) - p * q);\n }\n }\n }\n }\n }\n\n if (norm === 0) {\n return;\n }\n\n for (n = nn - 1; n >= 0; n--) {\n p = d[n];\n q = e[n];\n\n if (q === 0) {\n l = n;\n H.set(n, n, 1);\n for (i = n - 1; i >= 0; i--) {\n w = H.get(i, i) - p;\n r = 0;\n for (j = l; j <= n; j++) {\n r = r + H.get(i, j) * H.get(j, n);\n }\n\n if (e[i] < 0) {\n z = w;\n s = r;\n } else {\n l = i;\n if (e[i] === 0) {\n H.set(i, n, w !== 0 ? -r / w : -r / (eps * norm));\n } else {\n x = H.get(i, i + 1);\n y = H.get(i + 1, i);\n q = (d[i] - p) * (d[i] - p) + e[i] * e[i];\n t = (x * s - z * r) / q;\n H.set(i, n, t);\n H.set(\n i + 1,\n n,\n Math.abs(x) > Math.abs(z) ? (-r - w * t) / x : (-s - y * t) / z,\n );\n }\n\n t = Math.abs(H.get(i, n));\n if (eps * t * t > 1) {\n for (j = i; j <= n; j++) {\n H.set(j, n, H.get(j, n) / t);\n }\n }\n }\n }\n } else if (q < 0) {\n l = n - 1;\n\n if (Math.abs(H.get(n, n - 1)) > Math.abs(H.get(n - 1, n))) {\n H.set(n - 1, n - 1, q / H.get(n, n - 1));\n H.set(n - 1, n, -(H.get(n, n) - p) / H.get(n, n - 1));\n } else {\n cdivres = cdiv(0, -H.get(n - 1, n), H.get(n - 1, n - 1) - p, q);\n H.set(n - 1, n - 1, cdivres[0]);\n H.set(n - 1, n, cdivres[1]);\n }\n\n H.set(n, n - 1, 0);\n H.set(n, n, 1);\n for (i = n - 2; i >= 0; i--) {\n ra = 0;\n sa = 0;\n for (j = l; j <= n; j++) {\n ra = ra + H.get(i, j) * H.get(j, n - 1);\n sa = sa + H.get(i, j) * H.get(j, n);\n }\n\n w = H.get(i, i) - p;\n\n if (e[i] < 0) {\n z = w;\n r = ra;\n s = sa;\n } else {\n l = i;\n if (e[i] === 0) {\n cdivres = cdiv(-ra, -sa, w, q);\n H.set(i, n - 1, cdivres[0]);\n H.set(i, n, cdivres[1]);\n } else {\n x = H.get(i, i + 1);\n y = H.get(i + 1, i);\n vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;\n vi = (d[i] - p) * 2 * q;\n if (vr === 0 && vi === 0) {\n vr =\n eps *\n norm *\n (Math.abs(w) +\n Math.abs(q) +\n Math.abs(x) +\n Math.abs(y) +\n Math.abs(z));\n }\n cdivres = cdiv(\n x * r - z * ra + q * sa,\n x * s - z * sa - q * ra,\n vr,\n vi,\n );\n H.set(i, n - 1, cdivres[0]);\n H.set(i, n, cdivres[1]);\n if (Math.abs(x) > Math.abs(z) + Math.abs(q)) {\n H.set(\n i + 1,\n n - 1,\n (-ra - w * H.get(i, n - 1) + q * H.get(i, n)) / x,\n );\n H.set(\n i + 1,\n n,\n (-sa - w * H.get(i, n) - q * H.get(i, n - 1)) / x,\n );\n } else {\n cdivres = cdiv(\n -r - y * H.get(i, n - 1),\n -s - y * H.get(i, n),\n z,\n q,\n );\n H.set(i + 1, n - 1, cdivres[0]);\n H.set(i + 1, n, cdivres[1]);\n }\n }\n\n t = Math.max(Math.abs(H.get(i, n - 1)), Math.abs(H.get(i, n)));\n if (eps * t * t > 1) {\n for (j = i; j <= n; j++) {\n H.set(j, n - 1, H.get(j, n - 1) / t);\n H.set(j, n, H.get(j, n) / t);\n }\n }\n }\n }\n }\n }\n\n for (i = 0; i < nn; i++) {\n if (i < low || i > high) {\n for (j = i; j < nn; j++) {\n V.set(i, j, H.get(i, j));\n }\n }\n }\n\n for (j = nn - 1; j >= low; j--) {\n for (i = low; i <= high; i++) {\n z = 0;\n for (k = low; k <= Math.min(j, high); k++) {\n z = z + V.get(i, k) * H.get(k, j);\n }\n V.set(i, j, z);\n }\n }\n}\n\nfunction cdiv(xr, xi, yr, yi) {\n let r, d;\n if (Math.abs(yr) > Math.abs(yi)) {\n r = yi / yr;\n d = yr + r * yi;\n return [(xr + r * xi) / d, (xi - r * xr) / d];\n } else {\n r = yr / yi;\n d = yi + r * yr;\n return [(r * xr + xi) / d, (r * xi - xr) / d];\n }\n}\n\nclass CholeskyDecomposition {\n constructor(value) {\n value = WrapperMatrix2D.checkMatrix(value);\n if (!value.isSymmetric()) {\n throw new Error('Matrix is not symmetric');\n }\n\n let a = value;\n let dimension = a.rows;\n let l = new Matrix(dimension, dimension);\n let positiveDefinite = true;\n let i, j, k;\n\n for (j = 0; j < dimension; j++) {\n let d = 0;\n for (k = 0; k < j; k++) {\n let s = 0;\n for (i = 0; i < k; i++) {\n s += l.get(k, i) * l.get(j, i);\n }\n s = (a.get(j, k) - s) / l.get(k, k);\n l.set(j, k, s);\n d = d + s * s;\n }\n\n d = a.get(j, j) - d;\n\n positiveDefinite &&= d > 0;\n l.set(j, j, Math.sqrt(Math.max(d, 0)));\n for (k = j + 1; k < dimension; k++) {\n l.set(j, k, 0);\n }\n }\n\n this.L = l;\n this.positiveDefinite = positiveDefinite;\n }\n\n isPositiveDefinite() {\n return this.positiveDefinite;\n }\n\n solve(value) {\n value = WrapperMatrix2D.checkMatrix(value);\n\n let l = this.L;\n let dimension = l.rows;\n\n if (value.rows !== dimension) {\n throw new Error('Matrix dimensions do not match');\n }\n if (this.isPositiveDefinite() === false) {\n throw new Error('Matrix is not positive definite');\n }\n\n let count = value.columns;\n let B = value.clone();\n let i, j, k;\n\n for (k = 0; k < dimension; k++) {\n for (j = 0; j < count; j++) {\n for (i = 0; i < k; i++) {\n B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(k, i));\n }\n B.set(k, j, B.get(k, j) / l.get(k, k));\n }\n }\n\n for (k = dimension - 1; k >= 0; k--) {\n for (j = 0; j < count; j++) {\n for (i = k + 1; i < dimension; i++) {\n B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(i, k));\n }\n B.set(k, j, B.get(k, j) / l.get(k, k));\n }\n }\n\n return B;\n }\n\n get lowerTriangularMatrix() {\n return this.L;\n }\n}\n\nclass nipals {\n constructor(X, options = {}) {\n X = WrapperMatrix2D.checkMatrix(X);\n let { Y } = options;\n const {\n scaleScores = false,\n maxIterations = 1000,\n terminationCriteria = 1e-10,\n } = options;\n\n let u;\n if (Y) {\n if (isAnyArray(Y) && typeof Y[0] === 'number') {\n Y = Matrix.columnVector(Y);\n } else {\n Y = WrapperMatrix2D.checkMatrix(Y);\n }\n if (Y.rows !== X.rows) {\n throw new Error('Y should have the same number of rows as X');\n }\n u = Y.getColumnVector(0);\n } else {\n u = X.getColumnVector(0);\n }\n\n let diff = 1;\n let t, q, w, tOld;\n\n for (\n let counter = 0;\n counter < maxIterations && diff > terminationCriteria;\n counter++\n ) {\n w = X.transpose().mmul(u).div(u.transpose().mmul(u).get(0, 0));\n w = w.div(w.norm());\n\n t = X.mmul(w).div(w.transpose().mmul(w).get(0, 0));\n\n if (counter > 0) {\n diff = t.clone().sub(tOld).pow(2).sum();\n }\n tOld = t.clone();\n\n if (Y) {\n q = Y.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n q = q.div(q.norm());\n\n u = Y.mmul(q).div(q.transpose().mmul(q).get(0, 0));\n } else {\n u = t;\n }\n }\n\n if (Y) {\n let p = X.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n p = p.div(p.norm());\n let xResidual = X.clone().sub(t.clone().mmul(p.transpose()));\n let residual = u.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n let yResidual = Y.clone().sub(\n t.clone().mulS(residual.get(0, 0)).mmul(q.transpose()),\n );\n\n this.t = t;\n this.p = p.transpose();\n this.w = w.transpose();\n this.q = q;\n this.u = u;\n this.s = t.transpose().mmul(t);\n this.xResidual = xResidual;\n this.yResidual = yResidual;\n this.betas = residual;\n } else {\n this.w = w.transpose();\n this.s = t.transpose().mmul(t).sqrt();\n if (scaleScores) {\n this.t = t.clone().div(this.s.get(0, 0));\n } else {\n this.t = t;\n }\n this.xResidual = X.sub(t.mmul(w.transpose()));\n }\n }\n}\n\nexports.AbstractMatrix = AbstractMatrix;\nexports.CHO = CholeskyDecomposition;\nexports.CholeskyDecomposition = CholeskyDecomposition;\nexports.DistanceMatrix = DistanceMatrix;\nexports.EVD = EigenvalueDecomposition;\nexports.EigenvalueDecomposition = EigenvalueDecomposition;\nexports.LU = LuDecomposition;\nexports.LuDecomposition = LuDecomposition;\nexports.Matrix = Matrix;\nexports.MatrixColumnSelectionView = MatrixColumnSelectionView;\nexports.MatrixColumnView = MatrixColumnView;\nexports.MatrixFlipColumnView = MatrixFlipColumnView;\nexports.MatrixFlipRowView = MatrixFlipRowView;\nexports.MatrixRowSelectionView = MatrixRowSelectionView;\nexports.MatrixRowView = MatrixRowView;\nexports.MatrixSelectionView = MatrixSelectionView;\nexports.MatrixSubView = MatrixSubView;\nexports.MatrixTransposeView = MatrixTransposeView;\nexports.NIPALS = nipals;\nexports.Nipals = nipals;\nexports.QR = QrDecomposition;\nexports.QrDecomposition = QrDecomposition;\nexports.SVD = SingularValueDecomposition;\nexports.SingularValueDecomposition = SingularValueDecomposition;\nexports.SymmetricMatrix = SymmetricMatrix;\nexports.WrapperMatrix1D = WrapperMatrix1D;\nexports.WrapperMatrix2D = WrapperMatrix2D;\nexports.correlation = correlation;\nexports.covariance = covariance;\nexports.default = Matrix;\nexports.determinant = determinant;\nexports.inverse = inverse;\nexports.linearDependencies = linearDependencies;\nexports.pseudoInverse = pseudoInverse;\nexports.solve = solve;\nexports.wrap = wrap;\n//# sourceMappingURL=matrix.js.map\n","import * as matrix from './matrix.js';\n\nexport const AbstractMatrix = matrix.AbstractMatrix;\nexport const CHO = matrix.CHO;\nexport const CholeskyDecomposition = matrix.CholeskyDecomposition;\nexport const DistanceMatrix = matrix.DistanceMatrix;\nexport const EVD = matrix.EVD;\nexport const EigenvalueDecomposition = matrix.EigenvalueDecomposition;\nexport const LU = matrix.LU;\nexport const LuDecomposition = matrix.LuDecomposition;\nexport const Matrix = matrix.Matrix;\nexport const MatrixColumnSelectionView = matrix.MatrixColumnSelectionView;\nexport const MatrixColumnView = matrix.MatrixColumnView;\nexport const MatrixFlipColumnView = matrix.MatrixFlipColumnView;\nexport const MatrixFlipRowView = matrix.MatrixFlipRowView;\nexport const MatrixRowSelectionView = matrix.MatrixRowSelectionView;\nexport const MatrixRowView = matrix.MatrixRowView;\nexport const MatrixSelectionView = matrix.MatrixSelectionView;\nexport const MatrixSubView = matrix.MatrixSubView;\nexport const MatrixTransposeView = matrix.MatrixTransposeView;\nexport const NIPALS = matrix.NIPALS;\nexport const Nipals = matrix.Nipals;\nexport const QR = matrix.QR;\nexport const QrDecomposition = matrix.QrDecomposition;\nexport const SVD = matrix.SVD;\nexport const SingularValueDecomposition = matrix.SingularValueDecomposition;\nexport const SymmetricMatrix = matrix.SymmetricMatrix;\nexport const WrapperMatrix1D = matrix.WrapperMatrix1D;\nexport const WrapperMatrix2D = matrix.WrapperMatrix2D;\nexport const correlation = matrix.correlation;\nexport const covariance = matrix.covariance;\nexport default matrix.default.Matrix ? matrix.default.Matrix : matrix.Matrix;\nexport const determinant = matrix.determinant;\nexport const inverse = matrix.inverse;\nexport const linearDependencies = matrix.linearDependencies;\nexport const pseudoInverse = matrix.pseudoInverse;\nexport const solve = matrix.solve;\nexport const wrap = matrix.wrap;\n","import { xCheck } from \"./xCheck.js\";\nimport { xGetFromToIndex } from \"./xGetFromToIndex.js\";\n/**\n * Computes the mean value of an array of values.\n * @param array - array of numbers\n * @param options - options\n */\nexport function xMean(array, options = {}) {\n xCheck(array);\n const { fromIndex, toIndex } = xGetFromToIndex(array, options);\n let sumValue = array[fromIndex];\n for (let i = fromIndex + 1; i <= toIndex; i++) {\n sumValue += array[i];\n }\n return sumValue / (toIndex - fromIndex + 1);\n}\n//# sourceMappingURL=xMean.js.map","import { isAnyArray } from 'is-any-array';\nimport { xMean } from \"./xMean.js\";\n/**\n * Finds the variance of the data\n * @param values - the values of the array\n * @param options - options\n * @returns variance\n */\nexport function xVariance(values, options = {}) {\n if (!isAnyArray(values)) {\n throw new TypeError('input must be an array');\n }\n const { unbiased = true, mean = xMean(values) } = options;\n let sqrError = 0;\n for (let i = 0; i < values.length; i++) {\n const x = values[i] - mean;\n sqrError += x * x;\n }\n if (unbiased) {\n return sqrError / (values.length - 1);\n }\n else {\n return sqrError / values.length;\n }\n}\n//# sourceMappingURL=xVariance.js.map","import { xVariance } from \"./xVariance.js\";\n/**\n * Finds the standard deviation for the data at hand\n * @param values - values in the data\n * @param options - options\n * @returns standard deviation\n */\nexport function xStandardDeviation(values, options = {}) {\n return Math.sqrt(xVariance(values, options));\n}\n//# sourceMappingURL=xStandardDeviation.js.map","import { isAnyArray } from 'is-any-array';\nimport { getOutputArray } from \"./getOutputArray.js\";\n/**\n * This function divide the first array by the second array or a constant value to each element of the first array\n * @param array1 - first array\n * @param array2 - second array or number\n * @param options - options\n */\nexport function xDivide(array1, array2, options = {}) {\n let isConstant = false;\n let constant = 0;\n if (isAnyArray(array2)) {\n if (array1.length !== array2.length) {\n throw new RangeError('size of array1 and array2 must be identical');\n }\n }\n else {\n isConstant = true;\n constant = array2;\n }\n const array3 = getOutputArray(options.output, array1.length);\n if (isConstant) {\n for (let i = 0; i < array1.length; i++) {\n array3[i] = array1[i] / constant;\n }\n }\n else {\n for (let i = 0; i < array1.length; i++) {\n array3[i] = array1[i] / array2[i];\n }\n }\n return array3;\n}\n//# sourceMappingURL=xDivide.js.map","import { xCheck } from \"./xCheck.js\";\n/**\n * Returns a copy of the data as a Float64Array.\n * @param array - array of numbers\n */\nexport function xEnsureFloat64(array) {\n xCheck(array);\n if (array instanceof Float64Array) {\n return array.slice(0);\n }\n return Float64Array.from(array);\n}\n//# sourceMappingURL=xEnsureFloat64.js.map","const LITTLE_ENDIAN = new Uint8Array(new Uint16Array([1]).buffer)[0] === 1;\n/**\n * Check whether the host stores multi-byte values least significant byte first.\n * Needed whenever a typed array is reinterpreted through another view, because\n * the position of the words of a value then depends on the platform.\n * @returns true on a little-endian host.\n */\nexport function isLittleEndian() {\n return LITTLE_ENDIAN;\n}\n//# sourceMappingURL=isLittleEndian.js.map","'use strict';\n\nfunction FFT(size) {\n this.size = size | 0;\n if (this.size <= 1 || (this.size & (this.size - 1)) !== 0)\n throw new Error('FFT size must be a power of two and bigger than 1');\n\n this._csize = size << 1;\n\n // NOTE: Use of `var` is intentional for old V8 versions\n var table = new Array(this.size * 2);\n for (var i = 0; i < table.length; i += 2) {\n const angle = Math.PI * i / this.size;\n table[i] = Math.cos(angle);\n table[i + 1] = -Math.sin(angle);\n }\n this.table = table;\n\n // Find size's power of two\n var power = 0;\n for (var t = 1; this.size > t; t <<= 1)\n power++;\n\n // Calculate initial step's width:\n // * If we are full radix-4 - it is 2x smaller to give inital len=8\n // * Otherwise it is the same as `power` to give len=4\n this._width = power % 2 === 0 ? power - 1 : power;\n\n // Pre-compute bit-reversal patterns\n this._bitrev = new Array(1 << this._width);\n for (var j = 0; j < this._bitrev.length; j++) {\n this._bitrev[j] = 0;\n for (var shift = 0; shift < this._width; shift += 2) {\n var revShift = this._width - shift - 2;\n this._bitrev[j] |= ((j >>> shift) & 3) << revShift;\n }\n }\n\n this._out = null;\n this._data = null;\n this._inv = 0;\n}\nmodule.exports = FFT;\n\nFFT.prototype.fromComplexArray = function fromComplexArray(complex, storage) {\n var res = storage || new Array(complex.length >>> 1);\n for (var i = 0; i < complex.length; i += 2)\n res[i >>> 1] = complex[i];\n return res;\n};\n\nFFT.prototype.createComplexArray = function createComplexArray() {\n const res = new Array(this._csize);\n for (var i = 0; i < res.length; i++)\n res[i] = 0;\n return res;\n};\n\nFFT.prototype.toComplexArray = function toComplexArray(input, storage) {\n var res = storage || this.createComplexArray();\n for (var i = 0; i < res.length; i += 2) {\n res[i] = input[i >>> 1];\n res[i + 1] = 0;\n }\n return res;\n};\n\nFFT.prototype.completeSpectrum = function completeSpectrum(spectrum) {\n var size = this._csize;\n var half = size >>> 1;\n for (var i = 2; i < half; i += 2) {\n spectrum[size - i] = spectrum[i];\n spectrum[size - i + 1] = -spectrum[i + 1];\n }\n};\n\nFFT.prototype.transform = function transform(out, data) {\n if (out === data)\n throw new Error('Input and output buffers must be different');\n\n this._out = out;\n this._data = data;\n this._inv = 0;\n this._transform4();\n this._out = null;\n this._data = null;\n};\n\nFFT.prototype.realTransform = function realTransform(out, data) {\n if (out === data)\n throw new Error('Input and output buffers must be different');\n\n this._out = out;\n this._data = data;\n this._inv = 0;\n this._realTransform4();\n this._out = null;\n this._data = null;\n};\n\nFFT.prototype.inverseTransform = function inverseTransform(out, data) {\n if (out === data)\n throw new Error('Input and output buffers must be different');\n\n this._out = out;\n this._data = data;\n this._inv = 1;\n this._transform4();\n for (var i = 0; i < out.length; i++)\n out[i] /= this.size;\n this._out = null;\n this._data = null;\n};\n\n// radix-4 implementation\n//\n// NOTE: Uses of `var` are intentional for older V8 version that do not\n// support both `let compound assignments` and `const phi`\nFFT.prototype._transform4 = function _transform4() {\n var out = this._out;\n var size = this._csize;\n\n // Initial step (permute and transform)\n var width = this._width;\n var step = 1 << width;\n var len = (size / step) << 1;\n\n var outOff;\n var t;\n var bitrev = this._bitrev;\n if (len === 4) {\n for (outOff = 0, t = 0; outOff < size; outOff += len, t++) {\n const off = bitrev[t];\n this._singleTransform2(outOff, off, step);\n }\n } else {\n // len === 8\n for (outOff = 0, t = 0; outOff < size; outOff += len, t++) {\n const off = bitrev[t];\n this._singleTransform4(outOff, off, step);\n }\n }\n\n // Loop through steps in decreasing order\n var inv = this._inv ? -1 : 1;\n var table = this.table;\n for (step >>= 2; step >= 2; step >>= 2) {\n len = (size / step) << 1;\n var quarterLen = len >>> 2;\n\n // Loop through offsets in the data\n for (outOff = 0; outOff < size; outOff += len) {\n // Full case\n var limit = outOff + quarterLen;\n for (var i = outOff, k = 0; i < limit; i += 2, k += step) {\n const A = i;\n const B = A + quarterLen;\n const C = B + quarterLen;\n const D = C + quarterLen;\n\n // Original values\n const Ar = out[A];\n const Ai = out[A + 1];\n const Br = out[B];\n const Bi = out[B + 1];\n const Cr = out[C];\n const Ci = out[C + 1];\n const Dr = out[D];\n const Di = out[D + 1];\n\n // Middle values\n const MAr = Ar;\n const MAi = Ai;\n\n const tableBr = table[k];\n const tableBi = inv * table[k + 1];\n const MBr = Br * tableBr - Bi * tableBi;\n const MBi = Br * tableBi + Bi * tableBr;\n\n const tableCr = table[2 * k];\n const tableCi = inv * table[2 * k + 1];\n const MCr = Cr * tableCr - Ci * tableCi;\n const MCi = Cr * tableCi + Ci * tableCr;\n\n const tableDr = table[3 * k];\n const tableDi = inv * table[3 * k + 1];\n const MDr = Dr * tableDr - Di * tableDi;\n const MDi = Dr * tableDi + Di * tableDr;\n\n // Pre-Final values\n const T0r = MAr + MCr;\n const T0i = MAi + MCi;\n const T1r = MAr - MCr;\n const T1i = MAi - MCi;\n const T2r = MBr + MDr;\n const T2i = MBi + MDi;\n const T3r = inv * (MBr - MDr);\n const T3i = inv * (MBi - MDi);\n\n // Final values\n const FAr = T0r + T2r;\n const FAi = T0i + T2i;\n\n const FCr = T0r - T2r;\n const FCi = T0i - T2i;\n\n const FBr = T1r + T3i;\n const FBi = T1i - T3r;\n\n const FDr = T1r - T3i;\n const FDi = T1i + T3r;\n\n out[A] = FAr;\n out[A + 1] = FAi;\n out[B] = FBr;\n out[B + 1] = FBi;\n out[C] = FCr;\n out[C + 1] = FCi;\n out[D] = FDr;\n out[D + 1] = FDi;\n }\n }\n }\n};\n\n// radix-2 implementation\n//\n// NOTE: Only called for len=4\nFFT.prototype._singleTransform2 = function _singleTransform2(outOff, off,\n step) {\n const out = this._out;\n const data = this._data;\n\n const evenR = data[off];\n const evenI = data[off + 1];\n const oddR = data[off + step];\n const oddI = data[off + step + 1];\n\n const leftR = evenR + oddR;\n const leftI = evenI + oddI;\n const rightR = evenR - oddR;\n const rightI = evenI - oddI;\n\n out[outOff] = leftR;\n out[outOff + 1] = leftI;\n out[outOff + 2] = rightR;\n out[outOff + 3] = rightI;\n};\n\n// radix-4\n//\n// NOTE: Only called for len=8\nFFT.prototype._singleTransform4 = function _singleTransform4(outOff, off,\n step) {\n const out = this._out;\n const data = this._data;\n const inv = this._inv ? -1 : 1;\n const step2 = step * 2;\n const step3 = step * 3;\n\n // Original values\n const Ar = data[off];\n const Ai = data[off + 1];\n const Br = data[off + step];\n const Bi = data[off + step + 1];\n const Cr = data[off + step2];\n const Ci = data[off + step2 + 1];\n const Dr = data[off + step3];\n const Di = data[off + step3 + 1];\n\n // Pre-Final values\n const T0r = Ar + Cr;\n const T0i = Ai + Ci;\n const T1r = Ar - Cr;\n const T1i = Ai - Ci;\n const T2r = Br + Dr;\n const T2i = Bi + Di;\n const T3r = inv * (Br - Dr);\n const T3i = inv * (Bi - Di);\n\n // Final values\n const FAr = T0r + T2r;\n const FAi = T0i + T2i;\n\n const FBr = T1r + T3i;\n const FBi = T1i - T3r;\n\n const FCr = T0r - T2r;\n const FCi = T0i - T2i;\n\n const FDr = T1r - T3i;\n const FDi = T1i + T3r;\n\n out[outOff] = FAr;\n out[outOff + 1] = FAi;\n out[outOff + 2] = FBr;\n out[outOff + 3] = FBi;\n out[outOff + 4] = FCr;\n out[outOff + 5] = FCi;\n out[outOff + 6] = FDr;\n out[outOff + 7] = FDi;\n};\n\n// Real input radix-4 implementation\nFFT.prototype._realTransform4 = function _realTransform4() {\n var out = this._out;\n var size = this._csize;\n\n // Initial step (permute and transform)\n var width = this._width;\n var step = 1 << width;\n var len = (size / step) << 1;\n\n var outOff;\n var t;\n var bitrev = this._bitrev;\n if (len === 4) {\n for (outOff = 0, t = 0; outOff < size; outOff += len, t++) {\n const off = bitrev[t];\n this._singleRealTransform2(outOff, off >>> 1, step >>> 1);\n }\n } else {\n // len === 8\n for (outOff = 0, t = 0; outOff < size; outOff += len, t++) {\n const off = bitrev[t];\n this._singleRealTransform4(outOff, off >>> 1, step >>> 1);\n }\n }\n\n // Loop through steps in decreasing order\n var inv = this._inv ? -1 : 1;\n var table = this.table;\n for (step >>= 2; step >= 2; step >>= 2) {\n len = (size / step) << 1;\n var halfLen = len >>> 1;\n var quarterLen = halfLen >>> 1;\n var hquarterLen = quarterLen >>> 1;\n\n // Loop through offsets in the data\n for (outOff = 0; outOff < size; outOff += len) {\n for (var i = 0, k = 0; i <= hquarterLen; i += 2, k += step) {\n var A = outOff + i;\n var B = A + quarterLen;\n var C = B + quarterLen;\n var D = C + quarterLen;\n\n // Original values\n var Ar = out[A];\n var Ai = out[A + 1];\n var Br = out[B];\n var Bi = out[B + 1];\n var Cr = out[C];\n var Ci = out[C + 1];\n var Dr = out[D];\n var Di = out[D + 1];\n\n // Middle values\n var MAr = Ar;\n var MAi = Ai;\n\n var tableBr = table[k];\n var tableBi = inv * table[k + 1];\n var MBr = Br * tableBr - Bi * tableBi;\n var MBi = Br * tableBi + Bi * tableBr;\n\n var tableCr = table[2 * k];\n var tableCi = inv * table[2 * k + 1];\n var MCr = Cr * tableCr - Ci * tableCi;\n var MCi = Cr * tableCi + Ci * tableCr;\n\n var tableDr = table[3 * k];\n var tableDi = inv * table[3 * k + 1];\n var MDr = Dr * tableDr - Di * tableDi;\n var MDi = Dr * tableDi + Di * tableDr;\n\n // Pre-Final values\n var T0r = MAr + MCr;\n var T0i = MAi + MCi;\n var T1r = MAr - MCr;\n var T1i = MAi - MCi;\n var T2r = MBr + MDr;\n var T2i = MBi + MDi;\n var T3r = inv * (MBr - MDr);\n var T3i = inv * (MBi - MDi);\n\n // Final values\n var FAr = T0r + T2r;\n var FAi = T0i + T2i;\n\n var FBr = T1r + T3i;\n var FBi = T1i - T3r;\n\n out[A] = FAr;\n out[A + 1] = FAi;\n out[B] = FBr;\n out[B + 1] = FBi;\n\n // Output final middle point\n if (i === 0) {\n var FCr = T0r - T2r;\n var FCi = T0i - T2i;\n out[C] = FCr;\n out[C + 1] = FCi;\n continue;\n }\n\n // Do not overwrite ourselves\n if (i === hquarterLen)\n continue;\n\n // In the flipped case:\n // MAi = -MAi\n // MBr=-MBi, MBi=-MBr\n // MCr=-MCr\n // MDr=MDi, MDi=MDr\n var ST0r = T1r;\n var ST0i = -T1i;\n var ST1r = T0r;\n var ST1i = -T0i;\n var ST2r = -inv * T3i;\n var ST2i = -inv * T3r;\n var ST3r = -inv * T2i;\n var ST3i = -inv * T2r;\n\n var SFAr = ST0r + ST2r;\n var SFAi = ST0i + ST2i;\n\n var SFBr = ST1r + ST3i;\n var SFBi = ST1i - ST3r;\n\n var SA = outOff + quarterLen - i;\n var SB = outOff + halfLen - i;\n\n out[SA] = SFAr;\n out[SA + 1] = SFAi;\n out[SB] = SFBr;\n out[SB + 1] = SFBi;\n }\n }\n }\n};\n\n// radix-2 implementation\n//\n// NOTE: Only called for len=4\nFFT.prototype._singleRealTransform2 = function _singleRealTransform2(outOff,\n off,\n step) {\n const out = this._out;\n const data = this._data;\n\n const evenR = data[off];\n const oddR = data[off + step];\n\n const leftR = evenR + oddR;\n const rightR = evenR - oddR;\n\n out[outOff] = leftR;\n out[outOff + 1] = 0;\n out[outOff + 2] = rightR;\n out[outOff + 3] = 0;\n};\n\n// radix-4\n//\n// NOTE: Only called for len=8\nFFT.prototype._singleRealTransform4 = function _singleRealTransform4(outOff,\n off,\n step) {\n const out = this._out;\n const data = this._data;\n const inv = this._inv ? -1 : 1;\n const step2 = step * 2;\n const step3 = step * 3;\n\n // Original values\n const Ar = data[off];\n const Br = data[off + step];\n const Cr = data[off + step2];\n const Dr = data[off + step3];\n\n // Pre-Final values\n const T0r = Ar + Cr;\n const T1r = Ar - Cr;\n const T2r = Br + Dr;\n const T3r = inv * (Br - Dr);\n\n // Final values\n const FAr = T0r + T2r;\n\n const FBr = T1r;\n const FBi = -T3r;\n\n const FCr = T0r - T2r;\n\n const FDr = T1r;\n const FDi = T3r;\n\n out[outOff] = FAr;\n out[outOff + 1] = 0;\n out[outOff + 2] = FBr;\n out[outOff + 3] = FBi;\n out[outOff + 4] = FCr;\n out[outOff + 5] = 0;\n out[outOff + 6] = FDr;\n out[outOff + 7] = FDi;\n};\n","import FFT from 'fft.js';\n// An FFT instance precomputes size-dependent twiddle factors and a bit-reversal\n// table; for a 64k transform that setup dominates the cost. A single shared\n// cache keeps one instance per size so every FFT-based function in the library\n// (spectra, matrices, Hilbert transforms…) reuses it instead of rebuilding the\n// tables on each call.\nclass FFTCache {\n #maxSize;\n #instances = new Map();\n constructor(maxSize = 1) {\n this.#maxSize = maxSize;\n }\n get maxSize() {\n return this.#maxSize;\n }\n set maxSize(value) {\n if (!Number.isInteger(value) || value < 1) {\n throw new RangeError(`FFT cache size must be a positive integer, got ${value}.`);\n }\n this.#maxSize = value;\n // Honour the new bound right away: if already over it, drop everything,\n // matching the clear-when-full strategy used in get().\n if (this.#instances.size > value)\n this.#instances.clear();\n }\n get(size) {\n let fft = this.#instances.get(size);\n if (fft === undefined) {\n // Bound the cache so transforming many different sizes cannot grow it\n // without limit. Rather than track insertion order to evict a single\n // entry, drop everything once full: distinct sizes are rare, so the\n // common workload never hits this and stays at full speed.\n if (this.#instances.size >= this.#maxSize)\n this.#instances.clear();\n fft = new FFT(size);\n this.#instances.set(size, fft);\n }\n return fft;\n }\n clear() {\n this.#instances.clear();\n }\n}\n// Process-wide singleton: a single shared cache, like a static global, but kept\n// as an instance so its state stays fully encapsulated.\nconst fftCache = new FFTCache();\n/**\n * Returns a cached `fft.js` instance for the given transform size, building it\n * on first use. Shared by every FFT-based function in the library so the\n * size-dependent lookup tables are computed once and reused across calls.\n * @param size - number of points of the transform.\n * @returns a reusable FFT instance for that size.\n */\nexport function getFFT(size) {\n return fftCache.get(size);\n}\n/**\n * Releases every cached FFT instance. The library keeps one FFT instance per\n * transform size to avoid rebuilding its lookup tables on each call; each\n * instance holds tables proportional to its size (≈1 MB for a 64k transform).\n * Call this to free that memory once you are done transforming — for example\n * after processing a batch of large spectra. Subsequent transforms simply\n * rebuild whatever sizes they need.\n */\nexport function clearFFTCache() {\n fftCache.clear();\n}\n/**\n * Sets the maximum number of distinct transform sizes kept in the shared FFT\n * cache (default 1). When the cache is full and a new size is requested, the\n * whole cache is dropped and rebuilt on demand. Lowering the limit below the\n * current number of cached sizes clears the cache immediately.\n * @param maxSize - maximum number of cached sizes; must be a positive integer.\n */\nexport function setFFTCacheMaxSize(maxSize) {\n fftCache.maxSize = maxSize;\n}\n//# sourceMappingURL=fftCache.js.map","export function createNumberArray(ArrayConstructor, length) {\n if (ArrayConstructor === Array) {\n return new ArrayConstructor(length).fill(0);\n }\n else {\n return new ArrayConstructor(length);\n }\n}\nexport function createDoubleArray(ArrayConstructor, length) {\n if (ArrayConstructor === Array) {\n return new ArrayConstructor(length).fill(0);\n }\n else {\n return new ArrayConstructor(length);\n }\n}\n//# sourceMappingURL=createArray.js.map","/**\n * Create an array with numbers between \"from\" and \"to\" of length \"length\"\n * @param options - options.\n * @returns array of distributed numbers between \"from\" and \"to\".\n */\nexport function createFromToArray(options = {}) {\n const { from = 0, to = 1, length = 1000, includeFrom = true, includeTo = true, distribution = 'uniform', } = options;\n const array = new Float64Array(length);\n let div = length;\n if (includeFrom && includeTo) {\n div = length - 1;\n }\n else if ((!includeFrom && includeTo) || (includeFrom && !includeTo)) {\n div = length;\n }\n else if (!includeFrom && !includeTo) {\n div = length + 1;\n }\n const delta = (to - from) / div;\n if (distribution === 'uniform') {\n if (includeFrom) {\n let index = 0;\n while (index < length) {\n array[index] = from + delta * index;\n index++;\n }\n }\n else {\n let index = 0;\n while (index < length) {\n array[index] = from + delta * (index + 1);\n index++;\n }\n }\n }\n else if (distribution === 'log') {\n const base = (to / from) ** (1 / div);\n const firstExponent = Math.log(from) / Math.log(base);\n if (includeFrom) {\n let index = 0;\n while (index < length) {\n array[index] = base ** (firstExponent + index);\n index++;\n }\n }\n else {\n let index = 0;\n while (index < length) {\n array[index] = base ** (firstExponent + index + 1);\n index++;\n }\n }\n }\n else {\n throw new Error('distribution must be uniform or log');\n }\n return array;\n}\n//# sourceMappingURL=createFromToArray.js.map","/**\n * Check if a number is a power of two.\n * @param n - input number.\n */\nexport function isPowerOfTwo(n) {\n return n !== 0 && (n & (n - 1)) === 0;\n}\n//# sourceMappingURL=isPowerOfTwo.js.map","/**\n * Get the size of the next power of two.\n * @param n - input number.\n */\nexport function nextPowerOfTwo(n) {\n if (n === 0)\n return 1;\n n--;\n n |= n >> 1;\n n |= n >> 2;\n n |= n >> 4;\n n |= n >> 8;\n n |= n >> 16;\n return n + 1;\n}\n//# sourceMappingURL=nextPowerOfTwo.js.map","import { isAnyArray } from 'is-any-array';\n/**\n * This function xSubtract the first array by the second array or a constant value from each element of the first array\n * @param array1 - the array that will be rotated\n * @param array2 - second array or number\n * @returns array after subtraction\n */\nexport function xSubtract(array1, array2) {\n let isConstant = false;\n let constant = 0;\n if (isAnyArray(array2)) {\n if (array1.length !== array2.length) {\n throw new Error('size of array1 and array2 must be identical');\n }\n }\n else {\n isConstant = true;\n constant = array2;\n }\n const array3 = new Float64Array(array1.length);\n if (isConstant) {\n for (let i = 0; i < array1.length; i++) {\n array3[i] = array1[i] - constant;\n }\n }\n else {\n for (let i = 0; i < array1.length; i++) {\n array3[i] = array1[i] - array2[i];\n }\n }\n return array3;\n}\n//# sourceMappingURL=xSubtract.js.map","import { xAbsolute } from \"../x/xAbsolute.js\";\nimport { xMedian } from \"../x/xMedian.js\";\nimport { xSubtract } from \"../x/xSubtract.js\";\n/**\n * Calculate the weights based on the control points and the MAD between the original data and the new baseline.\n * MAD (Median Absolute Deviation) is more robust to outliers and\n * the factor (1.4826) makes MAD scaled to be equivalent to the standard deviation for\n * normal distributions. {@link https://en.m.wikipedia.org/wiki/Median_absolute_deviation}.\n * @param yData - the original data.\n * @param baseline - the new baseline calculated.\n * @param weights - the current weights to be updated.\n * @param options - options for updating weights.\n * @returns new array of weights.\n */\nexport function calculateAdaptiveWeights(yData, baseline, weights, options) {\n const { controlPoints, factorStd = 3, learningRate = 0.5 } = options;\n if (learningRate === 0) {\n return weights;\n }\n const absResiduals = xAbsolute(xSubtract(yData, baseline));\n const medAbsRes = xMedian(absResiduals);\n const mad = 1.4826 * medAbsRes;\n const threshold = mad > 0 ? factorStd * mad : 1;\n const rawWeights = new Float64Array(absResiduals.length);\n for (let i = 0; i < absResiduals.length; i++) {\n rawWeights[i] = Math.exp(-((absResiduals[i] / threshold) ** 2));\n }\n const oneMinusLearningRate = 1 - learningRate;\n for (let i = 0; i < weights.length; i++) {\n let weight = weights[i];\n weight = (oneMinusLearningRate * weight + learningRate * rawWeights[i]) / 4;\n if (controlPoints && controlPoints[i] > 0) {\n weight *= 4;\n }\n weights[i] = weight;\n }\n weights[0] = 1;\n weights[weights.length - 1] = 1;\n return weights;\n}\n//# sourceMappingURL=calculateAdaptiveWeights.js.map","import { getFFT } from \"../utils/fftCache.js\";\nimport { isPowerOfTwo, nextPowerOfTwo } from \"../utils/index.js\";\nimport { xCheck } from \"./xCheck.js\";\n/**\n * Performs the Hilbert transform\n * @link https://en.wikipedia.org/wiki/Hilbert_transform\n * @param array - array containing values.\n * @param options - options.\n * @returns a new vector with 90 degree shift regarding the phase of the original function.\n */\nexport function xHilbertTransform(array, options = {}) {\n xCheck(array);\n const { forceFFT = false } = options;\n const length = array.length;\n if (isPowerOfTwo(length)) {\n return hilbertTransformWithFFT(array);\n }\n else if (forceFFT) {\n return resampling(hilbertTransformWithFFT(resampling(array, nextPowerOfTwo(length))), length);\n }\n else {\n return hilbertTransform(array);\n }\n}\n/**\n * Performs the discrete Hilbert transform using fast Fourier transform\n * @param array - array containing values.\n * @returns a new vector with 90 degree shift regarding the phase of the original function.\n * @see DOI: 10.1109/TAU.1970.1162139 \"Discrete Hilbert transform\"\n */\nfunction hilbertTransformWithFFT(array) {\n const length = array.length;\n const fft = getFFT(length);\n // Single reusable buffer for FFT spectrum\n const spectrum = new Float64Array(length * 2);\n // Forward FFT\n fft.realTransform(spectrum, array);\n fft.completeSpectrum(spectrum);\n const half = length >> 1;\n // Zero Nyquist\n const j = half << 1;\n spectrum[j] = 0;\n spectrum[j + 1] = 0;\n // Negate negative frequencies\n for (let j = (half + 1) << 1; j < spectrum.length; j += 2) {\n spectrum[j] = -spectrum[j];\n spectrum[j + 1] = -spectrum[j + 1];\n }\n const hilbertSignal = new Float64Array(length * 2);\n fft.inverseTransform(hilbertSignal, spectrum);\n // Extract imaginary part directly into output\n const result = new Float64Array(length);\n for (let i = 0; i < length; i++) {\n result[i] = hilbertSignal[i * 2 + 1];\n }\n return result;\n}\n/**\n * Performs the discrete Hilbert transform\n * @param array - array containing values.\n * @param options - options.\n * @param options.inClockwise - whether to apply clockwise rotation.\n * @returns a new vector with 90 degree shift regarding the phase of the original function.\n */\nfunction hilbertTransform(array, options = {}) {\n const { inClockwise = true } = options;\n const input = [0, ...array, 0];\n const result = new Float64Array(array.length);\n for (let k = 1; k < input.length - 1; k++) {\n let aSum = 0;\n for (let i = 0; i < k - 1; i++) {\n const log = Math.log((k - i) / (k - i - 1));\n aSum += input[i] * log + (input[i + 1] - input[i]) * (-1 + (k - i) * log);\n }\n const b = input[k - 1] - input[k + 1];\n let cSum = 0;\n for (let i = k + 1; i < input.length - 1; i++) {\n const log = Math.log((i - k) / (i - k + 1));\n cSum += input[i] * log + (input[i - 1] - input[i]) * (1 + (i - k) * log);\n }\n result[k - 1] = ((inClockwise ? 1 : -1) * (aSum + b + cSum)) / Math.PI;\n }\n return result;\n}\n/**\n * Performs resampling of an input array to the desired length employing linear interpolation.\n * @param array - array containing values.\n * @param length - the length of the resulting array.\n * @returns a new array of the desired length.\n * @link https://en.wikipedia.org/wiki/Sample-rate_conversion\n */\nfunction resampling(array, length) {\n xCheck(array);\n const oldLength = array.length;\n const ratio = (oldLength - 1) / (length - 1);\n const result = new Float64Array(length);\n let currentIndex = 0;\n let floor = Math.floor(currentIndex);\n let ceil = Math.min(Math.ceil(currentIndex), oldLength - 1);\n let diff = currentIndex - floor;\n for (let i = 0; i < length; i++) {\n result[i] = array[floor] * (1 - diff) + array[ceil] * diff;\n currentIndex += ratio;\n floor = Math.floor(currentIndex);\n ceil = Math.min(Math.ceil(currentIndex), oldLength - 1);\n diff = currentIndex - floor;\n }\n return result;\n}\n//# sourceMappingURL=xHilbertTransform.js.map","import { xCheck } from \"./xCheck.js\";\nimport { xGetFromToIndex } from \"./xGetFromToIndex.js\";\n/**\n * Computes the maximal value of an array of values\n * @param array - array of numbers\n * @param options - options\n */\nexport function xMaxValue(array, options = {}) {\n xCheck(array);\n const { fromIndex, toIndex } = xGetFromToIndex(array, options);\n let maxValue = array[fromIndex];\n for (let i = fromIndex + 1; i <= toIndex; i++) {\n if (array[i] > maxValue) {\n maxValue = array[i];\n }\n }\n return maxValue;\n}\n//# sourceMappingURL=xMaxValue.js.map","import { xCheck } from \"./xCheck.js\";\nimport { xGetFromToIndex } from \"./xGetFromToIndex.js\";\n/**\n * Computes the minimal value of an array of values.\n * @param array - array of numbers\n * @param options - options\n */\nexport function xMinValue(array, options = {}) {\n xCheck(array);\n const { fromIndex, toIndex } = xGetFromToIndex(array, options);\n let minValue = array[fromIndex];\n for (let i = fromIndex + 1; i <= toIndex; i++) {\n if (array[i] < minValue) {\n minValue = array[i];\n }\n }\n return minValue;\n}\n//# sourceMappingURL=xMinValue.js.map","/**\n * Check if the values are separated always by the same difference\n * @param array - monotone growing array of number.\n * @param options - options.\n */\nexport function xIsEquallySpaced(array, options = {}) {\n if (array.length < 3)\n return true;\n const { tolerance = 0.05 } = options;\n let maxDx = 0;\n let minDx = Number.MAX_SAFE_INTEGER;\n for (let i = 0; i < array.length - 1; ++i) {\n const absoluteDifference = array[i + 1] - array[i];\n if (absoluteDifference < minDx) {\n minDx = absoluteDifference;\n }\n if (absoluteDifference > maxDx) {\n maxDx = absoluteDifference;\n }\n }\n return (maxDx - minDx) / maxDx < tolerance;\n}\n//# sourceMappingURL=xIsEquallySpaced.js.map","/**\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","import { xCheck } from \"./xCheck.js\";\nimport { xGetFromToIndex } from \"./xGetFromToIndex.js\";\n/**\n * Computes the maximal value of an array of values\n * @param array - array of numbers\n * @param options - options\n */\nexport function xMaxAbsoluteValue(array, options = {}) {\n xCheck(array);\n const { fromIndex, toIndex } = xGetFromToIndex(array, options);\n let maxValue = Math.abs(array[fromIndex]);\n for (let i = fromIndex + 1; i <= toIndex; i++) {\n if (array[i] >= 0) {\n if (array[i] > maxValue) {\n maxValue = array[i];\n }\n }\n else if (-array[i] > maxValue) {\n maxValue = -array[i];\n }\n }\n return maxValue;\n}\n//# sourceMappingURL=xMaxAbsoluteValue.js.map","import { xCheck } from \"./xCheck.js\";\nimport { xGetFromToIndex } from \"./xGetFromToIndex.js\";\n/**\n * Computes the index of the maximum of the given values\n * @param array - array of numbers\n * @param options - options\n * @returns - index\n */\nexport function xMaxIndex(array, options = {}) {\n xCheck(array);\n const { fromIndex, toIndex } = xGetFromToIndex(array, options);\n let maxIndex = fromIndex;\n for (let i = fromIndex + 1; i <= toIndex; i++) {\n if (array[i] > array[maxIndex]) {\n maxIndex = i;\n }\n }\n return maxIndex;\n}\n//# sourceMappingURL=xMaxIndex.js.map","import { xMedian } from \"./xMedian.js\";\n/**\n * This function calculates the median absolute deviation (MAD).\n * https://en.wikipedia.org/wiki/Median_absolute_deviation\n * @param array - array of numbers.\n */\nexport function xMedianAbsoluteDeviation(array) {\n const median = xMedian(array);\n const averageDeviations = new Float64Array(array.length);\n for (let i = 0; i < array.length; i++) {\n averageDeviations[i] = Math.abs(array[i] - median);\n }\n return {\n median,\n mad: xMedian(averageDeviations),\n };\n}\n//# sourceMappingURL=xMedianAbsoluteDeviation.js.map","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 (let i = 1; i < array.length; i++) {\n const value = array[i];\n if (value < min)\n min = value;\n else if (value > max)\n max = value;\n }\n return { min, max };\n}\n//# sourceMappingURL=xMinMaxValues.js.map","import { isAnyArray } from 'is-any-array';\nimport { getOutputArray } from \"./getOutputArray.js\";\n/**\n * This function xMultiply the first array by the second array or a constant value to each element of the first array\n * @param array1 - first array\n * @param array2 - second array\n * @param options - options\n */\nexport function xMultiply(array1, array2, options = {}) {\n let isConstant = false;\n let constant = 0;\n if (isAnyArray(array2)) {\n if (array1.length !== array2.length) {\n throw new Error('size of array1 and array2 must be identical');\n }\n }\n else {\n isConstant = true;\n constant = array2;\n }\n const array3 = getOutputArray(options.output, array1.length);\n if (isConstant) {\n for (let i = 0; i < array1.length; i++) {\n array3[i] = array1[i] * constant;\n }\n }\n else {\n for (let i = 0; i < array1.length; i++) {\n array3[i] = array1[i] * array2[i];\n }\n }\n return array3;\n}\n//# sourceMappingURL=xMultiply.js.map","/**\n * Applies the Rayleigh inverse cumulative distribution for a single value.\n * @param input - cumulative probability value.\n * @returns the inverse value for the Rayleigh distribution.\n */\nexport function simpleNormInvMagnitude(input) {\n return -Math.sqrt(-2 * Math.log(1 - input));\n}\n//# sourceMappingURL=simpleNormInvMagnitude.js.map","/*\nAdapted from: https://github.com/compute-io/erfcinv/blob/aa116e23883839359e310ad41a7c42f72815fc1e/lib/number.js\n\nThe MIT License (MIT)\n\nCopyright (c) 2014-2015 The Compute.io Authors. All rights reserved.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\n\nBoost Software License - Version 1.0 - August 17th, 2003\n\nPermission is hereby granted, free of charge, to any person or organization obtaining a copy of the software and accompanying documentation covered by this license (the \"Software\") to use, reproduce, display, distribute, execute, and transmit the Software, and to prepare derivative works of the Software, and to permit third-parties to whom the Software is furnished to do so, all subject to the following:\n\nThe copyright notices in the Software and this entire statement, including the above license grant, this restriction and the following disclaimer, must be included in all copies of the Software, in whole or in part, and all derivative works of the Software, unless such copies or derivative works are solely in the form of machine-executable object code generated by a source language processor.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n*/\n/* eslint-disable no-loss-of-precision */\n// Coefficients for erfcinv on [0, 0.5]:\nconst Y1 = 8.91314744949340820313e-2;\nconst P1 = [\n -5.38772965071242932965e-3, 8.22687874676915743155e-3,\n 2.19878681111168899165e-2, -3.65637971411762664006e-2,\n -1.26926147662974029034e-2, 3.34806625409744615033e-2,\n -8.36874819741736770379e-3, -5.08781949658280665617e-4,\n];\nconst Q1 = [\n 8.86216390456424707504e-4, -2.33393759374190016776e-3,\n 7.95283687341571680018e-2, -5.27396382340099713954e-2,\n -7.1228902341542847553e-1, 6.62328840472002992063e-1, 1.56221558398423026363,\n -1.56574558234175846809, -9.70005043303290640362e-1, 1,\n];\n// Coefficients for erfcinv for 0.5 > 1-x >= 0:\nconst Y2 = 2.249481201171875;\nconst P2 = [\n -3.67192254707729348546, 2.11294655448340526258e1, 1.7445385985570866523e1,\n -4.46382324441786960818e1, -1.88510648058714251895e1,\n 1.76447298408374015486e1, 8.37050328343119927838, 1.05264680699391713268e-1,\n -2.02433508355938759655e-1,\n];\nconst Q2 = [\n 1.72114765761200282724, -2.26436933413139721736e1, 1.08268667355460159008e1,\n 4.85609213108739935468e1, -2.01432634680485188801e1,\n -2.86608180499800029974e1, 3.9713437953343869095, 6.24264124854247537712, 1,\n];\n// Coefficients for erfcinv for sqrt( -log(1-x)):\nconst Y3 = 8.07220458984375e-1;\nconst P3 = [\n -6.81149956853776992068e-10, 2.85225331782217055858e-8,\n -6.79465575181126350155e-7, 2.14558995388805277169e-3,\n 2.90157910005329060432e-2, 1.42869534408157156766e-1,\n 3.37785538912035898924e-1, 3.87079738972604337464e-1,\n 1.17030156341995252019e-1, -1.63794047193317060787e-1,\n -1.31102781679951906451e-1,\n];\nconst Q3 = [\n 1.105924229346489121e-2, 1.52264338295331783612e-1, 8.48854343457902036425e-1,\n 2.59301921623620271374, 4.77846592945843778382, 5.38168345707006855425,\n 3.46625407242567245975, 1,\n];\nconst Y4 = 9.3995571136474609375e-1;\nconst P4 = [\n 2.66339227425782031962e-12, -2.30404776911882601748e-10,\n 4.60469890584317994083e-6, 1.57544617424960554631e-4,\n 1.87123492819559223345e-3, 9.50804701325919603619e-3,\n 1.85573306514231072324e-2, -2.22426529213447927281e-3,\n -3.50353787183177984712e-2,\n];\nconst Q4 = [\n 7.64675292302794483503e-5, 2.63861676657015992959e-3,\n 3.41589143670947727934e-2, 2.20091105764131249824e-1,\n 7.62059164553623404043e-1, 1.3653349817554063097, 1,\n];\nconst Y5 = 9.8362827301025390625e-1;\nconst P5 = [\n 9.9055709973310326855e-17, -2.81128735628831791805e-14,\n 4.62596163522878599135e-9, 4.49696789927706453732e-7,\n 1.49624783758342370182e-5, 2.09386317487588078668e-4,\n 1.05628862152492910091e-3, -1.12951438745580278863e-3,\n -1.67431005076633737133e-2,\n];\nconst Q5 = [\n 2.82243172016108031869e-7, 2.75335474764726041141e-5,\n 9.64011807005165528527e-4, 1.60746087093676504695e-2,\n 1.38151865749083321638e-1, 5.91429344886417493481e-1, 1,\n];\n/**\n * Evaluates a polynomial at x using Horner's method.\n * @param c - array of polynomial coefficients.\n * @param x - evaluation point.\n * @returns polynomial value.\n */\nfunction polyval(c, x) {\n let p = 0;\n for (const coef of c) {\n p = p * x + coef;\n }\n return p;\n}\n/**\n * Calculates a rational approximation.\n * @private\n * @param x - input value.\n * @param v - shift value.\n * @param P - array of numerator polynomial coefficients.\n * @param Q - array of denominator polynomial coefficients.\n * @param Y - scale factor.\n * @returns rational approximation.\n */\nfunction calc(x, v, P, Q, Y) {\n const s = x - v;\n const r = polyval(P, s) / polyval(Q, s);\n return Y * x + r * x;\n}\n/**\n * Evaluates the complementary inverse error function for an input value.\n * @private\n * @param x - input value.\n * @returns evaluated complementary inverse error function.\n */\nexport default function erfcinv(x) {\n let sign = false;\n let val;\n let q;\n let g;\n let r;\n // [1] Special cases...\n // NaN:\n if (Number.isNaN(x)) {\n return Number.NaN;\n }\n // x not on the interval: [0,2]\n if (x < 0 || x > 2) {\n throw new RangeError(`erfcinv()::invalid input argument. Value must be on the interval [0,2]. Value: \\`${x}\\`.`);\n }\n if (x === 0) {\n return Number.POSITIVE_INFINITY;\n }\n if (x === 2) {\n return Number.NEGATIVE_INFINITY;\n }\n if (x === 1) {\n return 0;\n }\n // [2] Get the sign and make use of `erfc` reflection formula: `erfc(-z)=2 - erfc(z)`...\n if (x > 1) {\n q = 2 - x;\n x = 1 - q;\n sign = true;\n }\n else {\n q = x;\n x = 1 - x;\n }\n // [3] |x| <= 0.5\n if (x <= 0.5) {\n g = x * (x + 10);\n r = polyval(P1, x) / polyval(Q1, x);\n val = g * Y1 + g * r;\n return sign ? -val : val;\n }\n // [4] 1-|x| >= 0.25\n if (q >= 0.25) {\n g = Math.sqrt(-2 * Math.log(q));\n q = q - 0.25;\n r = polyval(P2, q) / polyval(Q2, q);\n val = g / (Y2 + r);\n return sign ? -val : val;\n }\n q = Math.sqrt(-Math.log(q));\n // [5] q < 3\n if (q < 3) {\n return calc(q, 1.125, P3, Q3, Y3);\n }\n // [6] q < 6\n if (q < 6) {\n return calc(q, 3, P4, Q4, Y4);\n }\n // Note that the smallest number in JavaScript is 5e-324. Math.sqrt( -Math.log( 5e-324 ) ) ~27.2844\n return calc(q, 6, P5, Q5, Y5);\n // Note that in the boost library, they are able to go to much smaller values, as 128 bit long doubles support ~1e-5000; something which JavaScript does not natively support.\n}\n//# sourceMappingURL=erfcinv.js.map","import erfcinv from \"./erfcinv.js\";\n/**\n * Applies the inverse of the normal cumulative distribution for a single value.\n * @param input - cumulative probability value.\n * @returns the inverse value for the normal distribution.\n */\nexport function simpleNormInvRaw(input) {\n return -1 * Math.SQRT2 * erfcinv(2 * input);\n}\n//# sourceMappingURL=simpleNormInvRaw.js.map","import { isAnyArray } from 'is-any-array';\nimport { createFromToArray } from \"../utils/index.js\";\nimport { simpleNormInvMagnitude } from \"./utils/simpleNormInvMagnitude.js\";\nimport { simpleNormInvRaw } from \"./utils/simpleNormInvRaw.js\";\nimport { xEnsureFloat64 } from \"./xEnsureFloat64.js\";\n/**\n * Determine noise level by san plot methodology (https://doi.org/10.1002/mrc.4882)\n * @param array - real or magnitude spectra data.\n * @param options - options\n * @returns noise level\n */\nexport function xNoiseSanPlot(array, options = {}) {\n const { mask, cutOffFromPositive = true, refine = true, magnitudeMode = false, scaleFactor = 1, factorStd = 5, fixOffset = true, } = options;\n const input = prepareData(array, { scaleFactor, mask });\n if (fixOffset && !magnitudeMode) {\n const medianIndex = Math.floor(input.length / 2);\n const median = input.length % 2 === 0\n ? 0.5 * (input[medianIndex - 1] + input[medianIndex])\n : input[medianIndex];\n for (let i = 0; i < input.length; i++) {\n input[i] -= median;\n }\n }\n const firstNegativeValueIndex = input.at(-1) >= 0\n ? input.length\n : input.findIndex((e) => e < 0);\n let lastPositiveValueIndex = firstNegativeValueIndex - 1;\n for (let i = lastPositiveValueIndex; i >= 0; i--) {\n if (input[i] > 0) {\n lastPositiveValueIndex = i;\n break;\n }\n }\n const signPositive = input.slice(0, lastPositiveValueIndex + 1);\n const signNegative = createNegativeSign(input, firstNegativeValueIndex);\n const skyPoint = signPositive[0];\n const cutOff = options.cutOff ??\n (cutOffFromPositive\n ? determineCutOff(signPositive, { magnitudeMode })\n : null);\n const noiseLevelPositive = calculateNoiseLevel(signPositive, {\n factorStd,\n refine,\n magnitudeMode,\n cutOff,\n });\n const noiseLevelNegative = calculateNoiseLevel(signNegative, {\n factorStd,\n refine,\n magnitudeMode,\n cutOff,\n });\n return {\n positive: noiseLevelPositive,\n negative: noiseLevelNegative,\n snr: skyPoint / noiseLevelPositive,\n sanplot: generateSanPlot(input, {\n fromTo: {\n positive: { from: 0, to: lastPositiveValueIndex },\n negative: { from: firstNegativeValueIndex, to: input.length },\n },\n }),\n percentiles: {\n positive: getPercentiles(signPositive),\n negative: getPercentiles(signNegative),\n },\n };\n}\nfunction calculateNoiseLevel(sign, options) {\n const { factorStd, refine, magnitudeMode, cutOff } = options;\n if (sign.length === 0)\n return 0;\n const simpleNormInvValue = magnitudeMode\n ? simpleNormInvMagnitude\n : simpleNormInvRaw;\n const cutOffDist = cutOff || determineCutOff(sign, { magnitudeMode });\n let noiseLevel = sign[Math.floor(sign.length * cutOffDist)];\n let cloneSign = sign.slice();\n let cutOffSignalsIndex = -1;\n if (refine) {\n const cutOffSignals = noiseLevel * factorStd;\n cutOffSignalsIndex = sign.findIndex((e) => e < cutOffSignals);\n if (cutOffSignalsIndex > -1) {\n cloneSign = sign.slice(cutOffSignalsIndex);\n noiseLevel = cloneSign[Math.floor(cloneSign.length * cutOffDist)];\n }\n }\n if (refine && cutOffSignalsIndex > -1) {\n const effectiveCutOffDist = (cutOffDist * cloneSign.length + cutOffSignalsIndex) /\n (cloneSign.length + cutOffSignalsIndex);\n const refinedCorrectionFactor = -1 * simpleNormInvValue(effectiveCutOffDist / 2);\n noiseLevel /= refinedCorrectionFactor;\n }\n else {\n const correctionFactor = -simpleNormInvValue(cutOffDist / 2);\n noiseLevel /= correctionFactor;\n }\n return noiseLevel;\n}\n/**\n * Estimates where the \"noise region\" begins in a SAN-plot-style sorted\n * intensity series (Sheberstov et al., Magn. Reson. Chem. 2020, 58, 466-472,\n * https://doi.org/10.1002/mrc.4882).\n *\n * `signPositive` is expected to already be sorted by decreasing absolute\n * magnitude (the paper's S+ or S- series). For each quantile fraction `i`\n * (fraction of points at or beyond that position, counting from the tail),\n * we invert the Gaussian (or Rayleigh, in magnitude mode) order-statistics\n * relationship to get a local estimate of the noise standard deviation\n * (sigma). This estimate is only expected to be stable/flat once we are\n * fully inside the true noise region; in the signal and artifact regions\n * it will be biased and noisy. We therefore scan candidate quantile windows\n * and pick the one where the sigma estimates are most self-consistent\n * (lowest variance) - a proxy for \"where the noise region reliably starts\".\n * @param signPositive - an array of positive numbers.\n * @param options - optional parameters to configure the cut-off determination.\n * @param options.magnitudeMode - if true, uses magnitude mode for normalization. Default is false.\n * @param options.considerList - an object specifying the range and step for consideration.\n * @param options.considerList.from - the starting point of the range. Default is 0.5.\n * @param options.considerList.step - the step size for the range. Default is 0.1.\n * @param options.considerList.to - the ending point of the range. Default is 0.9.\n * @returns the optimal cut-off point as a number.\n */\nfunction determineCutOff(signPositive, options = {}) {\n const { magnitudeMode = false, considerList = { from: 0.5, step: 0.1, to: 0.9 }, } = options;\n const inverseQuantileFn = magnitudeMode\n ? simpleNormInvMagnitude\n : simpleNormInvRaw;\n const indexMax = signPositive.length - 1;\n // Ensure the configurable scan range stays inside the valid quantile domain.\n const { from, to, step } = considerList;\n const safeFrom = Math.min(Math.max(from, 0.001), 0.999);\n const safeTo = Math.min(Math.max(to, 0.001), 0.999);\n const safeStep = Math.max(0.001, Math.min(0.999, step));\n const normalizedFrom = Math.min(safeFrom, safeTo);\n const normalizedTo = Math.max(safeFrom, safeTo);\n // For each quantile fraction, compute a local sigma estimate by inverting\n // the theoretical relationship between order statistics and the assumed\n // noise distribution (Gaussian or Rayleigh).\n const sigmaEstimates = [];\n const startQuantile = Math.max(0.001, normalizedFrom - safeStep / 2);\n const endQuantile = Math.min(0.999, normalizedTo + safeStep / 2);\n for (let quantileFraction = startQuantile; quantileFraction <= endQuantile; quantileFraction += 0.01) {\n const index = Math.max(0, Math.min(indexMax, Math.round(indexMax * quantileFraction)));\n const sigma = -signPositive[index] / inverseQuantileFn(quantileFraction / 2);\n // Skip non-finite sigma estimates\n if (Number.isFinite(sigma)) {\n sigmaEstimates.push([quantileFraction, Math.abs(sigma)]);\n }\n }\n const halfWindow = safeStep / 2;\n let bestVariance = Number.MAX_SAFE_INTEGER;\n let bestQuantileFraction = 0.5;\n for (let windowCenter = normalizedFrom; windowCenter <= normalizedTo; windowCenter += safeStep) {\n const windowFloor = windowCenter - halfWindow;\n const windowTop = windowCenter + halfWindow;\n let count = 0;\n let sum = 0;\n // First pass: compute mean\n for (const [q, absSigma] of sigmaEstimates) {\n if (q > windowFloor && q < windowTop) {\n sum += absSigma;\n count++;\n }\n }\n // Guard against empty windows.\n if (count === 0)\n continue;\n const meanSigma = sum / count;\n // Second pass: compute variance\n let variance = 0;\n for (const [q, absSigma] of sigmaEstimates) {\n if (q > windowFloor && q < windowTop) {\n variance += (absSigma - meanSigma) ** 2;\n }\n }\n if (variance < bestVariance) {\n bestVariance = variance;\n bestQuantileFraction = windowCenter;\n }\n }\n return bestQuantileFraction;\n}\n/**\n * Generates a SAN plot from the given array based on the specified options.\n * @param array - the input array of numbers to be processed.\n * @param options - an optional object containing configuration options.\n * @param options.logBaseY - the logarithmic base for the Y-axis. Defaults to 2.\n * @param options.fromTo - an object specifying the range for each key. Each key maps to an object with `from` and `to` properties.\n * @returns an object where each key maps to a DataXY object containing the processed data.\n */\nfunction generateSanPlot(array, options = {}) {\n const { fromTo, logBaseY = 2 } = options;\n const sanplot = {};\n for (const key in fromTo) {\n const { from, to } = fromTo[key];\n sanplot[key] =\n from !== to\n ? scale(array.slice(from, to), {\n logBaseY,\n })\n : { x: [], y: [] };\n if (key === 'negative') {\n sanplot[key].y.reverse();\n }\n }\n return sanplot;\n}\n/**\n * Scales the input array based on the provided options.\n * @param array - the input array to be scaled.\n * @param options - an optional object containing scaling options.\n * @param options.logBaseY - if provided, the array values will be scaled using the logarithm of this base.\n * @returns an object containing the scaled x and y arrays.\n */\nfunction scale(array, options = {}) {\n const { log10, abs } = Math;\n const { logBaseY } = options;\n if (logBaseY) {\n array = array.slice(0);\n const logOfBase = log10(logBaseY);\n for (let i = 0; i < array.length; i++) {\n array[i] = log10(abs(array[i])) / logOfBase;\n }\n }\n const xAxis = createFromToArray({\n from: 0,\n to: array.length - 1,\n length: array.length,\n });\n return { x: xAxis, y: array };\n}\nfunction createNegativeSign(array, from) {\n const length = array.length - from;\n const result = new Float64Array(length);\n for (let i = 0; i < length; i++) {\n result[i] = -array[array.length - 1 - i];\n }\n return result;\n}\n/**\n * Calculates intensity percentiles ranging from the 0th to the 100th percentile\n * (in 1 percentile increments) from a pre-sorted array.\n * @param sorted - a pre-sorted array of numbers.\n * @returns An array where index `p` contains the `p`th percentile.\n */\nfunction getPercentiles(sorted) {\n const result = new Array(101).fill(0);\n if (sorted.length === 0)\n return result;\n const maxIndex = sorted.length - 1;\n for (let percentile = 0; percentile <= 100; percentile++) {\n const ratio = percentile / 100;\n const index = maxIndex - Math.floor(ratio * maxIndex);\n result[percentile] = sorted[index];\n }\n return result;\n}\n/**\n * Prepares and processes the input data array based on the provided options.\n * @param array - The input array of numbers to be processed.\n * @param options - The configuration options for processing the data.\n * @returns A new Float64Array containing the processed data, scaled by the\n * scaleFactor and sorted in descending order.\n */\nfunction prepareData(array, options) {\n const { scaleFactor, mask } = options;\n const input = xEnsureFloat64(isAnyArray(mask) && mask.length === array.length\n ? array.filter((_e, i) => !mask[i])\n : array);\n if (scaleFactor > 1) {\n for (let i = 0; i < input.length; i++) {\n input[i] *= scaleFactor;\n }\n }\n input.sort();\n input.reverse();\n return input;\n}\n//# sourceMappingURL=xNoiseSanPlot.js.map","import { xMedianAbsoluteDeviation } from \"./xMedianAbsoluteDeviation.js\";\n/**\n * Determine noise level using MAD https://en.wikipedia.org/wiki/Median_absolute_deviation\n * Constant to convert mad to sd calculated using https://www.wolframalpha.com/input?i=sqrt%282%29+inverse+erf%280.5%29\n * This assumes a gaussian distribution of the noise\n * @param array - array of numbers.\n * @returns noise level corresponding to one standard deviation.\n */\nexport function xNoiseStandardDeviation(array) {\n const { mad, median } = xMedianAbsoluteDeviation(array);\n return { sd: mad / 0.6744897501960817, mad, median };\n}\n//# sourceMappingURL=xNoiseStandardDeviation.js.map","/**\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 (let i = 0; i < array.length; i++) {\n const element = array[i];\n result += element * element;\n }\n return Math.sqrt(result);\n}\n//# sourceMappingURL=xNorm.js.map","import { xCheck } from \"./xCheck.js\";\nimport { xGetFromToIndex } from \"./xGetFromToIndex.js\";\n/**\n * Calculate the sum of the values\n * @param array - object that contains property x (an ordered increasing array) and y (an array).\n * @param options - options.\n * @returns sum value on the specified range.\n */\nexport function xSum(array, options = {}) {\n xCheck(array);\n const { fromIndex, toIndex } = xGetFromToIndex(array, options);\n let sumValue = array[fromIndex];\n for (let i = fromIndex + 1; i <= toIndex; i++) {\n sumValue += array[i];\n }\n return sumValue;\n}\n//# sourceMappingURL=xSum.js.map","import { getOutputArray } from \"./getOutputArray.js\";\nimport { xCheck } from \"./xCheck.js\";\nimport { xMaxValue } from \"./xMaxValue.js\";\nimport { xSum } from \"./xSum.js\";\n/**\n * Divides the data with either the sum, the absolute sum or the maximum of the data\n * @param input - array containing values.\n * @param options - options.\n * @returns normalized data.\n */\nexport function xNormed(input, options = {}) {\n const { algorithm = 'absolute', value = 1 } = options;\n xCheck(input);\n const output = getOutputArray(options.output, input.length);\n if (input.length === 0) {\n throw new Error('input must not be empty');\n }\n switch (algorithm) {\n case 'absolute': {\n const absoluteSumValue = absoluteSum(input) / value;\n if (absoluteSumValue === 0) {\n throw new Error('trying to divide by 0');\n }\n for (let i = 0; i < input.length; i++) {\n output[i] = input[i] / absoluteSumValue;\n }\n return output;\n }\n case 'max': {\n const currentMaxValue = xMaxValue(input);\n if (currentMaxValue === 0) {\n throw new Error('trying to divide by 0');\n }\n const factor = value / currentMaxValue;\n for (let i = 0; i < input.length; i++) {\n output[i] = input[i] * factor;\n }\n return output;\n }\n case 'sum': {\n const sumFactor = xSum(input) / value;\n if (sumFactor === 0) {\n throw new Error('trying to divide by 0');\n }\n for (let i = 0; i < input.length; i++) {\n output[i] = input[i] / sumFactor;\n }\n return output;\n }\n default:\n throw new Error(`unknown algorithm: ${String(algorithm)}`);\n }\n}\nfunction absoluteSum(input) {\n let sumValue = 0;\n for (let i = 0; i < input.length; i++) {\n sumValue += Math.abs(input[i]);\n }\n return sumValue;\n}\n//# sourceMappingURL=xNormed.js.map","import { xCheck } from \"./xCheck.js\";\nimport { xEnsureFloat64 } from \"./xEnsureFloat64.js\";\n/**\n * Pads an array symmetrically on both sides.\n * @param array - the array that will be padded\n * @param options - options\n * @returns padded Float64Array\n */\nexport function xPadding(array, options = {}) {\n const { size = 0, value = 0, algorithm } = options;\n xCheck(array);\n if (!algorithm) {\n return xEnsureFloat64(array);\n }\n const result = new Float64Array(array.length + size * 2);\n for (let i = 0; i < array.length; i++) {\n result[i + size] = array[i];\n }\n const fromEnd = size + array.length;\n const toEnd = 2 * size + array.length;\n switch (algorithm) {\n case 'value':\n for (let i = 0; i < size; i++) {\n result[i] = value;\n }\n for (let i = fromEnd; i < toEnd; i++) {\n result[i] = value;\n }\n break;\n case 'duplicate':\n for (let i = 0; i < size; i++) {\n result[i] = array[0];\n }\n for (let i = fromEnd; i < toEnd; i++) {\n result[i] = array.at(-1);\n }\n break;\n case 'circular':\n for (let i = 0; i < size; i++) {\n result[i] =\n array[(array.length - (size % array.length) + i) % array.length];\n }\n for (let i = 0; i < size; i++) {\n result[i + fromEnd] = array[i % array.length];\n }\n break;\n default:\n throw new Error(`unknown algorithm ${String(algorithm)}`);\n }\n return result;\n}\n//# sourceMappingURL=xPadding.js.map","import { xCheck } from \"./xCheck.js\";\nimport { xStandardDeviation } from \"./xStandardDeviation.js\";\n/**\n * Pareto scaling, which uses the square root of standard deviation as the scaling factor, circumvents the amplification of noise by retaining a small portion of magnitude information.\n * Noda, I. (2008). Scaling techniques to enhance two-dimensional correlation spectra. Journal of Molecular Structure, 883, 216-227.\n * DOI: 10.1016/j.molstruc.2007.12.026\n * @param array - array of number\n */\nexport function xParetoNormalization(array) {\n xCheck(array);\n const result = new Float64Array(array.length);\n const sqrtSD = Math.sqrt(xStandardDeviation(array));\n for (let i = 0; i < array.length; i++) {\n result[i] = array[i] / sqrtSD;\n }\n return result;\n}\n//# sourceMappingURL=xParetoNormalization.js.map","import { getOutputArray } from \"./getOutputArray.js\";\nimport { xCheck } from \"./xCheck.js\";\nimport { xMaxValue } from \"./xMaxValue.js\";\nimport { xMinValue } from \"./xMinValue.js\";\n/**\n * Function used to rescale data\n * @param input - input for the rescale\n * @param options - options\n * @returns rescaled data\n */\nexport function xRescale(input, options = {}) {\n xCheck(input);\n const output = getOutputArray(options.output, input.length);\n const currentMin = xMinValue(input);\n const currentMax = xMaxValue(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 = 0, max = 1 } = options;\n if (min >= max) {\n throw new RangeError('min option must be smaller than max option');\n }\n const factor = (max - min) / (currentMax - currentMin);\n for (let i = 0; i < input.length; i++) {\n output[i] = (input[i] - currentMin) * factor + min;\n }\n return output;\n}\n//# sourceMappingURL=xRescale.js.map","import { xCheck } from \"./xCheck.js\";\nimport { xPadding } from \"./xPadding.js\";\n/**\n * This function calculates a rolling average\n * @param array - array\n * @param fct - callback function that from an array returns a value\n * @param options - options\n */\nexport function xRolling(array, fct, options = {}) {\n xCheck(array);\n if (typeof fct !== 'function') {\n throw new TypeError('fct must be a function');\n }\n const { window = 5, padding = {} } = options;\n const { size = window - 1, algorithm, value } = padding;\n const padded = xPadding(array, { size, algorithm, value }); // ensure we get a copy and it is float64\n const newArray = [];\n for (let i = 0; i < padded.length - window + 1; i++) {\n // we will send a view to the original buffer\n newArray.push(fct(padded.subarray(i, i + window)));\n }\n return newArray;\n}\n//# sourceMappingURL=xRolling.js.map","import { xMean } from \"./xMean.js\";\nimport { xRolling } from \"./xRolling.js\";\n/**\n * This function calculates a rolling average\n * @param array - array\n * @param options - option\n */\nexport function xRollingAverage(array, options = {}) {\n return xRolling(array, xMean, options);\n}\n//# sourceMappingURL=xRollingAverage.js.map","import { xMedian } from \"./xMedian.js\";\nimport { xRolling } from \"./xRolling.js\";\n/**\n * This function calculates a rolling average\n * @param array - array\n * @param options - options\n */\nexport function xRollingMedian(array, options = {}) {\n return xRolling(array, xMedian, options);\n}\n//# sourceMappingURL=xRollingMedian.js.map","/**\n * This function performs a circular shift to an array.\n * Positive values of shifts will shift to the right and negative values will do to the left.\n * @example xRotate([1,2,3,4],1) -> [4,1,2,3]\n * @example xRotate([1,2,3,4],-1) -> [2,3,4,1]\n * @param array - array\n * @param shift - shift\n * @returns - rotated array\n */\nexport function xRotate(array, shift) {\n shift = shift % array.length;\n if (shift < 0)\n shift += array.length;\n const result = new Float64Array(array.length);\n result.set(array.slice(array.length - shift));\n result.set(array.slice(0, array.length - shift), shift);\n return result;\n}\n//# sourceMappingURL=xRotate.js.map","import { createNumberArray } from \"../utils/index.js\";\nexport function xSequentialFillFromStep(parameters, options = {}) {\n const { from, step, size } = parameters;\n const { ArrayConstructor = Float64Array } = options;\n const result = createNumberArray(ArrayConstructor, size);\n for (let i = 0; i < size; i++) {\n result[i] = from + i * step;\n }\n return result;\n}\n//# sourceMappingURL=xSequentialFillFromStep.js.map","import { xSequentialFillFromStep } from \"./xSequentialFillFromStep.js\";\nexport function xSequentialFillFromTo(parameters, options = {}) {\n const { from, to, size } = parameters;\n const step = size <= 1 ? 0 : (to - from) / (size - 1);\n return xSequentialFillFromStep({ from, step, size }, options);\n}\n//# sourceMappingURL=xSequentialFillFromTo.js.map","import { createNumberArray } from \"../utils/index.js\";\n/**\n * Create a new matrix based on the size of the current one or by using specific dimensions.\n * @param options - options.\n */\nexport function matrixCreateEmpty(options) {\n const { matrix, nbRows = matrix?.length || 1, nbColumns = matrix?.[0].length || 1, ArrayConstructor = Float64Array, } = options;\n const newMatrix = [];\n for (let row = 0; row < nbRows; row++) {\n newMatrix.push(createNumberArray(ArrayConstructor, nbColumns));\n }\n return newMatrix;\n}\n//# sourceMappingURL=matrixCreateEmpty.js.map","export function matrixCheck(data) {\n if (data.length === 0 || data[0].length === 0) {\n throw new RangeError('matrix must contain data');\n }\n const firstLength = data[0].length;\n for (let i = 1; i < data.length; i++) {\n if (data[i].length !== firstLength) {\n throw new RangeError('all rows must has the same length');\n }\n }\n}\n//# sourceMappingURL=matrixCheck.js.map","/*\nThe MIT License (MIT)\n\nCopyright (c) 2013 Eric Arnebäck\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n*/\n/**\n * Solves a system of linear equations using the Cholesky decomposition method.\n * It is a direct conversion to TS from {@link https://github.com/scijs/cholesky-solve}\n * @param nonZerosArray - the matrix in triplet form (array of arrays), where each sub-array contains three elements: row index, column index, and value.\n * @param dimension - the order of the matrix (number of rows/columns).\n * @param permutationEncoded - optional permutation array. If provided, it will be used to permute the matrix.\n * @returns a function that takes a right-hand side vector `b` and returns the solution vector `x`, or `null` if the decomposition fails.\n */\nexport function matrixCholeskySolver(nonZerosArray, dimension, permutationEncoded) {\n if (permutationEncoded) {\n const pinv = new Int32Array(dimension);\n for (let k = 0; k < dimension; k++) {\n pinv[permutationEncoded[k]] = k;\n }\n // Build a permuted copy (kept as plain arrays for compatibility)\n const mt = new Array(nonZerosArray.length);\n for (let a = 0; a < nonZerosArray.length; ++a) {\n const [r, c, value] = nonZerosArray[a];\n const ar = pinv[r];\n const ac = pinv[c];\n mt[a] = ac < ar ? [ac, ar, value] : [ar, ac, value];\n }\n nonZerosArray = mt;\n }\n else {\n // use a typed identity permutation\n const p = new Int32Array(dimension);\n for (let i = 0; i < dimension; ++i) {\n p[i] = i;\n }\n permutationEncoded = p;\n }\n const nnz = nonZerosArray.length;\n const ap = new Int32Array(dimension + 1);\n const ai = new Int32Array(nnz);\n const ax = new Float64Array(nnz);\n const lnz = new Int32Array(dimension);\n for (let idx = 0; idx < nnz; idx++) {\n const col = nonZerosArray[idx][1];\n lnz[col]++;\n }\n ap[0] = 0;\n for (let i = 0; i < dimension; ++i) {\n ap[i + 1] = ap[i] + lnz[i];\n }\n const colOffset = new Int32Array(dimension);\n for (let idx = 0; idx < nnz; idx++) {\n const e = nonZerosArray[idx];\n const col = e[1];\n const adr = ap[col] + colOffset[col];\n ai[adr] = e[0];\n ax[adr] = e[2];\n colOffset[col]++;\n }\n const d = new Float64Array(dimension);\n const y = new Float64Array(dimension);\n const lp = new Int32Array(dimension + 1);\n const parent = new Int32Array(dimension);\n const lnzArray = new Int32Array(dimension);\n const flag = new Int32Array(dimension);\n const pattern = new Int32Array(dimension);\n const bp1 = new Float64Array(dimension);\n const x = new Float64Array(dimension);\n ldlSymbolic(dimension, ap, ai, lp, parent, lnzArray, flag);\n const nz = lp[dimension];\n const lx = new Float64Array(nz);\n const li = new Int32Array(nz);\n const result = ldlNumeric(dimension, ap, ai, ax, lp, parent, lnzArray, li, lx, d, y, pattern, flag);\n if (result === dimension) {\n return (b) => {\n ldlPerm(dimension, bp1, b, permutationEncoded);\n ldlLsolve(dimension, bp1, lp, li, lx);\n ldlDsolve(dimension, bp1, d);\n ldlLTsolve(dimension, bp1, lp, li, lx);\n ldlPermt(dimension, x, bp1, permutationEncoded);\n return x;\n };\n }\n else {\n return null;\n }\n}\nfunction ldlSymbolic(dimension, ap, ai, lp, parent, lnz, flag) {\n const apLoc = ap;\n const aiLoc = ai;\n const parentLoc = parent;\n const lnzLoc = lnz;\n const flagLoc = flag;\n for (let k = 0; k < dimension; k++) {\n parentLoc[k] = -1;\n flagLoc[k] = k;\n lnzLoc[k] = 0;\n const kk = k;\n const p2 = apLoc[kk + 1];\n for (let p = apLoc[kk]; p < p2; p++) {\n let i = aiLoc[p];\n if (i < k) {\n for (; flagLoc[i] !== k; i = parentLoc[i]) {\n if (parentLoc[i] === -1)\n parentLoc[i] = k;\n lnzLoc[i]++;\n flagLoc[i] = k;\n }\n }\n }\n }\n lp[0] = 0;\n for (let k = 0; k < dimension; k++) {\n lp[k + 1] = lp[k] + lnz[k];\n }\n}\nfunction ldlNumeric(dimension, ap, ai, ax, lp, parent, lnz, li, lx, d, y, pattern, flag) {\n const apLoc = ap;\n const aiLoc = ai;\n const axLoc = ax;\n const lpLoc = lp;\n const parentLoc = parent;\n const lnzLoc = lnz;\n const liLoc = li;\n const lxLoc = lx;\n const dLoc = d;\n const yLoc = y;\n const patternLoc = pattern;\n const flagLoc = flag;\n let yi, lKi;\n for (let k = 0; k < dimension; k++) {\n yLoc[k] = 0;\n let top = dimension;\n flagLoc[k] = k;\n lnzLoc[k] = 0;\n const kk = k;\n const p2col = apLoc[kk + 1];\n for (let p = apLoc[kk]; p < p2col; p++) {\n let i = aiLoc[p];\n if (i <= k) {\n yLoc[i] += axLoc[p];\n let len = 0;\n for (; flagLoc[i] !== k; i = parentLoc[i]) {\n patternLoc[len++] = i;\n flagLoc[i] = k;\n }\n while (len > 0)\n patternLoc[--top] = patternLoc[--len];\n }\n }\n dLoc[k] = yLoc[k];\n yLoc[k] = 0;\n for (; top < dimension; top++) {\n const i = patternLoc[top];\n yi = yLoc[i];\n yLoc[i] = 0;\n const p2 = lpLoc[i] + lnzLoc[i];\n let p;\n for (p = lpLoc[i]; p < p2; p++) {\n yLoc[liLoc[p]] -= lxLoc[p] * yi;\n }\n lKi = yi / dLoc[i];\n dLoc[k] -= lKi * yi;\n liLoc[p] = k;\n lxLoc[p] = lKi;\n lnzLoc[i]++;\n }\n if (dLoc[k] === 0)\n return k;\n }\n return dimension;\n}\nfunction ldlLsolve(dimension, x, lp, li, lx) {\n const lpLoc = lp;\n const liLoc = li;\n const lxLoc = lx;\n const xLoc = x;\n for (let j = 0; j < dimension; j++) {\n const p2 = lpLoc[j + 1];\n for (let p = lpLoc[j]; p < p2; p++) {\n xLoc[liLoc[p]] -= lxLoc[p] * xLoc[j];\n }\n }\n}\nfunction ldlDsolve(dimension, x, d) {\n const xLoc = x;\n const dLoc = d;\n for (let j = 0; j < dimension; j++) {\n xLoc[j] /= dLoc[j];\n }\n}\nfunction ldlLTsolve(dimension, x, lp, li, lx) {\n const lpLoc = lp;\n const liLoc = li;\n const lxLoc = lx;\n const xLoc = x;\n for (let j = dimension - 1; j >= 0; j--) {\n const p2 = lpLoc[j + 1];\n for (let p = lpLoc[j]; p < p2; p++) {\n xLoc[j] -= lxLoc[p] * xLoc[liLoc[p]];\n }\n }\n}\nfunction ldlPerm(dimension, x, b, permutationEncoded) {\n const perm = permutationEncoded;\n const xLoc = x;\n const bLoc = b;\n for (let j = 0; j < dimension; j++) {\n xLoc[j] = bLoc[perm[j]];\n }\n}\nfunction ldlPermt(dimension, x, b, permutationEncoded) {\n const perm = permutationEncoded;\n const xLoc = x;\n const bLoc = b;\n for (let j = 0; j < dimension; j++) {\n xLoc[perm[j]] = bLoc[j];\n }\n}\n//# sourceMappingURL=matrixCholeskySolver.js.map","/**\n * The MIT License (MIT)\n *\n * Copyright (c) 2013 Mikola Lysenko\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to deal\n * in the Software without restriction, including without limitation the rights\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n * THE SOFTWARE.\n */\n/**\n * The reverse Cuthill-Mckee method is a fast and effective preconditioner for reducing the bandwidth of sparse linear systems.\n * When solving a positive semidefinite linear system using Cholesky factorization, it greatly reduces fill-in.\n * It is a direct conversion to TS from {@link github.com/mikolalysenko/cuthill-mckee}\n * @param list - lower triangular non zeros from a symmetric sparse matrix.\n * @param dimension - matrix dimension\n * @returns A Float64Array where the value at each index represents the new position of the node\n * in the bandwidth-reduced ordering.\n */\nexport function matrixCuthillMckee(list, dimension) {\n const adj = new Array(dimension);\n const visited = new Array(dimension).fill(false);\n for (let i = 0; i < dimension; ++i) {\n adj[i] = [];\n }\n for (const l of list) {\n adj[l[0]].push(l[1]);\n }\n const toVisit = new Float64Array(dimension);\n let eol = 0;\n let ptr = 0;\n for (let i = 0; i < dimension; ++i) {\n if (visited[i]) {\n continue;\n }\n toVisit[eol++] = i;\n visited[i] = true;\n while (ptr < eol) {\n const v = toVisit[ptr++];\n const nbhd = Float64Array.from(adj[v]);\n nbhd.sort();\n for (const u of nbhd) {\n if (visited[u]) {\n continue;\n }\n visited[u] = true;\n toVisit[eol++] = u;\n }\n }\n }\n const result = new Float64Array(dimension);\n for (let i = 0; i < dimension; ++i) {\n result[toVisit[i]] = i;\n }\n return result;\n}\n//# sourceMappingURL=matrixCuthillMckee.js.map","import { matrixCheck } from \"./matrixCheck.js\";\n/**\n * Get min and max Z.\n * @param matrix - matrix [rows][cols].\n */\nexport function matrixMinMaxZ(matrix) {\n matrixCheck(matrix);\n const nbRows = matrix.length;\n const nbColumns = matrix[0].length;\n let min = matrix[0][0];\n let max = matrix[0][0];\n for (let column = 0; column < nbColumns; column++) {\n for (let row = 0; row < nbRows; row++) {\n if (matrix[row][column] < min)\n min = matrix[row][column];\n if (matrix[row][column] > max)\n max = matrix[row][column];\n }\n }\n return { min, max };\n}\n//# sourceMappingURL=matrixMinMaxZ.js.map","/**\n * add the provided weights to a particular given system matrix (lD'D) in the triplet form and y data. This function is not general\n * it assumes that diagonal coefficients are in the even indexes, it is the case of the matrix generated by createSystemMatrix function.\n * It simulates the matrix operation W + lD'D and Wy.\n * @param leftHandSide - the original system matrix to be updated, a lower triangular non-zeros of the system matrix (lambda D'D).\n * @param rightHandSide - the original vector to be updated.\n * @param weights - the weights to apply to the system matrix and vector.\n * @returns an object that contains the new left and right hand-side of the system.\n */\nexport function addWeights(leftHandSide, rightHandSide, weights) {\n const nbPoints = rightHandSide.length;\n const l = nbPoints - 1;\n const newLeftHandSide = new Array(leftHandSide.length);\n const newRightHandSide = new Float64Array(nbPoints);\n for (let i = 0; i < l; i++) {\n const w = weights[i];\n const diag = i * 2;\n const next = diag + 1;\n newLeftHandSide[diag] = leftHandSide[diag].slice();\n newLeftHandSide[next] = leftHandSide[next].slice();\n newRightHandSide[i] = rightHandSide[i] * w;\n newLeftHandSide[diag][2] += w;\n }\n newRightHandSide[l] = rightHandSide[l] * weights[l];\n newLeftHandSide[l * 2] = leftHandSide[l * 2].slice();\n newLeftHandSide[l * 2][2] += weights[l];\n return {\n leftHandSide: newLeftHandSide,\n rightHandSide: newRightHandSide,\n };\n}\n//# sourceMappingURL=addWeights.js.map","import { matrixCuthillMckee } from \"../matrix/matrixCuthillMckee.js\";\n/**\n * Generates a lower triangular non-zeros of the first order smoother matrix (lambda D'D) where D is the derivate of the identity matrix\n * this function in combination with addWeights function can obtain (Q = W + lambda D'D) a penalized least square of Whittaker smoother,\n * it also generates a permutation encoded array.\n * @param dimension - the number of points in the matrix.\n * @param lambda - the factor of smoothness.\n * @returns an object containing the lower triangular non-zero elements of the matrix\n * and the permutation encoded array.\n * @property lowerTriangularNonZeros - The lower triangular non-zero elements of the matrix in triplet form.\n * @property permutationEncodedArray - The permutation encoded array generated using the Cuthill-McKee algorithm.\n */\nexport function createSystemMatrix(dimension, lambda) {\n const upperTriangularNonZeros = [];\n const last = dimension - 1;\n for (let i = 0; i < last; i++) {\n upperTriangularNonZeros.push([i, i, lambda * 2], [i, i + 1, -1 * lambda]);\n }\n upperTriangularNonZeros[0][2] = lambda;\n upperTriangularNonZeros.push([last, last, lambda]);\n const permutationEncodedArray = matrixCuthillMckee(upperTriangularNonZeros, dimension);\n return { upperTriangularNonZeros, permutationEncodedArray };\n}\n//# sourceMappingURL=createSystemMatrix.js.map","import { matrixCholeskySolver } from \"../matrix/index.js\";\nimport { addWeights } from \"../utils/addWeights.js\";\nimport { createSystemMatrix } from \"../utils/createSystemMatrix.js\";\nimport { calculateAdaptiveWeights } from \"../utils/index.js\";\nimport { xEnsureFloat64 } from \"./xEnsureFloat64.js\";\nimport { xMultiply } from \"./xMultiply.js\";\nexport function xWhittakerSmoother(yData, options = {}) {\n const { algorithm = 'thomas', ...restOptions } = options;\n if (algorithm === 'thomas') {\n return whittakerByThomas(yData, restOptions);\n }\n return whittakerByCholesky(yData, restOptions);\n}\n/**\n * @deprecated Use xWhittakerSmoother instead.\n * TODO: Remove in next major version.\n */\nexport const xWhitakerSmoother = xWhittakerSmoother;\n/**\n * Computes the baseline points for the given data using an iterative smoothing algorithm.\n * @param yData - the input data array.\n * @param options - the options for baseline computation.\n * @returns the computed baseline points.\n */\nfunction whittakerByCholesky(yData, options = {}) {\n const { lambda = 100, maxIterations = 100, tolerance = 1e-6, factorStd = 3, learningRate = 0.5, } = options;\n const size = yData.length;\n const { controlPoints, weights } = getWeightsAndControlPoints(yData, options);\n const prevBaseline = new Float64Array(size);\n let iteration = 0;\n let delta = Infinity;\n let baseline = xEnsureFloat64(yData);\n const { upperTriangularNonZeros, permutationEncodedArray } = createSystemMatrix(size, lambda);\n while (iteration < maxIterations && delta > tolerance) {\n const { leftHandSide, rightHandSide } = addWeights(upperTriangularNonZeros, yData, weights);\n const cho = matrixCholeskySolver(leftHandSide, size, permutationEncodedArray);\n if (!cho) {\n return baseline;\n }\n const newBaseline = cho(rightHandSide);\n //weights is updated inplace\n calculateAdaptiveWeights(yData, newBaseline, weights, {\n controlPoints,\n learningRate,\n factorStd,\n });\n delta = calculateDelta(newBaseline, prevBaseline, size);\n prevBaseline.set(newBaseline);\n baseline = xEnsureFloat64(newBaseline);\n iteration++;\n }\n return baseline;\n}\n/**\n * Calculates the delta between the current and previous baseline.\n * @param baseline - the current baseline array.\n * @param prevBaseline - the previous baseline array.\n * @param n - the length of the arrays.\n * @returns the calculated delta value.\n */\nfunction calculateDelta(baseline, prevBaseline, n) {\n let sum = 0;\n for (let i = 0; i < n; i++) {\n sum += (baseline[i] - prevBaseline[i]) ** 2;\n }\n return Math.sqrt(sum / n);\n}\n/**\n * Retrieves the control points and weights for the given data, the weights are modified multiplication of controlPoints if it exists.\n * @param y - the input data array.\n * @param options - the options for control points and weights.\n * @returns the control points and modified weights.\n */\nfunction getWeightsAndControlPoints(y, options = {}) {\n const { controlPoints } = options;\n const { weights = Float64Array.from({ length: y.length }).fill(1) } = options;\n if (controlPoints && controlPoints.length !== y.length) {\n throw new RangeError('controlPoints should match the length with X');\n }\n else if (weights.length !== y.length) {\n throw new RangeError('weights should match the length with X');\n }\n return {\n weights: controlPoints ? xMultiply(weights, controlPoints) : weights,\n controlPoints,\n };\n}\nfunction whittakerByThomas(yData, options = {}) {\n const { lambda = 100, maxIterations = 100, tolerance = 1e-6, factorStd = 3, learningRate = 0.5, } = options;\n const n = yData.length;\n const y = xEnsureFloat64(yData);\n const { controlPoints, weights } = getWeightsAndControlPoints(yData, options);\n const prevBaseline = new Float64Array(n);\n let baseline = xEnsureFloat64(yData);\n // Precompute base diagonal and constant off-diagonals (−lambda)\n const baseDiag = new Float64Array(n);\n if (n === 1) {\n baseDiag[0] = lambda;\n }\n else {\n baseDiag[0] = lambda;\n for (let i = 1; i < n - 1; i++)\n baseDiag[i] = 2 * lambda;\n baseDiag[n - 1] = lambda;\n }\n const lower = new Float64Array(Math.max(0, n - 1));\n const upper = new Float64Array(Math.max(0, n - 1));\n for (let i = 0; i < lower.length; i++) {\n lower[i] = -lambda;\n upper[i] = -lambda;\n }\n const main = new Float64Array(n);\n const rhs = new Float64Array(n);\n const solution = new Float64Array(n);\n const workC = new Float64Array(Math.max(0, n - 1));\n const workD = new Float64Array(n);\n let iteration = 0;\n let delta = Infinity;\n /**\n * Iterative Whittaker smoothing implementation using the Thomas algorithm\n * (tridiagonal solver). The method constructs the system\n * [W + lambda * D'D] z = W * y where D is the second-difference operator\n * and W are adaptive weights. At each iteration the tridiagonal system is\n * solved with the Thomas algorithm and the weights are updated using\n * `calculateAdaptiveWeights` until convergence.\n *\n * Use this algorithm by passing `algorithm: 'thomas'` to `xWhittakerSmoother`.\n * It is more memory-efficient for large inputs because it avoids forming\n * the full banded matrix, but both implementations aim to produce the same\n * baseline result.\n * @param yData - input signal/spectrum\n * @param options - smoothing options (supports `lambda`, `maxIterations`,\n * `tolerance`, `factorStd`, `learningRate`, and optional\n * `controlPoints` / `weights` inherited from\n * `CalculateAdaptiveWeightsOptions`).\n * @returns baseline as a `Float64Array`.\n */\n while (iteration < maxIterations && delta > tolerance) {\n // Build main diagonal = baseDiag + weights, and RHS = weights * y\n for (let i = 0; i < n; i++) {\n main[i] = baseDiag[i] + weights[i];\n rhs[i] = weights[i] * y[i];\n }\n // Solve tridiagonal system in-place into `solution`\n solveTridiagonalFloat64(lower, main, upper, rhs, solution, workC, workD);\n calculateAdaptiveWeights(y, solution, weights, {\n controlPoints,\n learningRate,\n factorStd,\n });\n delta = calculateDelta(solution, prevBaseline, n);\n prevBaseline.set(solution);\n baseline = xEnsureFloat64(solution);\n iteration++;\n }\n return baseline;\n}\nfunction solveTridiagonalFloat64(lower, diag, upper, rhs, out, cp, dp) {\n const n = diag.length;\n const x = out ?? new Float64Array(n);\n if (n === 0)\n return x;\n if (n === 1) {\n x[0] = rhs[0] / diag[0];\n return x;\n }\n const cprime = cp ?? new Float64Array(n - 1);\n const dprime = dp ?? new Float64Array(n);\n let denom = diag[0];\n cprime[0] = upper[0] / denom;\n dprime[0] = rhs[0] / denom;\n for (let i = 1; i < n - 1; i++) {\n denom = diag[i] - lower[i - 1] * cprime[i - 1];\n cprime[i] = upper[i] / denom;\n dprime[i] = (rhs[i] - lower[i - 1] * dprime[i - 1]) / denom;\n }\n denom = diag[n - 1] - lower[n - 2] * cprime[n - 2];\n dprime[n - 1] = (rhs[n - 1] - lower[n - 2] * dprime[n - 2]) / denom;\n x[n - 1] = dprime[n - 1];\n for (let i = n - 2; i >= 0; i--) {\n x[i] = dprime[i] - cprime[i] * x[i + 1];\n }\n return x;\n}\n//# sourceMappingURL=xWhittakerSmoother.js.map","/**\n * Phase correction filter.\n * @param data - complex spectrum\n * @param phi0 - angle in radians for zero order phase correction.\n * @param phi1 - angle in radians for first order phase correction.\n * @param options - options.\n * @returns a new object {re:[], im:[]} unless inPlace=true.\n */\nexport function reimPhaseCorrection(data, phi0 = 0, phi1 = 0, options = {}) {\n const { reverse = false, inPlace = false } = options;\n phi0 = Number.isFinite(phi0) ? phi0 : 0;\n phi1 = Number.isFinite(phi1) ? phi1 : 0;\n const length = data.re.length;\n // Decide target arrays\n const re = data.re;\n const im = data.im;\n const outRe = inPlace ? re : new Float64Array(length);\n const outIm = inPlace ? im : new Float64Array(length);\n let firstAngle = phi0;\n let delta = phi1 / length;\n if (reverse) {\n delta *= -1;\n firstAngle += phi1;\n }\n const alpha = 2 * Math.sin(delta / 2) ** 2;\n const beta = Math.sin(delta);\n let cosTheta = Math.cos(firstAngle);\n let sinTheta = Math.sin(firstAngle);\n for (let i = 0; i < length; i++) {\n const r = re[i];\n const ii = im[i];\n outRe[i] = r * cosTheta - ii * sinTheta;\n outIm[i] = ii * cosTheta + r * sinTheta;\n // Recursive angle update (stable incremental rotation)\n const newCosTheta = cosTheta - (alpha * cosTheta + beta * sinTheta);\n const newSinTheta = sinTheta - (alpha * sinTheta - beta * cosTheta);\n cosTheta = newCosTheta;\n sinTheta = newSinTheta;\n }\n return { re: outRe, im: outIm };\n}\n//# sourceMappingURL=reimPhaseCorrection.js.map","import { xNoiseSanPlot } from \"../x/index.js\";\nimport { reimAbsolute } from \"./reimAbsolute.js\";\nimport { reimPhaseCorrection } from \"./reimPhaseCorrection.js\";\n/**\n * Implementation of the algorithm for automatic phase correction: A robust, general automatic phase\n * correction algorithm for high-resolution NMR data. 10.1002/mrc.4586\n * @param data - complex spectrum\n * @param options - options\n * @returns phased spectrum together with the zero- and first-order correction angles (ph0, ph1) in degrees\n */\nexport function reimAutoPhaseCorrection(data, options = {}) {\n const { magnitudeMode = true, minRegSize = 30, factorNoise = 3, maxDistanceToJoin = 256, reverse = false, inPlace = false, } = options;\n const finalPeaks = detectBaselineRegions(data, {\n maxDistanceToJoin,\n magnitudeMode,\n factorNoise,\n });\n const { re, im } = data;\n const length = re.length;\n const indexMask = reverse ? (i) => length - 1 - i : (i) => i;\n let x0 = 0;\n let counter = -1;\n const res = [];\n while (counter < length) {\n const reTmp = [];\n const imTmp = [];\n while (!finalPeaks[indexMask(++counter)] && counter < length) {\n // Add some extra points(0.1 ppm) at rigth and left sides of the region.\n x0 = counter;\n }\n for (; finalPeaks[indexMask(counter)] && counter < length; counter += 2) {\n reTmp.push(re[indexMask(counter)]);\n imTmp.push(im[indexMask(counter)]);\n }\n if (reTmp.length > minRegSize) {\n res.push(autoPhaseRegion(reTmp, imTmp, x0));\n }\n }\n const { ph1, ph0 } = determiningGlobalValues(res.map((r) => r.x0 / length), res.map((r) => r.ph0), res.map((r) => r.area / 1e11));\n const phased = reimPhaseCorrection({ re, im }, toRadians(ph0), toRadians(ph1), { reverse, inPlace });\n return { data: phased, ph0, ph1 };\n}\nfunction determiningGlobalValues(x, ph0Values, weights) {\n if (x.length === 0) {\n return { ph0: 0, ph1: 0 };\n }\n else if (x.length === 1) {\n return { ph0: ph0Values[0], ph1: 0 };\n }\n const [ph1, ph0] = weightedLinearRegression(x, ph0Values, weights);\n let indexMax = -1;\n let maxDiff = Number.MIN_SAFE_INTEGER;\n for (let i = 0; i < x.length; i++) {\n const predictedPh0 = x[i] * ph1 + ph0;\n const diff = Math.abs(ph0Values[i] - predictedPh0);\n if (diff > 34 && maxDiff < diff) {\n indexMax = i;\n maxDiff = diff;\n }\n }\n if (indexMax > -1) {\n x.splice(indexMax, 1);\n ph0Values.splice(indexMax, 1);\n weights.splice(indexMax, 1);\n return determiningGlobalValues(x, ph0Values, weights);\n }\n return { ph0, ph1 };\n}\nfunction detectBaselineRegions(data, options) {\n const magnitudeData = options.magnitudeMode ? reimAbsolute(data) : data.re;\n const ds = holoborodko(magnitudeData);\n const peaksDs = robustBaseLineRegionsDetection(ds, options);\n const peaksSp = robustBaseLineRegionsDetection(magnitudeData, options);\n return peaksSp.map((sp, i) => sp && peaksDs[i]);\n}\n/**\n * Finds the best phase angle for a region by minimizing the negative area.\n * @param re - array of number.\n * @param im - array of number.\n * @param x0 - number.\n * @returns region.\n */\nfunction autoPhaseRegion(re, im, x0) {\n let start = -180;\n let stop = 180;\n const nSteps = 6;\n let maxSteps = 10;\n let bestAng = 0;\n let minArea = Number.MAX_SAFE_INTEGER;\n while (maxSteps > 0) {\n const dAng = (stop - start) / (nSteps + 1);\n for (let i = start; i <= stop; i += dAng) {\n const tmpPhased = reimPhaseCorrection({ re, im }, toRadians(i), 0);\n const negArea = getNegArea(tmpPhased.re);\n if (negArea < minArea) {\n [minArea, bestAng] = [negArea, i];\n }\n }\n start = bestAng - dAng;\n stop = bestAng + dAng;\n maxSteps--;\n }\n // Calculate the area for the best angle\n const phased = reimPhaseCorrection({ re, im }, toRadians(bestAng), 0);\n let area = 0;\n let sumX = 0;\n for (let j = 0; j < re.length; j++) {\n area += phased.re[j];\n sumX += phased.re[j] * (j + x0);\n }\n return { ph0: bestAng, area, x0: sumX / area };\n}\n/**\n * Applies the Holoborodko smoothing derivative filter.\n * @param s - array of float.\n * @returns array of float.\n */\nfunction holoborodko(s) {\n const dk = new Float64Array(s.length);\n for (let i = 5; i < s.length - 5; i++) {\n dk[i] =\n (42 * (s[i + 1] - s[i - 1]) +\n 48 * (s[i + 2] - s[i - 2]) +\n 27 * (s[i + 3] + s[i - 3]) +\n 8 * (s[i + 4] - s[i - 4]) +\n s[i + 5] -\n s[i - 5]) /\n 512;\n }\n //Fill the borders\n for (let i = 0; i < 5; i++) {\n dk[i] = dk[5];\n dk[s.length - i - 1] = dk[s.length - 6];\n }\n return dk;\n}\n/**\n * Detects baseline regions using robust iterative noise estimation.\n * @param s - input signal array.\n * @param options - detection options.\n * @param options.magnitudeMode - whether to use magnitude mode.\n * @param options.maxDistanceToJoin - max distance to join nearby regions.\n * @param options.factorNoise - noise factor for cutoff.\n */\nfunction robustBaseLineRegionsDetection(s, options) {\n const { maxDistanceToJoin, magnitudeMode, factorNoise } = options;\n const mask = new Uint8Array(s.length);\n for (let i = 0; i < s.length; i++) {\n mask[i] = 0;\n }\n let change = true;\n while (change) {\n const noiseLevel = xNoiseSanPlot(s, { magnitudeMode });\n const cutOff = factorNoise * noiseLevel.positive;\n change = false;\n for (let i = 0; i < s.length; i++) {\n if (Math.abs(s[i]) > cutOff && !mask[i]) {\n change = true;\n mask[i] = 1;\n }\n }\n }\n // Clean up mask by merging peaks blocks, separated by just a few points(4??).\n let count = 0;\n let prev = 0;\n for (let i = 0; i < s.length; i++) {\n if (!mask[i]) {\n count++;\n }\n else {\n if (count < maxDistanceToJoin) {\n for (let j = 0; j <= count; j++) {\n mask[prev + j] = 1;\n }\n }\n while (mask[++i] && i < s.length)\n ;\n prev = i;\n count = 0;\n }\n }\n return mask;\n}\n/**\n * Computes a weighted linear regression and returns [slope, intercept].\n * @param x - x values.\n * @param y - y values.\n * @param w - weights.\n */\nfunction weightedLinearRegression(x, y, w) {\n let sxtw = 0;\n let swx = 0;\n let sw = 0;\n let sxtwy = 0;\n let swy = 0;\n for (let i = 0; i < x.length; i++) {\n sxtw += x[i] * x[i] * w[i];\n swx += x[i] * w[i];\n sw += w[i];\n sxtwy += x[i] * w[i] * y[i];\n swy += w[i] * y[i];\n }\n /* Just to know what is the matrix system that we solve\n let Mx=[[sxtw, swx], [swx, sw]];\n let My=[[sxtwy], [swy]];\n */\n //Mx inverse\n const detMx = sxtw * sw - swx * swx;\n const inMx = [\n [sw / detMx, -swx / detMx],\n [-swx / detMx, sxtw / detMx],\n ];\n return [\n inMx[0][0] * sxtwy + inMx[0][1] * swy,\n inMx[1][0] * sxtwy + inMx[1][1] * swy,\n ];\n}\nfunction toRadians(degree) {\n return (degree * Math.PI) / 180;\n}\nfunction getNegArea(data) {\n let area = 0;\n for (let i = 0; i < data.length; i++) {\n const element = data[i];\n if (element < 0)\n area -= element;\n }\n return area;\n}\n//# sourceMappingURL=reimAutoPhaseCorrection.js.map","import { xRotate } from \"../x/index.js\";\n/**\n * Circular shift that moves the zero-frequency component to the center of the array (FFT shift).\n * For the inverse, the shift is reversed to restore the original layout.\n * @param data - input array\n * @param inverse - if true, shifts by ceil(n/2) instead of floor(n/2) to undo a previous zeroShift\n * @returns shifted array\n */\nexport function zeroShift(data, inverse) {\n const middle = inverse\n ? Math.ceil(data.length / 2)\n : Math.floor(data.length / 2);\n return xRotate(data, middle);\n}\n//# sourceMappingURL=zeroShift.js.map","import { getFFT } from \"../utils/fftCache.js\";\nimport { zeroShift } from \"./zeroShift.js\";\n/**\n * Computes the FFT of a complex spectrum.\n * @param data - complex spectrum.\n * @param options - options.\n * @returns FFT of complex spectrum.\n */\nexport function reimFFT(data, options = {}) {\n const { inverse = false, applyZeroShift = false, inPlace = false } = options;\n const { re, im } = data;\n const size = re.length;\n const csize = size << 1;\n let complexArray = new Float64Array(csize);\n for (let i = 0; i < csize; i += 2) {\n complexArray[i] = re[i >>> 1];\n complexArray[i + 1] = im[i >>> 1];\n }\n const fft = getFFT(size);\n let output = new Float64Array(csize);\n if (inverse) {\n if (applyZeroShift)\n complexArray = zeroShift(complexArray, true);\n fft.inverseTransform(output, complexArray);\n }\n else {\n fft.transform(output, complexArray);\n if (applyZeroShift)\n output = zeroShift(output);\n }\n if (inPlace) {\n for (let i = 0; i < csize; i += 2) {\n re[i >>> 1] = output[i];\n im[i >>> 1] = output[i + 1];\n }\n return data;\n }\n const newRe = new Float64Array(size);\n const newIm = new Float64Array(size);\n for (let i = 0; i < csize; i += 2) {\n newRe[i >>> 1] = output[i];\n newIm[i >>> 1] = output[i + 1];\n }\n return { re: newRe, im: newIm };\n}\n//# sourceMappingURL=reimFFT.js.map","import { createDoubleArray } from \"../utils/createArray.js\";\n/**\n * This function make a zero filling to re and im part.\n * @param data - object of kind {re:[], im:[]}\n * @param totalLength - final number of points\n * @returns new DataReIm object with zero-filled,\n * truncated arrays if totalLength is smaller current length or\n * the same input if totalLength is equal that current length\n */\nexport function reimZeroFilling(data, totalLength) {\n if (!Number.isInteger(totalLength) || totalLength < 0) {\n throw new RangeError('totalLength must be a non-negative integer');\n }\n const { re, im } = data;\n const length = re.length;\n if (totalLength === 0 || length === totalLength)\n return data;\n if (length > totalLength) {\n return {\n re: re.slice(0, totalLength),\n im: im.slice(0, totalLength),\n };\n }\n const newRE = createDoubleArray(re.constructor, totalLength);\n const newIM = createDoubleArray(im.constructor, totalLength);\n for (let i = 0; i < re.length; i++) {\n newRE[i] = re[i];\n newIM[i] = im[i];\n }\n return {\n re: newRE,\n im: newIM,\n };\n}\n//# sourceMappingURL=reimZeroFilling.js.map","import { zeroShift } from \"../reim/zeroShift.js\";\nimport { getFFT } from \"../utils/fftCache.js\";\n/**\n * Apply FFT to each row of a complex matrix, reusing a single FFT instance for\n * all rows to avoid repeated instantiation overhead.\n * All rows must have the same length.\n * @param data - complex matrix with re and im arrays of Float64Array rows\n * @param options - options\n * @returns complex matrix with transformed rows\n */\nexport function reimMatrixFFT(data, options = {}) {\n const { re, im } = data;\n const numRows = re.length;\n if (numRows === 0)\n return { re: [], im: [] };\n const { inverse = false, applyZeroShift = false, inPlace = false } = options;\n const size = re[0].length;\n const csize = size << 1;\n for (let j = 0; j < numRows; j++) {\n if (re[j].length !== size || im[j].length !== size) {\n throw new RangeError(`All rows must have the same length. Expected ${size} but row ${j} has length ${re[j].length}.`);\n }\n }\n // Single FFT instance and working buffers reused across all rows\n const fft = getFFT(size);\n const complexArray = new Float64Array(csize);\n const output = new Float64Array(csize);\n const resultRe = new Array(numRows);\n const resultIm = new Array(numRows);\n for (let j = 0; j < numRows; j++) {\n const reRow = re[j];\n const imRow = im[j];\n for (let i = 0; i < csize; i += 2) {\n complexArray[i] = reRow[i >>> 1];\n complexArray[i + 1] = imRow[i >>> 1];\n }\n const outRe = inPlace ? reRow : new Float64Array(size);\n const outIm = inPlace ? imRow : new Float64Array(size);\n if (inverse) {\n const input = applyZeroShift\n ? zeroShift(complexArray, true)\n : complexArray;\n fft.inverseTransform(output, input);\n for (let i = 0; i < csize; i += 2) {\n outRe[i >>> 1] = output[i];\n outIm[i >>> 1] = output[i + 1];\n }\n }\n else {\n fft.transform(output, complexArray);\n const source = applyZeroShift ? zeroShift(output) : output;\n for (let i = 0; i < csize; i += 2) {\n outRe[i >>> 1] = source[i];\n outIm[i >>> 1] = source[i + 1];\n }\n }\n resultRe[j] = outRe;\n resultIm[j] = outIm;\n }\n return { re: resultRe, im: resultIm };\n}\n//# sourceMappingURL=reimMatrixFFT.js.map","import { zeroShift } from \"../reim/zeroShift.js\";\nimport { getFFT } from \"../utils/fftCache.js\";\n/**\n * Apply FFT to each column of a complex matrix, reusing a single FFT instance for\n * all columns to avoid repeated instantiation overhead.\n * All columns must have the same length (all rows must have the same number of columns).\n * @param data - complex matrix with re and im arrays of Float64Array rows\n * @param options - options\n * @returns complex matrix with transformed columns\n */\nexport function reimMatrixFFTByColumns(data, options = {}) {\n const { re, im } = data;\n const numRows = re.length;\n if (numRows === 0)\n return { re: [], im: [] };\n const { inverse = false, applyZeroShift = false, inPlace = false } = options;\n const numColumns = re[0].length;\n const csize = numRows << 1;\n // Validate all rows have the same number of columns\n for (let j = 0; j < numRows; j++) {\n if (re[j].length !== numColumns || im[j].length !== numColumns) {\n throw new RangeError(`All rows must have the same length. Expected ${numColumns} but row ${j} has length ${re[j].length}.`);\n }\n }\n if (numColumns === 0) {\n return { re: new Array(numRows), im: new Array(numRows) };\n }\n // Single FFT instance and working buffers reused across all columns\n const fft = getFFT(numRows);\n const complexArray = new Float64Array(csize);\n const output = new Float64Array(csize);\n const resultRe = new Array(numRows);\n const resultIm = new Array(numRows);\n // Initialize result arrays\n for (let i = 0; i < numRows; i++) {\n resultRe[i] = inPlace ? re[i] : new Float64Array(numColumns);\n resultIm[i] = inPlace ? im[i] : new Float64Array(numColumns);\n }\n // Process each column\n for (let col = 0; col < numColumns; col++) {\n // Extract column data into complex array\n for (let row = 0; row < numRows; row++) {\n complexArray[row << 1] = re[row][col];\n complexArray[(row << 1) + 1] = im[row][col];\n }\n const colRe = new Float64Array(numRows);\n const colIm = new Float64Array(numRows);\n if (inverse) {\n const input = applyZeroShift\n ? zeroShift(complexArray, true)\n : complexArray;\n fft.inverseTransform(output, input);\n for (let i = 0; i < csize; i += 2) {\n colRe[i >>> 1] = output[i];\n colIm[i >>> 1] = output[i + 1];\n }\n }\n else {\n fft.transform(output, complexArray);\n const source = applyZeroShift ? zeroShift(output) : output;\n for (let i = 0; i < csize; i += 2) {\n colRe[i >>> 1] = source[i];\n colIm[i >>> 1] = source[i + 1];\n }\n }\n // Store transformed column back into result matrix\n for (let row = 0; row < numRows; row++) {\n resultRe[row][col] = colRe[row];\n resultIm[row][col] = colIm[row];\n }\n }\n return { re: resultRe, im: resultIm };\n}\n//# sourceMappingURL=reimMatrixFFTByColumns.js.map","/**\n * Apply phase correction to a complex matrix along rows or columns.\n * All rows must have the same length.\n * @param data - complex matrix with re and im arrays of Float64Array rows\n * @param phi0 - angle in radians for zero order phase correction.\n * @param phi1 - angle in radians for first order phase correction.\n * @param options - options including direction ('rows' or 'columns', default 'rows').\n * @returns complex matrix with corrected rows or columns.\n */\nexport function reimMatrixPhaseCorrection(data, phi0 = 0, phi1 = 0, options = {}) {\n const { reverse = false, inPlace = false, direction = 'rows' } = options;\n phi0 = Number.isFinite(phi0) ? phi0 : 0;\n phi1 = Number.isFinite(phi1) ? phi1 : 0;\n const { re, im } = data;\n const numRows = re.length;\n if (numRows === 0) {\n return {\n re: [],\n im: [],\n minRe: Number.NaN,\n maxRe: Number.NaN,\n minIm: Number.NaN,\n maxIm: Number.NaN,\n };\n }\n const numColumns = re[0].length;\n for (let j = 0; j < numRows; j++) {\n if (re[j].length !== numColumns || im[j].length !== numColumns) {\n throw new RangeError(`All rows must have the same length. Expected ${numColumns} but row ${j} has length ${re[j].length}.`);\n }\n }\n const resultRe = new Array(numRows);\n const resultIm = new Array(numRows);\n // Initialize result arrays\n for (let i = 0; i < numRows; i++) {\n resultRe[i] = inPlace ? re[i] : new Float64Array(numColumns);\n resultIm[i] = inPlace ? im[i] : new Float64Array(numColumns);\n }\n const size = direction === 'rows' ? numColumns : numRows;\n let delta = phi1 / (size - 1);\n if (reverse) {\n delta = -delta;\n }\n const alpha = 2 * Math.sin(delta / 2) ** 2;\n const beta = Math.sin(delta);\n let minRe = Number.POSITIVE_INFINITY;\n let maxRe = Number.NEGATIVE_INFINITY;\n let minIm = Number.POSITIVE_INFINITY;\n let maxIm = Number.NEGATIVE_INFINITY;\n if (direction === 'rows') {\n for (let j = 0; j < numRows; j++) {\n let firstAngle = phi0;\n if (reverse) {\n firstAngle += phi1;\n }\n let cosTheta = Math.cos(firstAngle);\n let sinTheta = Math.sin(firstAngle);\n for (let i = 0; i < numColumns; i++) {\n const r = re[j][i];\n const ii = im[j][i];\n resultRe[j][i] = r * cosTheta - ii * sinTheta;\n resultIm[j][i] = ii * cosTheta + r * sinTheta;\n if (resultRe[j][i] < minRe)\n minRe = resultRe[j][i];\n if (resultRe[j][i] > maxRe)\n maxRe = resultRe[j][i];\n if (resultIm[j][i] < minIm)\n minIm = resultIm[j][i];\n if (resultIm[j][i] > maxIm)\n maxIm = resultIm[j][i];\n const newCosTheta = cosTheta - (alpha * cosTheta + beta * sinTheta);\n const newSinTheta = sinTheta - (alpha * sinTheta - beta * cosTheta);\n cosTheta = newCosTheta;\n sinTheta = newSinTheta;\n }\n }\n }\n else {\n for (let col = 0; col < numColumns; col++) {\n let firstAngle = phi0;\n if (reverse) {\n firstAngle += phi1;\n }\n let cosTheta = Math.cos(firstAngle);\n let sinTheta = Math.sin(firstAngle);\n for (let row = 0; row < numRows; row++) {\n const r = re[row][col];\n const ii = im[row][col];\n resultRe[row][col] = r * cosTheta - ii * sinTheta;\n resultIm[row][col] = ii * cosTheta + r * sinTheta;\n if (resultRe[row][col] < minRe)\n minRe = resultRe[row][col];\n if (resultRe[row][col] > maxRe)\n maxRe = resultRe[row][col];\n if (resultIm[row][col] < minIm)\n minIm = resultIm[row][col];\n if (resultIm[row][col] > maxIm)\n maxIm = resultIm[row][col];\n const newCosTheta = cosTheta - (alpha * cosTheta + beta * sinTheta);\n const newSinTheta = sinTheta - (alpha * sinTheta - beta * cosTheta);\n cosTheta = newCosTheta;\n sinTheta = newSinTheta;\n }\n }\n }\n return { re: resultRe, im: resultIm, minRe, maxRe, minIm, maxIm };\n}\n//# sourceMappingURL=reimMatrixPhaseCorrection.js.map","import { isAnyArray } from 'is-any-array';\n/**\n * Verify that `data` is an object of x,y arrays.\n * Throws an error if it's not.\n * @param data - data to check.\n * @param options - options.\n * @param options.minLength - minimum required length.\n */\nexport function xyCheck(data, options = {}) {\n const { minLength } = options;\n if (data === null ||\n typeof data !== 'object' ||\n // @ts-expect-error Typechecking\n !isAnyArray(data.x) ||\n // @ts-expect-error Typechecking\n !isAnyArray(data.y)) {\n throw new Error('data must be an object of x and y arrays');\n }\n // @ts-expect-error Typechecking\n if (data.x.length !== data.y.length) {\n throw new Error('the x and y arrays must have the same length');\n }\n // @ts-expect-error Typechecking\n if (minLength && data.x.length < minLength) {\n throw new Error(`data.x must have a length of at least ${minLength}`);\n }\n}\n//# sourceMappingURL=xyCheck.js.map","import { xGetFromToIndex } from \"../x/index.js\";\nimport { xyCheck } from \"./xyCheck.js\";\n/**\n * Finds the max y value in a range and return a {x,y} point\n * @param data - object that contains property x (an ordered increasing array) and y (an array).\n * @param options - options.\n * @returns point with the highest y value in the range.\n */\nexport function xyMaxYPoint(data, options = {}) {\n xyCheck(data, { minLength: 1 });\n const { x, y } = data;\n if (x.length === 1) {\n return { x: x[0], y: y[0], index: 0 };\n }\n const { fromIndex, toIndex } = xGetFromToIndex(x, options);\n let current = { x: x[fromIndex], y: y[fromIndex], index: fromIndex };\n for (let i = fromIndex + 1; i <= toIndex; i++) {\n if (y[i] > current.y)\n current = { x: x[i], y: y[i], index: i };\n }\n return current;\n}\n//# sourceMappingURL=xyMaxYPoint.js.map","import { xIsMonotonic } from \"../x/index.js\";\nimport { xyCheck } from \"./xyCheck.js\";\n/**\n * Filters x,y values to allow strictly growing values in x-axis.\n * @param data - object that contains property x (an ordered increasing array) and y (an array).\n * @returns filtered data with strictly increasing x values.\n */\nexport function xyEnsureGrowingX(data) {\n xyCheck(data);\n if (xIsMonotonic(data.x) === 1)\n return data;\n const x = Array.from(data.x);\n const y = Array.from(data.y);\n let prevX = Number.NEGATIVE_INFINITY;\n let currentIndex = 0;\n for (let index = 0; index < x.length; index++) {\n if (prevX < x[index]) {\n if (currentIndex < index) {\n x[currentIndex] = x[index];\n y[currentIndex] = y[index];\n }\n currentIndex++;\n prevX = x[index];\n }\n }\n x.length = currentIndex;\n y.length = currentIndex;\n return { x, y };\n}\n//# sourceMappingURL=xyEnsureGrowingX.js.map","/**\n * Normalize an array of zones:\n * - ensure than from < to\n * - merge overlapping zones\n * - deal with exclusions zones\n * - if no zones is specified add one between -Infinity and +Infinity\n * @param zones - array of zones\n * @param options - options\n * @returns array of zones\n */\nexport function zonesNormalize(zones = [], options = {}) {\n const { exclusions = [] } = options;\n let { from = Number.NEGATIVE_INFINITY, to = Number.POSITIVE_INFINITY } = options;\n if (from > to)\n [from, to] = [to, from];\n zones = zones.map((zone) => zone.from > zone.to ? { from: zone.to, to: zone.from } : { ...zone });\n zones.sort((a, b) => {\n if (a.from !== b.from)\n return a.from - b.from;\n return a.to - b.to;\n });\n if (zones.length === 0) {\n zones.push({ from, to });\n }\n for (const zone of zones) {\n if (from > zone.from)\n zone.from = from;\n if (to < zone.to)\n zone.to = to;\n }\n zones = zones.filter((zone) => zone.from <= zone.to);\n if (zones.length === 0)\n return [];\n let currentZone = zones[0];\n const beforeExclusionsZones = [currentZone];\n for (let i = 1; i < zones.length; i++) {\n const zone = zones[i];\n if (zone.from <= currentZone.to) {\n if (currentZone.to < zone.to) {\n currentZone.to = zone.to;\n }\n }\n else {\n currentZone = zone;\n beforeExclusionsZones.push(currentZone);\n }\n }\n if (exclusions.length === 0)\n return beforeExclusionsZones;\n const normalizedExclusions = zonesNormalize(exclusions);\n let currentExclusionIndex = 0;\n const results = [];\n for (let zoneIndex = 0; zoneIndex < beforeExclusionsZones.length; zoneIndex++) {\n const zone = beforeExclusionsZones[zoneIndex];\n if (currentExclusionIndex === normalizedExclusions.length) {\n // we analysed all the exclusion zones\n results.push(zone);\n continue;\n }\n while (currentExclusionIndex < normalizedExclusions.length &&\n normalizedExclusions[currentExclusionIndex].to <= zone.from) {\n currentExclusionIndex++;\n }\n if (currentExclusionIndex === normalizedExclusions.length) {\n // we analysed all the exclusion zones\n results.push(zone);\n continue;\n }\n if (zone.to < normalizedExclusions[currentExclusionIndex].from) {\n // no problems, not yet in exclusion\n results.push(zone);\n continue;\n }\n if (normalizedExclusions[currentExclusionIndex].to >= zone.to) {\n // could be totally excluded\n if (normalizedExclusions[currentExclusionIndex].from <= zone.from) {\n continue;\n }\n results.push({\n from: normalizedExclusions[currentExclusionIndex].to,\n to: zone.to,\n });\n }\n // we cut in the middle, we need to create more zones, annoying !\n if (normalizedExclusions[currentExclusionIndex].from > zone.from) {\n results.push({\n from: zone.from,\n to: normalizedExclusions[currentExclusionIndex].from,\n });\n }\n zone.from = normalizedExclusions[currentExclusionIndex].to;\n zoneIndex--;\n }\n return results;\n}\n//# sourceMappingURL=zonesNormalize.js.map","import { zonesNormalize } from \"./zonesNormalize.js\";\n/**\n * Add the number of points per zone to reach a specified total\n * @param zones - array of zones\n * @param numberOfPoints - total number of points to distribute between zones\n * @param options - options\n * @returns array of zones with points\n */\nexport function zonesWithPoints(zones = [], \n/**\n * total number of points to distribute between zones\n * @default 10\n */\nnumberOfPoints = 10, options = {}) {\n if (zones.length === 0)\n return [];\n const normalizedZones = zonesNormalize(zones, options);\n const zonesWithNumberOfPoints = [];\n const totalSize = normalizedZones.reduce((previous, current) => {\n return previous + (current.to - current.from);\n }, 0);\n const unitsPerPoint = totalSize / numberOfPoints;\n let currentTotal = 0;\n for (let i = 0; i < normalizedZones.length - 1; i++) {\n const tempZone = normalizedZones[i];\n const tempZoneNumberOfPoints = Math.min(Math.round((tempZone.to - tempZone.from) / unitsPerPoint), numberOfPoints - currentTotal);\n zonesWithNumberOfPoints.push({\n ...tempZone,\n numberOfPoints: tempZoneNumberOfPoints,\n });\n currentTotal += tempZoneNumberOfPoints;\n }\n zonesWithNumberOfPoints.push({\n ...normalizedZones.at(-1),\n numberOfPoints: numberOfPoints - currentTotal,\n });\n return zonesWithNumberOfPoints;\n}\n//# sourceMappingURL=zonesWithPoints.js.map","/**\n * Function that retrieves the getEquallySpacedData with the variant \"slot\".\n * @param x - x coordinates.\n * @param y - y coordinates.\n * @param from - start of the output x range.\n * @param to - end of the output x range.\n * @param numberOfPoints - number of output points.\n * @returns array of y values equally spaced with the variant \"slot\".\n */\nexport default function equallySpacedSlot(\n/** x coordinates */\nx, \n/** y coordinates */\ny, \n/** from value */\nfrom, \n/** to value */\nto, \n/** number of points */\nnumberOfPoints) {\n const xLength = x.length;\n if (xLength < 2) {\n return Float64Array.from(x);\n }\n const step = (to - from) / (numberOfPoints > 1 ? numberOfPoints - 1 : 1);\n const halfStep = step / 2;\n const lastStep = x.at(-1) - x.at(-2);\n const start = from - halfStep;\n // Changed Array to Float64Array\n const output = new Float64Array(numberOfPoints);\n // Init main variables\n let min = start;\n let max = start + step;\n let previousX = -Number.MAX_VALUE;\n let previousY = 0;\n let nextX = x[0];\n let nextY = y[0];\n let frontOutsideSpectra = 0;\n let backOutsideSpectra = true;\n let currentValue = 0;\n // for slot algorithm\n let currentPoints = 0;\n let i = 1; // index of input\n let j = 0; // index of output\n main: while (true) {\n if (previousX >= nextX)\n throw new Error('x must be a growing series');\n while (previousX - max > 0) {\n // no overlap with original point, just consume current value\n if (backOutsideSpectra) {\n currentPoints++;\n backOutsideSpectra = false;\n }\n output[j] = currentPoints <= 0 ? 0 : currentValue / currentPoints;\n j++;\n if (j === numberOfPoints) {\n break main;\n }\n min = max;\n max += step;\n currentValue = 0;\n currentPoints = 0;\n }\n if (previousX > min) {\n currentValue += previousY;\n currentPoints++;\n }\n if (previousX === -Number.MAX_VALUE || frontOutsideSpectra > 1) {\n currentPoints--;\n }\n previousX = nextX;\n previousY = nextY;\n if (i < xLength) {\n nextX = x[i];\n nextY = y[i];\n i++;\n }\n else {\n nextX += lastStep;\n nextY = 0;\n frontOutsideSpectra++;\n }\n }\n return output;\n}\n//# sourceMappingURL=equallySpacedSlot.js.map","/**\n * Function that calculates the integral of the line between two\n * x-coordinates, given the slope and intercept of the line.\n * @param x0 - first x coordinate.\n * @param x1 - second x coordinate.\n * @param slope - slope of the line.\n * @param intercept - y-intercept of the line.\n * @returns integral value.\n */\nexport default function integral(\n/** first coordinate of point */\nx0, \n/** second coordinate of point */\nx1, \n/** slope of the line */\nslope, \n/** intercept of the line on the y axis */\nintercept) {\n return (0.5 * slope * x1 * x1 +\n intercept * x1 -\n (0.5 * slope * x0 * x0 + intercept * x0));\n}\n//# sourceMappingURL=integral.js.map","import integral from \"./integral.js\";\n/**\n * Function that retrieves the getEquallySpacedData with the variant \"smooth\".\n * @param x - x coordinates.\n * @param y - y coordinates.\n * @param from - start of the output x range.\n * @param to - end of the output x range.\n * @param numberOfPoints - number of output points.\n * @returns array of y values equally spaced with the variant \"smooth\".\n */\nexport default function equallySpacedSmooth(\n/** x coordinates */\nx, \n/** y coordinates */\ny, \n/** from value */\nfrom, \n/** to value */\nto, \n/** number of points */\nnumberOfPoints) {\n const xLength = x.length;\n const step = (to - from) / (numberOfPoints > 1 ? numberOfPoints - 1 : 1);\n const halfStep = step / 2;\n // Changed Array to Float64Array\n const output = new Float64Array(numberOfPoints);\n const initialOriginalStep = x[1] - x[0];\n const lastOriginalStep = x[xLength - 1] - x[xLength - 2];\n // Init main variables\n let min = from - halfStep;\n let max = from + halfStep;\n let previousX = Number.MIN_SAFE_INTEGER;\n let previousY = 0;\n let nextX = x[0] - initialOriginalStep;\n let nextY = 0;\n let currentValue = 0;\n let slope = 0;\n let intercept = 0;\n let sumAtMin = 0;\n let sumAtMax = 0;\n let i = 0; // index of input\n let j = 0; // index of output\n let add = 0;\n main: while (true) {\n if (previousX >= nextX)\n throw new Error('x must be a growing series');\n if (previousX <= min && min <= nextX) {\n add = integral(0, min - previousX, slope, previousY);\n sumAtMin = currentValue + add;\n }\n while (nextX - max >= 0) {\n // no overlap with original point, just consume current value\n add = integral(0, max - previousX, slope, previousY);\n sumAtMax = currentValue + add;\n output[j++] = (sumAtMax - sumAtMin) / step;\n if (j === numberOfPoints) {\n break main;\n }\n min = max;\n max += step;\n sumAtMin = sumAtMax;\n }\n currentValue += integral(previousX, nextX, slope, intercept);\n previousX = nextX;\n previousY = nextY;\n if (i < xLength) {\n nextX = x[i];\n nextY = y[i];\n i++;\n }\n else if (i === xLength) {\n nextX += lastOriginalStep;\n nextY = 0;\n }\n slope = getSlope(previousX, previousY, nextX, nextY);\n intercept = -slope * previousX + previousY;\n }\n return output;\n}\nfunction getSlope(x0, y0, x1, y1) {\n return (y1 - y0) / (x1 - x0);\n}\n//# sourceMappingURL=equallySpacedSmooth.js.map","import { createFromToArray } from \"../utils/index.js\";\nimport { zonesNormalize, zonesWithPoints } from \"../zones/index.js\";\nimport equallySpacedSlot from \"./utils/equallySpacedSlot.js\";\nimport equallySpacedSmooth from \"./utils/equallySpacedSmooth.js\";\nimport { xyCheck } from \"./xyCheck.js\";\n/**\n * Resample a spectrum to equally spaced x points.\n * When `exclusions` are provided they take precedence and `zones` is ignored.\n * @param data - object containing x and y arrays\n * @param options - options\n * @returns resampled spectrum with equally spaced x values\n */\nexport function xyEquallySpaced(data, options = {}) {\n const { x, y } = data;\n const xLength = x.length;\n const { from = x[0], to = x[xLength - 1], variant = 'smooth', numberOfPoints = 100, exclusions = [], zones = [{ from, to }], } = options;\n if (from > to) {\n throw new RangeError('from should be larger than to');\n }\n xyCheck(data);\n if (numberOfPoints < 2) {\n throw new RangeError(\"'numberOfPoints' option must be greater than 1\");\n }\n const normalizedZones = zonesNormalize(zones, { from, to, exclusions });\n const zonesWithPointsRes = zonesWithPoints(normalizedZones, numberOfPoints, {\n from,\n to,\n }).filter((zone) => zone.numberOfPoints);\n let xResult = [];\n let yResult = [];\n for (const zone of zonesWithPointsRes) {\n if (!zone.numberOfPoints) {\n zone.numberOfPoints = 0;\n }\n const zoneResult = processZone(Array.from(x), Array.from(y), zone.from, zone.to, zone.numberOfPoints, variant);\n xResult = xResult.concat(zoneResult.x);\n yResult = yResult.concat(zoneResult.y);\n }\n return { x: xResult, y: yResult };\n}\nfunction processZone(x, y, from, to, numberOfPoints, variant) {\n if (numberOfPoints < 1) {\n throw new RangeError('the number of points must be at least 1');\n }\n const output = variant === 'slot'\n ? Array.from(equallySpacedSlot(x, y, from, to, numberOfPoints))\n : Array.from(equallySpacedSmooth(x, y, from, to, numberOfPoints));\n return {\n x: Array.from(createFromToArray({\n from,\n to,\n length: numberOfPoints,\n })),\n y: output,\n };\n}\n//# sourceMappingURL=xyEquallySpaced.js.map","import { zonesNormalize } from \"../zones/index.js\";\nimport { xyCheck } from \"./xyCheck.js\";\n/**\n * Extract zones from a XY data.\n * @param data - object that contains property x (an ordered increasing array) and y (an array).\n * @param options - options.\n * @returns extracted DataXY containing only the points within the specified zones.\n */\nexport function xyExtract(data, options = {}) {\n xyCheck(data);\n const { x, y } = data;\n let { zones } = options;\n zones = zonesNormalize(zones);\n if (x === undefined ||\n y === undefined ||\n !Array.isArray(zones) ||\n zones.length === 0) {\n return data;\n }\n const newX = [];\n const newY = [];\n let currentZone = zones[0];\n let position = 0;\n loop: for (let i = 0; i < x.length; i++) {\n while (currentZone.to < x[i]) {\n position++;\n currentZone = zones[position];\n if (!currentZone) {\n i = x.length;\n break loop;\n }\n }\n if (x[i] >= currentZone.from) {\n newX.push(x[i]);\n newY.push(y[i]);\n }\n }\n return { x: newX, y: newY };\n}\n//# sourceMappingURL=xyExtract.js.map","import { zonesNormalize } from \"../zones/index.js\";\n/**\n * Filter an array x/y based on various criteria x points are expected to be sorted\n * @param data - object containing 2 properties x and y\n * @param options - options\n * @returns filtered array\n */\nexport function xyFilterX(data, options = {}) {\n const { x, y } = data;\n if (x.length === 0) {\n return {\n x: Array.from(x),\n y: Array.from(y),\n };\n }\n const { from = x[0], to = x.at(-1), zones = [{ from, to }], exclusions = [], } = options;\n // @ts-expect-error -- x.at(-1) returns number | undefined but array is guaranteed non-empty here\n const normalizedZones = zonesNormalize(zones, { from, to, exclusions });\n let currentZoneIndex = 0;\n const newX = [];\n const newY = [];\n let position = 0;\n while (position < x.length) {\n if (x[position] <= normalizedZones[currentZoneIndex].to &&\n x[position] >= normalizedZones[currentZoneIndex].from) {\n newX.push(x[position]);\n newY.push(y[position]);\n }\n else if (x[position] > normalizedZones[currentZoneIndex].to) {\n currentZoneIndex++;\n if (!normalizedZones[currentZoneIndex])\n break;\n }\n position++;\n }\n return {\n x: newX,\n y: newY,\n };\n}\n//# sourceMappingURL=xyFilterX.js.map","/**\n * Order object of array, x has to be monotone. Ensure x is growing\n * @param data - object of kind {x:[], y:[]}.\n * @returns data with x in ascending order (reversed in-place if descending).\n */\nexport function xyGrowingX(data) {\n const { x, y } = data;\n if (x.length !== y.length) {\n throw new TypeError('length of x and y must be identical');\n }\n if (x.length < 2 || x[0] < x.at(-1))\n return data;\n return {\n x: x.toReversed(),\n y: y.toReversed(),\n };\n}\n//# sourceMappingURL=xyGrowingX.js.map","import { xGetFromToIndex } from \"../x/index.js\";\nimport { xyCheck } from \"./xyCheck.js\";\n/**\n * Calculate integration\n * @param data - object that contains property x (an ordered increasing array) and y (an array).\n * @param options - options.\n * @returns integration value on the specified range.\n */\nexport function xyIntegration(data, options = {}) {\n xyCheck(data, { minLength: 1 });\n const { x, y } = data;\n if (x.length === 1)\n return 0;\n const { fromIndex, toIndex } = xGetFromToIndex(x, options);\n let currentxyIntegration = 0;\n for (let i = fromIndex; i < toIndex; i++) {\n currentxyIntegration += ((x[i + 1] - x[i]) * (y[i + 1] + y[i])) / 2;\n }\n return currentxyIntegration;\n}\n//# sourceMappingURL=xyIntegration.js.map","import { xyCheck } from \"./xyCheck.js\";\n/**\n * Interpolate y values at target x positions using linear interpolation.\n * This is useful for upsampling or resampling data to specific x positions.\n * Both the input data and target x values must be sorted in ascending order.\n * Uses a two-pointer approach with O(n + m) complexity.\n * @param data - object that contains property x (an ordered increasing array) and y (an array).\n * @param xTarget - target x positions where y values should be interpolated.\n * @returns interpolated y values at target x positions.\n */\nexport function xyInterpolateLinear(data, xTarget) {\n xyCheck(data);\n const { x, y } = data;\n if (x.length === 0) {\n return new Float64Array(0);\n }\n const result = new Float64Array(xTarget.length);\n let sparseIndex = 0;\n for (let i = 0; i < xTarget.length; i++) {\n const targetX = xTarget[i];\n // Move sparse index forward while we can\n while (sparseIndex < x.length - 1 && x[sparseIndex + 1] < targetX) {\n sparseIndex++;\n }\n // Handle edge cases\n if (sparseIndex === 0 && targetX <= x[0]) {\n // Before first point\n if (x.length === 1) {\n result[i] = y[0];\n }\n else {\n // Extrapolate using first two points\n const t = (targetX - x[0]) / (x[1] - x[0]);\n result[i] = y[0] * (1 - t) + y[1] * t;\n }\n }\n else if (sparseIndex >= x.length - 1) {\n // After last point\n result[i] = y[x.length - 1];\n }\n else {\n // Normal interpolation between two points\n const leftIdx = sparseIndex;\n const rightIdx = sparseIndex + 1;\n const t = (targetX - x[leftIdx]) / (x[rightIdx] - x[leftIdx]);\n result[i] = y[leftIdx] * (1 - t) + y[rightIdx] * t;\n }\n }\n return result;\n}\n//# sourceMappingURL=xyInterpolateLinear.js.map","import { xFindClosestIndex } from 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P,r,i,f,D,Q;if(i=en(t.V,e,A),f=-1,n&&t.X==8&&i!=-1&&(!ZW(t,t.e,i)||M1(t.V,t.e)!=0&&M1(t.V,t.e)==M1(t.V,i)&&!Fn(t.V,t.e)||uz(t,t.e,i))&&(i=-1),i!=-1&&(t.w==101||t.w==103||t.w==102?(f=NQ(t.V,i),i=-1):t.w==19&&i>=t.V.g&&(i=-1)),f==-1&&i==-1&&t.w!=22&&t.w!=19&&t.w!=3&&t.w!=10&&t.w!=27&&t.w!=16&&t.w!=11&&t.w!=12&&t.w!=13&&t.w!=28&&t.w!=29&&t.w!=30&&t.w!=14&&t.w!=31&&t.w!=15&&t.w!=32&&t.w!=33&&(f=kf(t.V,e,A)),f!=-1&&(t.w==101||t.w==103||t.w==102)&&!(q9(t.V,f)&&(xs(t,f)!=-1||es(t,f)!=-1))&&(f=-1),P=t.v,!n&&t.I&&(P=null,i==-1&&f==-1&&(t.w==2||t.w==4||t.w==21))){for(Q=new Zt(t.I);Q.a>24,Q[d][D][b][1]=t.B[d]<<24>>24;else{for(L9(t,o,r,!0,P),m0(o,P[L],0),Je(o,P[L],0),A=0;Ao.k[P[A]]&&w1(o,P[A],E0,!0);if(e)for(i=0;i12;)p1.a=at(p1.a,t[r]),p1.a=at(p1.a,Wt(t[r+1],8)),p1.a=at(p1.a,Wt(t[r+2],16)),p1.a=at(p1.a,Wt(t[r+3],24)),p1.b=at(p1.b,t[r+4]),p1.b=at(p1.b,Wt(t[r+5],8)),p1.b=at(p1.b,Wt(t[r+6],16)),p1.b=at(p1.b,Wt(t[r+7],24)),p1.c=at(p1.c,t[r+8]),p1.c=at(p1.c,Wt(t[r+9],8)),p1.c=at(p1.c,Wt(t[r+10],16)),p1.c=at(p1.c,Wt(t[r+11],24)),D$(p1),i-=12,r+=12;switch(i){case 12:p1.c=at(p1.c,Wt(t[r+11],24));case 11:p1.c=at(p1.c,Wt(t[r+10],16));case 10:p1.c=at(p1.c,Wt(t[r+9],8));case 9:p1.c=at(p1.c,t[8]);case 8:p1.b=at(p1.b,Wt(t[r+7],24));case 7:p1.b=at(p1.b,Wt(t[r+6],16));case 6:p1.b=at(p1.b,Wt(t[r+5],8));case 5:p1.b=at(p1.b,t[4]);case 4:p1.a=at(p1.a,Wt(t[r+3],24));case 3:p1.a=at(p1.a,Wt(t[r+2],16));case 2:p1.a=at(p1.a,Wt(t[r+1],8));case 1:p1.a=at(p1.a,t[r]);break;case 0:return Ot(p1.c)}return t$(p1),Ot(p1.c)}function mtt(t,e){Ws();var A,n,P,r,i,f,D,Q,o,L,s,y,l,h;if(D=O(t,0)<0,D&&(t=V0(t)),O(t,0)==0)switch(e){case 0:return\"0\";case 1:return\"0.0\";case 2:return\"0.00\";case 3:return\"0.000\";case 4:return Sy;case 5:return\"0.00000\";case 6:return\"0.000000\";default:return y=new M0,e<0?y.a+=\"0E+\":y.a+=\"0E\",y.a+=e==f1?\"2147483648\":\"\"+-e,y.a}o=18,L=c(Fr,Pe,6,o+1,15,1),A=o,h=t;do Q=h,h=In(h,10),L[--A]=Ot(at(48,Y1(Q,yn(h,10))))&d1;while(O(h,0)!=0);if(P=Y1(Y1(Y1(o,A),e),1),e==0)return D&&(L[--A]=45),PP(L,A,o-A);if(e>0&&O(P,-6)>=0){if(O(P,0)>=0){for(r=A+Ot(P),f=o-1;f>=r;f--)L[f+1]=L[f];return L[++r]=46,D&&(L[--A]=45),PP(L,A,o-A+1)}for(i=2;Si(i,at(V0(P),1));i++)L[--A]=48;return L[--A]=46,L[--A]=48,D&&(L[--A]=45),PP(L,A,o-A)}return l=A+1,n=o,s=new Vr,D&&(s.a+=\"-\"),n-l>=1?(Ye(s,L[A]),s.a+=\".\",s.a+=PP(L,A+1,o-A-1)):s.a+=PP(L,A,o-A),s.a+=\"E\",O(P,0)>0&&(s.a+=\"+\"),s.a+=\"\"+ve(P),s.a}function btt(t,e,A){var 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xp(t){var e,A,n,P,r,i,f,D,Q;for(A=m(k(B,1),F,6,15,[8,-2,8,-2,8]),Q=m(k(B,1),F,6,15,[8,-2,4,-2,12,-2,4,-2,12,-2,4,-2,12,-2,8]),fL(t.c,A,Q),t.o=$u(t.c,1,1),YP(t.o,t),f0(t.c,le(t.c,\"Atom Label:\"),1,1),f0(t.c,t.o,3,1),Fe(t.c,le(t.c,\"(examples: 'D', 'Li', 'Cys', 'R12', 'R3@C')\"),1,3,3,3),t.p=$u(t.c,1,1),YP(t.p,t),f0(t.c,le(t.c,\"Atom Mass:\"),1,5),f0(t.c,t.p,3,5),Fe(t.c,le(t.c,\"(empty for natural abundance)\"),1,7,3,7),t.q=$u(t.c,1,1),YP(t.q,t),f0(t.c,le(t.c,\"Abnormal Valence:\"),1,9),f0(t.c,t.q,3,9),Fe(t.c,le(t.c,\"(empty for default valence)\"),1,11,3,11),t.a==-1?(n=(O1(),J0)[t.k],Y8(t.o,t.n==null?n:t.n+\"@\"+n),t.g!=0&&Y8(t.p,\"\"+t.g),t.j!=-1&&Y8(t.q,\"\"+t.j)):(n=Vi(t.e,t.a),e=eP(t.e,t.a),Y8(t.o,e==null?n:e+\"@\"+n),we(t.e,t.a)!=0&&Y8(t.p,\"\"+we(t.e,t.a)),G9(t.e,t.a)!=-1&&Y8(t.q,\"\"+G9(t.e,t.a))),t.b=TA(t.c),r=SE,i=0,f=r.length;i=2&&!f&&i==Q){if(r=c(B,F,6,i,15,1),o=yf(t.j,e),o==0)if(J(t.j,e)==7&&R1(t.j,e)==0)o=1,m0(t.j,e,1);else if(J(t.j,e)>14)o=2;else continue;for(l=0,y=0;y>24,Q!=3)return Q;for(o=0,L=0,f=0;fn[2]&&n[1]-n[2]>l1)&&(L=3-L);break;case 1:n[2]-n[0]>l1&&(L=3-L);break;case 2:n[1]-n[0]4e6)return z1(),-v;for(e=0,i=c(B,F,6,t.a.a.length,15,1),y=c(B,F,6,t.a.a.length,15,1),D=0;D0&&(A=w.Math.abs(i)>=w.Math.abs(D.b)?D.b:i,i-=A,Ft(A<0?s:g,new Q3(P,D.a,A)),i==0))););}if(s.a.length==0||g.a.length==0)return null;for(kD(g,new Au),kD(s,new nu),h=new Zt(g);h.a-o.b)return t.i[lD(y.c,y.a)]+=o.b,t.i[lD(o.c,o.a)]-=o.b,new qQ(y.c,o.c,y.a,o.a);for(l=new Zt(g);l.at.H.g||t.g.i-t.q>t.H.i||t.g.g==0)return 0;for(e==6&&(t.N=63),sq(t,A),n=c(Pt,it,6,t.H.g,16,1),t.G=c(B,F,6,t.g.g,15,1),bU(t.G,-1),f=c(B,F,6,t.A,15,1),zr(f,f.length,-1),r=0;!t.P;){if(t.w!=0){if(Q=t.u[r]==-1?t.H.g:Ue(t.H,t.G[t.u[r]]),++f[r],f[r]==Q){if(f[r]=-1,r==0)break;--r,t.t[r]||(n[t.G[t.s[r]]]=!1);continue}if(t.u[r]==-1)n[f[r]]||sy(t,f[r],t.s[r])&&(t.G[t.s[r]]=f[r],n[f[r]]=!0,++r);else{if(Z(t.H,t.G[t.u[r]],f[r])>=t.H.g)continue;P=Z(t.H,t.G[t.u[r]],f[r]),t.t[r]?P==t.G[t.s[r]]&&Ay(t,Nt(t.H,t.G[t.u[r]],f[r]),t.v[r])&&++r:n[P]||sy(t,P,t.s[r])&&Ay(t,Nt(t.H,t.G[t.u[r]],f[r]),t.v[r])&&(n[P]=!0,t.G[t.s[r]]=P,++r)}}if(r==t.w){if(Mp(t,-1)&&hR(t,-1)&&AR(t,n,-1)){for(D=!1,i=0;iw.Math.abs(I)&&(I=s,a=R);L=w.Math.exp(-5*i/100),o[a]+=L*I}for(T=0;T<$;T++)C=T==0?$-1:T-1,A=o[C]+S2-o[T],A>k0&&(A-=k0),f5(t,tt[T],X[C],X[T],A)}function Gp(t){var e,A,n,P,r,i,f,D,Q,o,L,s,y;for(t.k=c(B,F,6,t.r,15,1),t.e=c(B,F,6,t.r,15,1),t.j=c(B,D0,7,t.r,0,2),t.n=c(B,D0,7,t.r,0,2),t.o=c(B,D0,7,t.r,0,2),t.p=c(B,F,6,t.g,15,1),o=c(B,F,6,t.r,15,1),i=0;i1&&(t.p[A]+=y-1)}for(D=0;D\")?0:h.endsWith(\"-\")||h.endsWith(\"+\")?1:h.endsWith(\"=\")?3:2),We(t.c,h,y)),n)for(A=c8(X1(n),\",\"),y.a=c(Yt,t1,6,A.length,15,1),Q=0;Q>16;if(s)for(L=c8(X1(s),\",\"),y.d=j1(Yt,[re,t1],[17,6],15,[L.length,2],2),Q=0;Q>16,y.d[Q][1]=L1(GD(L[Q],o+1),Fy,32767)<<16>>16;if(f)for(D=c8(X1(f),\",\"),y.c=c(Yt,t1,6,D.length,15,1),Q=0;Q>24;if(r)for(P=c8(X1(r),\",\"),y.b=c(_,At,6,P.length,15,1),Q=0;Q>24;h=X1(l)}}catch(T){if(T=_1(T),Jt(T,79))i=T,ur(i,(z1(),k9),\"\");else throw et(T)}}function si(t){var e,A,n,P,r,i,f,D,Q,o,L,s,y,l,h,T,g,v,d,b,j;if(t.R.g!=0&&!t.G){for(t.I=0,j=0,A=1;At.f[j]&&(j=A);for(n=c(Pt,it,6,t.R.g,16,1),i=c(Pt,it,6,t.R.i,16,1),t.H=c(B,F,6,t.R.g,15,1),t.B=c(B,F,6,t.R.g,15,1),t.F=c(B,F,6,t.R.g,15,1),t.C=c(B,F,6,t.R.i,15,1),t.B[0]=j,t.H[j]=0,n[j]=!0,P=1,D=0,Q=1,o=0;D=Ct(t.R,e))&&(f=Z(t.R,e,T),!n[f]&&t.f[f]>s&&(l=f,h=Nt(t.R,e,T),s=t.f[f]));if(s==-1)break;t.H[l]=Q,t.F[Q]=D,t.B[Q++]=l,t.C[o++]=h,n[l]=!0,i[h]=!0}++D}else{for(y=0,s=-1,e=0;es&&(y=e,s=t.f[e]);++P,t.H[y]=Q,t.F[Q]=-1,t.B[Q++]=y,n[y]=!0}for(t.D=c(B,F,6,2*(t.R.i-o),15,1);;){for(v=t.R.N,d=t.R.N,b=-1,r=0;rR&&(l=!0),Q=t.indexOf(R0(32),L),R>L&&(Q>R||Q==-1)&&(Q=R),o=Q==-1?(zt(L,t.length+1),t.substr(L)):(Mt(L,Q,t.length),t.substr(L,Q-L)),L=Q==-1||Q==t.length-1?-1:Q+1,v=null,e!=null&&e.length!=0&&(Q=e.indexOf(R0(32),d),Q==-1?v=(zt(d,e.length+1),e.substr(d)):(v=(Mt(d,Q,e.length),e.substr(d,Q-d)),d=Q+1)),f=null,A!=null&&A.length!=0&&(Q=A.indexOf(R0(32),D),Q==-1?f=(zt(D,A.length+1),A.substr(D)):(f=(Mt(D,Q,A.length),A.substr(D,Q-D)),D=Q+1)),j=new 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A,n,P,r,i,f,D,Q,o,L,s,y,l,h,T,g,v,d,b,j,R,C,E;for(Q=c(B,F,6,t.j.g,15,1),g=c(B,F,6,t.d.g,15,1),o=c(B,F,6,t.j.g,15,1),d=c(B,F,6,t.d.g,15,1),Q[0]=e.b,o[e.b]=e.b,g[0]=e.a,d[e.a]=e.a,t.n[e.b]==0&&(++t.c,t.n[e.b]=t.c,t.f[e.a]=t.c),f=0,L=0;f<=L;){for(R=Q[f],b=g[f],T=j1(B,[D0,F],[7,6],15,[N(t.j,R),N(t.d,b)],2),y=0;y0)||mn(t.k,P)&&t0(t.k,P)==0&&(t.j&4)==0||i!=0||o!=0||s!=0||G9(t.k,P)!=-1||jA(t.k,P)!=0||l!=null,g&&(A.a+=\"[\"),o!=0&&(A.a+=o),A.a+=\"\"+L,(B0(t.k,P)==1||B0(t.k,P)==2)&&(h=fe(t.k,P)-1,!(h!=-1&&t.i[h][OA(t.k,P)]<=1))&&(J(t.k,P)!=7||R1(t.k,P)>0)&&G(A,Ia(t,P,y)),(t.j&1)==0&&g&&(D=_d(t.k,P),D==1?A.a+=\"H\":D>1&&(A.a+=\"H\"+D)),i!=0&&(A.a+=String.fromCharCode(i>0?43:45),w.Math.abs(i)>1&&Hh(A,w.Math.abs(i))),l!=null&&(A.a+=\"\"+l),s!=0&&(A.a+=\":\",A.a+=s),g&&(A.a+=\"]\"),QX(t,e,A),e.e&&(A.a+=\")\")}function Gtt(t,e,A,n){var P,r,i,f,D,Q;if(i=1,O(t,0)==0)return n||A==1?\"0\":\"0.\"+HP(A-1);if(O(t,0)!=0){for(;A0(n9(t,10),0);)t=In(t,10),++e;for(i=0,f=t;O(f,0)!=0;f=In(f,10))++i}return r=new 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continue;if(ee(t,r)||ee(t,i)||t.D[r]<9&&G1(t,r)>3||t.D[i]<9&&G1(t,i)>3)continue;L=xt(t,r)!=0,t.t[r]-=1,t.t[i]+=1,L||(v=t.I[D],Q==1?t.I[D]=2:t.I[D]=4,(v==129||v==257)&&(R=t.F[0][D],g=$s(t,R,!1),t.F[0][g]!=R&&(t.F[1][g]=t.F[0][g],t.F[0][g]=R))),t.U=0}for(d=0,h=0,y=0,P=0;P0&&!xq(t,f)&&rA(t,f)&&(o=w.Math.min(xt(t,f),t.t[f]),o!=0&&j>=o&&(d-=o,b-=o,j-=o,t.t[f]-=o,t.U&=1));if(T=A?d:b,T<0){for(l=c(B,F,6,h,15,1),h=0,r=0;r=l.length-h;s--)n=l[s]&q0,rA(t,n)&&(o=w.Math.min(-T,-t.t[n]),d+=o,T+=o,t.t[n]+=o,t.U&=1)}return d}function Jp(t,e,A){var n,P,r,i,f,D,Q,o,L,s,y,l,h,T,g,v;for(o=c(ft,Dt,2,t.g.a.length+t.f.a.length,6,1),l=c(ft,Dt,2,t.g.a.length+t.f.a.length,6,1),r=c(ft,Dt,2,t.g.a.length+t.f.a.length,6,1),D=0;D0&&(L.a+=\" \",h.a+=\" \",i.a+=\" \"),L.a+=\"\"+o[s],h.a+=\"\"+l[s],i.a+=\"\"+r[s],o[s]=\"\"}for(L.a+=\"!\",t.f.a.length!=0&&t.g.a.length!=0&&(h.a+=\" \",i.a+=\" \"),f=t.g.a.length;ft.g.a.length&&(L.a+=\" \",h.a+=\" \",i.a+=\" \"),L.a+=\"\"+o[s],h.a+=\"\"+l[s],i.a+=\"\"+r[s],o[s]=\"\"}return 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Eh(D.a,D.c),r=!0)),c1(t.L,A)==386&&hL(P,D),c1(t.L,A)==8?r?(zQ(t,P,i,f),xn(t,D,i,f)):(xn(t,P,i,f),zQ(t,D,i,f)):(xn(t,P,i,f),xn(t,D,i,f))))}function Jtt(t,e){var A,n,P,r,i,f,D,Q,o,L,s,y,l,h,T,g,v;this.i=t,this.g=7,this.j=new n1,this.k=new n1,this.b=new n1,this.a=c(B,F,6,this.i.g,15,1),this.c=c(B,F,6,this.i.i,15,1),this.i._b(1),s=c(Pt,it,6,this.i.g,16,1),y=c(Pt,it,6,this.i.i,16,1);do for(i=!1,A=0;AQ){for(A=0;Aw.Math.abs(EL(E.a,I.a))?Tf(A,a,K,R.a):(DX(A,a,K),Tf(A,a,K,C.a)),Z5(t,e,A,P)}function Xtt(t){var e,A,n,P,r,i,f,D,Q,o,L,s,y,l,h,T,g,v,d,b,j,R,C,E,S;for(ut(t,7),E=0,y=c(Pt,it,6,t.g,16,1),S=c(B,F,6,t.g,15,1),r=0;rej(t.D[j[l]])&&(b=ej(t.D[j[l]]),d=j[l]);d!=-1&&(E-=t.t[d],t.t[d]=0,t.U=0)}}else E-=t.t[D],t.t[D]=0,t.U=0;return E>0&&(E=k_(t,E)),E}function d6(){d6=v1,yl=m(k(B,1),F,6,15,[0,yi,14286847,13402367,12779264,16758197,9474192,3166456,16715021,9494608,11789301,11230450,9109248,12560038,1578e4,16744448,16777008,2093087,8442339,9388244,4062976,15132390,12567239,10921643,9083335,10255047,14706227,15765664,5296208,13140019,8224944,12750735,6721423,12419299,16752896,10889513,6076625,7351984,DP,9764863,9756896,7586505,5551541,3907230,2396047,687500,27013,12632256,16767375,10909043,6717568,10380213,13924864,9699476,4366e3,5707663,51456,7394559,16777159,14286791,13107143,10747847,9437127,6422471,4587463,3211207,2097095,65436,58997,54354,48952,43812,5096191,5089023,2200790,2522539,2516630,1528967,13684960,16765219,12105936,10900557,5724513,10375093,11230208,7688005,4358806,4325478,32e3,7384058,47871,41471,36863,33023,27647,5528818,7888099,9064419,10565332,11739092,11739066,11734438,12389767,13041766,13369433,13697103,14221381,14680120,15073326,15400998,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13158600,1334015,56540,15075850,15132160,56540,15075850,15461355,8553170,1016335,1016335,1334015,15132160,3289770,14456450,16422400,16422400,11819700,3289770,1016335])}function oy(t,e,A){var n,P,r,i,f,D,Q,o,L,s,y,l,h;if(t.db[e]!=0||J(t.R,e)!=5&&J(t.R,e)!=6&&J(t.R,e)!=7&&J(t.R,e)!=14&&J(t.R,e)!=15&&J(t.R,e)!=16||(t.Q&E0)!=0&&J(t.R,e)>=14)return!1;if(t0(t.R,e)!=0){if(UT(t.R,e))return ya(t,e,A);if(J(t.R,e)!=15&&J(t.R,e)!=16)return!1}if(N(t.R,e)<3||Ct(t.R,e)>4||R1(t.R,e)>0&&J(t.R,e)==6||J(t.R,e)==5&&Ct(t.R,e)!=4)return!1;if(t.R.L){if(se(t.R,e))return!1;for(f=0;f4){ke(t,e,0,!1);return}for(A=t.e[e],l=!1,i=0;in[2]&&n[1]-n[2]>l1;break;case 1:o=n[2]-n[0]>l1;break;case 2:o=n[1]-n[0]3&&(l=3),(A&512)==0&&l>1&&(l=1),i=z(i,l<<6)),(A&ue)!=0&&(t.v[P]&8)!=0&&(i=z(i,256)),(A&q1)!=0&&Po;L--)y[L]=y[L-1];y[o]=i,++s}for(s>4&&(s=4),n=0,Q=0;Q9&&(T=9),T>2&&(T-=2),n=z(n,T<<4)):(A&1)!=0&&(t.v[e]&8)!=0&&(n=z(n,64)),(A&4)!=0&&e=0;Q--)a=at(Wt(C,32),V(I[Q],g1)),g=HY(a),I[Q]=Ot(g),C=Ot(G0(g,32));v=Ot(C),T=A;do b[--A]=48+v%10&d1;while((v=v/10|0)!=0&&A!=0);for(n=9-T+A,D=0;D0;D++)b[--A]=48;for(L=H-1;I[L]==0;L--)if(L==0)break t;H=L+1}for(;b[A]==48;)++A}if(y=E<0,i=d-A-e-1,e==0)return y&&(b[--A]=45),PP(b,A,d-A);if(e>0&&i>=-6){if(i>=0){for(o=A+i,s=d-1;s>=o;s--)b[s+1]=b[s];return b[++o]=46,y&&(b[--A]=45),PP(b,A,d-A+1)}for(L=2;L<-i+1;L++)b[--A]=48;return b[--A]=46,b[--A]=48,y&&(b[--A]=45),PP(b,A,d-A)}return S=A+1,r=d,j=new Vr,y&&(j.a+=\"-\"),r-S>=1?(Ye(j,b[A]),j.a+=\".\",j.a+=PP(b,A+1,d-A-1)):j.a+=PP(b,A,d-A),j.a+=\"E\",i>0&&(j.a+=\"+\"),j.a+=\"\"+i,j.a}function sy(t,e,A){var n,P,r,i,f,D,Q,o,L,s,y;if(f=N(t.H,e),n=t.o[A],n>f)return!1;if(Q=l0(t.H,e),r=l0(t.g,A),P=IA(t.g,A),D=IA(t.H,e),gt(V(r,1),0)){if(P!=null){if(gt(V(Q,1),0)){if(D==null||!Em(P,D))return!1}else if(D!=null){if(HK(D,P))return!1}else if(TF(J(t.H,e),P))return!1}}else{if(gt(V(Q,1),0))return!1;if(P!=null){if(D!=null){if(!Em(D,P))return!1}else if(!TF(J(t.H,e),P))return!1}else if(D!=null||t.J[e]!=t.k[A])return!1}if(gt(z(Q,r),0)){if(gt(V(r,q1),0)){if(t.H.L&&A0(V(Q,q1),0))return!1;if(n!=f)return!1}if(gt(V(r,E0),0)&&n>=f&&A0(V(Q,E0),0))return!1}if(gt(V(t.I[e],Wn(t.j[A])),0)||gt(V(t.B[A],Wn(t.M[e])),0))return!1;if(i=V(r,l9),t.H.L){if(o=V(r,l9),O(o,0)!=0&&(O(i,0)==0||gt(V(i,Wn(o)),0)))return!1}else if(O(i,0)!=0&&A0(V(i,t.M[e]),0))return!1;return R1(t.g,A)!=0&&R1(t.g,A)!=R1(t.H,e)||we(t.g,A)!=0&&we(t.g,A)!=we(t.H,e)||jA(t.g,A)!=0&&jA(t.g,A)!=jA(t.H,e)||(L=V(r,M2),O(L,0)!=0&&Ua(t.H,e)!=Ot(G0(L,47))-7)?!1:(s=V(r,lr),O(s,0)!=0?(s=G0(s,22),t.H.L?(y=Ot(G0(V(l0(t.H,e),lr),22)),Ot(s)==y):Or(t.H,e)==Ot(s)):!0)}function Ztt(t,e){var A,n,P,r,i,f,D,Q;if(A=0,i=0,r=e.length,f=null,Q=new Vr,i=-1&&(r=c(B,F,6,this.b,15,1),nZ(this,r,e)))for(L=0;L$&&($=X),n[y]=t.e[y];if($<12e-8)return 0;for(D=cj(t),$=0,W=w.Math.max(t.k*D,1),l=0;lw.Math.sqrt(3e-8*a*H)){for(P=1/P,r=1/i,T=0;T=0?t.a[i]:-t.a[0],n=Dn-A&&(o=n-A,L=i);if(o>15)break;r=L,f=r+1,r==-1?(e+=t.c[0],t.a[0]=0,t.c[0]=t.c[0]*2<<16>>16,t.d[0][0]=-t.d[0][1]):f>16,t.d[r][1]=360-t.d[r][0]<<16>>16):(z1(),Q=PQ(t,r,f,t.a[f]),Bf(t,Q,f,r,t.d[f][0],t.d[r][1]))):t.e==3?(f=t.a[r]==90?r-1:r,t.a[r]<90?(e+=t.c[r],t.a[r]=90,t.c[r]=t.c[r]*2<<16>>16,t.d[r][1]=180-t.d[r][0]<<16>>16):(z1(),Q=PQ(t,r,f,t.a[f]),Bf(t,Q,f,r,t.d[f][0],t.d[r][1]))):(f=0,Q=PQ(t,r,f,t.a[f]+360),Q<=360?Bf(t,Q,r,f,t.d[r][0],t.d[f][1]+360):Bf(t,Q-360,f,r,t.d[f][0],t.d[r][1]-360))}if(e!=0)for(P=0;P>16}function Wp(t,e){var A,n,P,r,i,f,D,Q;if(D=0,t.L){switch(e2&&(D=z(D,16)),Q>3&&(D=z(D,32))),A=t.t[e],A<0?D=z(D,Sf):A>0&&(D=z(D,Ef)),i=t.k[e],i){case 0:break;case 1:D=z(D,O0);break;case 2:D=z(D,Ti);break;case 3:D=z(D,NP);break;default:D=z(D,If)}switch(n=wT(t,e),n){case 0:break;case 1:D=z(D,p2);break;case 2:D=z(D,By);break;case 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gF(t,E);for(T=o1(e,n,P),f=m(k(Ut,1),Tt,6,15,[0,0]),D=m(k(Ut,1),Tt,6,15,[0,0]),Q=m(k(Ut,1),Tt,6,15,[0,0]),l=m(k(B,1),F,6,15,[e.D[n],e.D[P]]),i=(pn(t.a.b,L)-1)*(pn(t.a.b,s)-1),R=0;R<2;R++)switch(l[R]){case 6:f[R]=2,D[R]=2.12;break;case 7:f[R]=2,D[R]=1.5;break;case 8:f[R]=2,D[R]=.2,Q[R]=2;break;case 14:f[R]=1.25,D[R]=1.22;break;case 15:f[R]=1.25,D[R]=2.4;break;case 16:f[R]=1.25,D[R]=.49,Q[R]=8}return ZD(t.a.b,L)||ZD(t.a.b,s)?new Zn(0,0):sL(t.a.b,L)&&sL(t.a.b,s)&&T0)||pn(t.a.b,s)==2&&(u8(t.a.b,s)==3||NA(t.a.b,s)>0)?new xU:new Zn(0,w.Math.sqrt(D[0]*D[1])/i):pn(t.a.b,s)==4&&pn(t.a.b,L)!=4?pn(t.a.b,L)==3&&(u8(t.a.b,L)==4||u8(t.a.b,L)==34||NA(t.a.b,L)>0)||pn(t.a.b,L)==2&&(u8(t.a.b,L)==3||NA(t.a.b,L)>0)?new xU:new Zn(0,w.Math.sqrt(D[0]*D[1])/i):dt(e,T)==1&&NA(t.a.b,L)>0&&NA(t.a.b,s)>0||NA(t.a.b,L)>0&&X9(t.a.b,s)>0||X9(t.a.b,L)>0&&NA(t.a.b,s)>0?X9(t.a.b,L)>0&&X9(t.a.b,s)>0?new xU:X9(t.a.b,L)>0&&NA(t.a.b,s)>0?(tt=0,NA(t.a.b,L)==1?tt=.5:nP(l[0])==2&&nP(l[1])==2?tt=.3:(nP(l[0])!=2||nP(l[1])!=2)&&(tt=.15),new Zn(6*tt*w.Math.sqrt(f[0]*f[1]),0)):X9(t.a.b,s)>0&&NA(t.a.b,L)>0?(tt=0,NA(t.a.b,s)==1?tt=.5:nP(l[0])==2&&nP(l[1])==2?tt=.3:(nP(l[0])!=2||nP(l[1])!=2)&&(tt=.15),new Zn(6*tt*w.Math.sqrt(f[0]*f[1]),0)):(NA(t.a.b,L)==1||NA(t.a.b,s)==1)&&(l[0]!=6||l[1]!=6)?new Zn(2.4000000000000004*w.Math.sqrt(f[0]*f[1]),0):new Zn(.8999999999999999*w.Math.sqrt(f[0]*f[1]),0):(l[0]==8||l[0]==16)&&(l[1]==8||l[1]==16)?new Zn(-w.Math.sqrt(Q[0]*Q[1]),0):new Zn(0,w.Math.sqrt(D[0]*D[1])/i)}function w1t(t,e){var A,n,P,r,i,f,D,Q,o,L;if(!t.W){if(Q=e.b,e.j==1)if(Q==-3)t.Z=!0,or(t);else if(Q==-2)t.d=!0,or(t);else if(Q==-1)or(t);else if(e.a)Q==122?(Ov(t),t.eb=w.Math.max(t.eb,2),P1(t.k),N1(t,new x1(t,1,!0))):Q==99?a5(t,!0):Q==118&&((t.U&4)!=0&&t.r&&nF(),Wv(t));else if(Q==-4)t.w!=19&&(J1(t),pW(t)||LQ(t.V)&&(t.eb=w.Math.max(t.eb,1),P1(t.k),N1(t,new x1(t,1,!0))));else if(Q==-6||t.t==-1&&Q==63){E3(t._,tE,eE);return}else if(Q==-8)t.i.a.length!=0&&(uy(t,t.i.a),Ln(t.i,0));else if(t.u!=-1)Q==113&&t.V.L?Jv(t,t.u):Q==118?$Q(t.V,t.u,3,!1,Qn(t.V,24*t.ab))&&(t.eb=w.Math.max(t.eb,3),P1(t.k),N1(t,new x1(t,1,!0))):Q>=52&&Q<=55?$Q(t.V,t.u,Q-48,!1,Qn(t.V,24*t.ab))&&(t.eb=w.Math.max(t.eb,3),P1(t.k),N1(t,new x1(t,1,!0))):Q==97||Q==98?$Q(t.V,t.u,6,!0,Qn(t.V,24*t.ab))&&(t.eb=w.Math.max(t.eb,3),P1(t.k),N1(t,new x1(t,1,!0))):(P=Q==48?(J1(t),h8(t.V,t.u,16)):Q==49?(J1(t),h8(t.V,t.u,1)):Q==50?(J1(t),h8(t.V,t.u,2)):Q==51?(J1(t),h8(t.V,t.u,4)):Q==117?(J1(t),h8(t.V,t.u,257)):Q==100?(J1(t),h8(t.V,t.u,129)):Q==99?(J1(t),h8(t.V,t.u,386)):Q==109&&(J1(t),h8(t.V,t.u,16)),P&&(t.eb=w.Math.max(t.eb,1),P1(t.k),N1(t,new x1(t,1,!0))));else if(t.t!=-1)if(D=t.i.a.length==0,D?t.K=Q:(t.K==108&&(Ln(t.i,0),Ye(t.i,76)),t.K=-1),D&&Q==108)G(t.i,\"Cl\"),t.eb=w.Math.max(t.eb,1),P1(t.k);else if(D&&(Q==43||Q==45))J1(t),pT(t.V,t.t,Q==43)&&(t.eb=w.Math.max(t.eb,3),P1(t.k),N1(t,new x1(t,1,!0)));else if(D&&Q==46)J1(t),L=jA(t.V,t.t)==32?0:32,Je(t.V,t.t,L),t.eb=w.Math.max(t.eb,3),P1(t.k),N1(t,new x1(t,1,!0));else if(D&&Q==58)J1(t),L=jA(t.V,t.t)==48?16:jA(t.V,t.t)==16?0:48,Je(t.V,t.t,L),t.eb=w.Math.max(t.eb,3),P1(t.k),N1(t,new x1(t,1,!0));else if(D&&Q==113&&t.V.L)NF(t,t.t);else if(D&&t.V.L&&(Q==120||Q==88))o=m(k(B,1),F,6,15,[9,17,35,53]),ST(t.V,t.t,o),t.eb=w.Math.max(t.eb,3),P1(t.k),N1(t,new x1(t,1,!0));else if(D&&Q==63)J1(t),mf(t.V,t.t,0,0,-1,0)&&(t.eb=w.Math.max(t.eb,3),P1(t.k),N1(t,new x1(t,1,!0)));else if(D&&Q>48&&Q<=57){if(_i(t.V,t.t)>0){for(J1(t),r=Q-47,A=t.t,i=t.V.r-t.V.g,f=1;f=65&&Q<=90||Q>=97&&Q<=122||Q>=48&&Q<=57||Q==45?(Ye(t.i,Q&d1),t.eb=w.Math.max(t.eb,1),P1(t.k)):(Q==10||Q==13)&&uy(t,t.i.a);else Q==104||Q==118?(t.U&7)==0&&J5(t,Q==104):t.db&&y6(t.db,e);e.j==2&&(Q==-3?(t.Z=!1,or(t)):Q==-2?(t.d=!1,or(t)):Q==-1?or(t):t.db&&y6(t.db,e))}}function g1t(t){var e,A,n,P,r,i,f,D,Q,o,L,s,y,l,h,T,g,v,d,b,j,R,C,E,S,I,a,H,K,tt,X,W,$,st;for(n=0;n4){for(s=new VP(t.f,1+Ct(t.f,n),!1),s.a[Ct(t.f,n)]=0,s.b[Ct(t.f,n)]=0,s.k[Ct(t.f,n)]=32,s.e[Ct(t.f,n)]=n,t.b[n]=!0,l=0;l2)){for(s=new VP(t.f,R,!1),o=0,h=0;h2)break;t.b[e[j+1]]=!0,t.c[Nt(t.f,e[j],C)]=!0,++j}while(t0(t.f,e[j])==2&&Ct(t.f,e[j])==2);for(R=Ct(t.f,e[0])+Ct(t.f,e[j])+j-1,s=new VP(t.f,R,!1),d=0;d<=j;d++)s.a[d]=d,s.b[d]=0,s.k[d]=64,s.e[d]=e[d];for(L=j+1,y=!1,b=0;b4||W==1)return!1;P[t.b[C]]=!0}for(E=0;E<2;E++)for(X=0;X\":Qt==1?K?\"-\":\"+\":Qt==3?\"=\":\"\",t.d=H+A1,!0)}function B1t(t,e){var A,n,P;s8();var r,i,f,D,Q,o,L,s,y,l,h,T,g,v,d,b,j,R,C,E,S,I,a,H,K,tt,X,W,$,st,ht,wt,rt,ct,Xt,St,A1,Qt,bt,yt,Kt,S1,D1;for(d=new rp(e),T=new PM(e,d),D1=c(CS,DA,477,e.r,0,2),wt=1;wt1&&e.e[Q[1]]>1){if(dt(e,l)==3){for(tt=d.a[l],S1=c(B,F,6,2,15,1),ct=0;ct<2;ct++)for(Qt=0;QtC[0][1-Xt]&&(St=!St),C[1].length==2&&C[1][Qt]>C[1][1-Qt]&&(St=!St),A=d.a[l]-d.a[I[0][Xt]]*w.Math.cos(R[0][Xt])-d.a[I[1][Qt]]*w.Math.cos(R[1][Qt]),n=d.a[I[0][Xt]]*w.Math.sin(R[0][Xt]),St?n+=d.a[I[1][Qt]]*w.Math.sin(R[1][Qt]):n-=d.a[I[1][Qt]]*w.Math.sin(R[1][Qt]),P=c(B,F,6,2,15,1),P[0]=e.F[0][l],P[1]=e.F[1][l],OQ(D1,t9(C[0][Xt],C[1][Qt]),P,w.Math.sqrt(A*A+n*n),3)}else if(dt(e,l)!=0){for(Kt=c(B,F,6,2,15,1),ct=0;ct<2;ct++)for(Qt=0;Qt=39&&T.startsWith(\"V3000\",34)}catch($){if($=_1($),Jt($,19))return!1;throw et($)}if(t.g)return E=Wa(t,e),iu(t.j,d),E;if(!t.j&&(t.j=new SA(b,j)),iu(t.j,d),t.d||(t.j.M=!0),b==0){for(;T!=null&&!(e1(T,hr)||e1(T,Lo)||e1((zt(1,T.length+1),T.substr(1)),\"$\"));)T=X1(e);return!0}for(y=0;y=17||T.startsWith(\"M SDT\")&&T.length>=12||T.startsWith(\"M SED\")&&T.length>=12)&&(!Q&&(Q=new cP),S=(Mt(7,10,T.length),T.substr(7,3)),f=(h=S,t8(e9(Q,h))),f||(f=new tu,We(Q,S,f)),T.startsWith(\"M SAL\")&&T.startsWith(\" 1\",10)?f.a=L1(k1((Mt(13,17,T.length),T.substr(13,4))),f1,Et):T.startsWith(\"M SDT\")?f.b=(zt(11,T.length+1),T.substr(11)).startsWith(Iy):T.startsWith(\"M SED\")&&(f.c=k1((zt(11,T.length+1),T.substr(11))))),T=X1(e)}catch($){if($=_1($),Jt($,19))return o=$,ur(o,(z1(),k9),\"\"),!1;throw et($)}if(Q)for(D=(C=new oD(Q).a.ad().Tb(),new UD(C));D.a.cd();)f=(L=D.a.dd(),L.jd()),f.b&&f.a!=0&&f.c!=null&&IP(t.j,f.a-1,f.c);return t.e&&(e_(t),l$(t)),(t.i&1)!=0&&(t.f=Tu(t.j)),Db(t,K),ut(t.j,15),!0}function PM(t,e){var A,n,P,r,i,f,D,Q,o,L,s,y,l,h,T,g,v,d,b,j,R,C;for(this.b=e,this.e=t,this.a=c(Oe,DA,480,this.e.g,0,3),P=0;P4){for(D=1;D0)){if(i>2){if(this.c[n]==1)J(this.e,n)<=14?A=(HM-this.d[n])/2:A=VM+(VM-this.d[n])*.18000000715255737;else if(A=HM-this.d[n],i>3){if(this.c[n]==2){for(y=c(Pt,it,6,Ct(this.e,n),16,1),Q=1;Q10?NM:t0(this.e,n)==2?S2:xP(this.e,n,!0)?GM:t0(this.e,n)==0?NM:GM,D=1;D=2 (any ring count)\"),Gt(t.s,\"2 (in 1 ring)\"),Gt(t.s,\"3 (bridge head; 2 rings)\"),Gt(t.s,\">3 (in more than 2 rings)\"),f0(t.v,le(t.v,\"Ring bonds:\"),1,7),f0(t.v,t.s,3,7),t.t=TA(t.v),Gt(t.t,b8),Gt(t.t,\"3 members\"),Gt(t.t,\"4 members\"),Gt(t.t,\"5 members\"),Gt(t.t,\"6 members\"),Gt(t.t,\"7 members\"),f0(t.v,le(t.v,\"Smallest ring size:\"),1,9),f0(t.v,t.t,3,9),t.r=TA(t.v),v=VE,d=0,b=v.length;dy&&(rt=!rt);rt&&(p0=p0==1?2:1),ke(B1,Y0[yt],p0,!1)}else ke(B1,Y0[yt],0,!1);break}}for(Kt=0;Kt>18,l=(E.v[D1]&b1)>>18!=1&&(E.v[D1]&b1)>>18!=2?-1:(E.v[D1]&VA)>>20,v==1?l+=h:v==2&&(l+=T),Ne(B1,h0[D1],v,l),g=!0));for(g&&(h=Ae(B1,1),T=Ae(B1,2)),kr=c(Pt,it,6,E.r,16,1),Q1=0;Q1=0;R--)if(this.e[R]!=0){for(h=R+1;h=0;C--){if(C0;){for(b=K-2;b>=-1&&b!=-1;b--)if(w.Math.abs(P[b])<=f*(w.Math.abs(this.e[b])+w.Math.abs(this.e[b+1]))){P[b]=0;break}if(b==K-2)E=4;else{for(S=K-1;S>=b&&S!=b;S--)if(rt=(S!=K?w.Math.abs(P[S]):0)+(S!=b+1?w.Math.abs(P[S-1]):0),w.Math.abs(this.e[S])<=f*rt){this.e[S]=0;break}S==b?E=3:S==K-1?E=1:(E=2,b=S)}switch(++b,E){case 1:for(D=P[K-2],P[K-2]=0,h=K-2;h>=b;h--)for(rt=Q2(this.e[h],D),n=this.e[h]/rt,st=D/rt,this.e[h]=rt,h!=b&&(D=-st*P[h-1],P[h-1]=n*P[h-1]),o=0;o=this.e[b+1]);){if(rt=this.e[b],this.e[b]=this.e[b+1],this.e[b+1]=rt,b3||r>3?(this.b=c(Yt,t1,6,4,15,1),this.b[0]=45,this.b[1]=135,this.b[2]=225,this.b[2]=315,this.c=j1(Yt,[re,t1],[17,6],15,[4,2],2),this.c[0][0]=30,this.c[0][1]=60,this.c[1][0]=120,this.c[1][1]=150,this.c[2][0]=210,this.c[2][1]=240,this.c[2][0]=300,this.c[2][1]=330,this.a=c(Yt,t1,6,4,15,1),this.a[0]=25,this.a[1]=25,this.a[2]=25,this.a[3]=25):(t.p[A]==0||t.D[A]>9)&&(t.p[n]==0||t.D[n]>9)?P==3&&r==3||P==3&&r==2||P==3&&r==1||P==2&&r==3||P==1&&r==3||P==2&&r==2&&(e[0]!=-1||e[3]!=-1)?(this.b=c(Yt,t1,6,3,15,1),this.b[0]=60,this.b[1]=180,this.b[2]=300,this.c=j1(Yt,[re,t1],[17,6],15,[3,2],2),this.c[0][0]=45,this.c[0][1]=75,this.c[1][0]=165,this.c[1][1]=195,this.c[2][0]=285,this.c[2][1]=315,this.a=c(Yt,t1,6,3,15,1),this.a[0]=33,this.a[1]=33,this.a[2]=33):P==1&&r==2&&e[3]==-1||P==2&&r==1&&e[0]==-1?(this.b=c(Yt,t1,6,3,15,1),this.b[0]=60,this.b[1]=180,this.b[2]=300,this.c=j1(Yt,[re,t1],[17,6],15,[3,2],2),this.c[0][0]=45,this.c[0][1]=75,this.c[1][0]=165,this.c[1][1]=195,this.c[2][0]=285,this.c[2][1]=315,this.a=c(Yt,t1,6,3,15,1),this.a[0]=40,this.a[1]=20,this.a[2]=40):(P==1&&r==1||P==1&&r==2&&e[3]!=-1||P==2&&r==1&&e[0]!=-1||P==2&&r==2&&e[0]==-1&&e[3]==-1)&&(this.b=c(Yt,t1,6,3,15,1),this.b[0]=60,this.b[1]=180,this.b[2]=300,this.c=j1(Yt,[re,t1],[17,6],15,[3,2],2),this.c[0][0]=45,this.c[0][1]=75,this.c[1][0]=165,this.c[1][1]=195,this.c[2][0]=285,this.c[2][1]=315,this.a=c(Yt,t1,6,3,15,1),this.a[0]=25,this.a[1]=50,this.a[2]=25):(t.p[A]==0||t.D[A]>9)&&t.p[n]==1||(t.p[n]==0||t.D[n]>9)&&t.p[A]==1?P==3||r==3?(this.b=c(Yt,t1,6,6,15,1),this.b[0]=0,this.b[1]=60,this.b[2]=120,this.b[3]=180,this.b[4]=240,this.b[5]=300,this.c=j1(Yt,[re,t1],[17,6],15,[6,2],2),this.c[0][0]=-15,this.c[0][1]=15,this.c[1][0]=45,this.c[1][1]=75,this.c[2][0]=105,this.c[2][1]=135,this.c[3][0]=165,this.c[3][1]=195,this.c[4][0]=225,this.c[4][1]=255,this.c[5][0]=285,this.c[5][1]=315,this.a=c(Yt,t1,6,6,15,1),this.a[0]=16,this.a[1]=16,this.a[2]=16,this.a[3]=16,this.a[4]=16,this.a[5]=16):P==1&&r==1?(this.b=c(Yt,t1,6,2,15,1),this.b[0]=120,this.b[1]=240,this.c=j1(Yt,[re,t1],[17,6],15,[2,2],2),this.c[0][0]=105,this.c[0][1]=135,this.c[1][0]=225,this.c[1][1]=255,this.a=c(Yt,t1,6,2,15,1),this.a[0]=50,this.a[1]=50):t.p[A]==1&&P==2&&r==1||t.p[n]==1&&r==2&&P==1?(this.b=c(Yt,t1,6,2,15,1),this.b[0]=90,this.b[1]=270,this.c=j1(Yt,[re,t1],[17,6],15,[2,2],2),this.c[0][0]=75,this.c[0][1]=105,this.c[1][0]=255,this.c[1][1]=285,this.a=c(Yt,t1,6,2,15,1),this.a[0]=50,this.a[1]=50):t.p[A]==1&&P==1&&r==2&&e[3]==-1||t.p[n]==1&&r==1&&P==2&&e[0]==-1?(this.b=c(Yt,t1,6,3,15,1),this.b[0]=0,this.b[1]=120,this.b[2]=240,this.c=j1(Yt,[re,t1],[17,6],15,[3,2],2),this.c[0][0]=-15,this.c[0][1]=15,this.c[1][0]=105,this.c[1][1]=135,this.c[2][0]=225,this.c[2][1]=255,this.a=c(Yt,t1,6,3,15,1),this.a[0]=60,this.a[1]=20,this.a[2]=20):t.p[A]==1&&P==1&&r==2&&e[3]!=-1||t.p[n]==1&&r==1&&P==2&&e[0]!=-1?(this.b=c(Yt,t1,6,3,15,1),this.b[0]=0,this.b[1]=120,this.b[2]=240,this.c=j1(Yt,[re,t1],[17,6],15,[3,2],2),this.c[0][0]=-15,this.c[0][1]=15,this.c[1][0]=105,this.c[1][1]=135,this.c[2][0]=225,this.c[2][1]=255,this.a=c(Yt,t1,6,3,15,1),this.a[0]=20,this.a[1]=40,this.a[2]=40):P==2&&r==2&&(e[0]==-1||e[3]==-1?(this.b=c(Yt,t1,6,2,15,1),this.b[0]=0,this.b[1]=180,this.c=j1(Yt,[re,t1],[17,6],15,[2,2],2),this.c[0][0]=-15,this.c[0][1]=15,this.c[1][0]=165,this.c[1][1]=195,this.a=c(Yt,t1,6,2,15,1),this.a[0]=50,this.a[1]=50):(this.b=c(Yt,t1,6,2,15,1),this.b[0]=90,this.b[1]=270,this.c=j1(Yt,[re,t1],[17,6],15,[2,2],2),this.c[0][0]=75,this.c[0][1]=105,this.c[1][0]=255,this.c[1][1]=285,this.a=c(Yt,t1,6,2,15,1),this.a[0]=50,this.a[1]=50)):t.p[A]==1&&t.p[n]==1?P==1&&r==1?(this.b=c(Yt,t1,6,2,15,1),this.b[0]=0,this.b[1]=180,this.c=j1(Yt,[re,t1],[17,6],15,[2,2],2),this.c[0][0]=-15,this.c[0][1]=15,this.c[1][0]=165,this.c[1][1]=195,this.a=c(Yt,t1,6,2,15,1),this.a[0]=10,this.a[1]=90):(this.b=c(Yt,t1,6,6,15,1),this.b[0]=0,this.b[1]=50,this.b[2]=130,this.b[3]=180,this.b[4]=230,this.b[5]=310,this.c=j1(Yt,[re,t1],[17,6],15,[6,2],2),this.c[0][0]=-15,this.c[0][1]=15,this.c[1][0]=35,this.c[1][1]=65,this.c[2][0]=115,this.c[2][1]=145,this.c[3][0]=165,this.c[3][1]=195,this.c[4][0]=215,this.c[4][1]=245,this.c[5][0]=295,this.c[5][1]=325,this.a=c(Yt,t1,6,6,15,1),this.a[0]=40,this.a[1]=5,this.a[2]=5,this.a[3]=40,this.a[4]=5,this.a[5]=5):(this.b=c(Yt,t1,6,1,15,1),this.b[0]=180,this.c=j1(Yt,[re,t1],[17,6],15,[1,2],2),this.c[0][0]=165,this.c[0][1]=195,this.a=c(Yt,t1,6,1,15,1),this.a[0]=100)}function iM(t,e,A){var n,P,r,i,f,D,Q,o,L,s,y,l,h,T,g,v,d,b,j,R,C,E,S,I,a,H,K,tt,X,W,$,st,ht,wt,rt,ct;for(I=(z1(),nn),G(t.b,\"M V30 BEGIN CTAB\"+I),G(t.b,TM+e.r+\" \"+e.s+\" 0 0 0\"+I),G(t.b,\"M V30 BEGIN ATOM\"+I),D=0;D0&&G(t.b,\",\"),C=(O1(),J0)[s[R]],C.length){case 2:case 3:case 1:G(t.b,C);break;default:G(t.b,\"?\")}G(t.b,\"]\")}else gt(V(e.C[D],1),0)?G(t.b,\" A\"):e.D[D]>=129&&e.D[D]<=144||e.D[D]==154?G(t.b,\" R#\"):G(t.b,\" \"+(O1(),J0)[e.D[D]]);if(A?(G(t.b,\" \"+Bt(fP*t.c*e.K[D].a)/fP),G(t.b,\" \"+Bt(fP*t.c*-e.K[D].b)/fP),G(t.b,\" \"+Bt(fP*t.c*-e.K[D].c)/fP)):G(t.b,\" 0 0 0\"),G(t.b,\" \"+w.Math.abs(e.A[D])),e.t[D]!=0&&G(t.b,\" CHG=\"+e.t[D]),(e.v[D]&48)!=0)switch(G(t.b,\" RAD=\"),e.v[D]&48){case 16:G(t.b,\"1\");break;case 32:G(t.b,\"2\");break;case 48:G(t.b,\"3\")}((e.v[D]&3)==1||(e.v[D]&3)==2)&&(G(t.b,\" CFG=\"),(e.v[D]&3)==1?G(t.b,\"1\"):G(t.b,\"2\")),e.B[D]!=0&&G(t.b,\" MASS=\"+e.B[D]),ct=((e.v[D]&m8)>>>27)-1,ct!=-1&&G(t.b,\" VAL=\"+(ct==0?\"-1\":U1(ct))),y=e.D[D],(y>=129&&y<=144||y==154)&&G(t.b,\" RGROUPS=(1 \"+(y==154?0:y>=142?y-141:y-125)+\")\"),j=V(uP,e.C[D]),O(j,384)==0?G(t.b,\" HCOUNT=2\"):O(j,128)==0?G(t.b,\" HCOUNT=1\"):O(j,1792)==0?G(t.b,\" HCOUNT=-1\"):O(j,1664)==0&&G(t.b,\" HCOUNT=1\"),ht=V(e.C[D],6144),O(ht,0)!=0&&(gt(V(ht,E0),0)?G(t.b,\" SUBST=\"+(e.e[D]+1)):G(t.b,\" SUBST=-1\")),W=V(e.C[D],120),O(W,0)!=0&&(O(W,112)==0?G(t.b,\" RBCNT=-1\"):O(W,8)==0||O(W,104)==0?G(t.b,\" RBCNT=2\"):O(W,88)==0?G(t.b,\" RBCNT=3\"):O(W,56)==0&&G(t.b,\" RBCNT=4\")),G(t.b,I)}for(G(t.b,\"M V30 END ATOM\"+I),G(t.b,\"M V30 BEGIN BOND\"+I),h=0;h>18,rt==1?++r[(e.v[L]&b1)>>18!=1&&(e.v[L]&b1)>>18!=2?-1:(e.v[L]&VA)>>20]:rt==2?++H[(e.v[L]&b1)>>18!=1&&(e.v[L]&b1)>>18!=2?-1:(e.v[L]&VA)>>20]:++n);for(P=0,K=c(B,F,6,32,15,1),i=c(B,F,6,32,15,1),T=0;T>8,rt==1?++i[(e.G[T]&768)>>8!=1&&(e.G[T]&768)>>8!=2?-1:(e.G[T]&cn)>>10]:rt==2?++K[(e.G[T]&768)>>8!=1&&(e.G[T]&768)>>8!=2?-1:(e.G[T]&cn)>>10]:++P);if(X){if(G(t.b,\"M V30 BEGIN COLLECTION\"+I),n!=0){for(G(t.b,\"M V30 MDLV30/STEABS ATOMS=(\"+n),f=0;f>18==0&&G(t.b,\" \"+(f+1));G(t.b,\")\"+I)}if(P!=0){for(G(t.b,\"M V30 MDLV30/STEABS BONDS=(\"+P),l=0;l>8==0&&G(t.b,\" \"+(l+1));G(t.b,\")\"+I)}for(b=0;b<32;b++){if(H[b]!=0){for(G(t.b,cM+(b+1)+\" ATOMS=(\"+H[b]),f=0;f>18==2&&((e.v[f]&b1)>>18!=1&&(e.v[f]&b1)>>18!=2?-1:(e.v[f]&VA)>>20)==b&&G(t.b,\" \"+(f+1));G(t.b,\")\"+I)}if(r[b]!=0){for(G(t.b,wM+(b+1)+\" ATOMS=(\"+r[b]),f=0;f>18==1&&((e.v[f]&b1)>>18!=1&&(e.v[f]&b1)>>18!=2?-1:(e.v[f]&VA)>>20)==b&&G(t.b,\" \"+(f+1));G(t.b,\")\"+I)}if(K[b]!=0){for(G(t.b,cM+(b+1)+\" BONDS=(\"+K[b]),l=0;l>8==2&&((e.G[l]&768)>>8!=1&&(e.G[l]&768)>>8!=2?-1:(e.G[l]&cn)>>10)==b&&G(t.b,\" \"+(l+1));G(t.b,\")\"+I)}if(i[b]!=0){for(G(t.b,wM+(b+1)+\" BONDS=(\"+i[b]),l=0;l>8==1&&((e.G[l]&768)>>8!=1&&(e.G[l]&768)>>8!=2?-1:(e.G[l]&cn)>>10)==b&&G(t.b,\" \"+(l+1));G(t.b,\")\"+I)}}G(t.b,\"M V30 END COLLECTION\"+I)}G(t.b,t4+I)}function j6(t,e,A,n,P,r){var i,f,D,Q,o,L,s,y,l,h,T,g,v,d,b,j,R;if(e[A-1]==36){if(t.r=!0,t.q=new n1,A+=VR(t,e,A-1,t.q)-1,e[A++]!=93){if(!r)throw et(new H1(\"SmilesParser: A positive recursive SMARTS followed by another one or by atom query features is not supported. Position:\"+(A-1)));(t.k&128)==0&&(A=$Y(e,A))}return A}if(e[A-1]==42)t.d=6,t.c=z(t.c,1);else if(e[A-1]==63)t.d=0;else if(Q=e[A-1]==33,Q&&(t.r=!0,t.c=z(t.c,1),++A),e[A-1]==82&&P&&(Rn(e[A]&d1)||t.j&&e[A]==123))t.d=6,t.c=z(t.c,1),--A,Q&&--A;else{if(o=new U7,!mp(t,e,A-1,n,o))throw et(new H1(\"SmilesParser: Unexpected character in atom definition:'\"+String.fromCharCode(e[A-1]&d1)+FM+(A-1)));if(t.d=o.a,A+=o.b-1,(t.k&3)!=2&&(t.g=9),P&&(e[A]==44||Q)){for(y=o.d,s=o.c,d=A-o.b;d \"+n),R=e.length,Ek(d,d,R),pP(e,c(_,At,6,n-d,15,1),d,n)))+kM+d))}else fG(t,o.a),y=y|o.d,s=s|o.c;if(d+=o.b,Q&&e[d]!=59&&e[d]!=38||!Q&&e[d]!=44||Q&&e[d+1]!=33)break;++d,e[d]==33&&++d}t.b&&t.b.d.a.length>1&&(t.g=-1,s?y||(t.c=z(t.c,4)):t.c=z(t.c,2)),A=d}}for(h=new aw(e),g=c(B,F,6,1,15,1),v=!0;v;){if(e[A]==64){if(++A,e[A]==83&&e[A+1]==80||e[A]==84&&e[A+1]==66||e[A]==79&&e[A+1]==72){for(A+=2;Rn(e[A]&d1);)++A;continue}e[A]==64?(t.i=!0,++A):(e[A]==84&&e[A+1]==72||e[A]==65&&e[A+1]==76)&&(A+=2,t.i=e[A]==50,++A),t.p=!0;continue}if(e[A]==58){for(++A;Rn(e[A]&d1);)t.o=10*t.o+e[A]-48,++A;continue}if(e[A]==91)throw et(new H1(\"SmilesParser: nested square brackets found. 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w.Resources.registerFromNodejs=function(){throw new Error(\"Resources.registerFromNodejs can only be called from Node.js\")}}function Prt(){return new URL(\"resources.json\",import.meta.url).href}hw(X0);LI(X0);sI(X0);var Aot=X0,not=X0.CanvasEditor,Pot=X0.registerCustomElement,rot=X0.Canonizer,iot=X0.CanonizerUtil,fot=X0.ConformerGenerator,Dot=X0.DrugScoreCalculator,uot=X0.DruglikenessPredictor,Qot=X0.ForceFieldMMFF94,oot=X0.MCS,Uot=X0.Molecule,Lot=X0.MoleculeProperties,sot=X0.Reaction,yot=X0.ReactionEncoder,lot=X0.Reactor,hot=X0.Resources,Tot=X0.RingCollection,cot=X0.SDFileParser,wot=X0.SSSearcher,got=X0.SSSearcherWithIndex,dot=X0.SmilesParser,Bot=X0.ToxicityPredictor,vot=X0.Transformer,Fot=X0.Util,kot=X0.version;export{rot as Canonizer,iot as CanonizerUtil,not as CanvasEditor,fot as ConformerGenerator,Dot as DrugScoreCalculator,uot as DruglikenessPredictor,Qot as ForceFieldMMFF94,oot as MCS,Uot as Molecule,Lot as MoleculeProperties,sot as Reaction,yot as ReactionEncoder,lot as Reactor,hot as Resources,Tot as RingCollection,cot as SDFileParser,wot as SSSearcher,got as SSSearcherWithIndex,dot as SmilesParser,Bot as ToxicityPredictor,vot as Transformer,Fot as Util,Aot as default,Pot as registerCustomElement,kot as version};\n/**\n * openchemlib - Manipulate molecules\n * @version v9.25.0\n * @date 2026-08-14T08:23:36.230Z\n * @link https://github.com/cheminfo/openchemlib-js\n * @license BSD-3-Clause\n*/\n","// Generated by CoffeeScript 1.8.0\n(function() {\n var Heap, defaultCmp, floor, heapify, heappop, heappush, heappushpop, heapreplace, insort, min, nlargest, nsmallest, updateItem, _siftdown, _siftup;\n\n floor = Math.floor, min = Math.min;\n\n\n /*\n Default comparison function to be used\n */\n\n defaultCmp = function(x, y) {\n if (x < y) {\n return -1;\n }\n if (x > y) {\n return 1;\n }\n return 0;\n };\n\n\n /*\n Insert item x in list a, and keep it sorted assuming a is sorted.\n \n If x is already in a, insert it to the right of the rightmost x.\n \n 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(array.length) {\n returnitem = array[0];\n array[0] = lastelt;\n _siftup(array, 0, cmp);\n } else {\n returnitem = lastelt;\n }\n return returnitem;\n };\n\n\n /*\n Pop and return the current smallest value, and add the new item.\n \n This is more efficient than heappop() followed by heappush(), and can be\n more appropriate when using a fixed size heap. 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That constrains reasonable use of\n this routine unless written as part of a conditional replacement:\n if item > array[0]\n item = heapreplace(array, item)\n */\n\n heapreplace = function(array, item, cmp) {\n var returnitem;\n if (cmp == null) {\n cmp = defaultCmp;\n }\n returnitem = array[0];\n array[0] = item;\n _siftup(array, 0, cmp);\n return returnitem;\n };\n\n\n /*\n Fast version of a heappush followed by a heappop.\n */\n\n heappushpop = function(array, item, cmp) {\n var _ref;\n if (cmp == null) {\n cmp = defaultCmp;\n }\n if (array.length && cmp(array[0], item) < 0) {\n _ref = [array[0], item], item = _ref[0], array[0] = _ref[1];\n _siftup(array, 0, cmp);\n }\n return item;\n };\n\n\n /*\n Transform list into a heap, in-place, in O(array.length) time.\n */\n\n heapify = function(array, cmp) {\n var i, _i, _j, _len, _ref, _ref1, _results, _results1;\n if (cmp == null) {\n cmp = defaultCmp;\n }\n _ref1 = (function() {\n _results1 = [];\n for (var _j = 0, _ref = floor(array.length / 2); 0 <= _ref ? _j < _ref : _j > _ref; 0 <= _ref ? _j++ : _j--){ _results1.push(_j); }\n return _results1;\n }).apply(this).reverse();\n _results = [];\n for (_i = 0, _len = _ref1.length; _i < _len; _i++) {\n i = _ref1[_i];\n _results.push(_siftup(array, i, cmp));\n }\n return _results;\n };\n\n\n /*\n Update the position of the given item in the heap.\n This function should be called every time the item is being modified.\n */\n\n updateItem = function(array, item, cmp) {\n var pos;\n if (cmp == null) {\n cmp = defaultCmp;\n }\n pos = array.indexOf(item);\n if (pos === -1) {\n return;\n }\n _siftdown(array, 0, pos, cmp);\n return _siftup(array, pos, cmp);\n };\n\n\n /*\n Find the n largest elements in a dataset.\n */\n\n nlargest = function(array, n, cmp) {\n var elem, result, _i, _len, _ref;\n if (cmp == null) {\n cmp = defaultCmp;\n }\n result = array.slice(0, n);\n if (!result.length) {\n return result;\n }\n heapify(result, cmp);\n _ref = array.slice(n);\n for (_i = 0, _len = _ref.length; _i < _len; _i++) {\n elem = _ref[_i];\n heappushpop(result, elem, cmp);\n }\n return result.sort(cmp).reverse();\n };\n\n\n /*\n Find the n smallest elements in a dataset.\n */\n\n nsmallest = function(array, n, cmp) {\n var elem, i, los, result, _i, _j, _len, _ref, _ref1, _results;\n if (cmp == null) {\n cmp = defaultCmp;\n }\n if (n * 10 <= array.length) {\n result = array.slice(0, n).sort(cmp);\n if (!result.length) {\n return result;\n }\n los = result[result.length - 1];\n _ref = array.slice(n);\n for (_i = 0, _len = _ref.length; _i < _len; _i++) {\n elem = _ref[_i];\n if (cmp(elem, los) < 0) {\n insort(result, elem, 0, null, cmp);\n result.pop();\n los = result[result.length - 1];\n }\n }\n return result;\n }\n heapify(array, cmp);\n _results = [];\n for (i = _j = 0, _ref1 = min(n, array.length); 0 <= _ref1 ? _j < _ref1 : _j > _ref1; i = 0 <= _ref1 ? ++_j : --_j) {\n _results.push(heappop(array, cmp));\n }\n return _results;\n };\n\n _siftdown = function(array, startpos, pos, cmp) {\n var newitem, parent, parentpos;\n if (cmp == null) {\n cmp = defaultCmp;\n }\n newitem = array[pos];\n while (pos > startpos) {\n parentpos = (pos - 1) >> 1;\n parent = array[parentpos];\n if (cmp(newitem, parent) < 0) {\n array[pos] = parent;\n pos = parentpos;\n continue;\n }\n break;\n }\n return array[pos] = newitem;\n };\n\n _siftup = function(array, pos, cmp) {\n var childpos, endpos, newitem, rightpos, startpos;\n if (cmp == null) {\n cmp = defaultCmp;\n }\n endpos = array.length;\n startpos = pos;\n newitem = array[pos];\n childpos = 2 * pos + 1;\n while (childpos < endpos) {\n rightpos = childpos + 1;\n if (rightpos < endpos && !(cmp(array[childpos], array[rightpos]) < 0)) {\n childpos = rightpos;\n }\n array[pos] = array[childpos];\n pos = childpos;\n childpos = 2 * pos + 1;\n }\n array[pos] = newitem;\n return _siftdown(array, startpos, pos, cmp);\n };\n\n Heap = (function() {\n Heap.push = heappush;\n\n Heap.pop = heappop;\n\n Heap.replace = heapreplace;\n\n Heap.pushpop = heappushpop;\n\n Heap.heapify = heapify;\n\n Heap.updateItem = updateItem;\n\n Heap.nlargest = nlargest;\n\n Heap.nsmallest = nsmallest;\n\n function Heap(cmp) {\n this.cmp = cmp != null ? cmp : defaultCmp;\n this.nodes = [];\n }\n\n Heap.prototype.push = function(x) {\n return heappush(this.nodes, x, this.cmp);\n };\n\n Heap.prototype.pop = function() {\n return heappop(this.nodes, this.cmp);\n };\n\n Heap.prototype.peek = function() {\n return this.nodes[0];\n };\n\n Heap.prototype.contains = function(x) {\n return this.nodes.indexOf(x) !== -1;\n };\n\n Heap.prototype.replace = function(x) {\n return heapreplace(this.nodes, x, this.cmp);\n };\n\n Heap.prototype.pushpop = function(x) {\n return heappushpop(this.nodes, x, this.cmp);\n };\n\n Heap.prototype.heapify = function() {\n return heapify(this.nodes, this.cmp);\n };\n\n Heap.prototype.updateItem = function(x) {\n return updateItem(this.nodes, x, this.cmp);\n };\n\n Heap.prototype.clear = function() {\n return this.nodes = [];\n };\n\n Heap.prototype.empty = function() {\n return this.nodes.length === 0;\n };\n\n Heap.prototype.size = function() {\n return this.nodes.length;\n };\n\n Heap.prototype.clone = function() {\n var heap;\n heap = new Heap();\n heap.nodes = this.nodes.slice(0);\n return heap;\n };\n\n Heap.prototype.toArray = function() {\n return this.nodes.slice(0);\n };\n\n Heap.prototype.insert = Heap.prototype.push;\n\n Heap.prototype.top = Heap.prototype.peek;\n\n Heap.prototype.front = Heap.prototype.peek;\n\n Heap.prototype.has = Heap.prototype.contains;\n\n Heap.prototype.copy = Heap.prototype.clone;\n\n return Heap;\n\n })();\n\n (function(root, factory) {\n if (typeof define === 'function' && define.amd) {\n return define([], factory);\n } else if (typeof exports === 'object') {\n return module.exports = factory();\n } else {\n return root.Heap = factory();\n }\n })(this, function() {\n return Heap;\n });\n\n}).call(this);\n","export const GAUSSIAN_EXP_FACTOR = -4 * Math.LN2;\n/**\n * A pseudo-Voigt is a gaussian plus a lorentzian. Beyond 3.74 fwhm from the\n * centre the gaussian part is down to 1.4e-17 — too small to change a shape of\n * height one — while the lorentzian part is still 1.8e-2 and fades much more\n * slowly. Past that distance the gaussian is skipped: the `Math.exp` it costs\n * cannot change the result, and most points of a wide window lie out there.\n *\n * The limit is stored squared, so the test is `(x / fwhm)² > 14`, which saves a\n * square root. The same value works for every mixing ratio `mu`, except `mu = 1`\n * where the shape is a pure gaussian: with no lorentzian part left, skipping\n * would return zero instead of a very small number, so that case is never\n * skipped.\n */\nexport const GAUSSIAN_CUTOFF = 14;\nexport const ROOT_PI_OVER_LN2 = Math.sqrt(Math.PI / Math.LN2);\nexport const ROOT_LN2 = Math.sqrt(Math.LN2);\nexport const ROOT_THREE = Math.sqrt(3);\nexport const ROOT_2LN2 = Math.sqrt(2 * Math.LN2);\nexport const ROOT_2LN2_MINUS_ONE = Math.sqrt(2 * Math.LN2) - 1;\n//# sourceMappingURL=constants.js.map","// https://en.wikipedia.org/wiki/Error_function#Inverse_functions\n// This code yields to a good approximation\n// If needed a better implementation using polynomial can be found on https://en.wikipedia.org/wiki/Error_function#Inverse_functions\n/**\n * Approximate inverse error function.\n * @param x - value in the range (-1, 1).\n * @returns erfinv(x).\n * @see https://en.wikipedia.org/wiki/Error_function#Inverse_functions\n */\nexport default function erfinv(x) {\n const a = 0.147;\n if (x === 0)\n return 0;\n const ln1MinusXSqrd = Math.log(1 - x * x);\n const lnEtcBy2Plus2 = ln1MinusXSqrd / 2 + 2 / (Math.PI * a);\n const firstSqrt = Math.sqrt(lnEtcBy2Plus2 ** 2 - ln1MinusXSqrd / a);\n const secondSqrt = Math.sqrt(firstSqrt - lnEtcBy2Plus2);\n return secondSqrt * (x > 0 ? 1 : -1);\n}\n//# sourceMappingURL=erfinv.js.map","import { GAUSSIAN_EXP_FACTOR, ROOT_2LN2, ROOT_LN2, ROOT_PI_OVER_LN2, } from \"../../../util/constants.js\";\nimport erfinv from \"../../../util/erfinv.js\";\nexport class Gaussian {\n kind = 'gaussian';\n /**\n * Full width at half maximum.\n * @default 500\n */\n fwhm;\n constructor(options = {}) {\n const { fwhm = 500, sd } = options;\n this.fwhm = sd ? gaussianWidthToFWHM(2 * sd) : fwhm;\n }\n fwhmToWidth(fwhm = this.fwhm) {\n return gaussianFwhmToWidth(fwhm);\n }\n widthToFWHM(width) {\n return gaussianWidthToFWHM(width);\n }\n fct(x) {\n return gaussianFct(x, this.fwhm);\n }\n getArea(height = calculateGaussianHeight({ fwhm: this.fwhm })) {\n return getGaussianArea({ fwhm: this.fwhm, height });\n }\n getFactor(area) {\n return getGaussianFactor(area);\n }\n getData(options = {}) {\n return getGaussianData(this, options);\n }\n calculateHeight(area = 1) {\n return calculateGaussianHeight({ fwhm: this.fwhm, area });\n }\n getParameters() {\n return ['fwhm'];\n }\n /**\n * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n * @returns the shape descriptor.\n */\n toJSON() {\n return { kind: this.kind, fwhm: this.fwhm };\n }\n derivative(x) {\n const { fct, dx, dFwhm } = gaussianDerivative(x, this.fwhm);\n return { fct, dx, parameters: [dFwhm] };\n }\n}\n/**\n * Calculate the peak height for a given area and fwhm.\n * @param options - fwhm, area, and optional sd.\n * @returns the peak height.\n */\nexport function calculateGaussianHeight(options) {\n const { area = 1, sd } = options;\n let { fwhm = 500 } = options;\n if (sd)\n fwhm = gaussianWidthToFWHM(2 * sd);\n return (2 * area) / ROOT_PI_OVER_LN2 / fwhm;\n}\n/**\n * Evaluate the gaussian function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @returns the intensity at x.\n */\nexport function gaussianFct(x, fwhm) {\n return Math.exp(GAUSSIAN_EXP_FACTOR * (x / fwhm) ** 2);\n}\n/**\n * Analytical value and partial derivatives of the gaussian function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`) and `fwhm` (`dFwhm`).\n */\nexport function gaussianDerivative(x, fwhm) {\n const fct = gaussianFct(x, fwhm);\n const dx = ((2 * GAUSSIAN_EXP_FACTOR * x) / (fwhm * fwhm)) * fct;\n const dFwhm = ((-2 * GAUSSIAN_EXP_FACTOR * x * x) / (fwhm * fwhm * fwhm)) * fct;\n return { fct, dx, dFwhm };\n}\n/**\n * Convert inflection-point width to full width at half maximum.\n * @param width - width between inflection points.\n * @returns full width at half maximum.\n */\nexport function gaussianWidthToFWHM(width) {\n return width * ROOT_2LN2;\n}\n/**\n * Convert full width at half maximum to inflection-point width.\n * @param fwhm - full width at half maximum.\n * @returns width between inflection points.\n */\nexport function gaussianFwhmToWidth(fwhm) {\n return fwhm / ROOT_2LN2;\n}\n/**\n * Calculate the area under a gaussian peak.\n * @param options - fwhm, height, and optional sd.\n * @returns the area.\n */\nexport function getGaussianArea(options) {\n const { sd, height = 1 } = options;\n let { fwhm = 500 } = options;\n if (sd)\n fwhm = gaussianWidthToFWHM(2 * sd);\n return (height * ROOT_PI_OVER_LN2 * fwhm) / 2;\n}\n/**\n * Calculate the width factor corresponding to a given area coverage fraction.\n * @param area - target area fraction (0–1). Defaults to `0.9999`.\n * @returns the factor by which to multiply fwhm to cover the given area.\n */\nexport function getGaussianFactor(area = 0.9999) {\n if (area >= 1) {\n throw new Error('area should be (0 - 1)');\n }\n return erfinv(area) / ROOT_LN2;\n}\n/**\n * Generate an intensity array for a gaussian shape.\n * @param shape - gaussian shape parameters (fwhm, sd).\n * @param options - sampling options (length, factor, height).\n * @returns Float64Array of intensity values.\n */\nexport function getGaussianData(shape = {}, options = {}) {\n const { sd } = shape;\n let { fwhm = 500 } = shape;\n if (sd)\n fwhm = gaussianWidthToFWHM(2 * sd);\n const { factor = getGaussianFactor(), height = calculateGaussianHeight({ fwhm }), } = options;\n let { length } = options;\n if (!length) {\n length = Math.min(Math.ceil(fwhm * factor), 2 ** 25 - 1);\n if (length % 2 === 0)\n length++;\n }\n const center = (length - 1) / 2;\n const data = new Float64Array(length);\n for (let i = 0; i <= center; i++) {\n const value = gaussianFct(i - center, fwhm) * height;\n data[i] = value;\n data[length - 1 - i] = value;\n }\n return data;\n}\n//# sourceMappingURL=Gaussian.js.map","import { ROOT_THREE } from \"../../../util/constants.js\";\nexport class Lorentzian {\n kind = 'lorentzian';\n /**\n * Full width at half maximum.\n * @default 500\n */\n fwhm;\n constructor(options = {}) {\n const { fwhm = 500 } = options;\n this.fwhm = fwhm;\n }\n fwhmToWidth(fwhm = this.fwhm) {\n return lorentzianFwhmToWidth(fwhm);\n }\n widthToFWHM(width) {\n return lorentzianWidthToFWHM(width);\n }\n fct(x) {\n return lorentzianFct(x, this.fwhm);\n }\n getArea(height = 1) {\n return getLorentzianArea({ fwhm: this.fwhm, height });\n }\n getFactor(area) {\n return getLorentzianFactor(area);\n }\n getData(options = {}) {\n return getLorentzianData(this, options);\n }\n calculateHeight(area = 1) {\n return calculateLorentzianHeight({ fwhm: this.fwhm, area });\n }\n getParameters() {\n return ['fwhm'];\n }\n /**\n * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n * @returns the shape descriptor.\n */\n toJSON() {\n return { kind: this.kind, fwhm: this.fwhm };\n }\n derivative(x) {\n const { fct, dx, dFwhm } = lorentzianDerivative(x, this.fwhm);\n return { fct, dx, parameters: [dFwhm] };\n }\n}\nexport const calculateLorentzianHeight = ({ fwhm = 1, area = 1 }) => {\n return (2 * area) / Math.PI / fwhm;\n};\nexport const getLorentzianArea = (options) => {\n const { fwhm = 500, height = 1 } = options;\n return (height * Math.PI * fwhm) / 2;\n};\nexport const lorentzianFct = (x, fwhm) => {\n return fwhm ** 2 / (4 * x ** 2 + fwhm ** 2);\n};\n/**\n * Analytical value and partial derivatives of the lorentzian function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`) and `fwhm` (`dFwhm`).\n */\nexport function lorentzianDerivative(x, fwhm) {\n const denominator = 4 * x * x + fwhm * fwhm;\n const fct = (fwhm * fwhm) / denominator;\n const dx = (-8 * x * fwhm * fwhm) / (denominator * denominator);\n const dFwhm = (8 * fwhm * x * x) / (denominator * denominator);\n return { fct, dx, dFwhm };\n}\nexport const lorentzianWidthToFWHM = (width) => {\n return width * ROOT_THREE;\n};\nexport const lorentzianFwhmToWidth = (fwhm) => {\n return fwhm / ROOT_THREE;\n};\nconst lorentzianQuantile = (p) => Math.tan(Math.PI * (p - 0.5));\nexport const getLorentzianFactor = (area = 0.9999) => {\n if (area >= 1) {\n throw new Error('area should be (0 - 1)');\n }\n const halfResidual = (1 - area) * 0.5;\n return ((lorentzianQuantile(1 - halfResidual) - lorentzianQuantile(halfResidual)) /\n 2);\n};\nexport const getLorentzianData = (shape = {}, options = {}) => {\n const { fwhm = 500 } = shape;\n const { factor = getLorentzianFactor(), height = calculateLorentzianHeight({ fwhm, area: 1 }), } = options;\n let { length } = options;\n if (!length) {\n length = Math.min(Math.ceil(fwhm * factor), 2 ** 25 - 1);\n if (length % 2 === 0)\n length++;\n }\n const center = (length - 1) / 2;\n const data = new Float64Array(length);\n for (let i = 0; i <= center; i++) {\n const value = lorentzianFct(i - center, fwhm) * height;\n data[i] = value;\n data[length - 1 - i] = value;\n }\n return data;\n};\n//# sourceMappingURL=Lorentzian.js.map","import { calculateLorentzianHeight, getLorentzianFactor, lorentzianFwhmToWidth, lorentzianWidthToFWHM, } from \"../lorentzian/Lorentzian.js\";\nexport class LorentzianDispersive {\n kind = 'lorentzianDispersive';\n /**\n * Full width at half maximum.\n * @default 500\n */\n fwhm;\n constructor(options = {}) {\n const { fwhm = 500 } = options;\n this.fwhm = fwhm;\n }\n fwhmToWidth(fwhm = this.fwhm) {\n return lorentzianFwhmToWidth(fwhm);\n }\n widthToFWHM(width) {\n return lorentzianWidthToFWHM(width);\n }\n fct(x) {\n return lorentzianDispersiveFct(x, this.fwhm);\n }\n getArea() {\n return 0;\n }\n getFactor(area) {\n return getLorentzianFactor(area);\n }\n getData(options = {}) {\n return getLorentzianDispersiveData(this, options);\n }\n calculateHeight(area = 1) {\n return calculateLorentzianHeight({ fwhm: this.fwhm, area });\n }\n getParameters() {\n return ['fwhm'];\n }\n /**\n * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n * @returns the shape descriptor.\n */\n toJSON() {\n return { kind: this.kind, fwhm: this.fwhm };\n }\n derivative(x) {\n const { fct, dx, dFwhm } = lorentzianDispersiveDerivative(x, this.fwhm);\n return { fct, dx, parameters: [dFwhm] };\n }\n}\nexport const lorentzianDispersiveFct = (x, fwhm) => {\n return (2 * fwhm * x) / (4 * x ** 2 + fwhm ** 2);\n};\n/**\n * Analytical value and partial derivatives of the dispersive lorentzian function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`) and `fwhm` (`dFwhm`).\n */\nexport function lorentzianDispersiveDerivative(x, fwhm) {\n const denominator = 4 * x * x + fwhm * fwhm;\n const fct = (2 * fwhm * x) / denominator;\n const dx = (2 * fwhm * (fwhm * fwhm - 4 * x * x)) / (denominator * denominator);\n const dFwhm = (2 * x * (4 * x * x - fwhm * fwhm)) / (denominator * denominator);\n return { fct, dx, dFwhm };\n}\nexport const getLorentzianDispersiveData = (shape = {}, options = {}) => {\n const { fwhm = 500 } = shape;\n const { factor = getLorentzianFactor(), height = calculateLorentzianHeight({ fwhm, area: 1 }), } = options;\n let { length } = options;\n if (!length) {\n length = Math.min(Math.ceil(fwhm * factor), 2 ** 25 - 1);\n if (length % 2 === 0)\n length++;\n }\n const center = (length - 1) / 2;\n const data = new Float64Array(length);\n for (let i = 0; i <= center; i++) {\n const value = lorentzianDispersiveFct(i - center, fwhm) * height;\n data[i] = value;\n data[length - 1 - i] = -value;\n }\n return data;\n};\n//# sourceMappingURL=LorentzianDispersive.js.map","import { getGaussianFactor } from \"../gaussian/Gaussian.js\";\nimport { getLorentzianFactor } from \"../lorentzian/Lorentzian.js\";\n/**\n * Find the k factor for a pseudo-Voigt distribution such that the\n * cumulative probability pPseudoVoigt(k, mu) equals `pTarget`.\n *\n * Uses a simple bisection search (with exponential bracketing) to\n * invert the pseudo-Voigt cumulative function. Special cases:\n * - mu === 1 -> reduces to the gaussian case\n * - mu === 0 -> reduces to the lorentzian case\n * @param pTarget - Target cumulative probability in (0,1)\n * @param mu - Gaussian fraction in [0,1]\n * @param tol - Convergence tolerance\n * @param maxIter - Maximum number of bisection iterations\n * @returns the factor k such that pPseudoVoigt(k, mu) ~= pTarget\n */\nexport function pseudoVoigtFindFactor(pTarget, mu, tol = 1e-9, maxIter = 200) {\n if (pTarget <= 0 || pTarget >= 1) {\n throw new RangeError('pTarget must be in (0,1)');\n }\n if (mu === 1) {\n return getGaussianFactor(pTarget);\n }\n else if (mu === 0) {\n return getLorentzianFactor(pTarget);\n }\n // bisection\n let lo = 0;\n let hi = 10;\n let it = 0;\n while (pPseudoVoigt(hi, mu) < pTarget && it++ < 200)\n hi *= 2;\n for (let i = 0; i < maxIter; i++) {\n const mid = 0.5 * (lo + hi);\n const val = pPseudoVoigt(mid, mu);\n if (Math.abs(val - pTarget) < tol)\n return mid;\n if (val < pTarget) {\n lo = mid;\n }\n else {\n hi = mid;\n }\n }\n return 0.5 * (lo + hi);\n}\nfunction erf(x) {\n const sign = x < 0 ? -1 : 1;\n x = Math.abs(x);\n const a1 = 0.254829592;\n const a2 = -0.284496736;\n const a3 = 1.421413741;\n const a4 = -1.453152027;\n const a5 = 1.061405429;\n const p = 0.3275911;\n const t = 1 / (1 + p * x);\n const y = 1 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.exp(-x * x);\n return sign * y;\n}\nconst sqrtLn2 = Math.sqrt(Math.log(2));\nfunction pGaussian(k) {\n return erf(k * sqrtLn2);\n}\nfunction pLorentz(k) {\n return (2 / Math.PI) * Math.atan(k);\n}\nfunction pPseudoVoigt(k, mu) {\n return (1 - mu) * pLorentz(k) + mu * pGaussian(k);\n}\n//# sourceMappingURL=computeFactor.js.map","import { GAUSSIAN_CUTOFF, GAUSSIAN_EXP_FACTOR, ROOT_2LN2_MINUS_ONE, ROOT_PI_OVER_LN2, } from \"../../../util/constants.js\";\nimport { gaussianFct } from \"../gaussian/Gaussian.js\";\nimport { lorentzianFct } from \"../lorentzian/Lorentzian.js\";\nimport { pseudoVoigtFindFactor } from \"./computeFactor.js\";\nexport class PseudoVoigt {\n kind = 'pseudoVoigt';\n fwhm;\n /**\n * Ratio of gaussian contribution in the shape\n * @default 0.5\n */\n mu;\n constructor(options = {}) {\n const { fwhm = 500, mu = 0.5 } = options;\n this.mu = mu;\n this.fwhm = fwhm;\n }\n fwhmToWidth(fwhm = this.fwhm, mu = this.mu) {\n return pseudoVoigtFwhmToWidth(fwhm, mu);\n }\n widthToFWHM(width, mu = this.mu) {\n return pseudoVoigtWidthToFWHM(width, mu);\n }\n fct(x) {\n return pseudoVoigtFct(x, this.fwhm, this.mu);\n }\n getArea(height = 1) {\n return getPseudoVoigtArea({ fwhm: this.fwhm, height, mu: this.mu });\n }\n getFactor(area) {\n return getPseudoVoigtFactor(area, this.mu);\n }\n getData(options = {}) {\n const { length, factor, height = calculatePseudoVoigtHeight({\n fwhm: this.fwhm,\n mu: this.mu,\n area: 1,\n }), } = options;\n return getPseudoVoigtData(this, { factor, length, height });\n }\n calculateHeight(area = 1) {\n return calculatePseudoVoigtHeight({ fwhm: this.fwhm, mu: this.mu, area });\n }\n getParameters() {\n return ['fwhm', 'mu'];\n }\n /**\n * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n * @returns the shape descriptor.\n */\n toJSON() {\n return { kind: this.kind, fwhm: this.fwhm, mu: this.mu };\n }\n derivative(x) {\n const { fct, dx, dFwhm, dMu } = pseudoVoigtDerivative(x, this.fwhm, this.mu);\n return { fct, dx, parameters: [dFwhm, dMu] };\n }\n}\nexport const calculatePseudoVoigtHeight = (options = {}) => {\n const { fwhm = 1, mu = 0.5, area = 1 } = options;\n return (2 * area) / (fwhm * (mu * ROOT_PI_OVER_LN2 + (1 - mu) * Math.PI));\n};\nexport const pseudoVoigtFct = (x, fwhm, mu) => {\n // at mu = 1 the shape *is* the gaussian: there is no lorentzian half left to\n // carry the tail, so the gaussian is evaluated however far out it is asked for\n if (mu === 1)\n return gaussianFct(x, fwhm);\n const lorentzian = (1 - mu) * lorentzianFct(x, fwhm);\n const z = x / fwhm;\n if (z * z > GAUSSIAN_CUTOFF)\n return lorentzian;\n return lorentzian + mu * gaussianFct(x, fwhm);\n};\n/**\n * Analytical value and partial derivatives of the pseudo-Voigt function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @param mu - ratio of gaussian contribution in the shape.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`), `fwhm` (`dFwhm`) and `mu` (`dMu`).\n */\nexport function pseudoVoigtDerivative(x, fwhm, mu) {\n // gaussian and lorentzian derivative math is inlined (rather than calling\n // gaussianDerivative / lorentzianDerivative) to allocate a single object on\n // this hot path; the sub-calls would allocate three.\n //\n // Past {@link GAUSSIAN_CUTOFF} the gaussian half has underflowed, so it is\n // dropped here under the same condition as in `pseudoVoigtFct` — including its\n // mu = 1 exemption, so the two stay consistent — which also settles what the\n // derivatives are out there: `dx` and `dFwhm` keep only their lorentzian\n // halves, and `dMu` becomes `-lorentz`, the value the shape loses by trading\n // its lorentzian half for a gaussian one that contributes nothing.\n const z = x / fwhm;\n const e = mu !== 1 && z * z > GAUSSIAN_CUTOFF\n ? 0\n : Math.exp(GAUSSIAN_EXP_FACTOR * z * z);\n const denominator = 4 * x * x + fwhm * fwhm;\n const lorentz = (fwhm * fwhm) / denominator;\n const dEdt = ((2 * GAUSSIAN_EXP_FACTOR * x) / (fwhm * fwhm)) * e;\n const dLdt = (-8 * x * fwhm * fwhm) / (denominator * denominator);\n const dEdfwhm = ((-2 * GAUSSIAN_EXP_FACTOR * x * x) / (fwhm * fwhm * fwhm)) * e;\n const dLdfwhm = (8 * fwhm * x * x) / (denominator * denominator);\n return {\n fct: (1 - mu) * lorentz + mu * e,\n dx: (1 - mu) * dLdt + mu * dEdt,\n dFwhm: (1 - mu) * dLdfwhm + mu * dEdfwhm,\n dMu: e - lorentz,\n };\n}\nexport const pseudoVoigtWidthToFWHM = (width, mu = 0.5) => {\n return width * (mu * ROOT_2LN2_MINUS_ONE + 1);\n};\nexport const pseudoVoigtFwhmToWidth = (fwhm, mu = 0.5) => {\n return fwhm / (mu * ROOT_2LN2_MINUS_ONE + 1);\n};\nexport const getPseudoVoigtArea = (options) => {\n const { fwhm = 500, height = 1, mu = 0.5 } = options;\n return (fwhm * height * (mu * ROOT_PI_OVER_LN2 + (1 - mu) * Math.PI)) / 2;\n};\nexport const getPseudoVoigtFactor = (area = 0.9999, mu = 0.5) => {\n return pseudoVoigtFindFactor(area, mu);\n};\nexport const getPseudoVoigtData = (shape = {}, options = {}) => {\n const { fwhm = 500, mu = 0.5 } = shape;\n const { factor = getPseudoVoigtFactor(0.999, mu) } = options;\n let { length, height = calculatePseudoVoigtHeight({ fwhm, mu, area: 1 }) } = options;\n if (!height) {\n height =\n 1 /\n ((mu / Math.sqrt(-GAUSSIAN_EXP_FACTOR / Math.PI)) * fwhm +\n ((1 - mu) * fwhm * Math.PI) / 2);\n }\n if (!length) {\n length = Math.min(Math.ceil(fwhm * factor), 2 ** 25 - 1);\n if (length % 2 === 0)\n length++;\n }\n const center = (length - 1) / 2;\n const data = new Float64Array(length);\n for (let i = 0; i <= center; i++) {\n const value = pseudoVoigtFct(i - center, fwhm, mu) * height;\n data[i] = value;\n data[length - 1 - i] = value;\n }\n return data;\n};\n//# sourceMappingURL=PseudoVoigt.js.map","import { GAUSSIAN_CUTOFF, GAUSSIAN_EXP_FACTOR, } from \"../../../util/constants.js\";\nimport { calculatePseudoVoigtHeight, getPseudoVoigtArea, getPseudoVoigtData, getPseudoVoigtFactor, pseudoVoigtFct, pseudoVoigtFwhmToWidth, pseudoVoigtWidthToFWHM, } from \"../pseudoVoigt/PseudoVoigt.js\";\n/**\n * TCH-style pseudo-Voigt where gaussian and lorentzian widths are independent.\n * The effective fwhm and mixing parameter mu are derived from fwhmG and fwhmL\n * via the Thompson–Cox–Hastings approximation.\n */\nexport class PseudoVoigtTCH {\n kind = 'pseudoVoigtTCH';\n _fwhmG;\n _fwhmL;\n _fwhm;\n _mu;\n _lorentzianWidthFraction;\n constructor(options = {}) {\n const { fwhmG, fwhmL, fwhm, mu = 0.5 } = options;\n this._mu = mu;\n this._fwhm = 0;\n this._fwhmG = 0;\n this._fwhmL = 0;\n this._lorentzianWidthFraction = lorentzianWidthFraction(1 - mu);\n if (fwhmG !== undefined && fwhmL !== undefined) {\n this._fwhmG = fwhmG;\n this.fwhmL = fwhmL;\n }\n else if (fwhm !== undefined) {\n this.fwhm = fwhm;\n }\n }\n set fwhmG(value) {\n const effectiveFwhm = computeEffectiveWidth(value, this._fwhmL);\n const lorentzianFraction = this._fwhmL / effectiveFwhm;\n this._fwhm = effectiveFwhm;\n this._mu =\n 1 -\n (1.36603 * lorentzianFraction -\n 0.47719 * lorentzianFraction * lorentzianFraction +\n 0.11116 * lorentzianFraction * lorentzianFraction * lorentzianFraction);\n this._fwhmG = value;\n this._lorentzianWidthFraction = lorentzianFraction;\n }\n get fwhmG() {\n return this._fwhmG;\n }\n set fwhmL(value) {\n const effectiveFwhm = computeEffectiveWidth(this._fwhmG, value);\n const lorentzianFraction = value / effectiveFwhm;\n this._fwhm = effectiveFwhm;\n this._mu =\n 1 -\n (1.36603 * lorentzianFraction -\n 0.47719 * lorentzianFraction * lorentzianFraction +\n 0.11116 * lorentzianFraction * lorentzianFraction * lorentzianFraction);\n this._fwhmL = value;\n this._lorentzianWidthFraction = lorentzianFraction;\n }\n get fwhmL() {\n return this._fwhmL;\n }\n set mu(value) {\n const lorentzianFraction = lorentzianWidthFraction(1 - value);\n this._lorentzianWidthFraction = lorentzianFraction;\n this._fwhmL = this._fwhm * lorentzianFraction;\n this._fwhmG = this._fwhm * gaussianWidthFraction(lorentzianFraction);\n this._mu = value;\n }\n get mu() {\n return this._mu;\n }\n set fwhm(value) {\n const lorentzianFraction = this._lorentzianWidthFraction || lorentzianWidthFraction(1 - this._mu);\n this._fwhmL = value * lorentzianFraction;\n this._fwhmG = value * gaussianWidthFraction(lorentzianFraction);\n this._fwhm = value;\n }\n get fwhm() {\n return this._fwhm;\n }\n fwhmToWidth(fwhm = this._fwhm, mu = this._mu) {\n return pseudoVoigtFwhmToWidth(fwhm, mu);\n }\n widthToFWHM(width, mu = this._mu) {\n return pseudoVoigtWidthToFWHM(width, mu);\n }\n fct(x) {\n return pseudoVoigtFct(x, this._fwhm, this._mu);\n }\n getArea(height = 1) {\n return getPseudoVoigtArea({ fwhm: this._fwhm, height, mu: this._mu });\n }\n getFactor(area) {\n return getPseudoVoigtFactor(area, this._mu);\n }\n getData(options = {}) {\n const { length, factor, height = calculatePseudoVoigtHeight({\n fwhm: this._fwhm,\n mu: this._mu,\n area: 1,\n }), } = options;\n return getPseudoVoigtData(this, { factor, length, height });\n }\n calculateHeight(area = 1) {\n return calculatePseudoVoigtHeight({\n fwhm: this._fwhm,\n mu: this._mu,\n area,\n });\n }\n getParameters() {\n return ['fwhmG', 'fwhmL'];\n }\n /**\n * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n * The component widths are emitted rather than `fwhm`/`mu`, because they are\n * the state this shape is defined by: `getParameters` reports them and\n * `derivative` differentiates with respect to them. The effective width and\n * the mixing ratio are re-derived from them exactly.\n * @returns the shape descriptor.\n */\n toJSON() {\n return { kind: this.kind, fwhmG: this._fwhmG, fwhmL: this._fwhmL };\n }\n derivative(x) {\n const { fct, dx, dFwhmG, dFwhmL } = pseudoVoigtTCHDerivative(x, this._fwhmG, this._fwhmL);\n return { fct, dx, parameters: [dFwhmG, dFwhmL] };\n }\n}\n/**\n * Analytical value and partial derivatives of the TCH pseudo-Voigt function centered at x=0.\n * The effective fwhm `F` and mixing `mu` are functions of `fwhmG` and `fwhmL`, so the\n * derivatives chain `∂fct/∂F` and `∂fct/∂mu` through `∂F/∂·` and `∂mu/∂·`.\n * @param x - position at which to evaluate.\n * @param fwhmG - full width at half maximum of the gaussian component.\n * @param fwhmL - full width at half maximum of the lorentzian component.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`), `fwhmG` (`dFwhmG`) and `fwhmL` (`dFwhmL`).\n */\nexport function pseudoVoigtTCHDerivative(x, fwhmG, fwhmL) {\n const effectiveFwhm = computeEffectiveWidth(fwhmG, fwhmL);\n const w = effectiveFwhm ** 5; // the polynomial under the 1/5 power\n // ∂w/∂fwhmG and ∂w/∂fwhmL (derivatives of the TCH width polynomial).\n const dwDfwhmG = 5 * fwhmG ** 4 +\n 10.77076 * fwhmG ** 3 * fwhmL +\n 7.28529 * fwhmG ** 2 * fwhmL ** 2 +\n 8.94326 * fwhmG * fwhmL ** 3 +\n 0.07842 * fwhmL ** 4;\n const dwDfwhmL = 2.69269 * fwhmG ** 4 +\n 4.85686 * fwhmG ** 3 * fwhmL +\n 13.41489 * fwhmG ** 2 * fwhmL ** 2 +\n 0.31368 * fwhmG * fwhmL ** 3 +\n 5 * fwhmL ** 4;\n // F = w^0.2 => ∂F/∂· = 0.2 * F / w * ∂w/∂·\n const dFwhmDfwhmG = (0.2 * effectiveFwhm * dwDfwhmG) / w;\n const dFwhmDfwhmL = (0.2 * effectiveFwhm * dwDfwhmL) / w;\n // lorentzian width fraction L = fwhmL / F\n const lorentzianFraction = fwhmL / effectiveFwhm;\n const dLorentzianFractionDfwhmG = (-fwhmL / (effectiveFwhm * effectiveFwhm)) * dFwhmDfwhmG;\n const dLorentzianFractionDfwhmL = 1 / effectiveFwhm - (fwhmL / (effectiveFwhm * effectiveFwhm)) * dFwhmDfwhmL;\n // mu = 1 - (1.36603 L - 0.47719 L^2 + 0.11116 L^3)\n const dPolyDfraction = 1.36603 -\n 0.95438 * lorentzianFraction +\n 0.33348 * lorentzianFraction * lorentzianFraction;\n const dMuDfwhmG = -dPolyDfraction * dLorentzianFractionDfwhmG;\n const dMuDfwhmL = -dPolyDfraction * dLorentzianFractionDfwhmL;\n const mu = 1 -\n (1.36603 * lorentzianFraction -\n 0.47719 * lorentzianFraction * lorentzianFraction +\n 0.11116 * lorentzianFraction * lorentzianFraction * lorentzianFraction);\n // pseudoVoigt value and its ∂/∂x, ∂/∂F (dFwhm), ∂/∂mu (dMu) at the effective\n // fwhm, inlined to allocate a single object on this hot path.\n //\n // Past {@link GAUSSIAN_CUTOFF} the gaussian half has underflowed and is\n // dropped under the same condition as in `pseudoVoigtFct` — which this shape's\n // own `fct` delegates to, so the value and its derivatives stay consistent out\n // there. `fwhmL = 0` gives `mu = 1`, the pure gaussian that is never dropped.\n const z = x / effectiveFwhm;\n const e = mu !== 1 && z * z > GAUSSIAN_CUTOFF\n ? 0\n : Math.exp(GAUSSIAN_EXP_FACTOR * z * z);\n const denominator2 = 4 * x * x + effectiveFwhm * effectiveFwhm;\n const lorentz = (effectiveFwhm * effectiveFwhm) / denominator2;\n const dEdt = ((2 * GAUSSIAN_EXP_FACTOR * x) / (effectiveFwhm * effectiveFwhm)) * e;\n const dLdt = (-8 * x * effectiveFwhm * effectiveFwhm) / (denominator2 * denominator2);\n const dEdfwhm = ((-2 * GAUSSIAN_EXP_FACTOR * x * x) /\n (effectiveFwhm * effectiveFwhm * effectiveFwhm)) *\n e;\n const dLdfwhm = (8 * effectiveFwhm * x * x) / (denominator2 * denominator2);\n const dFwhm = (1 - mu) * dLdfwhm + mu * dEdfwhm;\n const dMu = e - lorentz;\n return {\n fct: (1 - mu) * lorentz + mu * e,\n dx: (1 - mu) * dLdt + mu * dEdt,\n dFwhmG: dFwhm * dFwhmDfwhmG + dMu * dMuDfwhmG,\n dFwhmL: dFwhm * dFwhmDfwhmL + dMu * dMuDfwhmL,\n };\n}\n/**\n * Compute the effective FWHM from gaussian and lorentzian component widths\n * using the Thompson–Cox–Hastings approximation.\n * @param fwhmG - gaussian component FWHM.\n * @param fwhmL - lorentzian component FWHM.\n * @returns effective combined FWHM.\n */\nfunction computeEffectiveWidth(fwhmG, fwhmL) {\n return ((fwhmG ** 5 +\n 2.69269 * fwhmG ** 4 * fwhmL +\n 2.42843 * fwhmG ** 3 * fwhmL ** 2 +\n 4.47163 * fwhmG ** 2 * fwhmL ** 3 +\n 0.07842 * fwhmG * fwhmL ** 4 +\n fwhmL ** 5) **\n 0.2);\n}\n/**\n * Solve for the lorentzian width fraction fwhmL/fwhm given lorentzianFraction = 1 - mu,\n * using Newton's method on: 1.36603·x - 0.47719·x² + 0.11116·x³ = lorentzianFraction.\n * @param lorentzianFraction - TCH lorentzian mixing parameter (= 1 - mu).\n * @returns the lorentzian width fraction fwhmL/fwhm.\n */\nfunction lorentzianWidthFraction(lorentzianFraction) {\n let fraction = lorentzianFraction;\n for (let i = 0; i < 6; i++) {\n const f = 1.36603 * fraction -\n 0.47719 * fraction * fraction +\n 0.11116 * fraction * fraction * fraction -\n lorentzianFraction;\n const df = 1.36603 - 2 * 0.47719 * fraction + 3 * 0.11116 * fraction * fraction;\n fraction -= f / df;\n }\n return fraction;\n}\n/**\n * Solve for the gaussian width fraction fwhmG/fwhm that pairs with a given\n * lorentzian width fraction fwhmL/fwhm. Writing fwhmG = g·fwhm and\n * fwhmL = q·fwhm in {@link computeEffectiveWidth} makes fwhm cancel, so `g` is\n * the root of the TCH width polynomial evaluated at 1. Solving it — rather than\n * taking `1 - q` — is what keeps `computeEffectiveWidth(fwhmG, fwhmL)` equal to\n * `fwhm`, and therefore keeps `fct` and `derivative` describing one same curve.\n * @param lorentzianFraction - the lorentzian width fraction fwhmL/fwhm.\n * @returns the gaussian width fraction fwhmG/fwhm.\n */\nfunction gaussianWidthFraction(lorentzianFraction) {\n const q = lorentzianFraction;\n let g = 1 - q;\n for (let i = 0; i < 8; i++) {\n const f = g ** 5 +\n 2.69269 * g ** 4 * q +\n 2.42843 * g ** 3 * q ** 2 +\n 4.47163 * g ** 2 * q ** 3 +\n 0.07842 * g * q ** 4 +\n q ** 5 -\n 1;\n const df = 5 * g ** 4 +\n 10.77076 * g ** 3 * q +\n 7.28529 * g ** 2 * q ** 2 +\n 8.94326 * g * q ** 3 +\n 0.07842 * q ** 4;\n if (df === 0)\n break;\n g -= f / df;\n }\n return g;\n}\n//# sourceMappingURL=PseudoVoigtTCH.js.map","import { ROOT_THREE } from \"../../../util/constants.js\";\n/**\n * This shape is a linear combination of rational function (n|n+2), for n = 0 (lorentzian function) and n = 2\n * the parameter that combines those two functions is `gamma` and it is called the kurtosis parameter, it is an\n * implementation of generalized lorentzian shape published by Stanislav Sykora in the SMASH 2010. DOI:10.3247/SL3nmr10.006\n * {@link https://www.ebyte.it/stan/Talk_ML_UserMeeting_SMASH_2010_GeneralizedLorentzian.html}\n */\nexport class GeneralizedLorentzian {\n kind = 'generalizedLorentzian';\n /**\n * Full width at half maximum.\n * @default 500\n */\n fwhm;\n /**\n * kurtosis parameter of the shape, between -1 to 2\n * @default 1\n */\n gamma;\n constructor(options = {}) {\n const { fwhm = 500, gamma = 0.5 } = options;\n this.fwhm = fwhm;\n this.gamma = gamma;\n }\n fwhmToWidth(fwhm = this.fwhm) {\n return generalizedLorentzianFwhmToWidth(fwhm);\n }\n widthToFWHM(width) {\n return generalizedLorentzianWidthToFWHM(width);\n }\n fct(x) {\n return generalizedLorentzianFct(x, this.fwhm, this.gamma);\n }\n getArea(height = 1) {\n return getGeneralizedLorentzianArea({\n fwhm: this.fwhm,\n height,\n gamma: this.gamma,\n });\n }\n getFactor(area) {\n return getGeneralizedLorentzianFactor(area);\n }\n getData(options = {}) {\n return getGeneralizedLorentzianData(this, options);\n }\n calculateHeight(area = 1) {\n const { gamma, fwhm } = this;\n return calculateGeneralizedLorentzianHeight({ fwhm, area, gamma });\n }\n getParameters() {\n return ['fwhm', 'gamma'];\n }\n /**\n * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n * @returns the shape descriptor.\n */\n toJSON() {\n return { kind: this.kind, fwhm: this.fwhm, gamma: this.gamma };\n }\n derivative(x) {\n const { fct, dx, dFwhm, dGamma } = generalizedLorentzianDerivative(x, this.fwhm, this.gamma);\n return { fct, dx, parameters: [dFwhm, dGamma] };\n }\n}\nexport const calculateGeneralizedLorentzianHeight = ({ fwhm = 1, gamma = 1, area = 1, }) => {\n return (area / fwhm / (3.14159 - 0.420894 * gamma)) * 2;\n};\n/**\n * Calculate the area under a generalized Lorentzian peak (integral from Mathematica).\n * @param options - shape parameters including fwhm, height, and gamma.\n * @returns the area under the peak.\n */\nexport const getGeneralizedLorentzianArea = (options) => {\n const { fwhm = 500, height = 1, gamma = 1 } = options;\n return (height * fwhm * (3.14159 - 0.420894 * gamma)) / 2;\n};\nexport const generalizedLorentzianFct = (x, fwhm, gamma) => {\n const u = ((2 * x) / fwhm) ** 2;\n return (1 - gamma) / (1 + u) + (gamma * (1 + u / 2)) / (1 + u + u ** 2);\n};\n/**\n * Analytical value and partial derivatives of the generalized lorentzian function centered at x=0.\n * @param x - position at which to evaluate.\n * @param fwhm - full width at half maximum.\n * @param gamma - kurtosis parameter of the shape.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`), `fwhm` (`dFwhm`) and `gamma` (`dGamma`).\n */\nexport function generalizedLorentzianDerivative(x, fwhm, gamma) {\n const u = ((2 * x) / fwhm) ** 2;\n const lorentzian = 1 / (1 + u); // A\n const rational = (1 + u / 2) / (1 + u + u * u); // B\n const fct = (1 - gamma) * lorentzian + gamma * rational;\n // dA/du and dB/du\n const dLorentzianDu = -1 / ((1 + u) * (1 + u));\n const denominator = 1 + u + u * u;\n const dRationalDu = -(0.5 + 2 * u + 0.5 * u * u) / (denominator * denominator);\n const dFctDu = (1 - gamma) * dLorentzianDu + gamma * dRationalDu;\n const duDx = (8 * x) / (fwhm * fwhm);\n const duDfwhm = (-8 * x * x) / (fwhm * fwhm * fwhm);\n const dx = dFctDu * duDx;\n const dFwhm = dFctDu * duDfwhm;\n const dGamma = rational - lorentzian; // B - A\n return { fct, dx, dFwhm, dGamma };\n}\nexport const generalizedLorentzianWidthToFWHM = (width) => {\n return width * ROOT_THREE;\n};\nexport const generalizedLorentzianFwhmToWidth = (fwhm) => {\n return fwhm / ROOT_THREE;\n};\nconst generalizedLorentzianQuantile = (p) => Math.tan(Math.PI * (p - 0.5));\nexport const getGeneralizedLorentzianFactor = (area = 0.9999) => {\n if (area >= 1) {\n throw new Error('area should be (0 - 1)');\n }\n const halfResidual = (1 - area) * 0.5;\n return ((generalizedLorentzianQuantile(1 - halfResidual) -\n generalizedLorentzianQuantile(halfResidual)) /\n 2);\n};\nexport const getGeneralizedLorentzianData = (shape = {}, options = {}) => {\n const { fwhm = 500, gamma = 1 } = shape;\n const { factor = getGeneralizedLorentzianFactor(), height = calculateGeneralizedLorentzianHeight({ fwhm, area: 1, gamma }), } = options;\n let { length } = options;\n if (!length) {\n length = Math.min(Math.ceil(fwhm * factor), 2 ** 25 - 1);\n if (length % 2 === 0)\n length++;\n }\n const center = (length - 1) / 2;\n const data = new Float64Array(length);\n for (let i = 0; i <= center; i++) {\n const value = generalizedLorentzianFct(i - center, fwhm, gamma) * height;\n data[i] = value;\n data[length - 1 - i] = value;\n }\n return data;\n};\n//# sourceMappingURL=GeneralizedLorentzian.js.map","import { ROOT_PI_OVER_LN2 } from \"../../../util/constants.js\";\nimport { gaussianDerivative, gaussianFct, gaussianFwhmToWidth, gaussianWidthToFWHM, getGaussianFactor, } from \"../gaussian/Gaussian.js\";\nexport class SplitGaussian {\n kind = 'splitGaussian';\n /**\n * Full width at half maximum of the lower-x half (x <= 0).\n * @default 500\n */\n fwhmLow;\n /**\n * Full width at half maximum of the higher-x half (x > 0).\n * @default 500\n */\n fwhmHigh;\n constructor(options = {}) {\n const { fwhmLow = 500, fwhmHigh = 500 } = options;\n this.fwhmLow = fwhmLow;\n this.fwhmHigh = fwhmHigh;\n }\n /**\n * Full width at half maximum of the peak. The half-maximum crossings are at\n * `-fwhmLow / 2` and `fwhmHigh / 2`, so the width between them is the mean of\n * both halves.\n * @returns the full width at half maximum.\n */\n get fwhm() {\n return (this.fwhmLow + this.fwhmHigh) / 2;\n }\n /**\n * Set the full width at half maximum. Both halves are scaled by the same\n * ratio, so their mean becomes `value` while the asymmetry between them is\n * preserved. A peak with no width has no ratio to preserve, so both halves\n * take `value` and the peak stays symmetric.\n * @param value - the new full width at half maximum.\n */\n set fwhm(value) {\n const { fwhm } = this;\n if (fwhm === 0) {\n this.fwhmLow = value;\n this.fwhmHigh = value;\n return;\n }\n const ratio = value / fwhm;\n this.fwhmLow *= ratio;\n this.fwhmHigh *= ratio;\n }\n /**\n * Convert a full width at half maximum to the width between the inflection\n * points. For this peak's own fwhm the result is exactly `σlow + σhigh`.\n * @param fwhm - full width at half maximum. Defaults to the peak's fwhm.\n * @returns the width between the inflection points.\n */\n fwhmToWidth(fwhm = this.fwhm) {\n return gaussianFwhmToWidth(fwhm);\n }\n /**\n * Convert a width between the inflection points back to a full width at half\n * maximum. A single width does not encode the asymmetry, so it cannot recover\n * `fwhmLow` and `fwhmHigh` individually.\n * @param width - width between the inflection points.\n * @returns the corresponding full width at half maximum.\n */\n widthToFWHM(width) {\n return gaussianWidthToFWHM(width);\n }\n fct(x) {\n return splitGaussianFct(x, this.fwhmLow, this.fwhmHigh);\n }\n getArea(height = calculateSplitGaussianHeight({\n fwhmLow: this.fwhmLow,\n fwhmHigh: this.fwhmHigh,\n })) {\n return getSplitGaussianArea({\n fwhmLow: this.fwhmLow,\n fwhmHigh: this.fwhmHigh,\n height,\n });\n }\n getFactor(area) {\n return getGaussianFactor(area);\n }\n getData(options = {}) {\n return getSplitGaussianData(this, options);\n }\n calculateHeight(area = 1) {\n return calculateSplitGaussianHeight({\n fwhmLow: this.fwhmLow,\n fwhmHigh: this.fwhmHigh,\n area,\n });\n }\n getParameters() {\n return ['fwhmLow', 'fwhmHigh'];\n }\n /**\n * Descriptor of this shape, so `JSON.stringify` round-trips through `getShape1D`.\n * @returns the shape descriptor.\n */\n toJSON() {\n return {\n kind: this.kind,\n fwhmLow: this.fwhmLow,\n fwhmHigh: this.fwhmHigh,\n };\n }\n derivative(x) {\n const { fct, dx, dFwhmLow, dFwhmHigh } = splitGaussianDerivative(x, this.fwhmLow, this.fwhmHigh);\n return { fct, dx, parameters: [dFwhmLow, dFwhmHigh] };\n }\n}\n/**\n * Calculate the peak height for a given area and both half-widths.\n * @param options - fwhmLow, fwhmHigh and area.\n * @returns the peak height.\n */\nexport function calculateSplitGaussianHeight(options) {\n const { fwhmLow = 500, fwhmHigh = 500, area = 1 } = options;\n return (4 * area) / ROOT_PI_OVER_LN2 / (fwhmLow + fwhmHigh);\n}\n/**\n * Evaluate the split (asymmetric) gaussian function centered at x=0.\n * The lower-x half (x <= 0) uses `fwhmLow`, the higher-x half (x > 0) uses `fwhmHigh`.\n * @param x - position at which to evaluate.\n * @param fwhmLow - full width at half maximum of the lower-x half.\n * @param fwhmHigh - full width at half maximum of the higher-x half.\n * @returns the intensity at x.\n */\nexport function splitGaussianFct(x, fwhmLow, fwhmHigh) {\n return x <= 0 ? gaussianFct(x, fwhmLow) : gaussianFct(x, fwhmHigh);\n}\n/**\n * Analytical value and partial derivatives of the split gaussian function centered at x=0.\n * Each half's fwhm only affects its own side, so the off-side derivative is 0.\n * @param x - position at which to evaluate.\n * @param fwhmLow - full width at half maximum of the lower-x half.\n * @param fwhmHigh - full width at half maximum of the higher-x half.\n * @returns the value `fct` and its partial derivatives with respect to `x` (`dx`), `fwhmLow` (`dFwhmLow`) and `fwhmHigh` (`dFwhmHigh`).\n */\nexport function splitGaussianDerivative(x, fwhmLow, fwhmHigh) {\n if (x <= 0) {\n const { fct, dx, dFwhm } = gaussianDerivative(x, fwhmLow);\n return { fct, dx, dFwhmLow: dFwhm, dFwhmHigh: 0 };\n }\n const { fct, dx, dFwhm } = gaussianDerivative(x, fwhmHigh);\n return { fct, dx, dFwhmLow: 0, dFwhmHigh: dFwhm };\n}\n/**\n * Calculate the area under a split gaussian peak.\n * @param options - fwhmLow, fwhmHigh and height.\n * @returns the area.\n */\nexport function getSplitGaussianArea(options) {\n const { fwhmLow = 500, fwhmHigh = 500, height = 1 } = options;\n return (height * ROOT_PI_OVER_LN2 * (fwhmLow + fwhmHigh)) / 4;\n}\n/**\n * Generate an intensity array for a split gaussian shape.\n * @param shape - split gaussian shape parameters (fwhmLow, fwhmHigh).\n * @param options - sampling options (length, factor, height).\n * @returns Float64Array of intensity values.\n */\nexport function getSplitGaussianData(shape = {}, options = {}) {\n const { fwhmLow = 500, fwhmHigh = 500 } = shape;\n const { factor = getGaussianFactor(), height = calculateSplitGaussianHeight({ fwhmLow, fwhmHigh }), } = options;\n let { length } = options;\n if (!length) {\n length = Math.min(Math.ceil(Math.max(fwhmLow, fwhmHigh) * factor), 2 ** 25 - 1);\n if (length % 2 === 0)\n length++;\n }\n const center = (length - 1) / 2;\n const data = new Float64Array(length);\n for (let i = 0; i < length; i++) {\n data[i] = splitGaussianFct(i - center, fwhmLow, fwhmHigh) * height;\n }\n return data;\n}\n//# sourceMappingURL=SplitGaussian.js.map","import { Gaussian } from \"./gaussian/Gaussian.js\";\nimport { GeneralizedLorentzian } from \"./generalizedLorentzian/GeneralizedLorentzian.js\";\nimport { Lorentzian } from \"./lorentzian/Lorentzian.js\";\nimport { LorentzianDispersive } from \"./lorentzianDispersive/LorentzianDispersive.js\";\nimport { PseudoVoigt } from \"./pseudoVoigt/PseudoVoigt.js\";\nimport { PseudoVoigtTCH } from \"./pseudoVoigtTCH/PseudoVoigtTCH.js\";\nimport { SplitGaussian } from \"./splitGaussian/SplitGaussian.js\";\nexport function getShape1D(shape) {\n const { kind } = shape;\n switch (kind) {\n case 'gaussian':\n return new Gaussian(shape);\n case 'lorentzian':\n return new Lorentzian(shape);\n case 'pseudoVoigt':\n return new PseudoVoigt(shape);\n case 'pseudoVoigtTCH':\n return new PseudoVoigtTCH(shape);\n case 'lorentzianDispersive':\n return new LorentzianDispersive(shape);\n case 'generalizedLorentzian':\n return new GeneralizedLorentzian(shape);\n case 'splitGaussian':\n return new SplitGaussian(shape);\n default:\n throw new Error(`Unknown distribution ${kind}`);\n }\n}\n//# sourceMappingURL=getShape1D.js.map","module.exports = function(haystack, needle, comparator, low, high) {\n var mid, cmp;\n\n if(low === undefined)\n low = 0;\n\n else {\n low = low|0;\n if(low < 0 || low >= haystack.length)\n throw new RangeError(\"invalid lower bound\");\n }\n\n if(high === undefined)\n high = haystack.length - 1;\n\n else {\n high = high|0;\n if(high < low || high >= haystack.length)\n throw new RangeError(\"invalid upper bound\");\n }\n\n while(low <= high) {\n // The naive `low + high >>> 1` could fail for array lengths > 2**31\n // because `>>>` converts its operands to int32. `low + (high - low >>> 1)`\n // works for array lengths <= 2**32-1 which is also Javascript's max array\n // length.\n mid = low + ((high - low) >>> 1);\n cmp = +comparator(haystack[mid], needle, mid, haystack);\n\n // Too low.\n if(cmp < 0.0)\n low = mid + 1;\n\n // Too high.\n else if(cmp > 0.0)\n high = mid - 1;\n\n // Key found.\n else\n return mid;\n }\n\n // Key not found.\n return ~low;\n}\n","'use strict';\n\nfunction assertNumber(number) {\n\tif (typeof number !== 'number') {\n\t\tthrow new TypeError('Expected a number');\n\t}\n}\n\nexports.ascending = (left, right) => {\n\tassertNumber(left);\n\tassertNumber(right);\n\n\tif (Number.isNaN(left)) {\n\t\treturn -1;\n\t}\n\n\tif (Number.isNaN(right)) {\n\t\treturn 1;\n\t}\n\n\treturn left - right;\n};\n\nexports.descending = (left, right) => {\n\tassertNumber(left);\n\tassertNumber(right);\n\n\tif (Number.isNaN(left)) {\n\t\treturn 1;\n\t}\n\n\tif (Number.isNaN(right)) {\n\t\treturn -1;\n\t}\n\n\treturn right - left;\n};\n","import binarySearch from 'binary-search';\nimport { ascending } from 'num-sort';\n\nexport const largestPrime = 0x7fffffff;\n\nconst primeNumbers = [\n // chunk #0\n largestPrime, // 2^31-1\n\n // chunk #1\n 5,\n 11,\n 23,\n 47,\n 97,\n 197,\n 397,\n 797,\n 1597,\n 3203,\n 6421,\n 12853,\n 25717,\n 51437,\n 102877,\n 205759,\n 411527,\n 823117,\n 1646237,\n 3292489,\n 6584983,\n 13169977,\n 26339969,\n 52679969,\n 105359939,\n 210719881,\n 421439783,\n 842879579,\n 1685759167,\n\n // chunk #2\n 433,\n 877,\n 1759,\n 3527,\n 7057,\n 14143,\n 28289,\n 56591,\n 113189,\n 226379,\n 452759,\n 905551,\n 1811107,\n 3622219,\n 7244441,\n 14488931,\n 28977863,\n 57955739,\n 115911563,\n 231823147,\n 463646329,\n 927292699,\n 1854585413,\n\n // chunk #3\n 953,\n 1907,\n 3821,\n 7643,\n 15287,\n 30577,\n 61169,\n 122347,\n 244703,\n 489407,\n 978821,\n 1957651,\n 3915341,\n 7830701,\n 15661423,\n 31322867,\n 62645741,\n 125291483,\n 250582987,\n 501165979,\n 1002331963,\n 2004663929,\n\n // chunk #4\n 1039,\n 2081,\n 4177,\n 8363,\n 16729,\n 33461,\n 66923,\n 133853,\n 267713,\n 535481,\n 1070981,\n 2141977,\n 4283963,\n 8567929,\n 17135863,\n 34271747,\n 68543509,\n 137087021,\n 274174111,\n 548348231,\n 1096696463,\n\n // chunk #5\n 31,\n 67,\n 137,\n 277,\n 557,\n 1117,\n 2237,\n 4481,\n 8963,\n 17929,\n 35863,\n 71741,\n 143483,\n 286973,\n 573953,\n 1147921,\n 2295859,\n 4591721,\n 9183457,\n 18366923,\n 36733847,\n 73467739,\n 146935499,\n 293871013,\n 587742049,\n 1175484103,\n\n // chunk #6\n 599,\n 1201,\n 2411,\n 4831,\n 9677,\n 19373,\n 38747,\n 77509,\n 155027,\n 310081,\n 620171,\n 1240361,\n 2480729,\n 4961459,\n 9922933,\n 19845871,\n 39691759,\n 79383533,\n 158767069,\n 317534141,\n 635068283,\n 1270136683,\n\n // chunk #7\n 311,\n 631,\n 1277,\n 2557,\n 5119,\n 10243,\n 20507,\n 41017,\n 82037,\n 164089,\n 328213,\n 656429,\n 1312867,\n 2625761,\n 5251529,\n 10503061,\n 21006137,\n 42012281,\n 84024581,\n 168049163,\n 336098327,\n 672196673,\n 1344393353,\n\n // chunk #8\n 3,\n 7,\n 17,\n 37,\n 79,\n 163,\n 331,\n 673,\n 1361,\n 2729,\n 5471,\n 10949,\n 21911,\n 43853,\n 87719,\n 175447,\n 350899,\n 701819,\n 1403641,\n 2807303,\n 5614657,\n 11229331,\n 22458671,\n 44917381,\n 89834777,\n 179669557,\n 359339171,\n 718678369,\n 1437356741,\n\n // chunk #9\n 43,\n 89,\n 179,\n 359,\n 719,\n 1439,\n 2879,\n 5779,\n 11579,\n 23159,\n 46327,\n 92657,\n 185323,\n 370661,\n 741337,\n 1482707,\n 2965421,\n 5930887,\n 11861791,\n 23723597,\n 47447201,\n 94894427,\n 189788857,\n 379577741,\n 759155483,\n 1518310967,\n\n // chunk #10\n 379,\n 761,\n 1523,\n 3049,\n 6101,\n 12203,\n 24407,\n 48817,\n 97649,\n 195311,\n 390647,\n 781301,\n 1562611,\n 3125257,\n 6250537,\n 12501169,\n 25002389,\n 50004791,\n 100009607,\n 200019221,\n 400038451,\n 800076929,\n 1600153859,\n\n // chunk #11\n 13,\n 29,\n 59,\n 127,\n 257,\n 521,\n 1049,\n 2099,\n 4201,\n 8419,\n 16843,\n 33703,\n 67409,\n 134837,\n 269683,\n 539389,\n 1078787,\n 2157587,\n 4315183,\n 8630387,\n 17260781,\n 34521589,\n 69043189,\n 138086407,\n 276172823,\n 552345671,\n 1104691373,\n\n // chunk #12\n 19,\n 41,\n 83,\n 167,\n 337,\n 677,\n 1361,\n 2729,\n 5471,\n 10949,\n 21911,\n 43853,\n 87719,\n 175447,\n 350899,\n 701819,\n 1403641,\n 2807303,\n 5614657,\n 11229331,\n 22458671,\n 44917381,\n 89834777,\n 179669557,\n 359339171,\n 718678369,\n 1437356741,\n\n // chunk #13\n 53,\n 107,\n 223,\n 449,\n 907,\n 1823,\n 3659,\n 7321,\n 14653,\n 29311,\n 58631,\n 117269,\n 234539,\n 469099,\n 938207,\n 1876417,\n 3752839,\n 7505681,\n 15011389,\n 30022781,\n 60045577,\n 120091177,\n 240182359,\n 480364727,\n 960729461,\n 1921458943\n];\n\nprimeNumbers.sort(ascending);\n\nexport function nextPrime(value) {\n let index = binarySearch(primeNumbers, value, ascending);\n if (index < 0) {\n index = ~index;\n }\n return primeNumbers[index];\n}\n","import { largestPrime, nextPrime } from './primeFinder';\n\nconst FREE = 0;\nconst FULL = 1;\nconst REMOVED = 2;\n\nconst defaultInitialCapacity = 150;\nconst defaultMinLoadFactor = 1 / 6;\nconst defaultMaxLoadFactor = 2 / 3;\n\nexport default class HashTable {\n constructor(options = {}) {\n if (options instanceof HashTable) {\n this.table = options.table.slice();\n this.values = options.values.slice();\n this.state = options.state.slice();\n this.minLoadFactor = options.minLoadFactor;\n this.maxLoadFactor = options.maxLoadFactor;\n this.distinct = options.distinct;\n this.freeEntries = options.freeEntries;\n this.lowWaterMark = options.lowWaterMark;\n this.highWaterMark = options.maxLoadFactor;\n return;\n }\n\n const initialCapacity =\n options.initialCapacity === undefined\n ? defaultInitialCapacity\n : options.initialCapacity;\n if (initialCapacity < 0) {\n throw new RangeError(\n `initial capacity must not be less than zero: ${initialCapacity}`\n );\n }\n\n const minLoadFactor =\n options.minLoadFactor === undefined\n ? defaultMinLoadFactor\n : options.minLoadFactor;\n const maxLoadFactor =\n options.maxLoadFactor === undefined\n ? defaultMaxLoadFactor\n : options.maxLoadFactor;\n if (minLoadFactor < 0 || minLoadFactor >= 1) {\n throw new RangeError(`invalid minLoadFactor: ${minLoadFactor}`);\n }\n if (maxLoadFactor <= 0 || maxLoadFactor >= 1) {\n throw new RangeError(`invalid maxLoadFactor: ${maxLoadFactor}`);\n }\n if (minLoadFactor >= maxLoadFactor) {\n throw new RangeError(\n `minLoadFactor (${minLoadFactor}) must be smaller than maxLoadFactor (${maxLoadFactor})`\n );\n }\n\n let capacity = initialCapacity;\n // User wants to put at least capacity elements. We need to choose the size based on the maxLoadFactor to\n // avoid the need to rehash before this capacity is reached.\n // actualCapacity * maxLoadFactor >= capacity\n capacity = (capacity / maxLoadFactor) | 0;\n capacity = nextPrime(capacity);\n if (capacity === 0) capacity = 1;\n\n this.table = newArray(capacity);\n this.values = newArray(capacity);\n this.state = newArray(capacity);\n\n this.minLoadFactor = minLoadFactor;\n if (capacity === largestPrime) {\n this.maxLoadFactor = 1;\n } else {\n this.maxLoadFactor = maxLoadFactor;\n }\n\n this.distinct = 0;\n this.freeEntries = capacity;\n\n this.lowWaterMark = 0;\n this.highWaterMark = chooseHighWaterMark(capacity, this.maxLoadFactor);\n }\n\n clone() {\n return new HashTable(this);\n }\n\n get size() {\n return this.distinct;\n }\n\n get(key) {\n const i = this.indexOfKey(key);\n if (i < 0) return 0;\n return this.values[i];\n }\n\n set(key, value) {\n let i = this.indexOfInsertion(key);\n if (i < 0) {\n i = -i - 1;\n this.values[i] = value;\n return false;\n }\n\n if (this.distinct > this.highWaterMark) {\n const newCapacity = chooseGrowCapacity(\n this.distinct + 1,\n this.minLoadFactor,\n this.maxLoadFactor\n );\n this.rehash(newCapacity);\n return this.set(key, value);\n }\n\n this.table[i] = key;\n this.values[i] = value;\n if (this.state[i] === FREE) this.freeEntries--;\n this.state[i] = FULL;\n this.distinct++;\n\n if (this.freeEntries < 1) {\n const newCapacity = chooseGrowCapacity(\n this.distinct + 1,\n this.minLoadFactor,\n this.maxLoadFactor\n );\n this.rehash(newCapacity);\n }\n\n return true;\n }\n\n remove(key, noRehash) {\n const i = this.indexOfKey(key);\n if (i < 0) return false;\n\n this.state[i] = REMOVED;\n this.distinct--;\n\n if (!noRehash) this.maybeShrinkCapacity();\n\n return true;\n }\n\n delete(key, noRehash) {\n const i = this.indexOfKey(key);\n if (i < 0) return false;\n\n this.state[i] = FREE;\n this.distinct--;\n\n if (!noRehash) this.maybeShrinkCapacity();\n\n return true;\n }\n\n maybeShrinkCapacity() {\n if (this.distinct < this.lowWaterMark) {\n const newCapacity = chooseShrinkCapacity(\n this.distinct,\n this.minLoadFactor,\n this.maxLoadFactor\n );\n this.rehash(newCapacity);\n }\n }\n\n containsKey(key) {\n return this.indexOfKey(key) >= 0;\n }\n\n indexOfKey(key) {\n const table = this.table;\n const state = this.state;\n const length = this.table.length;\n\n const hash = key & 0x7fffffff;\n let i = hash % length;\n let decrement = hash % (length - 2);\n if (decrement === 0) decrement = 1;\n\n while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {\n i -= decrement;\n if (i < 0) i += length;\n }\n\n if (state[i] === FREE) return -1;\n return i;\n }\n\n containsValue(value) {\n return this.indexOfValue(value) >= 0;\n }\n\n indexOfValue(value) {\n const values = this.values;\n const state = this.state;\n\n for (var i = 0; i < state.length; i++) {\n if (state[i] === FULL && values[i] === value) {\n return i;\n }\n }\n\n return -1;\n }\n\n indexOfInsertion(key) {\n const table = this.table;\n const state = this.state;\n const length = table.length;\n\n const hash = key & 0x7fffffff;\n let i = hash % length;\n let decrement = hash % (length - 2);\n if (decrement === 0) decrement = 1;\n\n while (state[i] === FULL && table[i] !== key) {\n i -= decrement;\n if (i < 0) i += length;\n }\n\n if (state[i] === REMOVED) {\n const j = i;\n while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {\n i -= decrement;\n if (i < 0) i += length;\n }\n if (state[i] === FREE) i = j;\n }\n\n if (state[i] === FULL) {\n return -i - 1;\n }\n\n return i;\n }\n\n ensureCapacity(minCapacity) {\n if (this.table.length < minCapacity) {\n const newCapacity = nextPrime(minCapacity);\n this.rehash(newCapacity);\n }\n }\n\n rehash(newCapacity) {\n const oldCapacity = this.table.length;\n\n if (newCapacity <= this.distinct) throw new Error('Unexpected');\n\n const oldTable = this.table;\n const oldValues = this.values;\n const oldState = this.state;\n\n const newTable = newArray(newCapacity);\n const newValues = newArray(newCapacity);\n const newState = newArray(newCapacity);\n\n this.lowWaterMark = chooseLowWaterMark(newCapacity, this.minLoadFactor);\n this.highWaterMark = chooseHighWaterMark(newCapacity, this.maxLoadFactor);\n\n this.table = newTable;\n this.values = newValues;\n this.state = newState;\n this.freeEntries = newCapacity - this.distinct;\n\n for (var i = 0; i < oldCapacity; i++) {\n if (oldState[i] === FULL) {\n var element = oldTable[i];\n var index = this.indexOfInsertion(element);\n newTable[index] = element;\n newValues[index] = oldValues[i];\n newState[index] = FULL;\n }\n }\n }\n\n forEachKey(callback) {\n for (var i = 0; i < this.state.length; i++) {\n if (this.state[i] === FULL) {\n if (!callback(this.table[i])) return false;\n }\n }\n return true;\n }\n\n forEachValue(callback) {\n for (var i = 0; i < this.state.length; i++) {\n if (this.state[i] === FULL) {\n if (!callback(this.values[i])) return false;\n }\n }\n return true;\n }\n\n forEachPair(callback) {\n for (var i = 0; i < this.state.length; i++) {\n if (this.state[i] === FULL) {\n if (!callback(this.table[i], this.values[i])) return false;\n }\n }\n return true;\n }\n}\n\nfunction chooseLowWaterMark(capacity, minLoad) {\n return (capacity * minLoad) | 0;\n}\n\nfunction chooseHighWaterMark(capacity, maxLoad) {\n return Math.min(capacity - 2, (capacity * maxLoad) | 0);\n}\n\nfunction chooseGrowCapacity(size, minLoad, maxLoad) {\n return nextPrime(\n Math.max(size + 1, ((4 * size) / (3 * minLoad + maxLoad)) | 0)\n );\n}\n\nfunction chooseShrinkCapacity(size, minLoad, maxLoad) {\n return nextPrime(\n Math.max(size + 1, ((4 * size) / (minLoad + 3 * maxLoad)) | 0)\n );\n}\n\nfunction newArray(size) {\n return Array(size).fill(0);\n}\n","/**\n * Converts a matrix from Coordinate (COO) format to Compressed Sparse Row (CSR) format.\n * @param {Object} cooMatrix - The matrix in COO format with properties: values, columns, rows.\n * @param {number} nbRows - The number of rows in the matrix.\n * @returns {{rows: Float64Array, columns: Float64Array, values: Float64Array}} The matrix in CSR format.\n */\nexport function cooToCsr(cooMatrix, nbRows) {\n const { values, columns, rows } = cooMatrix;\n const csrRowPtr = new Float64Array(nbRows + 1);\n // Count non-zeros per row\n const numberOfNonZeros = rows.length;\n for (let i = 0; i < numberOfNonZeros; i++) {\n csrRowPtr[rows[i] + 1]++;\n }\n // Compute cumulative sum\n for (let i = 1; i <= nbRows; i++) {\n csrRowPtr[i] += csrRowPtr[i - 1];\n }\n return { rows: csrRowPtr, columns, values };\n}\n//# sourceMappingURL=cooToCsr.js.map","import { SparseMatrix } from '../index.js';\n/**\n * Multiplies two sparse matrices, optimized for cases where the right-hand side matrix\n * has low density (number of rows > 100 and cardinality < 100).\n *\n * @private\n * @param {SparseMatrix} left - The left-hand side matrix.\n * @param {SparseMatrix} right - The right-hand side matrix to multiply with.\n * @returns {SparseMatrix} The resulting matrix after multiplication.\n */\nexport function mmulLowDensity(left, right) {\n const { columns: otherCols, rows: otherRows, values: otherValues, } = right.getNonZeros();\n const { columns: thisCols, rows: thisRows, values: thisValues, } = left.getNonZeros();\n const m = left.rows;\n const p = right.columns;\n const output = new SparseMatrix(m, p);\n const nbOtherActive = otherCols.length;\n const nbThisActive = thisCols.length;\n for (let t = 0; t < nbThisActive; t++) {\n const i = thisRows[t];\n const j = thisCols[t];\n for (let o = 0; o < nbOtherActive; o++) {\n if (j === otherRows[o]) {\n const l = otherCols[o];\n output.set(i, l, output.get(i, l) + otherValues[o] * thisValues[t]);\n }\n }\n }\n return output;\n}\n//# sourceMappingURL=mmulLowDensity.js.map","import { SparseMatrix } from '../index.js';\n/**\n * Multiplies two sparse matrices where the right-hand side matrix is of medium density.\n * Uses CSR format for the right-hand side for efficient multiplication.\n *\n * @private\n * @param {SparseMatrix} left - The left-hand side matrix.\n * @param {SparseMatrix} right - The right-hand side matrix to multiply with (in CSR format).\n * @returns {SparseMatrix} The resulting matrix after multiplication.\n */\nexport function mmulMediumDensity(left, right) {\n const m = left.rows;\n const p = right.columns;\n const result = new SparseMatrix(m, p);\n const { columns: thisCols, rows: thisRows, values: thisValues, } = left.getNonZeros();\n const { columns: otherCols, rows: otherRows, values: otherValues, } = right.getNonZeros({ csr: true });\n const nbThisActive = thisCols.length;\n for (let t = 0; t < nbThisActive; t++) {\n const i = thisRows[t];\n const j = thisCols[t];\n const oStart = otherRows[j];\n const oEnd = otherRows[j + 1];\n for (let k = oStart; k < oEnd; k++) {\n const l = otherCols[k];\n result.set(i, l, result.get(i, l) + otherValues[k] * thisValues[t]);\n }\n }\n return result;\n}\n//# sourceMappingURL=mmulMediumDensity.js.map","import { SparseMatrix } from '../index.js';\n/**\n * Multiplies two very small sparse matrices (both with cardinalities < 42).\n *\n * @private\n * @param {SparseMatrix} left - The left-hand side matrix.\n * @param {SparseMatrix} right - The right-hand side matrix to multiply with.\n * @returns {SparseMatrix} The resulting matrix after multiplication.\n */\nexport function mmulSmall(left, right) {\n const { columns: otherCols, rows: otherRows, values: otherValues, } = right.getNonZeros();\n const nbOtherActive = otherCols.length;\n const m = left.rows;\n const p = right.columns;\n const output = new SparseMatrix(m, p);\n left.withEachNonZero((i, j, v1) => {\n for (let o = 0; o < nbOtherActive; o++) {\n if (j === otherRows[o]) {\n const l = otherCols[o];\n output.set(i, l, output.get(i, l) + otherValues[o] * v1);\n }\n }\n });\n return output;\n}\n//# sourceMappingURL=mmulSmall.js.map","import HashTable from 'ml-hash-table';\nimport { cooToCsr } from './utils/cooToCsr.js';\nimport { mmulLowDensity } from './utils/mmulLowDensity.js';\nimport { mmulMediumDensity } from './utils/mmulMediumDensity.js';\nimport { mmulSmall } from './utils/mmulSmall.js';\n/** @typedef {(row: number, column: number, value: number) => void} WithEachNonZeroCallback */\n/** @typedef {(row: number, column: number, value: number) => number | false} ForEachNonZeroCallback */\nexport class SparseMatrix {\n constructor(rows, columns, options = {}) {\n if (rows instanceof SparseMatrix) {\n // clone\n const other = rows;\n this._init(other.rows, other.columns, other.elements.clone(), other.threshold);\n return;\n }\n if (Array.isArray(rows)) {\n const matrix = rows;\n const nbRows = matrix.length;\n options = columns || {};\n columns = matrix[0].length;\n this._init(nbRows, columns, new HashTable(options), options.threshold);\n for (let i = 0; i < nbRows; i++) {\n for (let j = 0; j < columns; j++) {\n let value = matrix[i][j];\n if (this.threshold && Math.abs(value) < this.threshold)\n value = 0;\n if (value !== 0) {\n this.elements.set(i * columns + j, matrix[i][j]);\n }\n }\n }\n this.elements.maybeShrinkCapacity();\n }\n else {\n this._init(rows, columns, new HashTable(options), options.threshold);\n }\n }\n _init(rows, columns, elements, threshold) {\n this.rows = rows;\n this.columns = columns;\n this.elements = elements;\n this.threshold = threshold || 0;\n }\n static eye(rows = 1, columns = rows) {\n const min = Math.min(rows, columns);\n const matrix = new SparseMatrix(rows, columns, { initialCapacity: min });\n for (let i = 0; i < min; i++) {\n matrix.set(i, i, 1);\n }\n return matrix;\n }\n clone() {\n return new SparseMatrix(this);\n }\n /**\n * @returns {number[][]}\n */\n to2DArray() {\n const copy = new Array(this.rows);\n for (let i = 0; i < this.rows; i++) {\n copy[i] = new Array(this.columns);\n for (let j = 0; j < this.columns; j++) {\n copy[i][j] = this.get(i, j);\n }\n }\n return copy;\n }\n isSquare() {\n return this.rows === this.columns;\n }\n isSymmetric() {\n if (!this.isSquare())\n return false;\n let symmetric = true;\n this.forEachNonZero((i, j, v) => {\n if (this.get(j, i) !== v) {\n symmetric = false;\n return false;\n }\n return v;\n });\n return symmetric;\n }\n /**\n * Search for the wither band in the main diagonals\n * @returns {number}\n */\n bandWidth() {\n let min = this.columns;\n let max = -1;\n this.forEachNonZero((i, j, v) => {\n let diff = i - j;\n min = Math.min(min, diff);\n max = Math.max(max, diff);\n return v;\n });\n return max - min;\n }\n /**\n * Test if a matrix is consider banded using a threshold\n * @param {number} width\n * @returns {boolean}\n */\n isBanded(width) {\n let bandWidth = this.bandWidth();\n return bandWidth <= width;\n }\n /**\n * @returns {number}\n */\n get cardinality() {\n return this.elements.size;\n }\n /**\n * @returns {number}\n */\n get size() {\n return this.rows * this.columns;\n }\n /**\n * @param {number} row\n * @param {number} column\n * @returns {number}\n */\n get(row, column) {\n return this.elements.get(row * this.columns + column);\n }\n /**\n * @param {number} row\n * @param {number} column\n * @param {number} value\n * @returns {this}\n */\n set(row, column, value) {\n if (this.threshold && Math.abs(value) < this.threshold)\n value = 0;\n if (value === 0) {\n this.elements.remove(row * this.columns + column);\n }\n else {\n this.elements.set(row * this.columns + column, value);\n }\n return this;\n }\n /**\n * Matrix multiplication, does not modify the current instance.\n * @param {SparseMatrix} other\n * @returns {SparseMatrix} returns a new matrix instance.\n * @throws {Error} If the number of columns of this matrix does not match the number of rows of the other matrix.\n */\n mmul(other) {\n if (this.columns !== other.rows) {\n throw new RangeError('Number of columns of left matrix are not equal to number of rows of right matrix.');\n }\n if (this.cardinality < 42 && other.cardinality < 42) {\n return mmulSmall(this, other);\n }\n else if (other.rows > 100 && other.cardinality < 100) {\n return mmulLowDensity(this, other);\n }\n return mmulMediumDensity(this, other);\n }\n /**\n * @param {SparseMatrix} other\n * @returns {SparseMatrix}\n */\n kroneckerProduct(other) {\n const m = this.rows;\n const n = this.columns;\n const p = other.rows;\n const q = other.columns;\n const result = new SparseMatrix(m * p, n * q, {\n initialCapacity: this.cardinality * other.cardinality + 10,\n });\n const { columns: otherCols, rows: otherRows, values: otherValues, } = other.getNonZeros();\n const { columns: thisCols, rows: thisRows, values: thisValues, } = this.getNonZeros();\n const nbThisActive = thisCols.length;\n const nbOtherActive = otherCols.length;\n for (let t = 0; t < nbThisActive; t++) {\n const pi = p * thisRows[t];\n const qj = q * thisCols[t];\n for (let o = 0; o < nbOtherActive; o++) {\n result.set(pi + otherRows[o], qj + otherCols[o], otherValues[o] * thisValues[t]);\n }\n }\n return result;\n }\n /**\n * Calls `callback` for each value in the matrix that is not zero.\n * Unlike `forEachNonZero`, the callback's return value has no effect.\n * @param {WithEachNonZeroCallback} callback\n * @param {boolean} [sort]\n * @returns {void}\n */\n withEachNonZero(callback, sort = false) {\n const { state, table, values } = this.elements;\n const nbStates = state.length;\n const activeIndex = new Float64Array(this.cardinality);\n for (let i = 0, j = 0; i < nbStates; i++) {\n if (state[i] === 1)\n activeIndex[j++] = i;\n }\n if (sort) {\n activeIndex.sort((a, b) => table[a] - table[b]);\n }\n const columns = this.columns;\n for (const i of activeIndex) {\n const key = table[i];\n callback((key / columns) | 0, key % columns, values[i]);\n }\n }\n /**\n * Calls `callback` for each value in the matrix that is not zero.\n * The callback can return:\n * - `false` to stop the iteration immediately\n * - the same value to keep it unchanged\n * - 0 to remove the value\n * - something else to change the value\n * @param {ForEachNonZeroCallback} callback\n * @returns {this}\n */\n forEachNonZero(callback) {\n this.elements.forEachPair((key, value) => {\n const i = (key / this.columns) | 0;\n const j = key % this.columns;\n let r = callback(i, j, value);\n if (r === false)\n return false; // stop iteration\n if (this.threshold && Math.abs(r) < this.threshold)\n r = 0;\n if (r !== value) {\n if (r === 0) {\n this.elements.remove(key, true);\n }\n else {\n this.elements.set(key, r);\n }\n }\n return true;\n });\n this.elements.maybeShrinkCapacity();\n return this;\n }\n /**\n * Returns the non-zero elements of the matrix in coordinates (COO) or CSR format.\n *\n * **COO (Coordinate) format:**\n * Stores the non-zero elements as three arrays: `rows`, `columns`, and `values`, where each index corresponds to a non-zero entry at (row, column) with the given value.\n *\n * **CSR (Compressed Sparse Row) format:**\n * Stores the matrix using three arrays:\n * - `rows`: Row pointer array of length `numRows + 1`, where each entry indicates the start of a row in the `columns` and `values` arrays.\n * - `columns`: Column indices of non-zero elements.\n * - `values`: Non-zero values.\n * This format is efficient for row slicing and matrix-vector multiplication.\n *\n * @param {Object} [options={}] - Options for output format and sorting.\n * @param {boolean} [options.csr] - If true, returns the result in CSR format. Otherwise, returns COO format.\n * @param {boolean} [options.sort] - If true, sorts the non-zero elements by their indices.\n * @returns {Object} If `csr` is not specified, returns an object with Float64Array `rows`, `columns`, and `values` (COO format).\n * If `csr` is true, returns `{ rows, columns, values }` in CSR format.\n */\n getNonZeros(options = {}) {\n const cardinality = this.cardinality;\n const rows = new Float64Array(cardinality);\n const columns = new Float64Array(cardinality);\n const values = new Float64Array(cardinality);\n const { csr, sort } = options;\n let idx = 0;\n this.withEachNonZero((i, j, value) => {\n rows[idx] = i;\n columns[idx] = j;\n values[idx] = value;\n idx++;\n }, sort || csr);\n return csr\n ? cooToCsr({ rows, columns, values }, this.rows)\n : { rows, columns, values };\n }\n /**\n * @param {number} newThreshold\n * @returns {this}\n */\n setThreshold(newThreshold) {\n if (newThreshold !== 0 && newThreshold !== this.threshold) {\n this.threshold = newThreshold;\n this.forEachNonZero((i, j, v) => v);\n }\n return this;\n }\n /**\n * @returns {SparseMatrix} - New transposed sparse matrix\n */\n transpose() {\n let trans = new SparseMatrix(this.columns, this.rows, {\n initialCapacity: this.cardinality,\n });\n this.withEachNonZero((i, j, value) => {\n trans.set(j, i, value);\n });\n return trans;\n }\n /**\n * @returns {boolean}\n */\n isEmpty() {\n return this.rows === 0 || this.columns === 0;\n }\n // The following was generated by `makeMathMethods.mjs`\n //#region Math methods.\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n add(value) {\n if (typeof value === 'number')\n return this.addS(value);\n return this.addM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n addS(value) {\n this.forEachNonZero((i, j, v) => v + value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n addM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) + v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static add(matrix, value) {\n return new SparseMatrix(matrix).addS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n sub(value) {\n if (typeof value === 'number')\n return this.subS(value);\n return this.subM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n subS(value) {\n this.forEachNonZero((i, j, v) => v - value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n subM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) - v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static sub(matrix, value) {\n return new SparseMatrix(matrix).subS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n subtract(value) {\n if (typeof value === 'number')\n return this.subtractS(value);\n return this.subtractM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n subtractS(value) {\n this.forEachNonZero((i, j, v) => v - value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n subtractM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) - v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static subtract(matrix, value) {\n return new SparseMatrix(matrix).subtractS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n mul(value) {\n if (typeof value === 'number')\n return this.mulS(value);\n return this.mulM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n mulS(value) {\n this.forEachNonZero((i, j, v) => v * value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n mulM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) * v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static mul(matrix, value) {\n return new SparseMatrix(matrix).mulS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n multiply(value) {\n if (typeof value === 'number')\n return this.multiplyS(value);\n return this.multiplyM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n multiplyS(value) {\n this.forEachNonZero((i, j, v) => v * value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n multiplyM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) * v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static multiply(matrix, value) {\n return new SparseMatrix(matrix).multiplyS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n div(value) {\n if (typeof value === 'number')\n return this.divS(value);\n return this.divM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n divS(value) {\n this.forEachNonZero((i, j, v) => v / value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n divM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) / v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static div(matrix, value) {\n return new SparseMatrix(matrix).divS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n divide(value) {\n if (typeof value === 'number')\n return this.divideS(value);\n return this.divideM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n divideS(value) {\n this.forEachNonZero((i, j, v) => v / value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n divideM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) / v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static divide(matrix, value) {\n return new SparseMatrix(matrix).divideS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n mod(value) {\n if (typeof value === 'number')\n return this.modS(value);\n return this.modM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n modS(value) {\n this.forEachNonZero((i, j, v) => v % value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n modM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) % v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static mod(matrix, value) {\n return new SparseMatrix(matrix).modS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n modulus(value) {\n if (typeof value === 'number')\n return this.modulusS(value);\n return this.modulusM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n modulusS(value) {\n this.forEachNonZero((i, j, v) => v % value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n modulusM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) % v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static modulus(matrix, value) {\n return new SparseMatrix(matrix).modulusS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n and(value) {\n if (typeof value === 'number')\n return this.andS(value);\n return this.andM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n andS(value) {\n this.forEachNonZero((i, j, v) => v & value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n andM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) & v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static and(matrix, value) {\n return new SparseMatrix(matrix).andS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n or(value) {\n if (typeof value === 'number')\n return this.orS(value);\n return this.orM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n orS(value) {\n this.forEachNonZero((i, j, v) => v | value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n orM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) | v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static or(matrix, value) {\n return new SparseMatrix(matrix).orS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n xor(value) {\n if (typeof value === 'number')\n return this.xorS(value);\n return this.xorM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n xorS(value) {\n this.forEachNonZero((i, j, v) => v ^ value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n xorM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) ^ v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static xor(matrix, value) {\n return new SparseMatrix(matrix).xorS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n leftShift(value) {\n if (typeof value === 'number')\n return this.leftShiftS(value);\n return this.leftShiftM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n leftShiftS(value) {\n this.forEachNonZero((i, j, v) => v << value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n leftShiftM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) << v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static leftShift(matrix, value) {\n return new SparseMatrix(matrix).leftShiftS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n signPropagatingRightShift(value) {\n if (typeof value === 'number') {\n return this.signPropagatingRightShiftS(value);\n }\n return this.signPropagatingRightShiftM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n signPropagatingRightShiftS(value) {\n this.forEachNonZero((i, j, v) => v >> value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n signPropagatingRightShiftM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) >> v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static signPropagatingRightShift(matrix, value) {\n return new SparseMatrix(matrix).signPropagatingRightShiftS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n rightShift(value) {\n if (typeof value === 'number')\n return this.rightShiftS(value);\n return this.rightShiftM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n rightShiftS(value) {\n this.forEachNonZero((i, j, v) => v >>> value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n rightShiftM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) >>> v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static rightShift(matrix, value) {\n return new SparseMatrix(matrix).rightShiftS(value);\n }\n /**\n * @param {number|SparseMatrix} value\n * @returns {this}\n */\n zeroFillRightShift(value) {\n if (typeof value === 'number')\n return this.zeroFillRightShiftS(value);\n return this.zeroFillRightShiftM(value);\n }\n /**\n * @param {number} value\n * @returns {this}\n */\n zeroFillRightShiftS(value) {\n this.forEachNonZero((i, j, v) => v >>> value);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {this}\n */\n zeroFillRightShiftM(matrix) {\n matrix.forEachNonZero((i, j, v) => {\n this.set(i, j, this.get(i, j) >>> v);\n return v;\n });\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @param {number} value\n * @returns {SparseMatrix}\n */\n static zeroFillRightShift(matrix, value) {\n return new SparseMatrix(matrix).zeroFillRightShiftS(value);\n }\n /**\n * @returns {this}\n */\n not() {\n this.forEachNonZero((i, j, v) => ~v);\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static not(matrix) {\n return new SparseMatrix(matrix).not();\n }\n /**\n * @returns {this}\n */\n abs() {\n this.forEachNonZero((i, j, v) => Math.abs(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static abs(matrix) {\n return new SparseMatrix(matrix).abs();\n }\n /**\n * @returns {this}\n */\n acos() {\n this.forEachNonZero((i, j, v) => Math.acos(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static acos(matrix) {\n return new SparseMatrix(matrix).acos();\n }\n /**\n * @returns {this}\n */\n acosh() {\n this.forEachNonZero((i, j, v) => Math.acosh(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static acosh(matrix) {\n return new SparseMatrix(matrix).acosh();\n }\n /**\n * @returns {this}\n */\n asin() {\n this.forEachNonZero((i, j, v) => Math.asin(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static asin(matrix) {\n return new SparseMatrix(matrix).asin();\n }\n /**\n * @returns {this}\n */\n asinh() {\n this.forEachNonZero((i, j, v) => Math.asinh(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static asinh(matrix) {\n return new SparseMatrix(matrix).asinh();\n }\n /**\n * @returns {this}\n */\n atan() {\n this.forEachNonZero((i, j, v) => Math.atan(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static atan(matrix) {\n return new SparseMatrix(matrix).atan();\n }\n /**\n * @returns {this}\n */\n atanh() {\n this.forEachNonZero((i, j, v) => Math.atanh(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static atanh(matrix) {\n return new SparseMatrix(matrix).atanh();\n }\n /**\n * @returns {this}\n */\n cbrt() {\n this.forEachNonZero((i, j, v) => Math.cbrt(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static cbrt(matrix) {\n return new SparseMatrix(matrix).cbrt();\n }\n /**\n * @returns {this}\n */\n ceil() {\n this.forEachNonZero((i, j, v) => Math.ceil(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static ceil(matrix) {\n return new SparseMatrix(matrix).ceil();\n }\n /**\n * @returns {this}\n */\n clz32() {\n this.forEachNonZero((i, j, v) => Math.clz32(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static clz32(matrix) {\n return new SparseMatrix(matrix).clz32();\n }\n /**\n * @returns {this}\n */\n cos() {\n this.forEachNonZero((i, j, v) => Math.cos(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static cos(matrix) {\n return new SparseMatrix(matrix).cos();\n }\n /**\n * @returns {this}\n */\n cosh() {\n this.forEachNonZero((i, j, v) => Math.cosh(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static cosh(matrix) {\n return new SparseMatrix(matrix).cosh();\n }\n /**\n * @returns {this}\n */\n exp() {\n this.forEachNonZero((i, j, v) => Math.exp(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static exp(matrix) {\n return new SparseMatrix(matrix).exp();\n }\n /**\n * @returns {this}\n */\n expm1() {\n this.forEachNonZero((i, j, v) => Math.expm1(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static expm1(matrix) {\n return new SparseMatrix(matrix).expm1();\n }\n /**\n * @returns {this}\n */\n floor() {\n this.forEachNonZero((i, j, v) => Math.floor(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static floor(matrix) {\n return new SparseMatrix(matrix).floor();\n }\n /**\n * @returns {this}\n */\n fround() {\n this.forEachNonZero((i, j, v) => Math.fround(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static fround(matrix) {\n return new SparseMatrix(matrix).fround();\n }\n /**\n * @returns {this}\n */\n log() {\n this.forEachNonZero((i, j, v) => Math.log(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static log(matrix) {\n return new SparseMatrix(matrix).log();\n }\n /**\n * @returns {this}\n */\n log1p() {\n this.forEachNonZero((i, j, v) => Math.log1p(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static log1p(matrix) {\n return new SparseMatrix(matrix).log1p();\n }\n /**\n * @returns {this}\n */\n log10() {\n this.forEachNonZero((i, j, v) => Math.log10(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static log10(matrix) {\n return new SparseMatrix(matrix).log10();\n }\n /**\n * @returns {this}\n */\n log2() {\n this.forEachNonZero((i, j, v) => Math.log2(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static log2(matrix) {\n return new SparseMatrix(matrix).log2();\n }\n /**\n * @returns {this}\n */\n round() {\n this.forEachNonZero((i, j, v) => Math.round(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static round(matrix) {\n return new SparseMatrix(matrix).round();\n }\n /**\n * @returns {this}\n */\n sign() {\n this.forEachNonZero((i, j, v) => Math.sign(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static sign(matrix) {\n return new SparseMatrix(matrix).sign();\n }\n /**\n * @returns {this}\n */\n sin() {\n this.forEachNonZero((i, j, v) => Math.sin(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static sin(matrix) {\n return new SparseMatrix(matrix).sin();\n }\n /**\n * @returns {this}\n */\n sinh() {\n this.forEachNonZero((i, j, v) => Math.sinh(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static sinh(matrix) {\n return new SparseMatrix(matrix).sinh();\n }\n /**\n * @returns {this}\n */\n sqrt() {\n this.forEachNonZero((i, j, v) => Math.sqrt(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static sqrt(matrix) {\n return new SparseMatrix(matrix).sqrt();\n }\n /**\n * @returns {this}\n */\n tan() {\n this.forEachNonZero((i, j, v) => Math.tan(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static tan(matrix) {\n return new SparseMatrix(matrix).tan();\n }\n /**\n * @returns {this}\n */\n tanh() {\n this.forEachNonZero((i, j, v) => Math.tanh(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static tanh(matrix) {\n return new SparseMatrix(matrix).tanh();\n }\n /**\n * @returns {this}\n */\n trunc() {\n this.forEachNonZero((i, j, v) => Math.trunc(v));\n return this;\n }\n /**\n * @param {SparseMatrix} matrix\n * @returns {SparseMatrix}\n */\n static trunc(matrix) {\n return new SparseMatrix(matrix).trunc();\n }\n}\nSparseMatrix.prototype.klass = 'Matrix';\nSparseMatrix.identity = SparseMatrix.eye;\nSparseMatrix.prototype.tensorProduct = SparseMatrix.prototype.kroneckerProduct;\n//# sourceMappingURL=index.js.map","import { matrixCuthillMckee } from 'ml-spectra-processing';\n/**\n * Updates the system matrix with new weights and computes the weighted right-hand side vector.\n * @param matrix - The lower triangular non-zeros matrix in triplet format.\n * @param y - The original data vector.\n * @param weights - The weights vector.\n * @returns A tuple of the updated matrix and the weighted right-hand side vector.\n */\nexport function updateSystem(matrix, y, weights) {\n const nbPoints = y.length;\n const l = nbPoints - 1;\n const newMatrix = new Array(matrix.length);\n const newVector = new Float64Array(nbPoints);\n for (let i = 0; i < l; i++) {\n const w = weights[i];\n const diag = i * 2;\n const next = diag + 1;\n newMatrix[diag] = matrix[diag].slice();\n newMatrix[next] = matrix[next].slice();\n if (w === 0) {\n newVector[i] = 0;\n }\n else {\n newVector[i] = y[i] * w;\n newMatrix[diag][2] += w;\n }\n }\n newVector[l] = y[l] * weights[l];\n newMatrix[l * 2] = matrix[l * 2].slice();\n newMatrix[l * 2][2] += weights[l];\n return [newMatrix, newVector];\n}\n/**\n * Constructs the delta matrix and applies Cuthill-McKee permutation.\n * @param nbPoints - Number of data points.\n * @param lambda - Smoothing parameter.\n * @returns An object containing the lower triangular non-zeros matrix and the permutation array.\n */\nexport function getDeltaMatrix(nbPoints, lambda) {\n const matrix = [];\n const last = nbPoints - 1;\n for (let i = 0; i < last; i++) {\n matrix.push([i, i, lambda * 2], [i + 1, i, -1 * lambda]);\n }\n matrix[0][2] = lambda;\n matrix.push([last, last, lambda]);\n return {\n lowerTriangularNonZeros: matrix,\n permutationEncodedArray: matrixCuthillMckee(matrix, nbPoints),\n };\n}\n//# sourceMappingURL=utils.js.map","import { matrixCholeskySolver, xAbsoluteSum, xBinning, xFindClosestIndex, xNoiseSanPlot, xSubtract, xyInterpolateLinear, } from 'ml-spectra-processing';\nimport { getDeltaMatrix, updateSystem } from \"./utils.js\";\n/**\n * Fit the baseline drift by iteratively changing weights of sum square error between the fitted baseline and original signals,\n * for further information about the parameters you can get the {@link https://github.com/zmzhang/airPLS/blob/main/airPLS_manuscript.pdf paper of airPLS}.\n * @param x - X axis data useful when control points or zones are submitted.\n * @param y - Original data.\n * @param options - Options object.\n * @returns An object containing the corrected data, baseline, iteration count, and error.\n */\nexport default function airPLS(x, y, options = {}) {\n const { xWork, yWork, optionsWork, shouldDownsample } = getDownSampleData(x, y, options);\n const { weights, controlPoints } = getControlPoints(xWork, yWork, optionsWork);\n const { maxIterations = 100, lambda = 10, tolerance = 0.001 } = optionsWork;\n let baseline = [];\n let iteration;\n let sumNegDifferences = Number.MAX_SAFE_INTEGER;\n const corrected = Float64Array.from(yWork);\n const stopCriterion = getStopCriterion(yWork, tolerance);\n const { length } = yWork;\n const { lowerTriangularNonZeros, permutationEncodedArray } = getDeltaMatrix(length, lambda);\n let threshold = 1;\n const l = length - 1;\n let prevNegSum = Number.MAX_SAFE_INTEGER;\n for (iteration = 0; iteration < maxIterations && Math.abs(sumNegDifferences) > stopCriterion; iteration++) {\n const [leftHandSide, rightHandSide] = updateSystem(lowerTriangularNonZeros, yWork, weights);\n const cho = matrixCholeskySolver(leftHandSide, length, permutationEncodedArray);\n if (cho === null) {\n throw new Error('Cholesky decomposition failed');\n }\n baseline = cho(rightHandSide);\n sumNegDifferences = applyCorrection(yWork, baseline, corrected);\n if (iteration === 1) {\n const { positive } = xNoiseSanPlot(corrected);\n threshold = positive;\n }\n else {\n const absChange = Math.abs(prevNegSum / sumNegDifferences);\n if (absChange < 1.01 && absChange > 0.99) {\n break;\n }\n }\n prevNegSum = sumNegDifferences + 0;\n for (let i = 1; i < l; i++) {\n const diff = Math.abs(corrected[i]);\n if (controlPoints[i] < 1 && diff > threshold * 2) {\n weights[i] = 0;\n }\n else {\n weights[i] = Math.exp(-((diff / threshold) ** 2));\n }\n }\n weights[0] = 1;\n weights[l] = 1;\n }\n // Interpolate results back to original resolution if downsampling was applied\n let finalBaseline = baseline;\n let finalCorrected = corrected;\n if (shouldDownsample) {\n finalBaseline = xyInterpolateLinear({ x: xWork, y: baseline }, x);\n finalCorrected = xSubtract(y, finalBaseline);\n }\n return {\n corrected: finalCorrected,\n baseline: finalBaseline,\n iteration,\n error: sumNegDifferences,\n };\n}\nfunction applyCorrection(y, baseline, corrected) {\n let sumNegDifferences = 0;\n for (let i = 0; i < y.length; i++) {\n const diff = y[i] - baseline[i];\n if (diff < 0)\n sumNegDifferences += diff;\n corrected[i] = diff;\n }\n return sumNegDifferences;\n}\nfunction getDownSampleData(x, y, options = {}) {\n const { autoDownsample = false, maxResolution = 5000, controlPoints, } = options;\n const shouldDownsample = autoDownsample && y.length > maxResolution;\n if (!shouldDownsample) {\n return { xWork: x, yWork: y, optionsWork: options, shouldDownsample };\n }\n const binSize = getDownsampleFactor(y.length, maxResolution);\n const xWork = xBinning(x, {\n binSize,\n keepFirstAndLast: true,\n });\n const yWork = xBinning(y, {\n binSize,\n keepFirstAndLast: true,\n });\n let optionsWork = options;\n // Downsample controlPoints if provided, to match downsampled x and y\n if (controlPoints) {\n const downsampledControlPoints = new Int8Array(xWork.length);\n for (let i = 0; i < x.length; i++) {\n if (controlPoints[i] > 0) {\n const closestIndex = xFindClosestIndex(xWork, x[i]);\n downsampledControlPoints[closestIndex] = 1;\n }\n }\n optionsWork = {\n ...options,\n controlPoints: downsampledControlPoints,\n };\n }\n return { xWork, yWork, optionsWork, shouldDownsample };\n}\nfunction getStopCriterion(y, tolerance) {\n const sum = xAbsoluteSum(y);\n return tolerance * sum;\n}\nfunction getControlPoints(x, y, options = {}) {\n const { length } = x;\n const { controlPoints = Int8Array.from({ length }).fill(0) } = options;\n const { zones = [], weights = Float64Array.from({ length }).fill(1) } = options;\n if (x.length !== y.length) {\n throw new RangeError('Y should match the length with X');\n }\n else if (controlPoints.length !== x.length) {\n throw new RangeError('controlPoints should match the length with X');\n }\n else if (weights.length !== x.length) {\n throw new RangeError('weights should match the length with X');\n }\n for (const range of zones) {\n let indexFrom = xFindClosestIndex(x, range.from);\n let indexTo = xFindClosestIndex(x, range.to);\n if (indexFrom > indexTo)\n [indexFrom, indexTo] = [indexTo, indexFrom];\n for (let i = indexFrom; i < indexTo; i++) {\n controlPoints[i] = 1;\n }\n }\n return {\n weights,\n controlPoints,\n };\n}\n/**\n * Calculate the downsampling factor to reduce data to target resolution.\n * @param originalLength - Original data length.\n * @param targetResolution - Target number of points.\n * @returns Downsampling factor.\n */\nfunction getDownsampleFactor(originalLength, targetResolution) {\n return Math.max(1, Math.ceil(originalLength / targetResolution));\n}\n//# sourceMappingURL=index.js.map","import { isAnyArray } from 'is-any-array';\n/**\n * Check that x and y are arrays with the same length.\n * @param x - first array\n * @param y - second array\n * @throws if x or y are not the same length, or if they are not arrays\n */\nexport function checkArrayLength(x, y) {\n if (!isAnyArray(x) || !isAnyArray(y)) {\n throw new TypeError('x and y must be arrays');\n }\n if (x.length !== y.length) {\n throw new RangeError('x and y arrays must have the same length');\n }\n}\n//# sourceMappingURL=checkArrayLength.js.map","import { isAnyArray } from 'is-any-array';\nimport { checkArrayLength } from './checkArrayLength';\nexport class BaseRegression {\n constructor() {\n if (new.target === BaseRegression) {\n throw new Error('BaseRegression must be subclassed');\n }\n }\n predict(x) {\n if (typeof x === 'number') {\n return this._predict(x);\n }\n else if (isAnyArray(x)) {\n const y = [];\n for (const xVal of x) {\n y.push(this._predict(xVal));\n }\n return y;\n }\n else {\n throw new TypeError('x must be a number or array');\n }\n }\n // eslint-disable-next-line @typescript-eslint/no-unused-vars\n _predict(x) {\n throw new Error('_predict must be implemented');\n }\n train() {\n // Do nothing for this package\n }\n // eslint-disable-next-line @typescript-eslint/no-unused-vars\n toString(precision) {\n return '';\n }\n // eslint-disable-next-line @typescript-eslint/no-unused-vars\n toLaTeX(precision) {\n return '';\n }\n /**\n * Return the correlation coefficient of determination (r) and chi-square.\n * @param x - explanatory variable\n * @param y - response variable\n * @return - Object with further statistics.\n */\n score(x, y) {\n checkArrayLength(x, y);\n const n = x.length;\n const y2 = new Array(n);\n for (let i = 0; i < n; i++) {\n y2[i] = this._predict(x[i]);\n }\n let xSum = 0;\n let ySum = 0;\n let chi2 = 0;\n let rmsd = 0;\n let xSquared = 0;\n let ySquared = 0;\n let xY = 0;\n for (let i = 0; i < n; i++) {\n xSum += y2[i];\n ySum += y[i];\n xSquared += y2[i] * y2[i];\n ySquared += y[i] * y[i];\n xY += y2[i] * y[i];\n if (y[i] !== 0) {\n chi2 += ((y[i] - y2[i]) * (y[i] - y2[i])) / y[i];\n }\n rmsd += (y[i] - y2[i]) * (y[i] - y2[i]);\n }\n const r = (n * xY - xSum * ySum) /\n Math.sqrt((n * xSquared - xSum * xSum) * (n * ySquared - ySum * ySum));\n return {\n r,\n r2: r * r,\n chi2,\n rmsd: Math.sqrt(rmsd / n),\n };\n }\n}\n//# sourceMappingURL=BaseRegression.js.map","/**\n * Cast `number` to string. Optionally `digits` specifies significant figures.\n * @param number\n * @param figures\n * @returns - A string representation of `number`.\n */\nexport function maybeToPrecision(number, figures) {\n if (number < 0) {\n number = 0 - number;\n if (typeof figures === 'number') {\n return `- ${number.toPrecision(figures)}`;\n }\n else {\n return `- ${number.toString()}`;\n }\n }\n else if (typeof figures === 'number') {\n return number.toPrecision(figures);\n }\n else {\n return number.toString();\n }\n}\n//# sourceMappingURL=maybeToPrecision.js.map","import { Matrix, MatrixTransposeView, solve } from 'ml-matrix';\nimport { BaseRegression, checkArrayLength, maybeToPrecision, } from 'ml-regression-base';\nexport class PolynomialRegression extends BaseRegression {\n degree;\n powers;\n coefficients;\n /**\n * Build a polynomial regression model from the given data set.\n * @param x - independent or explanatory variable\n * @param y - dependent or response variable\n * @param degree - degree of the polynomial regression, or array of powers to be used. When degree is an array, intercept at zero is forced to false/ignored.\n * @param options - regression options\n * @example `new PolynomialRegression(x, y, 2)`, in this case, you can pass the option `interceptAtZero`, if you need it.\n * @example `new PolynomialRegression(x, y, [1, 3, 5])`\n * Each of the degrees corresponds to a column, so if you have them switched, just do:\n * @example `new PolynomialRegression(x, y, [3, 1, 5])`\n */\n constructor(x, y, degree, options = {}) {\n super();\n // @ts-expect-error internal use only\n if (x === true) {\n // @ts-expect-error internal use only\n this.degree = y.degree;\n // @ts-expect-error internal use only\n this.powers = y.powers;\n // @ts-expect-error internal use only\n this.coefficients = y.coefficients;\n }\n else {\n checkArrayLength(x, y);\n const result = regress(x, y, degree, options);\n this.degree = result.degree;\n this.powers = result.powers;\n this.coefficients = result.coefficients;\n }\n }\n _predict(x) {\n const { coefficients, powers } = this;\n let y = 0;\n for (let k = 0; k < powers.length; k++) {\n const coefficient = coefficients[k] ?? 0;\n const power = powers[k] ?? 0;\n y += coefficient * x ** power;\n }\n return y;\n }\n toJSON() {\n return {\n name: 'polynomialRegression',\n degree: this.degree,\n powers: this.powers,\n coefficients: this.coefficients,\n };\n }\n toString(precision) {\n return this._toFormula(precision, false);\n }\n toLaTeX(precision) {\n return this._toFormula(precision, true);\n }\n _toFormula(precision, isLaTeX) {\n let sup = '^';\n let closeSup = '';\n let times = ' * ';\n if (isLaTeX) {\n sup = '^{';\n closeSup = '}';\n times = '';\n }\n let fn = '';\n let str = '';\n for (let k = 0; k < this.coefficients.length; k++) {\n str = '';\n const coefficient = this.coefficients[k] ?? 0;\n const power = this.powers[k] ?? 0;\n if (coefficient !== 0) {\n if (power === 0) {\n str = maybeToPrecision(coefficient, precision);\n }\n else if (power === 1) {\n str = `${maybeToPrecision(coefficient, precision) + times}x`;\n }\n else {\n str = `${maybeToPrecision(coefficient, precision) + times}x${sup}${power}${closeSup}`;\n }\n if (coefficient > 0 && k !== this.coefficients.length - 1) {\n str = ` + ${str}`;\n }\n else if (k !== this.coefficients.length - 1) {\n str = ` ${str}`;\n }\n }\n fn = str + fn;\n }\n if (fn.startsWith('+')) {\n fn = fn.slice(1);\n }\n return `f(x) = ${fn}`;\n }\n static load(json) {\n if (json.name !== 'polynomialRegression') {\n throw new TypeError('not a polynomial regression model');\n }\n // @ts-expect-error internal use only\n return new PolynomialRegression(true, json);\n }\n}\n/**\n * Perform a polynomial regression on the given data set.\n * This is an internal function.\n * @param x - independent or explanatory variable\n * @param y - dependent or response variable\n * @param degree - degree of the polynomial regression\n * @param options - regression options\n * @returns degree, powers and coefficients of the fitted polynomial\n */\nfunction regress(x, y, degree, options = {}) {\n const n = x.length;\n let { interceptAtZero = false } = options;\n let powers = [];\n if (Array.isArray(degree)) {\n powers = degree;\n interceptAtZero = false;\n }\n else if (typeof degree === 'number') {\n if (interceptAtZero) {\n powers = new Array(degree);\n for (let k = 0; k < degree; k++) {\n powers[k] = k + 1;\n }\n }\n else {\n powers = new Array(degree + 1);\n for (let k = 0; k <= degree; k++) {\n powers[k] = k;\n }\n }\n }\n const nCoefficients = powers.length;\n const F = new Matrix(n, nCoefficients);\n const Y = new Matrix([Array.from(y)]);\n for (let k = 0; k < nCoefficients; k++) {\n const power = powers[k] ?? 0;\n for (let i = 0; i < n; i++) {\n if (power === 0) {\n F.set(i, k, 1);\n }\n else {\n F.set(i, k, (x[i] ?? 0) ** power);\n }\n }\n }\n const FT = new MatrixTransposeView(F);\n const A = FT.mmul(F);\n const B = FT.mmul(new MatrixTransposeView(Y));\n return {\n coefficients: solve(A, B).to1DArray(),\n degree: Math.max(...powers),\n powers,\n };\n}\n//# sourceMappingURL=index.js.map","import { xMean } from 'ml-spectra-processing';\n/**\n * Center the mean\n * @param data\n */\nexport function centerMean(data) {\n const { y } = data;\n const mean = xMean(y);\n for (let i = 0; i < y.length; i++) {\n y[i] -= mean;\n }\n return { data };\n}\n//# sourceMappingURL=centerMean.js.map","import { xMedian } from 'ml-spectra-processing';\n/**\n * Center the median\n * @param data\n */\nexport function centerMedian(data) {\n const { y } = data;\n const median = xMedian(y);\n for (let i = 0; i < y.length; i++) {\n y[i] -= median;\n }\n return { data };\n}\n//# sourceMappingURL=centerMedian.js.map","import { xGetFromToIndex } from 'ml-spectra-processing';\n/**\n * Filter that allows to\n * @param data\n * @param options\n */\nexport function fromTo(data, options = {}) {\n const { fromIndex, toIndex } = xGetFromToIndex(data.x, options);\n return {\n data: {\n x: data.x.subarray(fromIndex, toIndex + 1),\n y: data.y.subarray(fromIndex, toIndex + 1),\n },\n };\n}\n//# sourceMappingURL=fromTo.js.map","import { xNormed } from 'ml-spectra-processing';\n/**\n * Norm the Y values\n * @param data\n * @param options\n */\nexport function normed(data, options = {}) {\n xNormed(data.y, { ...options, output: data.y });\n return { data };\n}\n//# sourceMappingURL=normed.js.map","import { xStandardDeviation } from 'ml-spectra-processing';\n/**\n * Center the mean\n * @param data\n */\nexport function divideBySD(data) {\n const { y } = data;\n const sd = xStandardDeviation(y);\n for (let i = 0; i < y.length; i++) {\n y[i] /= sd;\n }\n return { data };\n}\n//# sourceMappingURL=divideBySD.js.map","import { xRescale } from 'ml-spectra-processing';\n/**\n * Center the mean\n * @param data\n * @param options\n */\nexport function rescale(data, options = {}) {\n xRescale(data.y, { ...options, output: data.y });\n return { data };\n}\n//# sourceMappingURL=rescale.js.map","import { xParetoNormalization } from 'ml-spectra-processing';\n/**\n * Filter that allows to\n * @param data\n */\nexport function paretoNormalization(data) {\n return {\n data: {\n x: data.x,\n y: xParetoNormalization(data.y),\n },\n };\n}\n//# sourceMappingURL=paretoNormalization.js.map","import airpls from 'ml-airpls';\nimport { xSequentialFillFromTo } from 'ml-spectra-processing/x';\n/**\n * Adaptive iteratively reweighted penalized least squares [1]\n *\n * This function calls ml-airpls\n *\n * References:\n * [1] Zhang, Z.-M.; Chen, S.; Liang, Y.-Z.\n * Baseline Correction Using Adaptive Iteratively Reweighted Penalized Least Squares.\n * Analyst 2010, 135 (5), 1138–1146. https://doi.org/10.1039/B922045C.\n * @export\n * @param {Array} ys\n * @param {object} [options] - Options object\n * @param {Array} [options.x] Optional, Independent axis variable. If not specified, we use a linear grid\n * @param {object} [options.regression] - Options for the regression\n * @param {number} [options.regression.maxIterations = 100] - Maximum number of allowed iterations\n * @param {function} [options.regression.§Regression = PolynomialRegression] - Regression class with a predict method\n * @param {*} [options.regression.regressionOptions] - Options for regressionFunction\n * @param {number} [options.regression.tolerance = 0.001] - Convergence error tolerance\n * @returns {BaselineOutput}\n */\nexport function airPLSBaseline(ys, options = {}) {\n const numberPoints = ys.length;\n let { x, regressionOptions } = options;\n if (!x) {\n x = xSequentialFillFromTo({\n from: 0,\n to: numberPoints - 1,\n size: numberPoints,\n });\n }\n let output = airpls(x, ys, regressionOptions);\n\n return { baseline: output.baseline, correctedSpectrum: output.corrected };\n}\n","import { PolynomialRegression } from 'ml-regression-polynomial';\n\n/**\n * Iterative regression-based baseline correction\n * @param {Array} x - Independent axis variable\n * @param {Array} y - Dependent axis variable\n * @param {object} [options] - Options object\n * @param {number} [options.maxIterations = 100] - Maximum number of allowed iterations\n * @param {function} [options.Regression = PolynomialRegression] - Regression class with a predict method\n * @param {*} [options.regressionOptions] - Options for regressionFunction\n * @param {number} [options.tolerance = 0.001] - Convergence error tolerance\n * @return {{corrected: Array, delta: number, iteration: number, baseline: Array}}\n */\nexport function baselineCorrectionRegression(x, y, options = {}) {\n let {\n maxIterations = 100,\n Regression = PolynomialRegression,\n regressionOptions,\n tolerance = 0.001,\n } = options;\n\n if (!regressionOptions && Regression === PolynomialRegression) {\n regressionOptions = 3;\n }\n\n let baseline = y.slice();\n let fitting = y.slice();\n let oldFitting = y;\n let iteration = 0;\n let delta;\n let regression;\n while (iteration < maxIterations) {\n // Calculate the fitting result\n regression = new Regression(x, baseline, regressionOptions);\n\n delta = 0;\n for (let i = 0; i < baseline.length; i++) {\n fitting[i] = regression.predict(x[i]);\n if (baseline[i] > fitting[i]) {\n baseline[i] = fitting[i];\n }\n\n delta += Math.abs((fitting[i] - oldFitting[i]) / oldFitting[i]);\n }\n\n // Stop criterion\n if (delta < tolerance) {\n break;\n } else {\n oldFitting = fitting.slice();\n iteration++;\n }\n }\n\n // removes baseline\n let corrected = new Array(baseline.length);\n for (let j = 0; j < baseline.length; j++) {\n corrected[j] = y[j] - baseline[j];\n }\n\n return {\n corrected,\n delta,\n iteration,\n baseline,\n regression,\n };\n}\n","import { baselineCorrectionRegression } from 'ml-baseline-correction-regression';\nimport { xSequentialFillFromTo } from 'ml-spectra-processing/x';\n\n/**\n * Iterative polynomial fitting [1]\n *\n * Implementation based on ml-baseline-correction-regression\n *\n * References:\n * [1] Gan, F.; Ruan, G.; Mo, J.\n * Baseline Correction by Improved Iterative Polynomial Fitting with Automatic Threshold.\n * Chemometrics and Intelligent Laboratory Systems 2006, 82 (1), 59–65.\n * https://doi.org/10.1016/j.chemolab.2005.08.009.\n * @export\n * @param {Array} ys\n * @param {object} [options] - Options object\n * @param {Array} [options.x] Optional, Independent axis variable. If not specified, we use a linear grid\n * @param {Object} [options.regression]\n * @param {number} [options.regression.maxIterations = 100] - Maximum number of allowed iterations\n * @param {Object} [options.regression]\n * @param {function} [options.regression.Regression = PolynomialRegression] - Regression class with a predict method\n * @param {Object} [options.regression.regressionOptions] - Options for regressionFunction\n * @param {number} [options.regression.tolerance = 0.001] - Convergence error tolerance\n * @returns {BaselineOutput}\n */\nexport function iterativePolynomialBaseline(ys, options = {}) {\n const numberPoints = ys.length;\n let { x, regressionOptions } = options;\n if (!x) {\n x = xSequentialFillFromTo({\n from: 0,\n to: numberPoints - 1,\n size: numberPoints,\n });\n }\n\n let output = baselineCorrectionRegression(x, ys, regressionOptions);\n\n return { baseline: output.baseline, correctedSpectrum: output.corrected };\n}\n","import { xRollingAverage } from 'ml-spectra-processing';\n\n/**\n\n *\n * @export\n * @param {Array} ys\n * @param {Object} [options={}]\n * @param {number} [options.window] rolling window size, defaults to 10% of the length of the spectrum\n * @param {string} [options.padding.size=window-1] none, value, circular, duplicate\n * @param {string} [options.padding.algorithm='duplicate'] none, value, circular, duplicate\n * @param {number} [options.padding.value=0] value to use for padding (if algorithm='value')\n * @returns {BaselineOutput}\n */\nexport function rollingAverageBaseline(ys, options = {}) {\n let window = Math.max(Math.round(ys.length * 0.1), 2);\n let defaults = {\n window,\n padding: {\n size: window - 1,\n algorithm: 'duplicate',\n value: 0,\n },\n };\n let actualOptions = { ...defaults, ...options };\n let baseline = xRollingAverage(ys, actualOptions);\n let corrected = new Float64Array(ys.length);\n for (let i = 0; i < corrected.length; i++) {\n corrected[i] = ys[i] - baseline[i];\n }\n\n return { baseline, correctedSpectrum: corrected };\n}\n","import { isAnyArray } from 'is-any-array';\nimport { xMaxValue, xMean, xMinValue } from 'ml-spectra-processing';\n/**\n * Rolling ball baseline correction algorithm.\n * From the abstract of (1):\n * \"This algorithm behaves equivalently to traditional polynomial backgrounds in simple spectra,\n * [...] and is considerably more robust for multiple overlapping peaks, rapidly varying background [...]\n *\n * The baseline is the trace one gets by rolling a ball below a spectrum. Algorithm has three steps:\n * Finding the minima in each window, find maxima among minima and then smooth over them by averaging.\n *\n * Reference:\n * (1) Kneen, M. A.; Annegarn, H. J.\n * Algorithm for Fitting XRF, SEM and PIXE X-Ray Spectra Backgrounds.\n * Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 1996, 109–110, 209–213.\n * https://doi.org/10.1016/0168-583X(95)00908-6.\n * (2) Kristian Hovde Liland, Bjørn-Helge Mevik, Roberto Canteri: baseline.\n * https://cran.r-project.org/web/packages/baseline/index.html\n * @param spectrum - Input spectrum.\n * @param options - Rolling ball options.\n * @returns The computed baseline.\n */\nexport function rollingBall(spectrum, options = {}) {\n if (!isAnyArray(spectrum)) {\n throw new Error('Spectrum must be an array');\n }\n if (spectrum.length === 0) {\n throw new TypeError('Spectrum must not be empty');\n }\n const numberPoints = spectrum.length;\n const maxima = new Float64Array(numberPoints);\n const minima = new Float64Array(numberPoints);\n const baseline = new Float64Array(numberPoints);\n // windowM 4 percent of spectrum length\n // windowS 8 percent of spectrum length\n const { windowM = Math.round(numberPoints * 0.04), windowS = Math.round(numberPoints * 0.08), } = options;\n // fi(1) in original paper\n for (let i = 0; i < spectrum.length; i++) {\n const windowLeft = Math.max(0, i - windowM);\n const windowRight = Math.min(i + windowM + 1, spectrum.length);\n minima[i] = xMinValue(spectrum, {\n fromIndex: windowLeft,\n toIndex: windowRight,\n });\n }\n // fi in original paper\n for (let i = 0; i < minima.length; i++) {\n const windowLeft = Math.max(0, i - windowM);\n const windowRight = Math.min(i + windowM + 1, minima.length);\n maxima[i] = xMaxValue(minima, {\n fromIndex: windowLeft,\n toIndex: windowRight,\n });\n }\n for (let i = 0; i < minima.length; i++) {\n const windowLeft = Math.max(0, i - windowS);\n const windowRight = Math.min(i + windowS + 1, maxima.length);\n baseline[i] = xMean(maxima.subarray(windowLeft, windowRight));\n }\n return baseline;\n}\n//# sourceMappingURL=rollingBall.js.map","import { rollingBall } from 'ml-rolling-ball-baseline';\n\n/**\n * Rolling ball baseline correction algorithm.\n * From the abstract of (1):\n * \"This algorithm behaves equivalently to traditional polynomial backgrounds in simple spectra,\n * [...] and is considerably more robust for multiple overlapping peaks, rapidly varying background [...]\n *\n * The baseline is the trace one gets by rolling a ball below a spectrum. Algorithm has three steps:\n * Finding the minima in each window, find maxima among minima and then smooth over them by averaging.\n *\n * Algorithm described in (1), but in the implementation here the window width does not change.\n *\n * Reference:\n * (1) Kneen, M. A.; Annegarn, H. J.\n * Algorithm for Fitting XRF, SEM and PIXE X-Ray Spectra Backgrounds.\n * Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 1996, 109–110, 209–213.\n * https://doi.org/10.1016/0168-583X(95)00908-6.\n * (2) Kristian Hovde Liland, Bjørn-Helge Mevik, Roberto Canteri: baseline.\n * https://cran.r-project.org/web/packages/baseline/index.html\n *\n * @export\n * @param {Array} ys\n * @param {Object} [options={}]\n * @param {Number} [options.windowM] - width of local window for minimization/maximization, defaults to 4% of the spectrum length\n * @param {Number} [options.windowS] - width of local window for smoothing, defaults to 8% of the specturm length\n * @returns {BaselineOutput}\n */\nexport function rollingBallBaseline(ys, options = {}) {\n const baseline = rollingBall(ys, options);\n let corrected = new Float64Array(ys.length);\n for (let i = 0; i < corrected.length; i++) {\n corrected[i] = ys[i] - baseline[i];\n }\n\n return { baseline, correctedSpectrum: corrected };\n}\n","import { xRollingMedian } from 'ml-spectra-processing';\n\n/**\n\n *\n * @export\n * @param {Array} ys\n * @param {Object} [options={}]\n * @param {number} [options.window] rolling window size, defaults to 10% of the length of the spectrum\n * @param {string} [options.padding.size=window-1] none, value, circular, duplicate\n * @param {string} [options.padding.algorithm='duplicate'] none, value, circular, duplicate\n * @param {number} [options.padding.value=0] value to use for padding (if algorithm='value')\n * @returns {BaselineOutput}\n */\nexport function rollingMedianBaseline(ys, options = {}) {\n let window = Math.max(Math.round(ys.length * 0.1), 2);\n let defaults = {\n window,\n padding: {\n size: window - 1,\n algorithm: 'duplicate',\n value: 0,\n },\n };\n let actualOptions = { ...defaults, ...options };\n let baseline = xRollingMedian(ys, actualOptions);\n let corrected = new Float64Array(ys.length);\n for (let i = 0; i < corrected.length; i++) {\n corrected[i] = ys[i] - baseline[i];\n }\n\n return { baseline, correctedSpectrum: corrected };\n}\n","//@ts-expect-error no type definition for baselines\nimport { airPLSBaseline as baselineFct } from 'baselines';\n/**\n * @param data\n */\nexport function airPLSBaseline(data) {\n data.y = baselineFct(data.y).correctedSpectrum;\n return { data };\n}\n//# sourceMappingURL=airPLSBaseline.js.map","//@ts-expect-error no type definition for baselines\nimport { iterativePolynomialBaseline as baselineFct } from 'baselines';\n/**\n * @param data\n */\nexport function iterativePolynomialBaseline(data) {\n data.y = baselineFct(data.y).correctedSpectrum;\n return { data };\n}\n//# sourceMappingURL=iterativePolynomialBaseline.js.map","//@ts-expect-error no type definition for baselines\nimport { rollingAverageBaseline as baselineFct } from 'baselines';\n/**\n * @param data\n */\nexport function rollingAverageBaseline(data) {\n data.y = baselineFct(data.y).correctedSpectrum;\n return { data };\n}\n//# sourceMappingURL=rollingAverageBaseline.js.map","//@ts-expect-error no type definition for baselines\nimport { rollingBallBaseline as baselineFct } from 'baselines';\n/**\n * @param data\n */\nexport function rollingBallBaseline(data) {\n data.y = baselineFct(data.y).correctedSpectrum;\n return { data };\n}\n//# sourceMappingURL=rollingBallBaseline.js.map","//@ts-expect-error no type definition for baselines\nimport { rollingMedianBaseline as baselineFct } from 'baselines';\n/**\n * @param data\n */\nexport function rollingMedianBaseline(data) {\n data.y = baselineFct(data.y).correctedSpectrum;\n return { data };\n}\n//# sourceMappingURL=rollingMedianBaseline.js.map","/**\n * The spacing of an x axis over a window, which every derivative divides by.\n */\n/**\n * The mean spacing of `h` over the window centred on `center`.\n *\n * The sum of consecutive differences telescopes, so only the two ends of the\n * window are read instead of every point.\n * @param h - The x values.\n * @param center - Index the window is centred on.\n * @param half - Half the window size.\n * @returns The mean spacing.\n */\nexport function meanGap(h, center, half) {\n const lo = Math.max(center - half, 0);\n const hi = Math.min(center + half - 1, h.length - 2);\n return (h[hi + 1] - h[lo]) / (hi - lo + 1);\n}\n/**\n * A spacing raised to a derivative order.\n *\n * Orders 1 and 2 are spelled out because `x ** 1` is far slower than `x`.\n * @param spacing - The mean spacing.\n * @param derivative - The order.\n * @returns The divisor for that order.\n */\nexport function raise(spacing, derivative) {\n if (derivative === 1)\n return spacing;\n if (derivative === 2)\n return spacing * spacing;\n return spacing ** derivative;\n}\n//# sourceMappingURL=spacing.js.map","/**\n * The argument checks `sgg` and `sggDerivatives` share.\n */\nimport { isAnyArray } from 'is-any-array';\n/**\n * Throw unless the arguments describe a filter that can be applied.\n * @param ys - Array of y values.\n * @param xs - Array of x values, or a constant spacing.\n * @param windowSize - Number of points in the window.\n * @param polynomial - Polynomial grade.\n * @param derivatives - Every derivative order asked for.\n */\nexport function checkArguments(ys, xs, windowSize, polynomial, derivatives) {\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 for (const derivative of derivatives) {\n if (derivative < 0 || !Number.isInteger(derivative)) {\n throw new RangeError('Derivative should be a positive integer');\n }\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}\n//# sourceMappingURL=validate.js.map","/**\n * The Savitzky-Golay weight matrix, and a bounded cache of recent ones.\n *\n * The matrix depends only on the window size, the polynomial order and the\n * derivative, never on the data, so it is a constant across a run of spectra.\n * Building one costs thousands of recursive `gramPoly` calls.\n */\n/**\n * How many weight matrices are kept.\n *\n * Two at least: a derivative pair asks for the same window twice, so one slot\n * would always be overwritten before it was read back.\n *\n * Bounds the cache at `KEPT_WEIGHTS * windowSize ** 2 * 8` bytes: 2.5 kB at\n * windowSize 9, 319 kB at windowSize 101.\n */\nconst KEPT_WEIGHTS = 4;\n/** The shape each kept matrix was built for, three numbers per slot. */\nconst keptShapes = new Int32Array(KEPT_WEIGHTS * 3).fill(-1);\n/** The matrices themselves, in the same slots. */\nconst keptWeights = new Array(KEPT_WEIGHTS);\n/** The slot the next matrix built will land in, oldest first. */\nlet nextSlot = 0;\n/**\n * The weight matrix for a window, built once per shape rather than once per\n * call.\n *\n * Slots are compared on the three numbers, not on a key built from them, so\n * there is no string allocation per call. The caller must not mutate the\n * result.\n * @param windowSize - Number of points in the window.\n * @param polynomial - Polynomial grade.\n * @param derivative - Derivative order.\n * @returns The weight vectors, one per position in the window.\n */\nexport function cachedWeights(windowSize, polynomial, derivative) {\n for (let slot = 0; slot < KEPT_WEIGHTS; slot++) {\n const at = slot * 3;\n if (keptShapes[at] === windowSize &&\n keptShapes[at + 1] === polynomial &&\n keptShapes[at + 2] === derivative) {\n const known = keptWeights[slot];\n if (known !== undefined)\n return known;\n }\n }\n const built = fullWeights(windowSize, polynomial, derivative);\n const at = nextSlot * 3;\n keptShapes[at] = windowSize;\n keptShapes[at + 1] = polynomial;\n keptShapes[at + 2] = derivative;\n keptWeights[nextSlot] = built;\n nextSlot = (nextSlot + 1) % KEPT_WEIGHTS;\n return built;\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=weights.js.map","/**\n * The Savitzky-Golay filter, for one derivative order.\n */\nimport { isAnyArray } from 'is-any-array';\nimport { meanGap, raise } from \"./spacing.js\";\nimport { checkArguments } from \"./validate.js\";\nimport { cachedWeights } from \"./weights.js\";\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 checkArguments(ys, xs, windowSize, polynomial, [derivative]);\n const half = Math.floor(windowSize / 2);\n const np = ys.length;\n const ans = new Float64Array(np);\n const weights = cachedWeights(windowSize, polynomial, derivative);\n let hs = 0;\n let constantH = true;\n if (isAnyArray(xs)) {\n constantH = false;\n }\n else {\n hs = raise(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 = raise(meanGap(xs, half - i - 1, half), derivative);\n ans[half - i - 1] = d1 / hs;\n hs = raise(meanGap(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 = raise(meanGap(xs, i - half - 1, half), derivative);\n }\n ans[i - half - 1] = d / hs;\n }\n return ans;\n}\n//# sourceMappingURL=sgg.js.map","/**\n * Two derivatives of one spectrum, in a single pass.\n */\nimport { isAnyArray } from 'is-any-array';\nimport { meanGap, raise } from \"./spacing.js\";\nimport { checkArguments } from \"./validate.js\";\nimport { cachedWeights } from \"./weights.js\";\n/**\n * Apply the Savitzky Golay algorithm for two derivative orders at once.\n *\n * Each y value is read once and multiplied into both accumulators, and the\n * window spacing is measured once and raised twice, where calling `sgg` twice\n * repeats both. Peak picking wants the first and second derivative of the same\n * trace, which is what the default asks for.\n *\n * Two and not a list: an accumulator per order behind a loop costs more than\n * sharing the reads saves, so a general version measured slower than calling\n * `sgg` repeatedly. Ask for more than two by calling this and `sgg`.\n * @param ys - Array of y values.\n * @param xs - Array of X or deltaX.\n * @param options - Options controlling window size, derivatives and polynomial order.\n * @returns The two derivative arrays, in the order requested.\n */\nexport function sggPair(ys, xs, options = {}) {\n const { windowSize = 9, derivatives = [1, 2], polynomial = 3 } = options;\n const [firstOrder, secondOrder] = derivatives;\n checkArguments(ys, xs, windowSize, polynomial, derivatives);\n const half = Math.floor(windowSize / 2);\n const np = ys.length;\n const first = new Float64Array(np);\n const second = new Float64Array(np);\n const firstWeights = cachedWeights(windowSize, polynomial, firstOrder);\n const secondWeights = cachedWeights(windowSize, polynomial, secondOrder);\n const constantH = !isAnyArray(xs);\n const firstConstant = constantH ? raise(xs, firstOrder) : 0;\n const secondConstant = constantH ? raise(xs, secondOrder) : 0;\n for (let i = 0; i < half; i++) {\n const low = half - i - 1;\n const high = np - half + i;\n const lowFirst = firstWeights[low];\n const lowSecond = secondWeights[low];\n const highFirst = firstWeights[half + i + 1];\n const highSecond = secondWeights[half + i + 1];\n let lowFirstSum = 0;\n let lowSecondSum = 0;\n let highFirstSum = 0;\n let highSecondSum = 0;\n for (let l = 0; l < windowSize; l++) {\n const atLow = ys[l];\n const atHigh = ys[np - windowSize + l];\n lowFirstSum += lowFirst[l] * atLow;\n lowSecondSum += lowSecond[l] * atLow;\n highFirstSum += highFirst[l] * atHigh;\n highSecondSum += highSecond[l] * atHigh;\n }\n const lowGap = constantH ? 0 : meanGap(xs, low, half);\n const highGap = constantH ? 0 : meanGap(xs, high, half);\n first[low] =\n lowFirstSum / (constantH ? firstConstant : raise(lowGap, firstOrder));\n second[low] =\n lowSecondSum / (constantH ? secondConstant : raise(lowGap, secondOrder));\n first[high] =\n highFirstSum / (constantH ? firstConstant : raise(highGap, firstOrder));\n second[high] =\n highSecondSum /\n (constantH ? secondConstant : raise(highGap, secondOrder));\n }\n // The loop this exists for: one load of each sample feeds both sums, and one\n // spacing measurement feeds both divisors. The constant-spacing case is a\n // second loop rather than a test inside this one, and the orders are raised\n // inline rather than through a call, because both cost more per point than\n // the sharing saves.\n const firstCentre = firstWeights[half];\n const secondCentre = secondWeights[half];\n if (constantH) {\n for (let i = windowSize; i <= np; i++) {\n const from = i - windowSize;\n let firstSum = 0;\n let secondSum = 0;\n for (let l = 0; l < windowSize; l++) {\n const value = ys[from + l];\n firstSum += firstCentre[l] * value;\n secondSum += secondCentre[l] * value;\n }\n const into = i - half - 1;\n first[into] = firstSum / firstConstant;\n second[into] = secondSum / secondConstant;\n }\n }\n else {\n const firstIsOne = firstOrder === 1;\n const firstIsTwo = firstOrder === 2;\n const secondIsOne = secondOrder === 1;\n const secondIsTwo = secondOrder === 2;\n for (let i = windowSize; i <= np; i++) {\n const from = i - windowSize;\n let firstSum = 0;\n let secondSum = 0;\n for (let l = 0; l < windowSize; l++) {\n const value = ys[from + l];\n firstSum += firstCentre[l] * value;\n secondSum += secondCentre[l] * value;\n }\n const into = i - half - 1;\n const gap = meanGap(xs, into, half);\n first[into] =\n firstSum /\n (firstIsOne ? gap : firstIsTwo ? gap * gap : gap ** firstOrder);\n second[into] =\n secondSum /\n (secondIsOne ? gap : secondIsTwo ? gap * gap : gap ** secondOrder);\n }\n }\n return [first, second];\n}\n//# sourceMappingURL=sggPair.js.map","import { sgg } from 'ml-savitzky-golay-generalized';\n/**\n * Calculate the first derivative using Savitzky–Golay filter.\n * @param data\n * @param options\n */\nexport function firstDerivative(data, options = {}) {\n const { x, y } = data;\n return { data: { x, y: sgg(y, x, { ...options, derivative: 1 }) } };\n}\n//# sourceMappingURL=firstDerivative.js.map","import { sgg } from 'ml-savitzky-golay-generalized';\n/**\n * Calculate the second derivative using Savitzky–Golay filter.\n * @param data\n * @param options\n */\nexport function secondDerivative(data, options = {}) {\n const { x, y } = data;\n return { data: { x, y: sgg(y, x, { ...options, derivative: 2 }) } };\n}\n//# sourceMappingURL=secondDerivative.js.map","import { sgg } from 'ml-savitzky-golay-generalized';\n/**\n * Calculate the third derivative using Savitzky–Golay filter.\n * @param data\n * @param options\n */\nexport function thirdDerivative(data, options = {}) {\n const { x, y } = data;\n return { data: { x, y: sgg(y, x, { ...options, derivative: 3 }) } };\n}\n//# sourceMappingURL=thirdDerivative.js.map","import { sgg } from 'ml-savitzky-golay-generalized';\n/**\n * Apply the Savitzky Golay Generalized Filter\n * @param data\n * @param options\n */\nexport function savitzkyGolay(data, options = {}) {\n const { x, y } = data;\n return { data: { x, y: sgg(y, x, options) } };\n}\n//# sourceMappingURL=savitzkyGolay.js.map","import { xyEnsureGrowingX } from 'ml-spectra-processing';\n/**\n * Ensure X values are strictly monotonic increasing\n * https://www-groups.mcs.st-andrews.ac.uk/~john/analysis/Lectures/L8.html\n * @param data\n */\nexport function ensureGrowing(data) {\n return { data: xyEnsureGrowingX(data) };\n}\n//# sourceMappingURL=ensureGrowing.js.map","import { xyGrowingX } from 'ml-spectra-processing';\n/**\n * Ensure X values are strictly monotonic increasing\n * https://www-groups.mcs.st-andrews.ac.uk/~john/analysis/Lectures/L8.html\n * @param data\n */\nexport function reverseIfNeeded(data) {\n return { data: xyGrowingX(data) };\n}\n//# sourceMappingURL=reverseIfNeeded.js.map","import { xyEquallySpaced } from 'ml-spectra-processing';\n/**\n * Filter that allows to\n * @param data\n * @param options\n */\nexport function equallySpaced(data, options = {}) {\n return { data: xyEquallySpaced(data, options) };\n}\n//# sourceMappingURL=equallySpaced.js.map","import { xyFilterX } from 'ml-spectra-processing';\n/**\n * Filter that allows to\n * @param data\n * @param options\n */\nexport function filterX(data, options = {}) {\n return {\n data: xyFilterX(data, options),\n };\n}\n//# sourceMappingURL=filterX.js.map","import { xAdd, xMaxValue } from 'ml-spectra-processing';\n/**\n * Filter that allows to\n * @param data\n * @param options\n */\nexport function setMaxX(data, options = {}) {\n const { max = 1 } = options;\n const existingMax = xMaxValue(data.x);\n if (existingMax === max) {\n return { data };\n }\n return {\n data: {\n x: xAdd(data.x, max - existingMax),\n y: data.y,\n },\n };\n}\n//# sourceMappingURL=setMaxX.js.map","import { xAdd, xMinValue } from 'ml-spectra-processing';\n/**\n * Filter that allows to\n * @param data\n * @param options\n */\nexport function setMinX(data, options = {}) {\n const { min = 0 } = options;\n const existingMin = xMinValue(data.x);\n if (existingMin === min) {\n return { data };\n }\n return {\n data: {\n x: xAdd(data.x, min - existingMin),\n y: data.y,\n },\n };\n}\n//# sourceMappingURL=setMinX.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 { 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","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","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","/**\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 { sgg, sggPair } from 'ml-savitzky-golay-generalized';\nimport { xIsEquallySpaced, xIsMonotonic, xMaxAbsoluteValue, xMaxValue, xMinMaxValues, xNoiseStandardDeviation, } from 'ml-spectra-processing';\nimport { autoAlgorithm } from \"./algorithms/autoAlgorithm.js\";\nimport { firstDerivative } from \"./algorithms/firstDerivative.js\";\nimport { secondDerivative } from \"./algorithms/secondDerivative.js\";\nimport { optimizeTop } from \"./utils/optimizeTop.js\";\n/**\n * Global spectra deconvolution.\n * @param data - Object with `x` and `y` arrays. `x` must be monotone increasing.\n * @param options - Peak detection options.\n * @returns The detected peaks, sorted by ascending `x`.\n */\nexport function gsd(data, options = {}) {\n let { noiseLevel } = options;\n const { sgOptions = {\n windowSize: 9,\n polynomial: 3,\n }, smoothY = false, maxCriteria = true, maxAbsoluteRatio = 0, minMaxRatio = 0.00025, realTopDetection = false, peakDetectionAlgorithm = 'second', } = options;\n if (maxAbsoluteRatio < 0 || maxAbsoluteRatio > 1) {\n throw new Error('maxAbsoluteRatio must be between 0 and 1');\n }\n const { x } = data;\n let { y } = data;\n if (xIsMonotonic(x) !== 1) {\n throw new Error('GSD only accepts monotone increasing x values');\n }\n // Copy so the `maxCriteria` / clipping loops below don't mutate the caller's array.\n y = y.slice();\n // If the max difference between delta x is less than 5%, then,\n // we can assume it to be equally spaced variable\n const isEquallySpaced = xIsEquallySpaced(x);\n if (noiseLevel === undefined) {\n if (isEquallySpaced) {\n const noiseInfo = xNoiseStandardDeviation(y);\n if (maxCriteria) {\n noiseLevel = noiseInfo.median + 1.5 * noiseInfo.sd;\n }\n else {\n noiseLevel = -noiseInfo.median + 1.5 * noiseInfo.sd;\n }\n }\n else {\n noiseLevel = 0;\n }\n }\n else if (!maxCriteria) {\n noiseLevel *= -1;\n }\n if (!maxCriteria) {\n for (let i = 0; i < y.length; i++) {\n y[i] = -y[i];\n }\n }\n const maxAbsoluteValue = maxAbsoluteRatio > 0 ? maxAbsoluteRatio * xMaxAbsoluteValue(y) : noiseLevel;\n if (noiseLevel !== undefined) {\n for (let i = 0; i < y.length; i++) {\n if (y[i] < noiseLevel) {\n y[i] = noiseLevel;\n }\n }\n }\n const xValue = isEquallySpaced ? x[1] - x[0] : x;\n const yData = smoothY\n ? sgg(y, xValue, {\n ...sgOptions,\n derivative: 0,\n })\n : y;\n const { min: minY, max: maxY } = xMinMaxValues(yData);\n if (minY > maxY || minY === maxY)\n return [];\n const [dY, ddY] = sggPair(y, xValue, {\n windowSize: sgOptions.windowSize,\n polynomial: sgOptions.polynomial,\n derivatives: [1, 2],\n });\n const yThreshold = xMaxValue([\n noiseLevel,\n minY + (maxY - minY) * minMaxRatio,\n maxAbsoluteValue,\n ]);\n const dX = x[1] - x[0];\n const peakData = { x, y, yData, dY, ddY, dX, yThreshold };\n let peaks = [];\n if (peakDetectionAlgorithm === 'first') {\n peaks = firstDerivative(peakData);\n }\n else if (peakDetectionAlgorithm === 'second') {\n peaks = secondDerivative(peakData);\n }\n else {\n peaks = autoAlgorithm(peakData);\n }\n if (realTopDetection) {\n optimizeTop({ x, y: yData }, peaks);\n }\n for (const peak of peaks) {\n if (!maxCriteria) {\n peak.y *= -1;\n peak.ddY = peak.ddY * -1;\n }\n }\n peaks.sort((a, b) => {\n return a.x - b.x;\n });\n return peaks;\n}\n//# sourceMappingURL=gsd.js.map","/**\n * This function returns the sumOfShapes function\n * This function gives sumOfShapes access to the peak list and the associated data\n * @param internalPeaks\n */\nexport function getSumOfShapes(internalPeaks) {\n return function sumOfShapes(parameters) {\n for (const peak of internalPeaks) {\n for (let i = 2; i < peak.parameters.length; i++) {\n const shapeFctKey = peak.parameters[i];\n peak.shapeFct[shapeFctKey] = parameters[peak.fromIndex + i];\n }\n }\n return (x) => {\n let totalY = 0;\n for (const peak of internalPeaks) {\n const peakX = parameters[peak.fromIndex];\n const y = parameters[peak.fromIndex + 1];\n totalY += y * peak.shapeFct.fct(x - peakX);\n }\n return totalY;\n };\n };\n}\n//# sourceMappingURL=getSumOfShapes.js.map","/**\n * Asserts that value is truthy.\n * @param value - Value to check.\n * @param message - Optional error message to throw.\n */\nexport function assert(value, message) {\n if (!value) {\n throw new Error(message || 'unreachable');\n }\n}\n//# sourceMappingURL=assert.js.map","import { xMean } from 'ml-spectra-processing';\nimport { assert } from \"./assert.js\";\n/**\n * Build an optimization layout mapping actual per-peak parameter slots\n * to optimizer variables. The layout describes slots, grouped/shared\n * variables, variable bounds/initials, and provides a helper to\n * materialize actual peak parameter values from a variable vector.\n * @param internalPeaks - normalized internal peaks with parameter indices\n * @param peaks - original peak objects (for per-peak optimize flags)\n * @param options - user `OptimizeOptions`, may contain `linkedParameters`\n * @param yScale - y normalization factor (used when converting offsets)\n * @returns an `OptimizationLayout` describing variables and slots\n */\nexport function buildOptimizationLayout(internalPeaks, peaks, options, yScale = 1) {\n const slots = buildParameterSlots(internalPeaks, peaks, options);\n const variables = buildOptimizationVariables(slots, options.linkedParameters, yScale);\n const variableMin = new Float64Array(variables.length);\n const variableMax = new Float64Array(variables.length);\n const variableInit = new Float64Array(variables.length);\n const variableGrad = new Float64Array(variables.length);\n const freeIndices = [];\n for (let i = 0; i < variables.length; i++) {\n const variable = variables[i];\n variableMin[i] = variable.min;\n variableMax[i] = variable.max;\n variableInit[i] = variable.init;\n variableGrad[i] = variable.gradientDifference;\n if (variable.optimize) {\n freeIndices.push(i);\n }\n }\n return {\n slots,\n variables,\n freeIndices,\n variableMin,\n variableMax,\n variableInit,\n variableGrad,\n variableToPeakValues(variableValues) {\n const actualValues = new Array(slots.length);\n for (let i = 0; i < variables.length; i++) {\n const variableValue = variableValues[i];\n const members = variables[i].members;\n for (const member of members) {\n actualValues[member.actualIndex] =\n variableValue * member.factor + member.offset;\n }\n }\n return actualValues;\n },\n };\n}\n/**\n * Builds concrete parameter slots for each peak parameter.\n * @param internalPeaks - normalized peaks containing parameter metadata\n * @param peaks - original peaks used to resolve optimize flags\n * @param options - optimization options with parameter settings\n * @returns flattened parameter slots across all peaks\n */\nfunction buildParameterSlots(internalPeaks, peaks, options) {\n const slots = [];\n for (let peakIndex = 0; peakIndex < internalPeaks.length; peakIndex++) {\n const internalPeak = internalPeaks[peakIndex];\n for (let i = 0; i < internalPeak.parameters.length; i++) {\n const parameter = internalPeak.parameters[i];\n slots.push({\n actualIndex: internalPeak.fromIndex + i,\n peakIndex,\n peakId: internalPeak.id,\n parameter,\n init: internalPeak.propertiesValues.init[i],\n min: internalPeak.propertiesValues.min[i],\n max: internalPeak.propertiesValues.max[i],\n gradientDifference: internalPeak.propertiesValues.gradientDifference[i],\n optimize: getOptimizeFlag(peaks[peakIndex], parameter, options),\n });\n }\n }\n return slots;\n}\n/**\n * Builds optimization variables from concrete parameter slots.\n * @param slots - flattened per-peak parameter slots\n * @param linkedParameters - optional linked parameter groups\n * @param yScale - y normalization factor for y-offset conversion\n * @returns sorted optimization variables ready for the optimizer\n */\nfunction buildOptimizationVariables(slots, linkedParameters, yScale) {\n const groupedActualIndices = new Set();\n const variables = [];\n const slotLookup = new Map();\n const idToIndices = new Map();\n for (const slot of slots) {\n slotLookup.set(getSlotKey(slot.peakIndex, slot.parameter), slot);\n if (slot.peakId) {\n const indices = idToIndices.get(slot.peakId) ?? [];\n if (!indices.includes(slot.peakIndex)) {\n indices.push(slot.peakIndex);\n }\n idToIndices.set(slot.peakId, indices);\n }\n }\n for (const linkedParameter of linkedParameters ?? []) {\n variables.push(buildLinkedVariable(linkedParameter, slotLookup, groupedActualIndices, idToIndices, yScale));\n }\n for (const slot of slots) {\n if (groupedActualIndices.has(slot.actualIndex)) {\n continue;\n }\n variables.push({\n sortKey: slot.actualIndex,\n parameter: slot.parameter,\n init: slot.init,\n min: slot.min,\n max: slot.max,\n gradientDifference: slot.gradientDifference,\n optimize: slot.optimize,\n members: [\n {\n actualIndex: slot.actualIndex,\n peakIndex: slot.peakIndex,\n parameter: slot.parameter,\n factor: 1,\n offset: 0,\n },\n ],\n });\n }\n variables.sort((a, b) => a.sortKey - b.sortKey);\n return variables.map(({ sortKey: _sortKey, ...variable }) => variable);\n}\nfunction buildLinkedVariable(linkedParameter, slotLookup, groupedActualIndices, idToIndices, yScale) {\n if (linkedParameter.peaks.length === 0) {\n throw new Error(`Linked parameter for ${linkedParameter.parameter} must contain at least one peak`);\n }\n const resolvedMembers = linkedParameter.peaks.map((peak) => {\n const slot = resolveLinkedSlot(peak, linkedParameter.parameter, slotLookup, idToIndices);\n if (groupedActualIndices.has(slot.actualIndex)) {\n throw new Error(`Peak ${String(peak.id)} parameter ${linkedParameter.parameter} is already linked`);\n }\n return {\n slot,\n factor: getFactor(peak, linkedParameter.parameter),\n offset: getOffset(peak, linkedParameter.parameter, yScale),\n };\n });\n const memberActualIndices = new Set();\n for (const member of resolvedMembers) {\n if (memberActualIndices.has(member.slot.actualIndex)) {\n throw new Error(`Linked parameter for ${linkedParameter.parameter} contains the same peak more than once`);\n }\n memberActualIndices.add(member.slot.actualIndex);\n }\n const firstMember = resolvedMembers[0];\n let sharedMin = Number.NEGATIVE_INFINITY;\n let sharedMax = Number.POSITIVE_INFINITY;\n const optimize = firstMember.slot.optimize;\n const sharedInitCandidates = [];\n for (const member of resolvedMembers) {\n if (member.slot.optimize !== optimize) {\n throw new Error(`Linked parameter ${linkedParameter.parameter} must use a consistent optimize flag across all members`);\n }\n if (member.slot.min > member.slot.max) {\n throw new Error(`Linked parameter ${linkedParameter.parameter} has incompatible bounds across its members`);\n }\n const variableBounds = getMemberVariableBounds(member);\n sharedMin = Math.max(sharedMin, variableBounds.min);\n sharedMax = Math.min(sharedMax, variableBounds.max);\n sharedInitCandidates.push((member.slot.init - member.offset) / member.factor);\n }\n if (sharedMin > sharedMax) {\n throw new Error(`Linked parameter ${linkedParameter.parameter} has incompatible bounds across its members`);\n }\n for (const member of resolvedMembers) {\n groupedActualIndices.add(member.slot.actualIndex);\n }\n return {\n sortKey: Math.min(...resolvedMembers.map((member) => member.slot.actualIndex)),\n parameter: linkedParameter.parameter,\n init: xMean(sharedInitCandidates),\n min: sharedMin,\n max: sharedMax,\n gradientDifference: Math.min(...resolvedMembers.map((m) => Math.abs(m.slot.gradientDifference))),\n optimize,\n members: resolvedMembers.map((member) => ({\n actualIndex: member.slot.actualIndex,\n peakIndex: member.slot.peakIndex,\n parameter: member.slot.parameter,\n factor: member.factor,\n offset: member.offset,\n })),\n };\n}\nfunction resolveLinkedSlot(peak, parameter, slotLookup, idToIndices) {\n const peakIndex = typeof peak.id === 'number'\n ? peak.id\n : resolvePeakIndexById(peak.id, idToIndices);\n if (!Number.isInteger(peakIndex) || peakIndex < 0) {\n throw new Error(`Invalid peak reference ${String(peak.id)}`);\n }\n const slot = slotLookup.get(getSlotKey(peakIndex, parameter));\n if (!slot) {\n throw new Error(`Unknown parameter ${parameter} for peak ${String(peak.id)}`);\n }\n return slot;\n}\nfunction resolvePeakIndexById(peakId, idToIndices) {\n const indices = idToIndices.get(peakId);\n if (!indices || indices.length === 0) {\n throw new Error(`Unknown peak id ${peakId}`);\n }\n if (new Set(indices).size > 1) {\n throw new Error(`Peak id ${peakId} is ambiguous because it is used by multiple peaks`);\n }\n return indices[0];\n}\nfunction getFactor(peak, parameter) {\n const factor = peak.factor ?? 1;\n if (!Number.isFinite(factor) || factor === 0) {\n throw new Error(`Linked parameter ${parameter} must use a non-zero finite factor`);\n }\n return factor;\n}\nfunction getOffset(peak, parameter, yScale) {\n const offset = peak.offset ?? 0;\n if (!Number.isFinite(offset)) {\n throw new Error(`Linked parameter ${parameter} must use a finite offset`);\n }\n if (parameter === 'y') {\n return offset / yScale;\n }\n return offset;\n}\nfunction getMemberVariableBounds(member) {\n const transformedMin = (member.slot.min - member.offset) / member.factor;\n const transformedMax = (member.slot.max - member.offset) / member.factor;\n return {\n min: Math.min(transformedMin, transformedMax),\n max: Math.max(transformedMin, transformedMax),\n };\n}\nfunction getOptimizeFlag(peak, parameter, options) {\n assert(peak);\n let optimizeFlag = true;\n const perPeakParam = peak.parameters?.[parameter];\n const globalParam = options.parameters?.[parameter];\n if (perPeakParam?.optimize !== undefined) {\n if (typeof perPeakParam.optimize === 'function') {\n optimizeFlag = perPeakParam.optimize(peak);\n }\n else {\n const { optimize = true } = perPeakParam;\n optimizeFlag = optimize;\n }\n }\n else if (globalParam?.optimize !== undefined) {\n if (typeof globalParam.optimize === 'function') {\n optimizeFlag = globalParam.optimize(peak);\n }\n else {\n const { optimize = true } = globalParam;\n optimizeFlag = optimize;\n }\n }\n return optimizeFlag;\n}\nfunction getSlotKey(peakIndex, parameter) {\n return `${peakIndex}:${parameter}`;\n}\n//# sourceMappingURL=buildOptimizationLayout.js.map","/**\n * Reconstruct user-facing peak objects from internal peaks and a full\n * actual-parameter vector.\n * @template T - original Peak type\n * @param internalPeaks - internal peaks produced by `getInternalPeaks`\n * @param actualValues - flattened actual parameter values (not normalized for Y)\n * @param yScale - normalization factor previously applied to Y values\n * @returns array of optimized peaks with reconstructed shapes and ids\n */\nexport function reconstructPeaks(internalPeaks, actualValues, yScale) {\n const newPeaks = [];\n for (const peak of internalPeaks) {\n const { id, shape, parameters, fromIndex } = peak;\n let newPeak = { x: 0, y: 0, shape };\n if (id) {\n newPeak = { ...newPeak, id };\n }\n newPeak.x = actualValues[fromIndex];\n newPeak.y = actualValues[fromIndex + 1] * yScale;\n for (let i = 2; i < parameters.length; i++) {\n //@ts-expect-error should be fixed once\n newPeak.shape[parameters[i]] = actualValues[fromIndex + i];\n }\n newPeaks.push(newPeak);\n }\n return newPeaks;\n}\n//# sourceMappingURL=reconstructPeaks.js.map","import { reconstructPeaks } from \"./reconstructPeaks.js\";\n/**\n * Build result when no parameters are free to optimize.\n * Computes the fit error using the provided `globalInit` parameter vector\n * and reconstructs the output peak objects from `internalPeaks`.\n * @template T - input Peak type\n * @param internalPeaks - internal representation of peaks (with parameter indices)\n * @param normalizedY - observed Y values normalized by the global scale\n * @param x - X axis values\n * @param globalInit - full parameter vector (actual-space) used to evaluate the model\n * @param baseSumOfShapes - function that returns the spectrum function given parameters\n * @param yScale - the scale factor used to normalize Y (used to reconstruct peak amplitudes)\n * @returns an object containing `error`, `iterations` (0) and the reconstructed `peaks`\n */\nexport function getFixedParametersResult(internalPeaks, normalizedY, x, globalInit, baseSumOfShapes, yScale) {\n const fct = baseSumOfShapes(globalInit);\n let error = 0;\n for (let i = 0; i < normalizedY.length; i++) {\n error += (normalizedY[i] - fct(x[i])) ** 2;\n }\n return {\n error,\n iterations: 0,\n peaks: reconstructPeaks(internalPeaks, globalInit, yScale),\n };\n}\n//# sourceMappingURL=getFixedParametersResult.js.map","export const DefaultParameters = {\n x: {\n init: (peak) => peak.x,\n min: (peak, peakShape) => peak.x - peakShape.fwhm * 2,\n max: (peak, peakShape) => peak.x + peakShape.fwhm * 2,\n gradientDifference: (peak, peakShape) => peakShape.fwhm * 2e-3,\n },\n y: {\n init: (peak) => peak.y,\n min: (peak) => (peak.y < 0 ? -1.1 : 0),\n max: (peak) => (peak.y < 0 ? 0 : 1.1),\n gradientDifference: () => 1e-3,\n },\n fwhm: {\n init: (peak, peakShape) => peakShape.fwhm,\n min: (peak, peakShape) => peakShape.fwhm * 0.25,\n max: (peak, peakShape) => peakShape.fwhm * 4,\n gradientDifference: (peak, peakShape) => peakShape.fwhm * 2e-3,\n },\n fwhmG: {\n init: (peak, peakShape) => peakShape.fwhm * 0.6,\n min: (peak, peakShape) => peakShape.fwhm * 0.6 * 0.25,\n max: (peak, peakShape) => peakShape.fwhm * 0.6 * 4,\n gradientDifference: (peak, peakShape) => peakShape.fwhm * 0.6 * 2e-3,\n },\n fwhmL: {\n init: (peak, peakShape) => peakShape.fwhm * 0.4,\n min: (peak, peakShape) => peakShape.fwhm * 0.4 * 0.25,\n max: (peak, peakShape) => peakShape.fwhm * 0.4 * 4,\n gradientDifference: (peak, peakShape) => peakShape.fwhm * 0.4 * 2e-3,\n },\n mu: {\n init: (peak, peakShape) => peakShape.mu,\n min: () => 0,\n max: () => 1,\n gradientDifference: () => 0.01,\n },\n gamma: {\n init: (peak, peakShape) => peakShape.gamma || 0.5,\n min: () => -1,\n max: () => 2,\n gradientDifference: () => 0.01,\n },\n};\n//# sourceMappingURL=DefaultParameters.js.map","import { getShape1D } from 'ml-peak-shape-generator';\nimport { assert } from \"../assert.js\";\nimport { DefaultParameters } from \"./DefaultParameters.js\";\nconst properties = ['init', 'min', 'max', 'gradientDifference'];\n/**\n * Return an array of internalPeaks that contains the exact init, min, max values based on the options\n * @param peaks\n * @param minMaxY\n * @param yScale\n * @param options\n * @returns\n */\nexport function getInternalPeaks(peaks, yScale, options = {}) {\n let index = 0;\n const internalPeaks = [];\n for (const originalPeak of peaks) {\n const normalizedPeak = {\n ...originalPeak,\n y: originalPeak.y / yScale,\n };\n const peak = normalizedPeak;\n const { id, shape = options.shape || { kind: 'gaussian' } } = peak;\n const shapeFct = getShape1D(shape);\n const parameters = ['x', 'y', ...shapeFct.getParameters()];\n const propertiesValuesInternal = {\n min: [],\n max: [],\n init: [],\n gradientDifference: [],\n };\n for (const parameter of parameters) {\n for (const property of properties) {\n // check if the property is specified in the peak\n let propertyValue = peak?.parameters?.[parameter]?.[property];\n if (propertyValue !== undefined) {\n propertyValue = getNormalizedValue(propertyValue, parameter, property, yScale);\n propertiesValuesInternal[property].push(propertyValue);\n continue;\n }\n // check if there are some global option, it could be a number or a callback\n let generalParameterValue = options?.parameters?.[parameter]?.[property];\n if (generalParameterValue !== undefined) {\n if (typeof generalParameterValue === 'number') {\n generalParameterValue = getNormalizedValue(generalParameterValue, parameter, property, yScale);\n propertiesValuesInternal[property].push(generalParameterValue);\n continue;\n }\n else {\n // callbacks receive user-provided peak values (not Y-normalized)\n let value = generalParameterValue(originalPeak);\n value = getNormalizedValue(value, parameter, property, yScale);\n propertiesValuesInternal[property].push(value);\n continue;\n }\n }\n // we just need to take the default parameters\n assert(DefaultParameters[parameter], `No default parameter for ${parameter}`);\n const defaultParameterValues = DefaultParameters[parameter][property];\n propertiesValuesInternal[property].push(\n //@ts-expect-error parameters and shape instance are guaranteed to be present in the defaultParameterValues function\n defaultParameterValues(peak, shapeFct));\n }\n }\n const fromIndex = index;\n const toIndex = fromIndex + parameters.length - 1;\n index += toIndex - fromIndex + 1;\n const propertiesValues = {\n min: propertiesValuesInternal.min,\n max: propertiesValuesInternal.max,\n init: propertiesValuesInternal.init,\n gradientDifference: propertiesValuesInternal.gradientDifference,\n };\n internalPeaks.push({\n id,\n shape,\n shapeFct,\n parameters,\n propertiesValues,\n fromIndex,\n toIndex,\n });\n }\n return internalPeaks;\n}\nfunction getNormalizedValue(value, parameter, property, yScale) {\n if (parameter === 'y') {\n if (property === 'gradientDifference') {\n return value;\n }\n else {\n return value / yScale;\n }\n }\n return value;\n}\n//# sourceMappingURL=getInternalPeaks.js.map","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 { 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 { CholeskyDecomposition, Matrix, inverse } from 'ml-matrix';\nimport gradientFunction from \"./gradient_function.js\";\n/**\n * Builds the (nbParams x nbPoints) Jacobian of the residuals from an analytical\n * model gradient. The residual is `y - model`, so the residual Jacobian is the\n * negative of the model gradient — matching the sign convention produced by the\n * finite-difference `gradientFunction`.\n * @param data - points to fit\n * @param params - current parameter values\n * @param jacobianFunction - returns, for an x, the model partials over params\n */\nfunction analyticalGradient(data, params, jacobianFunction) {\n const nbParams = params.length;\n const nbPoints = data.x.length;\n const ans = Matrix.zeros(nbParams, nbPoints);\n const gradient = jacobianFunction(params);\n for (let point = 0; point < nbPoints; point++) {\n const partials = gradient(data.x[point]);\n for (let param = 0; param < nbParams; param++) {\n ans.set(param, point, -partials[param]);\n }\n }\n return ans;\n}\n/**\n * Matrix function over the samples\n *\n * @param data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param evaluatedData - Array of previous evaluated function values\n */\nfunction matrixFunction(data, evaluatedData) {\n const m = data.x.length;\n const ans = new Matrix(m, 1);\n for (let point = 0; point < m; point++) {\n ans.set(point, 0, data.y[point] - evaluatedData[point]);\n }\n return ans;\n}\n/**\n * Iteration for Levenberg-Marquardt\n *\n * @param data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param params - Array of previous parameter values\n * @param damping - Levenberg-Marquardt parameter\n * @param gradientDifference - The step size to approximate the jacobian matrix\n * @param centralDifference - If true the jacobian matrix is approximated by central differences otherwise by forward differences\n * @param parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @param weights - scale the gradient and residual error by weights\n * @param jacobianFunction - optional analytical Jacobian, replaces finite differences when provided\n */\nexport default function step(data, params, damping, gradientDifference, parameterizedFunction, centralDifference, weights, jacobianFunction) {\n const func = parameterizedFunction(params);\n const evaluatedData = new Float64Array(data.x.length);\n for (let i = 0; i < data.x.length; i++) {\n evaluatedData[i] = func(data.x[i]);\n }\n const gradientFunc = jacobianFunction\n ? analyticalGradient(data, params, jacobianFunction)\n : gradientFunction(data, evaluatedData, params, gradientDifference, parameterizedFunction, centralDifference);\n const residualError = matrixFunction(data, evaluatedData);\n const hessianApproximation = gradientFunc.mmulByTranspose(weights);\n for (let i = 0; i < params.length; i++) {\n hessianApproximation.set(i, i, hessianApproximation.get(i, i) + damping);\n }\n const jacobianWeightResidualError = gradientFunc.mmul(residualError.scale('row', { scale: weights }));\n // (damping * I + Jᵀ W J) is symmetric positive-definite for damping > 0, so a\n // Cholesky solve is faster and more numerically stable than forming the full\n // inverse and multiplying. Fall back to the inverse only in the rare case the\n // approximated Hessian is not positive-definite.\n const cholesky = new CholeskyDecomposition(hessianApproximation);\n const perturbations = cholesky.isPositiveDefinite()\n ? cholesky.solve(jacobianWeightResidualError)\n : inverse(hessianApproximation).mmul(jacobianWeightResidualError);\n return {\n perturbations,\n jacobianWeightResidualError,\n };\n}\n//# sourceMappingURL=step.js.map","import checkOptions from \"./check_options.js\";\nimport errorCalculation from \"./error_calculation.js\";\nimport step from \"./step.js\";\n/**\n * Curve fitting algorithm\n * @param data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param parameterizedFunction - Takes an array of parameters and returns a function with the independent variable as its sole argument\n * @param options - Options object\n */\nexport function levenbergMarquardt(data, parameterizedFunction, options) {\n const checkedOptions = checkOptions(data, options);\n const { checkTimeout, minValues, maxValues, parameters, weightSquare, dampingStepUp, dampingStepDown, maxIterations, errorTolerance, centralDifference, gradientDifference, improvementThreshold, } = checkedOptions;\n let damping = checkedOptions.damping;\n const { jacobianFunction } = options;\n let error = errorCalculation(data, parameters, parameterizedFunction, weightSquare);\n let optimalError = error;\n let optimalParameters = parameters.slice();\n let converged = error <= errorTolerance;\n let iteration = 0;\n for (; iteration < maxIterations && !converged; iteration++) {\n const previousError = error;\n const { perturbations, jacobianWeightResidualError } = step(data, parameters, damping, gradientDifference, parameterizedFunction, centralDifference, weightSquare, jacobianFunction);\n for (let k = 0; k < parameters.length; k++) {\n parameters[k] = Math.min(Math.max(minValues[k], parameters[k] - perturbations.get(k, 0)), maxValues[k]);\n }\n error = errorCalculation(data, parameters, parameterizedFunction, weightSquare);\n if (isNaN(error))\n break;\n if (error < optimalError - errorTolerance) {\n optimalError = error;\n optimalParameters = parameters.slice();\n }\n const improvementMetric = (previousError - error) /\n perturbations\n .transpose()\n .mmul(perturbations.mul(damping).add(jacobianWeightResidualError))\n .get(0, 0);\n if (improvementMetric > improvementThreshold) {\n damping = Math.max(damping / dampingStepDown, 1e-7);\n }\n else {\n damping = Math.min(damping * dampingStepUp, 1e7);\n }\n if (checkTimeout()) {\n throw new Error(`The execution time is over to ${options.timeout} seconds`);\n }\n converged = error <= errorTolerance;\n }\n return {\n parameterValues: optimalParameters,\n parameterError: optimalError,\n iterations: iteration,\n };\n}\n//# sourceMappingURL=levenberg_marquardt.js.map","/**\n * Preparata, F. P., & Shamos, M. I. (2012). Computational geometry: an introduction. Springer Science & Business Media.\n * @param {Array} x - The array with x coordinates of the points.\n * @param {Array} y - The array with y coordinates of the points.\n * @return {Array} The indices of the points of anticlockwise lower convex hull\n * @private\n */\nexport default function antiLowerConvexHull(x, y) {\n if (x.length !== y.length) {\n throw new RangeError('X and Y vectors has different dimensions');\n }\n\n const nbPoints = x.length - 1;\n if (nbPoints === 0) return [0];\n if (nbPoints === 1) return [0, 1];\n\n let currentPoint = 0;\n let result = new Array(x.length).fill(true);\n while (true) {\n const a = currentPoint;\n const b = moveOn(currentPoint, nbPoints, result);\n const c = moveOn(moveOn(currentPoint, nbPoints, result), nbPoints, result);\n\n const det =\n x[c] * (y[a] - y[b]) + x[a] * (y[b] - y[c]) + x[b] * (y[c] - y[a]);\n\n const leftTurn = det >= 0;\n\n if (leftTurn) {\n currentPoint = b;\n } else {\n result[b] = false;\n currentPoint = moveBack(currentPoint, nbPoints, result);\n }\n if (c === nbPoints) break;\n }\n\n return result\n .map((item, index) => (item === false ? false : index))\n .filter((item) => item !== false);\n}\n\n/**\n * @param {number} currentPoint - The index of the current point to make the move\n * @param {number} nbPoints - The total number of points in the array\n * @param {Array} vector - The array with the points\n * @return {number} the index of the point after the move\n * @private\n */\n\nfunction moveBack(currentPoint, nbPoints, vector) {\n let counter = currentPoint - 1;\n while (vector[counter] === false) counter--;\n return currentPoint === 0 ? nbPoints : counter;\n}\n\nfunction moveOn(currentPoint, nbPoints, vector) {\n let counter = currentPoint + 1;\n while (vector[counter] === false) counter++;\n return currentPoint === nbPoints ? 0 : counter;\n}\n","import { xNorm, xMaxValue, xMinValue } from 'ml-spectra-processing';\n\nimport antiLowerConvexHull from './util/antiLowerConvexHull';\n\n/**\n * Performs a global optimization of required parameters\n * It will return an object containing:\n * - `minFunctionValue`: The minimum value found for the objetive function\n * - `optima`: Array of Array of values for all the variables where the function reach its minimum value\n * - `iterations`: Number of iterations performed in the process\n * - `finalState`: Internal state allowing to continue optimization (initialState)\n * @param {function} objectiveFunction Function to evaluate. It should accept an array of variables\n * @param {Array} lowerBoundaries Array containing for each variable the lower boundary\n * @param {Array} upperBoundaries Array containing for each variable the higher boundary\n * @param {Object} [options={}]\n * @param {number} [options.iterations] - Number of iterations.\n * @param {number} [options.epsilon] - Tolerance to choose best current value.\n * @param {number} [options.tolerance] - Minimum tollerance of the function.\n * @param {number} [options.tolerance2] - Minimum tollerance of the function.\n * @param {Object} [options.initialState={}}] - finalState of previous optimization.\n * @return {Object} {finalState, iterations, minFunctionValue}\n * */\n\nexport default function direct(\n objectiveFunction,\n lowerBoundaries,\n upperBoundaries,\n options = {},\n) {\n const {\n iterations = 50,\n epsilon = 1e-4,\n tolerance = 1e-16,\n tolerance2 = 1e-12,\n initialState = {},\n } = options;\n\n if (\n objectiveFunction === undefined ||\n lowerBoundaries === undefined ||\n upperBoundaries === undefined\n ) {\n throw new RangeError('There is something undefined');\n }\n\n lowerBoundaries = new Float64Array(lowerBoundaries);\n upperBoundaries = new Float64Array(upperBoundaries);\n\n if (lowerBoundaries.length !== upperBoundaries.length) {\n throw new Error(\n 'Lower bounds and Upper bounds for x are not of the same length',\n );\n }\n\n //-------------------------------------------------------------------------\n // STEP 1. Initialization\n //-------------------------------------------------------------------------\n let n = lowerBoundaries.length;\n let diffBorders = upperBoundaries.map((x, i) => x - lowerBoundaries[i]);\n\n let {\n numberOfRectangles = 0,\n totalIterations = 0,\n unitaryCoordinates = [new Float64Array(n).fill(0.5)],\n middlePoint = new Float64Array(n).map((value, index) => {\n return (\n lowerBoundaries[index] +\n unitaryCoordinates[0][index] * diffBorders[index]\n );\n }),\n bestCurrentValue = objectiveFunction(middlePoint),\n fCalls = 1,\n smallerDistance = 0,\n edgeSizes = [new Float64Array(n).fill(0.5)],\n diagonalDistances = [Math.sqrt(n * 0.5 ** 2)],\n functionValues = [bestCurrentValue],\n differentDistances = diagonalDistances,\n smallerValuesByDistance = [bestCurrentValue],\n choiceLimit = undefined,\n } = initialState;\n if (\n initialState.originalCoordinates &&\n initialState.originalCoordinates.length > 0\n ) {\n bestCurrentValue = xMinValue(functionValues);\n choiceLimit =\n epsilon * Math.abs(bestCurrentValue) > 1e-8\n ? epsilon * Math.abs(bestCurrentValue)\n : 1e-8;\n\n smallerDistance = getMinIndex(\n functionValues,\n diagonalDistances,\n choiceLimit,\n bestCurrentValue,\n );\n\n unitaryCoordinates = initialState.originalCoordinates.slice();\n for (let j = 0; j < unitaryCoordinates.length; j++) {\n for (let i = 0; i < lowerBoundaries.length; i++) {\n unitaryCoordinates[j][i] =\n (unitaryCoordinates[j][i] - lowerBoundaries[i]) / diffBorders[i];\n }\n }\n }\n\n let iteration = 0;\n //-------------------------------------------------------------------------\n // Iteration loop\n //-------------------------------------------------------------------------\n\n while (iteration < iterations) {\n //----------------------------------------------------------------------\n // STEP 2. Identify the set S of all potentially optimal rectangles\n //----------------------------------------------------------------------\n\n let S1 = [];\n let idx = differentDistances.findIndex(\n // eslint-disable-next-line no-loop-func\n (e) => e === diagonalDistances[smallerDistance],\n );\n let counter = 0;\n for (let i = idx; i < differentDistances.length; i++) {\n for (let f = 0; f < functionValues.length; f++) {\n if (\n (functionValues[f] === smallerValuesByDistance[i]) &\n (diagonalDistances[f] === differentDistances[i])\n ) {\n S1[counter++] = f;\n }\n }\n }\n\n let optimumValuesIndex, S3;\n if (differentDistances.length - idx > 1) {\n let a1 = diagonalDistances[smallerDistance];\n let b1 = functionValues[smallerDistance];\n let a2 = differentDistances[differentDistances.length - 1];\n let b2 = smallerValuesByDistance[differentDistances.length - 1];\n let slope = (b2 - b1) / (a2 - a1);\n let constant = b1 - slope * a1;\n let S2 = new Uint32Array(counter);\n counter = 0;\n for (let i = 0; i < S2.length; i++) {\n let j = S1[i];\n if (\n functionValues[j] <=\n slope * diagonalDistances[j] + constant + tolerance2\n ) {\n S2[counter++] = j;\n }\n }\n\n let xHull = [];\n let yHull = [];\n for (let i = 0; i < counter; i++) {\n xHull.push(diagonalDistances[S2[i]]);\n yHull.push(functionValues[S2[i]]);\n }\n\n let lowerIndexHull = antiLowerConvexHull(xHull, yHull);\n\n S3 = [];\n for (let i = 0; i < lowerIndexHull.length; i++) {\n S3.push(S2[lowerIndexHull[i]]);\n }\n } else {\n S3 = S1.slice(0, counter);\n }\n optimumValuesIndex = S3;\n //--------------------------------------------------------------\n // STEPS 3,5: Select any rectangle j in S\n //--------------------------------------------------------------\n for (let k = 0; k < optimumValuesIndex.length; k++) {\n let j = optimumValuesIndex[k];\n let largerSide = xMaxValue(edgeSizes[j]);\n let largeSidesIndex = new Uint32Array(edgeSizes[j].length);\n counter = 0;\n for (let i = 0; i < edgeSizes[j].length; i++) {\n if (Math.abs(edgeSizes[j][i] - largerSide) < tolerance) {\n largeSidesIndex[counter++] = i;\n }\n }\n let delta = (2 * largerSide) / 3;\n let bestFunctionValues = [];\n for (let r = 0; r < counter; r++) {\n let i = largeSidesIndex[r];\n let firstMiddleCenter = unitaryCoordinates[j].slice();\n let secondMiddleCenter = unitaryCoordinates[j].slice();\n firstMiddleCenter[i] += delta;\n secondMiddleCenter[i] -= delta;\n let firstMiddleValue = new Float64Array(firstMiddleCenter.length);\n let secondMiddleValue = new Float64Array(secondMiddleCenter.length);\n for (let i = 0; i < firstMiddleCenter.length; i++) {\n firstMiddleValue[i] =\n lowerBoundaries[i] + firstMiddleCenter[i] * diffBorders[i];\n secondMiddleValue[i] =\n lowerBoundaries[i] + secondMiddleCenter[i] * diffBorders[i];\n }\n let firstMinValue = objectiveFunction(firstMiddleValue);\n let secondMinValue = objectiveFunction(secondMiddleValue);\n fCalls += 2;\n bestFunctionValues.push({\n minValue: Math.min(firstMinValue, secondMinValue),\n index: r,\n });\n // [Math.min(firstMinValue, secondMinValue), r];\n unitaryCoordinates.push(firstMiddleCenter, secondMiddleCenter);\n functionValues.push(firstMinValue, secondMinValue);\n }\n\n let b = bestFunctionValues.sort((a, b) => a.minValue - b.minValue);\n for (let r = 0; r < counter; r++) {\n let u = largeSidesIndex[b[r].index];\n let ix1 = numberOfRectangles + 2 * (b[r].index + 1) - 1;\n let ix2 = numberOfRectangles + 2 * (b[r].index + 1);\n edgeSizes[j][u] = delta / 2;\n edgeSizes[ix1] = edgeSizes[j].slice();\n edgeSizes[ix2] = edgeSizes[j].slice();\n diagonalDistances[j] = xNorm(edgeSizes[j]);\n diagonalDistances[ix1] = diagonalDistances[j];\n diagonalDistances[ix2] = diagonalDistances[j];\n }\n numberOfRectangles += 2 * counter;\n }\n\n //--------------------------------------------------------------\n // Update\n //--------------------------------------------------------------\n\n bestCurrentValue = xMinValue(functionValues);\n\n choiceLimit =\n epsilon * Math.abs(bestCurrentValue) > 1e-8\n ? epsilon * Math.abs(bestCurrentValue)\n : 1e-8;\n\n smallerDistance = getMinIndex(\n functionValues,\n diagonalDistances,\n choiceLimit,\n bestCurrentValue,\n iteration,\n );\n\n differentDistances = Array.from(new Set(diagonalDistances));\n differentDistances = differentDistances.sort((a, b) => a - b);\n\n smallerValuesByDistance = [];\n for (let i = 0; i < differentDistances.length; i++) {\n let minIndex;\n let minValue = Number.POSITIVE_INFINITY;\n for (let k = 0; k < diagonalDistances.length; k++) {\n if (diagonalDistances[k] === differentDistances[i]) {\n if (functionValues[k] < minValue) {\n minValue = functionValues[k];\n minIndex = k;\n }\n }\n }\n smallerValuesByDistance.push(functionValues[minIndex]);\n }\n\n let currentMin = [];\n for (let j = 0; j < functionValues.length; j++) {\n if (functionValues[j] === bestCurrentValue) {\n let temp = [];\n for (let i = 0; i < lowerBoundaries.length; i++) {\n temp.push(\n lowerBoundaries[i] + unitaryCoordinates[j][i] * diffBorders[i],\n );\n }\n currentMin.push(temp);\n }\n }\n iteration += 1;\n }\n //--------------------------------------------------------------\n // Saving results\n //--------------------------------------------------------------\n\n let result = {};\n result.minFunctionValue = bestCurrentValue;\n result.iterations = iteration;\n let originalCoordinates = [];\n for (let j = 0; j < numberOfRectangles + 1; j++) {\n let pair = [];\n for (let i = 0; i < lowerBoundaries.length; i++) {\n pair.push(lowerBoundaries[i] + unitaryCoordinates[j][i] * diffBorders[i]);\n }\n originalCoordinates.push(pair);\n }\n\n result.finalState = {\n numberOfRectangles,\n totalIterations: (totalIterations += iterations),\n originalCoordinates,\n middlePoint,\n fCalls,\n smallerDistance,\n edgeSizes,\n diagonalDistances,\n functionValues,\n differentDistances,\n smallerValuesByDistance,\n choiceLimit,\n };\n\n let minimizer = [];\n for (let i = 0; i < functionValues.length; i++) {\n if (functionValues[i] === bestCurrentValue) {\n minimizer.push(originalCoordinates[i]);\n }\n }\n\n result.optima = minimizer;\n return result;\n}\n\nfunction getMinIndex(\n functionValues,\n diagonalDistances,\n choiceLimit,\n bestCurrentValue,\n) {\n let item = [];\n for (let i = 0; i < functionValues.length; i++) {\n item[i] =\n Math.abs(functionValues[i] - (bestCurrentValue + choiceLimit)) /\n diagonalDistances[i];\n }\n const min = xMinValue(item);\n let result = item.findIndex((x) => x === min);\n return result;\n}\n","import direct from 'ml-direct';\n/**\n * Run a direct optimization on the provided data using a sum-of-shapes model.\n * @param data - The observed x/y data to fit.\n * @param sumOfShapes - A function returning the model prediction for a given parameter vector.\n * @param options - Optimization bounds and solver options.\n * @returns The optimized parameter values, the final objective error, and the number of iterations.\n */\nexport function directOptimization(data, sumOfShapes, options) {\n const { minValues, maxValues, maxIterations, epsilon, tolerance, tolerance2, initialState, } = options;\n const objectiveFunction = getObjectiveFunction(data, sumOfShapes);\n const result = direct(objectiveFunction, \n // direct internally converts ArrayLike to Float64Array,\n // so we can safely cast minValues and maxValues to number[]\n minValues, maxValues, {\n iterations: maxIterations,\n epsilon,\n tolerance,\n tolerance2,\n initialState,\n });\n const { optima, minFunctionValue, iterations } = result;\n return {\n parameterError: minFunctionValue,\n iterations,\n parameterValues: optima[0],\n };\n}\nfunction getObjectiveFunction(data, sumOfShapes) {\n const { x, y } = data;\n const nbPoints = x.length;\n return (parameters) => {\n const fct = sumOfShapes(parameters);\n let error = 0;\n for (let i = 0; i < nbPoints; i++) {\n error += (y[i] - fct(x[i])) ** 2;\n }\n return error;\n };\n}\n//# sourceMappingURL=directOptimization.js.map","import { levenbergMarquardt } from 'ml-levenberg-marquardt';\nimport { directOptimization } from \"./wrappers/directOptimization.js\";\n/**\n * Algorithm to select the method.\n * @param optimizationOptions - Optimization options\n * @returns - The algorithm and optimization options\n */\nexport function selectMethod(optimizationOptions = {}) {\n const { kind = 'lm', options } = optimizationOptions;\n switch (kind) {\n case 'lm':\n case 'levenbergMarquardt':\n return {\n algorithm: levenbergMarquardt,\n optimizationOptions: {\n damping: 1.5,\n maxIterations: 100,\n errorTolerance: 1e-8,\n ...options,\n },\n };\n case 'direct': {\n return {\n algorithm: directOptimization,\n optimizationOptions: {\n maxIterations: 20,\n epsilon: 1e-4,\n tolerance: 1e-16,\n tolerance2: 1e-12,\n initialState: {},\n ...options,\n },\n };\n }\n default:\n throw new Error(`Unknown fitting algorithm`);\n }\n}\n//# sourceMappingURL=selectMethod.js.map","import { xMaxAbsoluteValue } from 'ml-spectra-processing';\nimport { getSumOfShapes } from \"./shapes/getSumOfShapes.js\";\nimport { buildOptimizationLayout } from \"./util/buildOptimizationLayout.js\";\nimport { getFixedParametersResult } from \"./util/getFixedParametersResult.js\";\nimport { getInternalPeaks } from \"./util/internalPeaks/getInternalPeaks.js\";\nimport { reconstructPeaks } from \"./util/reconstructPeaks.js\";\nimport { selectMethod } from \"./util/selectMethod.js\";\n/**\n * Fits a set of points to the sum of a set of bell functions.\n * @param data - An object containing the x and y data to be fitted.\n * @param peaks - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].\n * @param options - Options for optimize\n * @returns - An object with fitting error and the list of optimized parameters { parameters: [ {x, y, width} ], error } if the kind of shape is pseudoVoigt mu parameter is optimized.\n */\nexport function optimize(data, peaks, options = {}) {\n // rescale data so the maximum Y value becomes 1\n const max = xMaxAbsoluteValue(data.y);\n const yScale = max === 0 ? 1 : max;\n const internalPeaks = getInternalPeaks(peaks, yScale, options);\n // need to rescale what is related to Y\n const normalizedY = new Float64Array(data.y.length);\n for (let i = 0; i < data.y.length; i++) {\n normalizedY[i] = data.y[i] / yScale;\n }\n const optimizationLayout = buildOptimizationLayout(internalPeaks, peaks, options, yScale);\n const { freeIndices, variableMin, variableMax, variableInit, variableGrad, variables, } = optimizationLayout;\n const { algorithm, optimizationOptions } = selectMethod(options.optimization);\n const baseSumOfShapes = getSumOfShapes(internalPeaks);\n const sumOfShapesForVariables = (variableValues) => {\n return baseSumOfShapes(optimizationLayout.variableToPeakValues(variableValues));\n };\n if (freeIndices.length === 0) {\n return getFixedParametersResult(internalPeaks, normalizedY, data.x, optimizationLayout.variableToPeakValues(variableInit), baseSumOfShapes, yScale);\n }\n // prepare arrays to pass to the algorithm (reduced if needed)\n let minValues;\n let maxValues;\n let initialValues;\n let gradientDifferences;\n let sumOfShapesToUse = sumOfShapesForVariables;\n if (freeIndices.length === variables.length) {\n // nothing to reduce\n minValues = variableMin;\n maxValues = variableMax;\n initialValues = variableInit;\n gradientDifferences = variableGrad;\n }\n else {\n // wrapper that maps reduced (free) parameters into the full parameter vector\n const sumOfShapesForReduced = (reducedParameters) => {\n const full = new Float64Array(variables.length);\n full.set(variableInit);\n for (let k = 0; k < freeIndices.length; k++) {\n full[freeIndices[k]] = reducedParameters[k];\n }\n return sumOfShapesForVariables(full);\n };\n minValues = new Float64Array(freeIndices.length);\n maxValues = new Float64Array(freeIndices.length);\n initialValues = new Float64Array(freeIndices.length);\n gradientDifferences = new Float64Array(freeIndices.length);\n for (let j = 0; j < freeIndices.length; j++) {\n const i = freeIndices[j];\n minValues[j] = variableMin[i];\n maxValues[j] = variableMax[i];\n initialValues[j] = variableInit[i];\n gradientDifferences[j] = variableGrad[i];\n }\n sumOfShapesToUse = sumOfShapesForReduced;\n }\n const fitted = algorithm({ x: data.x, y: normalizedY }, sumOfShapesToUse, {\n minValues,\n maxValues,\n initialValues,\n gradientDifference: gradientDifferences,\n ...optimizationOptions,\n });\n let fittedVariableValues;\n if (freeIndices.length === variables.length) {\n fittedVariableValues = fitted.parameterValues;\n }\n else {\n const full = variableInit.slice();\n for (let k = 0; k < freeIndices.length; k++) {\n full[freeIndices[k]] = fitted.parameterValues[k];\n }\n fittedVariableValues = full;\n }\n const fittedValues = optimizationLayout.variableToPeakValues(fittedVariableValues);\n return {\n error: fitted.parameterError,\n iterations: fitted.iterations,\n peaks: reconstructPeaks(internalPeaks, fittedValues, yScale),\n };\n}\n//# sourceMappingURL=index.js.map","import { getShape1D } from 'ml-peak-shape-generator';\n/**\n * Add a `shape` property to peaks that do not have one.\n * If a peak already has a `shape` but no `fwhm`, the FWHM is computed from `peak.width`.\n * @param peaks - Peaks with a `width` property.\n * @param options - Shape options.\n * @returns A peak list where every peak has a `shape` property.\n */\nexport function addMissingShape(peaks, options = {}) {\n const { shape = { kind: 'gaussian' }, output = structuredClone(peaks) } = options;\n const defaultShapeInstance = getShape1D(shape);\n return output.map((peak) => {\n if (hasShape(peak)) {\n if (!('fwhm' in peak.shape)) {\n peak.shape.fwhm = getShape1D(peak.shape).widthToFWHM(peak.width);\n }\n return peak;\n }\n return {\n ...peak,\n shape: {\n fwhm: defaultShapeInstance.widthToFWHM(peak.width),\n ...shape,\n },\n };\n });\n}\nfunction hasShape(peak) {\n return 'shape' in peak;\n}\n//# sourceMappingURL=addMissingShape.js.map","/**\n * Splits a group of peaks into smaller subgroups whose size is approximately\n * limited by `maxNumberOfPeaks`.\n *\n * If the group contains more peaks than allowed, the function identifies the\n * largest normalized gaps between adjacent peaks and uses them as split points.\n *\n * The gap score is computed as:\n *\n * `score = distanceBetweenCenters / averagePeakWidth`\n *\n * where:\n * - `distanceBetweenCenters = peak[i + 1].x - peak[i].x`\n * - `averagePeakWidth = (peak[i].width + peak[i + 1].width) / 2`\n *\n * Larger scores indicate that two neighboring peaks are well separated\n * relative to their widths, making them good candidates for dividing the\n * fitting problem into independent subgroups.\n *\n * The algorithm:\n * 1. Computes the number of cuts required.\n * 2. Scores every boundary between adjacent peaks.\n * 3. Selects the boundaries with the largest scores.\n * 4. Splits the original group at those boundaries.\n * @param group - Ordered collection of peaks. Peaks should be sorted by\n * increasing `x` position before calling this function.\n * @param maxNumberOfPeaks - Desired maximum number of peaks per subgroup.\n * If the group size is less than or equal to this value, no splitting occurs.\n * @returns An array of peak subgroups. The returned groups preserve the\n * original peak ordering.\n */\nexport function splitGroup(group, maxNumberOfPeaks) {\n const groups = [group];\n while (true) {\n const index = groups.findIndex((g) => g.length > maxNumberOfPeaks);\n if (index === -1)\n break;\n const current = groups[index];\n const cut = findBestCut(current);\n groups.splice(index, 1, current.slice(0, cut), current.slice(cut));\n }\n return groups;\n}\nfunction findBestCut(group) {\n let bestScore = -Infinity;\n const candidates = [];\n for (let i = 1; i < group.length; i++) {\n const score = (group[i].x - group[i - 1].x) /\n ((group[i].width + group[i - 1].width) / 2);\n if (score > bestScore) {\n bestScore = score;\n candidates.length = 0;\n }\n if (score === bestScore) {\n candidates.push({\n index: i,\n balance: Math.abs(i - (group.length - i)),\n });\n }\n }\n let bestScored = candidates[0];\n for (const candidate of candidates) {\n if (candidate.balance < bestScored.balance) {\n bestScored = candidate;\n }\n }\n return bestScored.index;\n}\n//# sourceMappingURL=splitGroup.js.map","import { splitGroup } from \"./splitGroup.js\";\n/**\n * Group peaks based on a width-aware factor.\n * Only `x` and `width` are used, so the current implementation does not take\n * peak asymmetry into account.\n * @param peaks - Peaks with `x` and `width` properties.\n * @param options - Grouping options.\n * @returns Groups of peaks sorted by ascending `x`.\n */\nexport function groupPeaks(peaks, options = {}) {\n if (peaks.length === 0)\n return [];\n const { groupingFactor = 1, maxNumberOfPeaks = 15 } = options;\n const sortedPeaks = peaks.toSorted((a, b) => a.x - b.x);\n let previousPeak = sortedPeaks[0];\n let currentGroup = [previousPeak];\n const groups = [currentGroup];\n for (let i = 1; i < sortedPeaks.length; i++) {\n const peak = sortedPeaks[i];\n if ((peak.x - previousPeak.x) / ((peak.width + previousPeak.width) / 2) <=\n groupingFactor) {\n currentGroup.push(peak);\n }\n else {\n currentGroup = [peak];\n groups.push(currentGroup);\n }\n previousPeak = peak;\n }\n if (maxNumberOfPeaks !== undefined) {\n return groups.flatMap((group) => group.length > maxNumberOfPeaks\n ? splitGroup(group, maxNumberOfPeaks)\n : [group]);\n }\n return groups;\n}\n//# sourceMappingURL=groupPeaks.js.map","import { getShape1D } from 'ml-peak-shape-generator';\nimport { optimize } from 'ml-spectra-fitting';\nimport { xGetFromToIndex } from 'ml-spectra-processing';\nimport { addMissingShape } from \"../utils/addMissingShape.js\";\nimport { groupPeaks } from \"../utils/groupPeaks.js\";\n/**\n * Optimize the position (x), max intensity (y), full width at half maximum (fwhm)\n * and the ratio of gaussian contribution (mu) if it's required.\n * It currently supports three kind of shapes: gaussian, lorentzian and pseudovoigt.\n * Returns both the optimized peaks and per-group diagnostic logs.\n * @param data - An object containing the x and y data to be fitted.\n * @param peakList - A list of initial parameters to be optimized. e.g. coming from a peak picking [{x, y, width}].\n * @param options - Optimization options.\n * @returns An object with the optimized peaks and the per-group logs.\n */\nexport function optimizePeaksWithLogs(data, peakList, options = {}) {\n const { fromTo = {}, shape = { kind: 'gaussian' }, maxNumberOfPeaks, groupingFactor, factorLimits = 2, parameters, optimization = {\n kind: 'lm',\n options: {\n timeout: 10,\n },\n }, } = options;\n // Optimize peaks in groups: fitting everything at once would be too slow and\n // have too many free parameters.\n const groups = groupPeaks(peakList, {\n groupingFactor,\n maxNumberOfPeaks,\n });\n const logs = [];\n const results = [];\n for (const peakGroup of groups) {\n const start = Date.now();\n const peaks = addMissingShape(peakGroup, { shape });\n const firstPeak = peaks[0];\n const lastPeak = peaks.at(-1);\n const { from = firstPeak.x - firstPeak.width * factorLimits, to = lastPeak.x + lastPeak.width * factorLimits, } = fromTo;\n const { fromIndex, toIndex } = xGetFromToIndex(data.x, { from, to });\n const x = data.x instanceof Float64Array\n ? data.x.subarray(fromIndex, toIndex)\n : data.x.slice(fromIndex, toIndex);\n const y = data.y instanceof Float64Array\n ? data.y.subarray(fromIndex, toIndex)\n : data.y.slice(fromIndex, toIndex);\n const log = {\n range: { from, to },\n parameters: { optimization, parameters },\n groupSize: peakGroup.length,\n time: Date.now() - start,\n };\n if (x.length > 5) {\n const { iterations, error, peaks: optimizedPeaks, } = optimize({ x, y }, peaks, {\n shape,\n parameters,\n optimization,\n });\n for (const optimizedPeak of optimizedPeaks) {\n results.push({\n ...optimizedPeak,\n width: getShape1D(optimizedPeak.shape).fwhmToWidth(optimizedPeak.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","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","/**\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 * 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","import { gsd } from 'ml-gsd';\nimport { xAdd, xFindClosestIndex, xMean } from 'ml-spectra-processing';\n/**\n * Filter that allows to calibrateX the x axis based on the presence of peaks\n * @param data\n * @param options\n */\nexport function calibrateX(data, options = {}) {\n const { targetX = 0, nbPeaks = 1, from = data.x[0], to = data.x.at(-1), gsd: gsdOptions = {\n minMaxRatio: 0.1,\n realTopDetection: true,\n smoothY: true,\n sgOptions: {\n windowSize: 7,\n polynomial: 3,\n },\n }, } = options;\n const fromIndex = xFindClosestIndex(data.x, from);\n const toIndex = xFindClosestIndex(data.x, to);\n let peaks = gsd({\n x: data.x.subarray(fromIndex, toIndex),\n y: data.y.subarray(fromIndex, toIndex),\n }, gsdOptions);\n peaks.sort((a, b) => b.y - a.y);\n peaks = peaks.slice(0, nbPeaks);\n if (peaks.length < nbPeaks)\n return { data };\n const middle = xMean(peaks.map((peak) => peak.x));\n return { data: { x: xAdd(data.x, targetX - middle), y: data.y } };\n}\n//# sourceMappingURL=calibrateX.js.map","import { xApplyFunctionStr } from 'ml-spectra-processing';\n/**\n * Filter that allows to\n * @param data\n * @param options\n */\nexport function xFunction(data, options = {}) {\n return {\n data: {\n x: xApplyFunctionStr(data.x, {\n variableLabel: 'x',\n fctString: options.function,\n }),\n y: data.y,\n },\n };\n}\n//# sourceMappingURL=xFunction.js.map","import { xApplyFunctionStr } from 'ml-spectra-processing';\n/**\n * Filter that allows to\n * @param data\n * @param options\n */\nexport function yFunction(data, options = {}) {\n return {\n data: {\n x: data.x,\n y: xApplyFunctionStr(data.y, {\n variableLabel: 'y',\n fctString: options.function,\n }),\n },\n };\n}\n//# sourceMappingURL=yFunction.js.map","import { xAdd, xMaxValue } from 'ml-spectra-processing';\n/**\n * Filter that allows to\n * @param data\n * @param options\n */\nexport function setMaxY(data, options = {}) {\n const { max = 1 } = options;\n const existingMax = xMaxValue(data.y);\n if (existingMax === max) {\n return { data };\n }\n return {\n data: {\n x: data.x,\n y: xAdd(data.y, max - existingMax),\n },\n };\n}\n//# sourceMappingURL=setMaxY.js.map","import { xAdd, xMinValue } from 'ml-spectra-processing';\n/**\n * Filter that allows to\n * @param data\n * @param options\n */\nexport function setMinY(data, options = {}) {\n const { min = 0 } = options;\n const existingMin = xMinValue(data.y);\n if (existingMin === min) {\n return { data };\n }\n return {\n data: {\n x: data.x,\n y: xAdd(data.y, min - existingMin),\n },\n };\n}\n//# sourceMappingURL=setMinY.js.map","import { xEnsureFloat64, xyGrowingX } from 'ml-spectra-processing';\nimport * as Filters from \"./filters/filters.js\";\n/**\n * Apply filters on {x:[], y:[]}\n * @param data\n * @param filters\n * @returns\n */\nexport function filterXY(data, filters) {\n let result = {\n data: xyGrowingX({ x: xEnsureFloat64(data.x), y: xEnsureFloat64(data.y) }),\n };\n const logs = [];\n for (const filter of filters) {\n const start = Date.now();\n if (!filter.name) {\n // we ignore empty filter names\n continue;\n }\n const filterFct = Filters[filter.name];\n if (!filterFct) {\n throw new Error(`Unknown filter: ${filter.name}`);\n }\n // @ts-expect-error some method have options and some other ones don't have any options\n result = filterFct(result.data, filter.options);\n result.data = xyGrowingX(result.data);\n logs.push({\n name: filter.name,\n time: Date.now() - start,\n });\n }\n return { logs, data: result.data };\n}\n//# sourceMappingURL=filterXY.js.map","// eslint-disable-next-line jsdoc/require-jsdoc\nexport function filterMatrix(matrix, \n// eslint-disable-next-line @typescript-eslint/no-unused-vars\nfilters) {\n const result = {\n data: matrix,\n };\n const logs = [];\n /**\n * todo\n * for (let filter of filters) {\n * const start = Date.now();\n * // eslint-disable-next-line import/namespace\n * const filterFct = Filters[filter.name];\n * if (!filterFct) {\n * throw new Error(`Unknown filter: ${filter.name}`);\n * }\n * result = filterFct(result.data, filter.options);\n * logs.push({\n * name: filter.name,\n * time: Date.now() - start,\n * });\n * }\n */\n return { logs, data: result.data };\n}\n//# sourceMappingURL=filterMatrix.js.map","/*! @preserve\n * numeral.js\n * version : 2.0.6\n * author : Adam Draper\n * license : MIT\n * http://adamwdraper.github.com/Numeral-js/\n */\n\n(function (global, factory) {\n if (typeof define === 'function' && define.amd) {\n define(factory);\n } else if (typeof module === 'object' && module.exports) {\n module.exports = factory();\n } else {\n global.numeral = factory();\n }\n}(this, function () {\n /************************************\n Variables\n ************************************/\n\n var numeral,\n _,\n VERSION = '2.0.6',\n formats = {},\n locales = {},\n defaults = {\n currentLocale: 'en',\n zeroFormat: null,\n nullFormat: null,\n defaultFormat: '0,0',\n scalePercentBy100: true\n },\n options = {\n currentLocale: defaults.currentLocale,\n zeroFormat: defaults.zeroFormat,\n nullFormat: defaults.nullFormat,\n defaultFormat: defaults.defaultFormat,\n scalePercentBy100: defaults.scalePercentBy100\n };\n\n\n /************************************\n Constructors\n ************************************/\n\n // Numeral prototype object\n function Numeral(input, number) {\n this._input = input;\n\n this._value = number;\n }\n\n numeral = function(input) {\n var value,\n kind,\n unformatFunction,\n regexp;\n\n if (numeral.isNumeral(input)) {\n value = input.value();\n } else if (input === 0 || typeof input === 'undefined') {\n value = 0;\n } else if (input === null || _.isNaN(input)) {\n value = null;\n } else if (typeof input === 'string') {\n if (options.zeroFormat && input === options.zeroFormat) {\n value = 0;\n } else if (options.nullFormat && input === options.nullFormat || !input.replace(/[^0-9]+/g, '').length) {\n value = null;\n } else {\n for (kind in formats) {\n regexp = typeof formats[kind].regexps.unformat === 'function' ? formats[kind].regexps.unformat() : formats[kind].regexps.unformat;\n\n if (regexp && input.match(regexp)) {\n unformatFunction = formats[kind].unformat;\n\n break;\n }\n }\n\n unformatFunction = unformatFunction || numeral._.stringToNumber;\n\n value = unformatFunction(input);\n }\n } else {\n value = Number(input)|| null;\n }\n\n return new Numeral(input, value);\n };\n\n // version number\n numeral.version = VERSION;\n\n // compare numeral object\n numeral.isNumeral = function(obj) {\n return obj instanceof Numeral;\n };\n\n // helper functions\n numeral._ = _ = {\n // formats numbers separators, decimals places, signs, abbreviations\n numberToFormat: function(value, format, roundingFunction) {\n var locale = locales[numeral.options.currentLocale],\n negP = false,\n optDec = false,\n leadingCount = 0,\n abbr = '',\n trillion = 1000000000000,\n billion = 1000000000,\n million = 1000000,\n thousand = 1000,\n decimal = '',\n neg = false,\n abbrForce, // force abbreviation\n abs,\n min,\n max,\n power,\n int,\n precision,\n signed,\n thousands,\n output;\n\n // make sure we never format a null value\n value = value || 0;\n\n abs = Math.abs(value);\n\n // see if we should use parentheses for negative number or if we should prefix with a sign\n // if both are present we default to parentheses\n if (numeral._.includes(format, '(')) {\n negP = true;\n format = format.replace(/[\\(|\\)]/g, '');\n } else if (numeral._.includes(format, '+') || numeral._.includes(format, '-')) {\n signed = numeral._.includes(format, '+') ? format.indexOf('+') : value < 0 ? format.indexOf('-') : -1;\n format = format.replace(/[\\+|\\-]/g, '');\n }\n\n // see if abbreviation is wanted\n if (numeral._.includes(format, 'a')) {\n abbrForce = format.match(/a(k|m|b|t)?/);\n\n abbrForce = abbrForce ? abbrForce[1] : false;\n\n // check for space before abbreviation\n if (numeral._.includes(format, ' a')) {\n abbr = ' ';\n }\n\n format = format.replace(new RegExp(abbr + 'a[kmbt]?'), '');\n\n if (abs >= trillion && !abbrForce || abbrForce === 't') {\n // trillion\n abbr += locale.abbreviations.trillion;\n value = value / trillion;\n } else if (abs < trillion && abs >= billion && !abbrForce || abbrForce === 'b') {\n // billion\n abbr += locale.abbreviations.billion;\n value = value / billion;\n } else if (abs < billion && abs >= million && !abbrForce || abbrForce === 'm') {\n // million\n abbr += locale.abbreviations.million;\n value = value / million;\n } else if (abs < million && abs >= thousand && !abbrForce || abbrForce === 'k') {\n // thousand\n abbr += locale.abbreviations.thousand;\n value = value / thousand;\n }\n }\n\n // check for optional decimals\n if (numeral._.includes(format, '[.]')) {\n optDec = true;\n format = format.replace('[.]', '.');\n }\n\n // break number and format\n int = value.toString().split('.')[0];\n precision = format.split('.')[1];\n thousands = format.indexOf(',');\n leadingCount = (format.split('.')[0].split(',')[0].match(/0/g) || []).length;\n\n if (precision) {\n if (numeral._.includes(precision, '[')) {\n precision = precision.replace(']', '');\n precision = precision.split('[');\n decimal = numeral._.toFixed(value, (precision[0].length + precision[1].length), roundingFunction, precision[1].length);\n } else {\n decimal = numeral._.toFixed(value, precision.length, roundingFunction);\n }\n\n int = decimal.split('.')[0];\n\n if (numeral._.includes(decimal, '.')) {\n decimal = locale.delimiters.decimal + decimal.split('.')[1];\n } else {\n decimal = '';\n }\n\n if (optDec && Number(decimal.slice(1)) === 0) {\n decimal = '';\n }\n } else {\n int = numeral._.toFixed(value, 0, roundingFunction);\n }\n\n // check abbreviation again after rounding\n if (abbr && !abbrForce && Number(int) >= 1000 && abbr !== locale.abbreviations.trillion) {\n int = String(Number(int) / 1000);\n\n switch (abbr) {\n case locale.abbreviations.thousand:\n abbr = locale.abbreviations.million;\n break;\n case locale.abbreviations.million:\n abbr = locale.abbreviations.billion;\n break;\n case locale.abbreviations.billion:\n abbr = locale.abbreviations.trillion;\n break;\n }\n }\n\n\n // format number\n if (numeral._.includes(int, '-')) {\n int = int.slice(1);\n neg = true;\n }\n\n if (int.length < leadingCount) {\n for (var i = leadingCount - int.length; i > 0; i--) {\n int = '0' + int;\n }\n }\n\n if (thousands > -1) {\n int = int.toString().replace(/(\\d)(?=(\\d{3})+(?!\\d))/g, '$1' + locale.delimiters.thousands);\n }\n\n if (format.indexOf('.') === 0) {\n int = '';\n }\n\n output = int + decimal + (abbr ? abbr : '');\n\n if (negP) {\n output = (negP && neg ? '(' : '') + output + (negP && neg ? ')' : '');\n } else {\n if (signed >= 0) {\n output = signed === 0 ? (neg ? '-' : '+') + output : output + (neg ? '-' : '+');\n } else if (neg) {\n output = '-' + output;\n }\n }\n\n return output;\n },\n // unformats numbers separators, decimals places, signs, abbreviations\n stringToNumber: function(string) {\n var locale = locales[options.currentLocale],\n stringOriginal = string,\n abbreviations = {\n thousand: 3,\n million: 6,\n billion: 9,\n trillion: 12\n },\n abbreviation,\n value,\n i,\n regexp;\n\n if (options.zeroFormat && string === options.zeroFormat) {\n value = 0;\n } else if (options.nullFormat && string === options.nullFormat || !string.replace(/[^0-9]+/g, '').length) {\n value = null;\n } else {\n value = 1;\n\n if (locale.delimiters.decimal !== '.') {\n string = string.replace(/\\./g, '').replace(locale.delimiters.decimal, '.');\n }\n\n for (abbreviation in abbreviations) {\n regexp = new RegExp('[^a-zA-Z]' + locale.abbreviations[abbreviation] + '(?:\\\\)|(\\\\' + locale.currency.symbol + ')?(?:\\\\))?)?$');\n\n if (stringOriginal.match(regexp)) {\n value *= Math.pow(10, abbreviations[abbreviation]);\n break;\n }\n }\n\n // check for negative number\n value *= (string.split('-').length + Math.min(string.split('(').length - 1, string.split(')').length - 1)) % 2 ? 1 : -1;\n\n // remove non numbers\n string = string.replace(/[^0-9\\.]+/g, '');\n\n value *= Number(string);\n }\n\n return value;\n },\n isNaN: function(value) {\n return typeof value === 'number' && isNaN(value);\n },\n includes: function(string, search) {\n return string.indexOf(search) !== -1;\n },\n insert: function(string, subString, start) {\n return string.slice(0, start) + subString + string.slice(start);\n },\n reduce: function(array, callback /*, initialValue*/) {\n if (this === null) {\n throw new TypeError('Array.prototype.reduce called on null or undefined');\n }\n\n if (typeof callback !== 'function') {\n throw new TypeError(callback + ' is not a function');\n }\n\n var t = Object(array),\n len = t.length >>> 0,\n k = 0,\n value;\n\n if (arguments.length === 3) {\n value = arguments[2];\n } else {\n while (k < len && !(k in t)) {\n k++;\n }\n\n if (k >= len) {\n throw new TypeError('Reduce of empty array with no initial value');\n }\n\n value = t[k++];\n }\n for (; k < len; k++) {\n if (k in t) {\n value = callback(value, t[k], k, t);\n }\n }\n return value;\n },\n /**\n * Computes the multiplier necessary to make x >= 1,\n * effectively eliminating miscalculations caused by\n * finite precision.\n */\n multiplier: function (x) {\n var parts = x.toString().split('.');\n\n return parts.length < 2 ? 1 : Math.pow(10, parts[1].length);\n },\n /**\n * Given a variable number of arguments, returns the maximum\n * multiplier that must be used to normalize an operation involving\n * all of them.\n */\n correctionFactor: function () {\n var args = Array.prototype.slice.call(arguments);\n\n return args.reduce(function(accum, next) {\n var mn = _.multiplier(next);\n return accum > mn ? accum : mn;\n }, 1);\n },\n /**\n * Implementation of toFixed() that treats floats more like decimals\n *\n * Fixes binary rounding issues (eg. (0.615).toFixed(2) === '0.61') that present\n * problems for accounting- and finance-related software.\n */\n toFixed: function(value, maxDecimals, roundingFunction, optionals) {\n var splitValue = value.toString().split('.'),\n minDecimals = maxDecimals - (optionals || 0),\n boundedPrecision,\n optionalsRegExp,\n power,\n output;\n\n // Use the smallest precision value possible to avoid errors from floating point representation\n if (splitValue.length === 2) {\n boundedPrecision = Math.min(Math.max(splitValue[1].length, minDecimals), maxDecimals);\n } else {\n boundedPrecision = minDecimals;\n }\n\n power = Math.pow(10, boundedPrecision);\n\n // Multiply up by precision, round accurately, then divide and use native toFixed():\n output = (roundingFunction(value + 'e+' + boundedPrecision) / power).toFixed(boundedPrecision);\n\n if (optionals > maxDecimals - boundedPrecision) {\n optionalsRegExp = new RegExp('\\\\.?0{1,' + (optionals - (maxDecimals - boundedPrecision)) + '}$');\n output = output.replace(optionalsRegExp, '');\n }\n\n return output;\n }\n };\n\n // avaliable options\n numeral.options = options;\n\n // avaliable formats\n numeral.formats = formats;\n\n // avaliable formats\n numeral.locales = locales;\n\n // This function sets the current locale. If\n // no arguments are passed in, it will simply return the current global\n // locale key.\n numeral.locale = function(key) {\n if (key) {\n options.currentLocale = key.toLowerCase();\n }\n\n return options.currentLocale;\n };\n\n // This function provides access to the loaded locale data. If\n // no arguments are passed in, it will simply return the current\n // global locale object.\n numeral.localeData = function(key) {\n if (!key) {\n return locales[options.currentLocale];\n }\n\n key = key.toLowerCase();\n\n if (!locales[key]) {\n throw new Error('Unknown locale : ' + key);\n }\n\n return locales[key];\n };\n\n numeral.reset = function() {\n for (var property in defaults) {\n options[property] = defaults[property];\n }\n };\n\n numeral.zeroFormat = function(format) {\n options.zeroFormat = typeof(format) === 'string' ? format : null;\n };\n\n numeral.nullFormat = function (format) {\n options.nullFormat = typeof(format) === 'string' ? format : null;\n };\n\n numeral.defaultFormat = function(format) {\n options.defaultFormat = typeof(format) === 'string' ? format : '0.0';\n };\n\n numeral.register = function(type, name, format) {\n name = name.toLowerCase();\n\n if (this[type + 's'][name]) {\n throw new TypeError(name + ' ' + type + ' already registered.');\n }\n\n this[type + 's'][name] = format;\n\n return format;\n };\n\n\n numeral.validate = function(val, culture) {\n var _decimalSep,\n _thousandSep,\n _currSymbol,\n _valArray,\n _abbrObj,\n _thousandRegEx,\n localeData,\n temp;\n\n //coerce val to string\n if (typeof val !== 'string') {\n val += '';\n\n if (console.warn) {\n console.warn('Numeral.js: Value is not string. It has been co-erced to: ', val);\n }\n }\n\n //trim whitespaces from either sides\n val = val.trim();\n\n //if val is just digits return true\n if (!!val.match(/^\\d+$/)) {\n return true;\n }\n\n //if val is empty return false\n if (val === '') {\n return false;\n }\n\n //get the decimal and thousands separator from numeral.localeData\n try {\n //check if the culture is understood by numeral. if not, default it to current locale\n localeData = numeral.localeData(culture);\n } catch (e) {\n localeData = numeral.localeData(numeral.locale());\n }\n\n //setup the delimiters and currency symbol based on culture/locale\n _currSymbol = localeData.currency.symbol;\n _abbrObj = localeData.abbreviations;\n _decimalSep = localeData.delimiters.decimal;\n if (localeData.delimiters.thousands === '.') {\n _thousandSep = '\\\\.';\n } else {\n _thousandSep = localeData.delimiters.thousands;\n }\n\n // validating currency symbol\n temp = val.match(/^[^\\d]+/);\n if (temp !== null) {\n val = val.substr(1);\n if (temp[0] !== _currSymbol) {\n return false;\n }\n }\n\n //validating abbreviation symbol\n temp = val.match(/[^\\d]+$/);\n if (temp !== null) {\n val = val.slice(0, -1);\n if (temp[0] !== _abbrObj.thousand && temp[0] !== _abbrObj.million && temp[0] !== _abbrObj.billion && temp[0] !== _abbrObj.trillion) {\n return false;\n }\n }\n\n _thousandRegEx = new RegExp(_thousandSep + '{2}');\n\n if (!val.match(/[^\\d.,]/g)) {\n _valArray = val.split(_decimalSep);\n if (_valArray.length > 2) {\n return false;\n } else {\n if (_valArray.length < 2) {\n return ( !! _valArray[0].match(/^\\d+.*\\d$/) && !_valArray[0].match(_thousandRegEx));\n } else {\n if (_valArray[0].length === 1) {\n return ( !! _valArray[0].match(/^\\d+$/) && !_valArray[0].match(_thousandRegEx) && !! _valArray[1].match(/^\\d+$/));\n } else {\n return ( !! _valArray[0].match(/^\\d+.*\\d$/) && !_valArray[0].match(_thousandRegEx) && !! _valArray[1].match(/^\\d+$/));\n }\n }\n }\n }\n\n return false;\n };\n\n\n /************************************\n Numeral Prototype\n ************************************/\n\n numeral.fn = Numeral.prototype = {\n clone: function() {\n return numeral(this);\n },\n format: function(inputString, roundingFunction) {\n var value = this._value,\n format = inputString || options.defaultFormat,\n kind,\n output,\n formatFunction;\n\n // make sure we have a roundingFunction\n roundingFunction = roundingFunction || Math.round;\n\n // format based on value\n if (value === 0 && options.zeroFormat !== null) {\n output = options.zeroFormat;\n } else if (value === null && options.nullFormat !== null) {\n output = options.nullFormat;\n } else {\n for (kind in formats) {\n if (format.match(formats[kind].regexps.format)) {\n formatFunction = formats[kind].format;\n\n break;\n }\n }\n\n formatFunction = formatFunction || numeral._.numberToFormat;\n\n output = formatFunction(value, format, roundingFunction);\n }\n\n return output;\n },\n value: function() {\n return this._value;\n },\n input: function() {\n return this._input;\n },\n set: function(value) {\n this._value = Number(value);\n\n return this;\n },\n add: function(value) {\n var corrFactor = _.correctionFactor.call(null, this._value, value);\n\n function cback(accum, curr, currI, O) {\n return accum + Math.round(corrFactor * curr);\n }\n\n this._value = _.reduce([this._value, value], cback, 0) / corrFactor;\n\n return this;\n },\n subtract: function(value) {\n var corrFactor = _.correctionFactor.call(null, this._value, value);\n\n function cback(accum, curr, currI, O) {\n return accum - Math.round(corrFactor * curr);\n }\n\n this._value = _.reduce([value], cback, Math.round(this._value * corrFactor)) / corrFactor;\n\n return this;\n },\n multiply: function(value) {\n function cback(accum, curr, currI, O) {\n var corrFactor = _.correctionFactor(accum, curr);\n return Math.round(accum * corrFactor) * Math.round(curr * corrFactor) / Math.round(corrFactor * corrFactor);\n }\n\n this._value = _.reduce([this._value, value], cback, 1);\n\n return this;\n },\n divide: function(value) {\n function cback(accum, curr, currI, O) {\n var corrFactor = _.correctionFactor(accum, curr);\n return Math.round(accum * corrFactor) / Math.round(curr * corrFactor);\n }\n\n this._value = _.reduce([this._value, value], cback);\n\n return this;\n },\n difference: function(value) {\n return Math.abs(numeral(this._value).subtract(value).value());\n }\n };\n\n /************************************\n Default Locale && Format\n ************************************/\n\n numeral.register('locale', 'en', {\n delimiters: {\n thousands: ',',\n decimal: '.'\n },\n abbreviations: {\n thousand: 'k',\n million: 'm',\n billion: 'b',\n trillion: 't'\n },\n ordinal: function(number) {\n var b = number % 10;\n return (~~(number % 100 / 10) === 1) ? 'th' :\n (b === 1) ? 'st' :\n (b === 2) ? 'nd' :\n (b === 3) ? 'rd' : 'th';\n },\n currency: {\n symbol: '$'\n }\n });\n\n \n\n(function() {\n numeral.register('format', 'bps', {\n regexps: {\n format: /(BPS)/,\n unformat: /(BPS)/\n },\n format: function(value, format, roundingFunction) {\n var space = numeral._.includes(format, ' BPS') ? ' ' : '',\n output;\n\n value = value * 10000;\n\n // check for space before BPS\n format = format.replace(/\\s?BPS/, '');\n\n output = numeral._.numberToFormat(value, format, roundingFunction);\n\n if (numeral._.includes(output, ')')) {\n output = output.split('');\n\n output.splice(-1, 0, space + 'BPS');\n\n output = output.join('');\n } else {\n output = output + space + 'BPS';\n }\n\n return output;\n },\n unformat: function(string) {\n return +(numeral._.stringToNumber(string) * 0.0001).toFixed(15);\n }\n });\n})();\n\n\n(function() {\n var decimal = {\n base: 1000,\n suffixes: ['B', 'KB', 'MB', 'GB', 'TB', 'PB', 'EB', 'ZB', 'YB']\n },\n binary = {\n base: 1024,\n suffixes: ['B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB', 'ZiB', 'YiB']\n };\n\n var allSuffixes = decimal.suffixes.concat(binary.suffixes.filter(function (item) {\n return decimal.suffixes.indexOf(item) < 0;\n }));\n var unformatRegex = allSuffixes.join('|');\n // Allow support for BPS (http://www.investopedia.com/terms/b/basispoint.asp)\n unformatRegex = '(' + unformatRegex.replace('B', 'B(?!PS)') + ')';\n\n numeral.register('format', 'bytes', {\n regexps: {\n format: /([0\\s]i?b)/,\n unformat: new RegExp(unformatRegex)\n },\n format: function(value, format, roundingFunction) {\n var output,\n bytes = numeral._.includes(format, 'ib') ? binary : decimal,\n suffix = numeral._.includes(format, ' b') || numeral._.includes(format, ' ib') ? ' ' : '',\n power,\n min,\n max;\n\n // check for space before\n format = format.replace(/\\s?i?b/, '');\n\n for (power = 0; power <= bytes.suffixes.length; power++) {\n min = Math.pow(bytes.base, power);\n max = Math.pow(bytes.base, power + 1);\n\n if (value === null || value === 0 || value >= min && value < max) {\n suffix += bytes.suffixes[power];\n\n if (min > 0) {\n value = value / min;\n }\n\n break;\n }\n }\n\n output = numeral._.numberToFormat(value, format, roundingFunction);\n\n return output + suffix;\n },\n unformat: function(string) {\n var value = numeral._.stringToNumber(string),\n power,\n bytesMultiplier;\n\n if (value) {\n for (power = decimal.suffixes.length - 1; power >= 0; power--) {\n if (numeral._.includes(string, decimal.suffixes[power])) {\n bytesMultiplier = Math.pow(decimal.base, power);\n\n break;\n }\n\n if (numeral._.includes(string, binary.suffixes[power])) {\n bytesMultiplier = Math.pow(binary.base, power);\n\n break;\n }\n }\n\n value *= (bytesMultiplier || 1);\n }\n\n return value;\n }\n });\n})();\n\n\n(function() {\n numeral.register('format', 'currency', {\n regexps: {\n format: /(\\$)/\n },\n format: function(value, format, roundingFunction) {\n var locale = numeral.locales[numeral.options.currentLocale],\n symbols = {\n before: format.match(/^([\\+|\\-|\\(|\\s|\\$]*)/)[0],\n after: format.match(/([\\+|\\-|\\)|\\s|\\$]*)$/)[0]\n },\n output,\n symbol,\n i;\n\n // strip format of spaces and $\n format = format.replace(/\\s?\\$\\s?/, '');\n\n // format the number\n output = numeral._.numberToFormat(value, format, roundingFunction);\n\n // update the before and after based on value\n if (value >= 0) {\n symbols.before = symbols.before.replace(/[\\-\\(]/, '');\n symbols.after = symbols.after.replace(/[\\-\\)]/, '');\n } else if (value < 0 && (!numeral._.includes(symbols.before, '-') && !numeral._.includes(symbols.before, '('))) {\n symbols.before = '-' + symbols.before;\n }\n\n // loop through each before symbol\n for (i = 0; i < symbols.before.length; i++) {\n symbol = symbols.before[i];\n\n switch (symbol) {\n case '$':\n output = numeral._.insert(output, locale.currency.symbol, i);\n break;\n case ' ':\n output = numeral._.insert(output, ' ', i + locale.currency.symbol.length - 1);\n break;\n }\n }\n\n // loop through each after symbol\n for (i = symbols.after.length - 1; i >= 0; i--) {\n symbol = symbols.after[i];\n\n switch (symbol) {\n case '$':\n output = i === symbols.after.length - 1 ? output + locale.currency.symbol : numeral._.insert(output, locale.currency.symbol, -(symbols.after.length - (1 + i)));\n break;\n case ' ':\n output = i === symbols.after.length - 1 ? output + ' ' : numeral._.insert(output, ' ', -(symbols.after.length - (1 + i) + locale.currency.symbol.length - 1));\n break;\n }\n }\n\n\n return output;\n }\n });\n})();\n\n\n(function() {\n numeral.register('format', 'exponential', {\n regexps: {\n format: /(e\\+|e-)/,\n unformat: /(e\\+|e-)/\n },\n format: function(value, format, roundingFunction) {\n var output,\n exponential = typeof value === 'number' && !numeral._.isNaN(value) ? value.toExponential() : '0e+0',\n parts = exponential.split('e');\n\n format = format.replace(/e[\\+|\\-]{1}0/, '');\n\n output = numeral._.numberToFormat(Number(parts[0]), format, roundingFunction);\n\n return output + 'e' + parts[1];\n },\n unformat: function(string) {\n var parts = numeral._.includes(string, 'e+') ? string.split('e+') : string.split('e-'),\n value = Number(parts[0]),\n power = Number(parts[1]);\n\n power = numeral._.includes(string, 'e-') ? power *= -1 : power;\n\n function cback(accum, curr, currI, O) {\n var corrFactor = numeral._.correctionFactor(accum, curr),\n num = (accum * corrFactor) * (curr * corrFactor) / (corrFactor * corrFactor);\n return num;\n }\n\n return numeral._.reduce([value, Math.pow(10, power)], cback, 1);\n }\n });\n})();\n\n\n(function() {\n numeral.register('format', 'ordinal', {\n regexps: {\n format: /(o)/\n },\n format: function(value, format, roundingFunction) {\n var locale = numeral.locales[numeral.options.currentLocale],\n output,\n ordinal = numeral._.includes(format, ' o') ? ' ' : '';\n\n // check for space before\n format = format.replace(/\\s?o/, '');\n\n ordinal += locale.ordinal(value);\n\n output = numeral._.numberToFormat(value, format, roundingFunction);\n\n return output + ordinal;\n }\n });\n})();\n\n\n(function() {\n numeral.register('format', 'percentage', {\n regexps: {\n format: /(%)/,\n unformat: /(%)/\n },\n format: function(value, format, roundingFunction) {\n var space = numeral._.includes(format, ' %') ? ' ' : '',\n output;\n\n if (numeral.options.scalePercentBy100) {\n value = value * 100;\n }\n\n // check for space before %\n format = format.replace(/\\s?\\%/, '');\n\n output = numeral._.numberToFormat(value, format, roundingFunction);\n\n if (numeral._.includes(output, ')')) {\n output = output.split('');\n\n output.splice(-1, 0, space + '%');\n\n output = output.join('');\n } else {\n output = output + space + '%';\n }\n\n return output;\n },\n unformat: function(string) {\n var number = numeral._.stringToNumber(string);\n if (numeral.options.scalePercentBy100) {\n return number * 0.01;\n }\n return number;\n }\n });\n})();\n\n\n(function() {\n numeral.register('format', 'time', {\n regexps: {\n format: /(:)/,\n unformat: /(:)/\n },\n format: function(value, format, roundingFunction) {\n var hours = Math.floor(value / 60 / 60),\n minutes = Math.floor((value - (hours * 60 * 60)) / 60),\n seconds = Math.round(value - (hours * 60 * 60) - (minutes * 60));\n\n return hours + ':' + (minutes < 10 ? '0' + minutes : minutes) + ':' + (seconds < 10 ? '0' + seconds : seconds);\n },\n unformat: function(string) {\n var timeArray = string.split(':'),\n seconds = 0;\n\n // turn hours and minutes into seconds and add them all up\n if (timeArray.length === 3) {\n // hours\n seconds = seconds + (Number(timeArray[0]) * 60 * 60);\n // minutes\n seconds = seconds + (Number(timeArray[1]) * 60);\n // seconds\n seconds = seconds + Number(timeArray[2]);\n } else if (timeArray.length === 2) {\n // minutes\n seconds = seconds + (Number(timeArray[0]) * 60);\n // seconds\n seconds = seconds + Number(timeArray[1]);\n }\n return Number(seconds);\n }\n });\n})();\n\nreturn numeral;\n}));\n","/**\n * Fast Fourier Transform module\n * 1D-FFT/IFFT, 2D-FFT/IFFT (radix-2)\n */\nvar FFT = (function(){\n var FFT; \n \n if(typeof exports !== 'undefined') {\n FFT = exports; // for CommonJS\n } else {\n FFT = {};\n }\n \n var version = {\n release: '0.3.0',\n date: '2013-03'\n };\n FFT.toString = function() {\n return \"version \" + version.release + \", released \" + version.date;\n };\n\n // core operations\n var _n = 0, // order\n _bitrev = null, // bit reversal table\n _cstb = null; // sin/cos table\n\n var core = {\n init : function(n) {\n if(n !== 0 && (n & (n - 1)) === 0) {\n _n = n;\n core._initArray();\n core._makeBitReversalTable();\n core._makeCosSinTable();\n } else {\n throw new Error(\"init: radix-2 required\");\n }\n },\n // 1D-FFT\n fft1d : function(re, im) {\n core.fft(re, im, 1);\n },\n // 1D-IFFT\n ifft1d : function(re, im) {\n var n = 1/_n;\n core.fft(re, im, -1);\n for(var i=0; i<_n; i++) {\n re[i] *= n;\n im[i] *= n;\n }\n },\n // 1D-IFFT\n bt1d : function(re, im) {\n core.fft(re, im, -1);\n },\n // 2D-FFT Not very useful if the number of rows have to be equal to cols\n fft2d : function(re, im) {\n var tre = [],\n tim = [],\n i = 0;\n // x-axis\n for(var y=0; y<_n; y++) {\n i = y*_n;\n for(var x1=0; x1<_n; x1++) {\n tre[x1] = re[x1 + i];\n tim[x1] = im[x1 + i];\n }\n core.fft1d(tre, tim);\n for(var x2=0; x2<_n; x2++) {\n re[x2 + i] = tre[x2];\n im[x2 + i] = tim[x2];\n }\n }\n // y-axis\n for(var x=0; x<_n; x++) {\n for(var y1=0; y1<_n; y1++) {\n i = x + y1*_n;\n tre[y1] = re[i];\n tim[y1] = im[i];\n }\n core.fft1d(tre, tim);\n for(var y2=0; y2<_n; y2++) {\n i = x + y2*_n;\n re[i] = tre[y2];\n im[i] = tim[y2];\n }\n }\n },\n // 2D-IFFT\n ifft2d : function(re, im) {\n var tre = [],\n tim = [],\n i = 0;\n // x-axis\n for(var y=0; y<_n; y++) {\n i = y*_n;\n for(var x1=0; x1<_n; x1++) {\n tre[x1] = re[x1 + i];\n tim[x1] = im[x1 + i];\n }\n core.ifft1d(tre, tim);\n for(var x2=0; x2<_n; x2++) {\n re[x2 + i] = tre[x2];\n im[x2 + i] = tim[x2];\n }\n }\n // y-axis\n for(var x=0; x<_n; x++) {\n for(var y1=0; y1<_n; y1++) {\n i = x + y1*_n;\n tre[y1] = re[i];\n tim[y1] = im[i];\n }\n core.ifft1d(tre, tim);\n for(var y2=0; y2<_n; y2++) {\n i = x + y2*_n;\n re[i] = tre[y2];\n im[i] = tim[y2];\n }\n }\n },\n // core operation of FFT\n fft : function(re, im, inv) {\n var d, h, ik, m, tmp, wr, wi, xr, xi,\n n4 = _n >> 2;\n // bit reversal\n for(var l=0; l<_n; l++) {\n m = _bitrev[l];\n if(l < m) {\n tmp = re[l];\n re[l] = re[m];\n re[m] = tmp;\n tmp = im[l];\n im[l] = im[m];\n im[m] = tmp;\n }\n }\n // butterfly operation\n for(var k=1; k<_n; k<<=1) {\n h = 0;\n d = _n/(k << 1);\n for(var j=0; j> 1;\n while(k <= j) {\n j -= k;\n k >>= 1;\n }\n j += k;\n _bitrev[i] = j;\n }\n },\n // makes trigonometiric function table\n _makeCosSinTable : function() {\n var n2 = _n >> 1,\n n4 = _n >> 2,\n n8 = _n >> 3,\n n2p4 = n2 + n4,\n t = Math.sin(Math.PI/_n),\n dc = 2*t*t,\n ds = Math.sqrt(dc*(2 - dc)),\n c = _cstb[n4] = 1,\n s = _cstb[0] = 0;\n t = 2*dc;\n for(var i=1; i{for(var t in n)bl(e,t,{get:n[t],enumerable:!0})};export*from\"gyromagnetic-ratio\";import{xEnsureFloat64 as hl}from\"ml-spectra-processing\";function L(e,n){let{windowData:t,output:s=new Float64Array(e)}=n,i=s.length;if(i!==t.length)throw new RangeError(\"window function and data should have the same length\");for(let o=0;oMath.exp(i*o)}function kn(e,n){let{dw:t,length:s}=n,{lineBroadening:i,lineBroadeningCenter:o}=e;if(o>1||o<0)throw new Error(\"The lineBroadeningCenter of gaussian shape should be inside of the window function: 0 - 1\");let a=Math.PI*i*t,r=o*(s-1);return m=>Math.exp(-1*((r-m)*a)**2)}function An(e,n){return Nt({exponent:1,...e},n)}function Nt(e,n){let{length:t}=n,{exponent:s,offset:i}=e;if(t===void 0)throw new Error(\"length options should exists for sineBell shape\");let o=Math.PI*i,a=Math.PI*(1-i)/(t-1);return r=>Math.sin(o+a*r)**s}function Rn(e,n){return Nt({...e,exponent:2},n)}function Nn(e,n){let{length:t,dw:s}=n,i=-s/e.lineBroadening;return o=>{let a=Math.exp(i*o);return a/(a**2+Math.exp(-i*(o-t))**2)}}function wn(e,n){let{kind:t,options:s}=e;switch(t){case\"exponential\":return Mn(s,n);case\"gaussian\":return kn(s,n);case\"sineBell\":return An(s,n);case\"sineSquare\":return Rn(s,n);case\"traf\":return Nn(s,n);default:throw new Error(`Unknown distribution ${t}`)}}function me(e){let{shapes:n,windowOptions:t}=e,s=new Float64Array(t.length).fill(1);for(let[i,o]of Object.entries(n)){let{options:a,apply:r=!0}=o;if(!r)continue;let m=jn(wn({kind:i,options:a},t),t.length);L(s,{windowData:m,output:s})}return Dl(s,xl(s),{output:s})}import{TopicMolecule as Nl}from\"openchemlib-utils\";import{Molecule as Al}from\"openchemlib\";import{agnes as Ol}from\"ml-hclust\";import{Matrix as Sl}from\"ml-matrix\";import{Molecule as En}from\"openchemlib\";function In(e,n={}){let t=n.output||structuredClone(e);for(let s of t)s.id=crypto.randomUUID();return t}var Hl={h:\"1H\",c:\"13C\"};function Tn(e){return Object.values(e).map(n=>Hl[n.toLowerCase()])}function vl(e){if(!e.from||!e.to)throw new Error(\"options from and to are mandatory\")}async function ce(e,n){vl(n);let{from:t,to:s,minLength:i=0,maxLength:o,predictions:a,includeDiagonal:r=!1}=n,m={...n.joinDistance,H:.05,C:.5},{correlationPaths:l=Pl(e,{fromLabel:t,toLabel:s,minLength:i,maxLength:o})}=n,c=e.getGroupedDiastereotopicAtomIDs()||[],u=[];for(let D of c||[])u.push({...D,pathInfo:structuredClone(l[D.atoms[0]])});let f=a[s],g=a[t];if(!g||!f)throw new Error(\"predictions are not availaible\");let d={x:g,y:f},p={x:{},y:{}};for(let D in p)for(let h of d[D].joinedSignals){if(!h.diaIDs)throw new Error(\"Signal has not diaIDs\");p[D][h.diaIDs[0]]=h}let C={};for(let D of u){let h=D.pathInfo;if(h.paths.length===0||!p.x[h.oclID])continue;let S=h.paths;for(let P of S){if(!p.y[l[P.to].oclID])continue;let H={x:h,y:l[P.to]},x=`${H.x.oclID}-${H.y.oclID}`;if(x===`${h.oclID}-${h.oclID}`||C[x])continue;let O={id:crypto.randomUUID(),x:0,y:0,z:100},v={id:crypto.randomUUID(),x:{delta:0,diaIDs:[],atoms:[]},y:{delta:0,diaIDs:[],atoms:[]},peaks:[]};for(let F of[\"x\",\"y\"]){let j=H[F].oclID;O[F]=p[F][j].delta,v[F].delta=p[F][j].delta,v[F].diaIDs=[j];let{atoms:k}=p[F][j];k&&(v[F].atoms=k)}v.peaks=[O],C[x]=v}}r&&jl(C,{paths:l,signalsByDiaID:p});let y=Object.values(C),b=Ml(y,{joinDistance:m,from:t,to:s});return{nuclei:Tn({from:t,to:s}),joinedSignals:y,signals:Fl(y),zones:b}}function Pl(e,n){let{fromLabel:t,toLabel:s,minLength:i,maxLength:o=Number.MAX_SAFE_INTEGER}=n,a=e.diaIDs,r=e.moleculeWithH,m=e.distanceMatrix,l=En.getAtomicNoFromLabel(t),c=En.getAtomicNoFromLabel(s),u=[];for(let f=0;f=i&&p<=o&&g.paths.push({from:f,to:d})}u.push(g)}return u}function Fl(e){let n=[];for(let t of e)for(let s of t.x.atoms||[])for(let i of t.y.atoms||[]){let o=structuredClone(t);o.x.atoms=[s],o.y.atoms=[i],n.push(o)}return In(n)}function jl(e,n){let{paths:t=[],signalsByDiaID:s}=n;for(let i of t){let o=i.oclID;if(!s.x[o]||e[`${o}-${o}`])continue;let a=s.x[o].delta,r=s.y[o].delta,m={id:crypto.randomUUID(),x:{delta:a,diaIDs:[o],atoms:s.x[o].atoms},y:{delta:r,diaIDs:[o],atoms:s.y[o].atoms},peaks:[{id:crypto.randomUUID(),x:a,y:r,z:1,minX:a,maxX:a,minY:r,maxY:r}]};e[`${i.oclID}-${i.oclID}`]=m}}function Ml(e,n){if(e.length===0)return[];let{joinDistance:t,from:s,to:i}=n,o=new Sl(e.map(d=>[d.x.delta,d.y.delta])),a=o.minColumn(0),r=o.maxColumn(0),m=o.minColumn(1),l=o.maxColumn(1);o.mulColumn(0,1/(r-a)),o.mulColumn(1,1/(l-m));let c=Math.hypot(t[s]/(r-a),t[i]/(l-m)),u=Ol(o.to2DArray(),{method:\"centroid\"}),f=zn([u],c),g=[];for(let d of f){let p=[];d.isLeaf&&p.push(e[d.index]);for(let C of d.children)for(let y of C.indices())p.push(e[y]);g.push({id:crypto.randomUUID(),...kl(p,{joinDistance:t,from:s,to:i}),signals:p})}return g}function kl(e,n){let{joinDistance:t,from:s,to:i}=n,o=Number.MAX_SAFE_INTEGER,a=Number.MAX_SAFE_INTEGER,r=Number.MIN_SAFE_INTEGER,m=Number.MIN_SAFE_INTEGER;for(let l of e)l.x.deltar&&(r=l.x.delta),l.y.deltam&&(m=l.y.delta);return{x:{from:o-t[s],to:r+t[s]},y:{from:a-t[i],to:m+t[i]}}}function zn(e,n){let t=e.length;for(let s=0;sn&&(e.push(...i.children),e.splice(s--,1))}return e.length=t&&c.paths.push({from:l,to:u});i.push(c)}return i}function Ln(e,n){async function t(s){let i=await fetch(n,{headers:{accept:\"application/json\",\"content-type\":\"application/json\"},method:\"POST\",body:JSON.stringify({molfile:s.toMolfile({version:3})})});if(i.status!==200){let a=await i.text();throw new Error(`${i.statusText}: ${a}`)}let{data:o}=await i.json();return{nucleus:o.nucleus,joinedSignals:o.joinedSignals??[],signals:o.signals??[],ranges:o.ranges??[]}}return Object.defineProperty(t,\"name\",{value:`default${e[0].toUpperCase()}${e.slice(1)}Predictor`}),t}var Xn=Ln(\"proton\",\"https://nmr-prediction.service.zakodium.com/v1/predict/proton\"),qn=Ln(\"carbon\",\"https://nmr-prediction.service.zakodium.com/v1/predict/carbon\");async function Ye(e,n,t){let{cache:s,predictorOptions:i}=t;if(typeof s==\"function\"){let a=await s(e);if(a)return a}let o=await n(e,i);return typeof s==\"function\"&&await s(e,o),o}async function Yn(e,n={}){let{predictor:t=qn,predictorOptions:s={},cache:i}=n;return e.molecule.getAllAtoms()===0?{nucleus:\"13C\",joinedSignals:[],signals:[],ranges:[]}:Ye(e,t,{cache:i,predictorOptions:s})}async function Wn(e,n){let{minLength:t=2,maxLength:s=3,joinDistance:i}=n;return ce(e,{...n,from:\"H\",to:\"C\",minLength:t,maxLength:s,joinDistance:i})}async function Zn(e,n){let{minLength:t=1,maxLength:s=1,joinDistance:i}=n;return ce(e,{...n,from:\"H\",to:\"C\",minLength:t,maxLength:s,joinDistance:i})}async function Un(e,n={}){let{predictor:t=Xn,cache:s,predictorOptions:i={}}=n;return e.molecule.getAllAtoms()===0?{nucleus:\"1H\",joinedSignals:[],signals:[],ranges:[]}:Ye(e,t,{cache:s,predictorOptions:i})}function wt(e,n){if(e in n)return!0;let t=\"hsqc\"in n||\"hmbc\"in n;return e===\"C\"?t:e===\"H\"?t||\"cosy\"in n:!1}var wl={cosy:{predictor:Bn,required:[\"H\"],message:\"Proton prediction is missing so COSY could not be generated.\"},hsqc:{predictor:Zn,required:[\"H\",\"C\"],message:\"Carbon and / or proton prediction is missing so HSQC could not be generated.\"},hmbc:{predictor:Wn,required:[\"H\",\"C\"],message:\"Carbon and / or proton prediction is missing so HMBC could not be generated.\"}};async function V(e,n={}){let{logger:t,joinDistance:s={H:.05,C:.5},predictOptions:i={H:{},C:{},hsqc:{},hmbc:{},cosy:{}}}=n,o=new Nl(e),a=wt(\"H\",i)?await Un(o,{...i?.H}):void 0,r=wt(\"C\",i)?await Yn(o,i?.C):void 0,m={},l=o.diaIDs,c={};Vn(a,l)&&(c.H=a,\"H\"in i&&(m.proton=a)),Vn(r,l)&&(c.C=r,\"C\"in i&&(m.carbon=r));let f=[\"cosy\",\"hsqc\",\"hmbc\"].filter(d=>d in i).map(async d=>{let{predictor:p,required:C,message:y}=wl[d];if(C.every(b=>b in c)){let b=await p(o,{predictions:c,...i[d],joinDistance:s});return{key:d,result:b}}else t&&t.warn(y);return{key:d,result:void 0}}),g=await Promise.all(f);for(let{key:d,result:p}of g)p&&(m[d]=p);return{diaIDs:o.diaIDs,hoses:o.hoseCodes,distanceMatrix:o.distanceMatrix,moleculeWithHydrogens:o.moleculeWithH,groupedDiaIDs:o.getGroupedDiastereotopicAtomIDs()||[],molfile:o.toMolfile(),molfileWithH:o.toMolfileWithH(),spectra:m}}function Vn(e,n=[]){if(!e||e.ranges.length===0)return!1;let{signals:t}=e;for(let s of t)if(!s.diaIDs?.every(o=>n.includes(o)))return!1;return!0}import{xIsMonotonic as om,xRescale as am,xSequentialFillFromTo as rm}from\"ml-spectra-processing\";var 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My(e,n={}){let{restrictionByCS:t={},justAssign:s,minScore:i=.01,maxSolutions:o=10,nbAllowedUnAssigned:a,timeout:r=6e3,predictionOptions:m={},predictions:l={},correlation:c={}}=n,{tolerance:u={H:.2,C:1},useChemicalShiftScore:f=!1,chemicalShiftRestriction:g=!0}=t,d=e.molecule;if(!d)throw new Error(\"It is needed a molecule to assign\");d.addImplicitHydrogens(),Jm(d);let p=Vs(e.spectra),{targets:C,correlations:y}=$s(p,c),{assignmentOrder:b}=Qt(y,s);return await Zs({restrictionByCS:{tolerance:u,useChemicalShiftScore:f,chemicalShiftRestriction:g},spectra:p,molecule:d,timeout:r,minScore:i,maxSolutions:o,assignmentOrder:b,nbAllowedUnAssigned:a,correlations:y,targets:C,predictionOptions:m,predictions:l})}var $m={unspecified:\"Unspecified\",unknown:\"Unknown\",signal:\"Signal\",residualSolvent:\"Residual solvent\",reference:\"Reference\",nmrSolvent:\"NMR Solvent\",impurity:\"Impurity\",standard:\"Standard\",artifact:\"Artifact\",p1:\"Product 1\",p2:\"Product 2\",p3:\"Product 3\"};function 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n)[\"nmrSolvent\",\"residualSolvent\"].includes(c.kind||\"\")?s.push(c):i.push(c);if(s.length===0)return{peaks:i,solventSignals:[]};let{solvent:a,...r}=t,l=le(e,s,{...r,frequencyCluster:50}).map(c=>({...c,assignment:a}));return{peaks:i,solventSignals:l}}import{xMaxValue as fp}from\"ml-spectra-processing\";var 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gp(e,n){let{frequency:t,solventFeatures:s,tolerance:i=.1,minScore:o=.99}=n,{delta:a,js:r}=s.ranges[0].signals[0],m=hp(r[0],t),l=e.map(yp),c=pp(l,m,{delta:a,minScore:o,tolerance:i});if(c)for(let u of c.peaks){let f=l.findIndex(g=>g.id===u.id);l[f].kind=\"nmrSolvent\"}return l}function Lr(e,n){let{solvent:t=\"\",logger:s,...i}=n,o=dp(t);return o?gp(e,{...i,solventFeatures:o}):(s&&s.error(`There is not support for solvent: ${t}`),e)}function dp(e){let n=e.toLocaleLowerCase();return n===\"(cd3)2so\"&&(n=\"dmso\"),zr.find(s=>s.solvent===n)}function pp(e,n,t){let{delta:s,minScore:i=.7,tolerance:o=.1}=t,a=n.toSorted((g,d)=>g.x-d.x),[r,m=r]=[a[0],a.at(-1)],l=s+r.x-o,c=s+m.x+o,u=e.filter(g=>g.x>=l&&g.x<=c).toSorted((g,d)=>g.x-d.x),f;for(let g of u){let d=a.findIndex(H=>H.x===0);if(d===-1||Math.abs(g.x-s)>=o)continue;let p=a.slice(0,d),C=a.slice(d+1),y=u.filter(H=>H.xH.x>g.x),D=Br(p,y,g,o),h=Br(C,b,g,o);if(!D||!h)continue;let 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s==\"number\"&&s!==-1?e.filters=[...e.filters.slice(0,s+1),o,...e.filters.slice(s+1)]:e.filters.push(o)}function At(e,n,t){let{name:s,value:i}=n,o=Date.now();re[s].apply(e,i);let a=Date.now()-o;t?.debug(`Filter: ${s} takes ${a} ms`)}export{De as Filters1D,cl as Filters1DManager,re as Filters2D,Cl as Filters2DManager,ne as MultiplicityPatterns,Ho as RangeUtilities,Dn as XYAutoPeaksPickingWindowSizeError,nn as apodizationXReIm,L as apodize,at as autoPhaseCorrection,is as calculateRelativeFrequency,r3 as carbonImpurities,rn as checkIntegralKind,FD as checkMultiplicity,zs as correlationApi,tt as couplingACSPatterns,en as couplingPatterns,me as createApodizationWindowData,xc as default1DApodization,ae as ensureValidSignalKind,$o as fftDirectDimension,ia as fftIndirectDimension,$a as findMultiplet,ar as findPivot,YC as get13CAssignments,rt as get1DErrorFactor,BC as get1HAssignments,Za as get2DErrorFactor,My as getAssignments,Z6 as getBaselineAnchors,S6 as getBaselineZones,go as getBaselineZonesByDietrich,h3 as getDatabase,sC as getFrequency,ln as getPeakDelta,Ne as getRelativeFrequency,xe as getShiftX,rr as isEmptyMolecule,mx as isMolfileNotEmpty,Co as isRangeExists,Ua as isZoneExists,yo as mapIntegrals,bo as mapPeaks,Do as mapRanges,Ga as mapZones,xx as markSolventPeaks,hD as matrixToBoxPlot,SD as matrixToStocsy,FH as normalizeNucleus,uH as optimizeSignals,o3 as parseCT,ZD as peakToXY,Qa as peaksToFID,Ka as peaksToXY,V as predict,HC as predictSpectra,ui as protonImpurities,nt as rangeFromSignal,Nx as rangesToACS,Ix as rangesToTSV,Lx as rangesToXY,ye as reimComplexBackwardLinearPrediction,AH as resurrect,Ht as signalJoinCouplings,$m as signalKindLabelMapping,et as signalKindsToInclude,ut as signalMultiplicityPattern,ss as signals2DToZ,pH as signalsJoin,xH as signalsToFID,SH as signalsToRanges,Ue as signalsToXY,Qn as simulate1D,ir as solventSuppression,Ce as splitPatterns,RD as translateMultiplet,xo as updateIntegralsRelativeValues,y8 as updatePeaksRelativeValues,So as updateRanges,Oo as updateRangesRelativeValues,wH as xBackwardLinearPrediction,Yi as xForwardLinearPrediction,zH as xreimMultipletAnalysis,to as xreimTrim,te as xyAutoPeaksPicking,FO as xyAutoRangesPicking,ji as xyBaselineCalculation,RO as xyCalculateDiffussionCoefficient,EO as xyCalculateT1,sS as xyGetPivot,XO as xyKineticFirstOrder,UO as xyPeaksOptimization,QO as xyPeaksOptimizationByStages,Fa as xyzAutoPhaseCorrection,nl as xyzAutoSignalsPicking,OS as xyzAutoZonesPicking,Cn as xyzPhaseCorrection,PS as xyzZoneIntegration,Ft as xyzZonesIntegration};\n","/**\n * Returns a list of likely nuclei based on an experiment string.\n * This is really a hypothesis and should not be used as ground truth.\n * @param experiment - The experiment type string.\n * @returns An array of nucleus strings.\n */\nexport function getNucleusFrom2DExperiment(experiment) {\n if (typeof experiment !== 'string') {\n return [];\n }\n const lowerExperiment = experiment.toLowerCase();\n if (lowerExperiment.includes('jres')) {\n return ['1H'];\n }\n if (lowerExperiment.includes('hmbc') || lowerExperiment.includes('hsqc')) {\n return ['1H', '13C'];\n }\n return ['1H', '1H'];\n}\n//# sourceMappingURL=getNucleusFrom2DExperiment.js.map","/**\n * Returns an experiment string based on a pulse sequence.\n * @param meta - Metadata object or pulse string.\n * @param info - Info object containing $SPECTYP.\n * @returns The experiment type string.\n */\nexport function getSpectrumType(meta = {}, info = {}) {\n if (meta === null)\n meta = {};\n if (typeof meta === 'string') {\n meta = { pulse: meta };\n }\n let spectyp;\n if (Array.isArray(info.$SPECTYP)) {\n spectyp = (info.$SPECTYP[0] || '')\n .replace(/^<(?.*)>$/, '$1')\n .toLowerCase();\n }\n else {\n spectyp = (info.$SPECTYP || '')\n .replace(/^<(?.*)>$/, '$1')\n .toLowerCase();\n }\n if (spectyp)\n return spectyp;\n const pulse = meta.pulse;\n if (typeof pulse !== 'string') {\n return '';\n }\n const lowerPulse = pulse.toLowerCase();\n if (lowerPulse.includes('zg')) {\n return '1d';\n }\n if (lowerPulse.includes('hsqct') ||\n (lowerPulse.includes('invi') &&\n (lowerPulse.includes('ml') || lowerPulse.includes('di')))) {\n return 'hsqctocsy';\n }\n if (lowerPulse.includes('hsqc') || lowerPulse.includes('invi')) {\n return 'hsqc';\n }\n if (lowerPulse.includes('hmbc') ||\n (lowerPulse.includes('inv4') && lowerPulse.includes('lp'))) {\n return 'hmbc';\n }\n if (lowerPulse.includes('cosy')) {\n return 'cosy';\n }\n if (lowerPulse.includes('jres')) {\n return 'jres';\n }\n if (lowerPulse.includes('tocsy') ||\n lowerPulse.includes('mlev') ||\n lowerPulse.includes('dipsi')) {\n return 'tocsy';\n }\n if (lowerPulse.includes('noesy')) {\n return 'noesy';\n }\n if (lowerPulse.includes('roesy')) {\n return 'roesy';\n }\n if (lowerPulse.includes('dept')) {\n return 'dept';\n }\n if (lowerPulse.includes('jmod') || lowerPulse.includes('apt')) {\n return 'aptjmod';\n }\n return '';\n}\n//# sourceMappingURL=getSpectrumType.js.map","import { getNucleusFrom2DExperiment } from \"./getNucleusFrom2DExperiment.js\";\nimport { getSpectrumType } from \"./getSpectrumType.js\";\n/**\n * Extracts NMR metadata from parsed JCAMP info and meta records.\n * @param info - The info record from parsed JCAMP.\n * @param meta - The meta record from parsed JCAMP.\n * @returns The extracted NMR metadata.\n */\nexport function getMetaData(info, meta) {\n const metadata = {\n dimension: 1,\n nucleus: [],\n isFid: false,\n isFt: false,\n isComplex: false,\n };\n maybeAdd(metadata, 'title', info.TITLE);\n maybeAdd(metadata, 'solvent', info['.SOLVENTNAME']);\n maybeAdd(metadata, 'pulse', info['.PULSESEQUENCE'] || info['.PULPROG'] || meta.PULPROG);\n maybeAdd(metadata, 'experiment', getSpectrumType(metadata, info));\n maybeAdd(metadata, 'temperature', Number.parseFloat(asString(meta.TE || info['.TE'])));\n maybeAdd(metadata, 'frequency', Number.parseFloat(asString(info['.OBSERVEFREQUENCY'])));\n maybeAdd(metadata, 'type', info.DATATYPE);\n maybeAdd(metadata, 'probe', meta.PROBHD);\n if (meta.FNTYPE !== undefined) {\n maybeAdd(metadata, 'acquisitionMode', Number.parseInt(asString(meta.FNTYPE), 10));\n }\n maybeAdd(metadata, 'expno', Number.parseInt(asString(meta.EXPNO), 10));\n if (metadata.type) {\n if (metadata.type.toUpperCase().includes('FID')) {\n metadata.isFid = true;\n }\n else if (metadata.type.toUpperCase().includes('SPECTRUM')) {\n metadata.isFt = true;\n }\n }\n const nucleus = info['.NUCLEUS'];\n const observeNucleus = info['.OBSERVENUCLEUS'];\n if (typeof nucleus === 'string') {\n metadata.nucleus = nucleus.split(',').map((nuc) => nuc.trim());\n }\n else if (typeof observeNucleus === 'string') {\n metadata.nucleus = [observeNucleus.replaceAll(/[^A-Za-z0-9]/g, '')];\n }\n else {\n metadata.nucleus = getNucleusFrom2DExperiment(metadata.experiment);\n }\n metadata.dimension = metadata.nucleus.length;\n const symbol = info.SYMBOL;\n if (typeof symbol === 'string') {\n const symbols = symbol.split(/[, ]+/);\n if (symbols.includes('R') && symbols.includes('I')) {\n metadata.isComplex = true;\n }\n }\n if (meta.DATE) {\n try {\n metadata.date = new Date(Number(meta.DATE) * 1000).toISOString();\n }\n catch {\n // eslint-disable-next-line no-console\n console.error('Could not parse date from meta.DATE');\n }\n }\n return metadata;\n}\nfunction asString(value) {\n if (typeof value === 'string')\n return value;\n if (typeof value === 'number')\n return String(value);\n return '';\n}\nfunction maybeAdd(object, name, value) {\n if (value !== undefined) {\n if (typeof value === 'string') {\n if (value.startsWith('<') && value.endsWith('>')) {\n object[name] = value.slice(1, -1).trim();\n }\n else {\n object[name] = value.trim();\n }\n }\n else {\n object[name] = value;\n }\n }\n}\n//# sourceMappingURL=getMetaData.js.map","import { convert } from 'jcampconverter';\nimport { xyAutoRangesPicking } from 'nmr-processing';\nimport { getMetaData } from \"./getMetaData.js\";\nconst defaultRangesOptions = {\n integrationSum: 100,\n clean: 0.4,\n compile: true,\n integralType: 'sum',\n};\nconst defaultPeaksOptions = {\n thresholdFactor: 0.85,\n};\n/**\n * Returns a metadata object from JCAMP data.\n * @param jcampData - The JCAMP string or ArrayBuffer.\n * @param options - Options for parsing and range computation.\n * @returns The extracted NMR metadata.\n */\nexport function fromJcamp(jcampData, options = {}) {\n const { computeRanges = false } = options;\n const parsedJcamp = convert(jcampData, {\n keepRecordsRegExp: /.*/,\n canonicMetadataLabels: true,\n withoutXY: false,\n }).flatten[0];\n if (!parsedJcamp) {\n throw new Error('Failed to parse JCAMP data');\n }\n const metadata = getMetaData(parsedJcamp.info, parsedJcamp.meta);\n if (computeRanges &&\n metadata.isFt &&\n metadata.dimension === 1 &&\n metadata.nucleus[0] === '1H') {\n const { ranges = {}, impurities = {}, peakPicking = {} } = options;\n const rangesOptions = { ...defaultRangesOptions, ...ranges };\n const peaksOptions = {\n ...defaultPeaksOptions,\n frequency: metadata.frequency || 400,\n ...peakPicking,\n };\n if (metadata.solvent) {\n impurities.solvent = metadata.solvent;\n }\n const spectrumData = parsedJcamp.spectra[0]?.data;\n if (spectrumData) {\n const x = Float64Array.from(spectrumData.x);\n const y = Float64Array.from(spectrumData.y);\n // xyAutoRangesPicking requires monotone increasing x values\n const first = x[0];\n const last = x.at(-1);\n if (x.length > 1 &&\n first !== undefined &&\n last !== undefined &&\n first > last) {\n x.reverse();\n y.reverse();\n }\n metadata.range = xyAutoRangesPicking({ x, y }, {\n impurities,\n ranges: rangesOptions,\n peakPicking: peaksOptions,\n });\n }\n }\n return metadata;\n}\nexport { getSpectrumType } from \"./getSpectrumType.js\";\nexport { getMetaData as fromInfo } from \"./getMetaData.js\";\n//# sourceMappingURL=index.js.map","import { fromJcamp } from 'nmr-metadata';\nimport { findBy, getExtension, getReference, getTargetProperty, getTextContent, } from \"../common.js\";\nconst isFid = /[^a-z]fid[^a-z]/i;\nconst replaceFid = /[^a-z]fid[^a-z]?/i;\nconst nmr = {\n find: findBy(getNmrReference),\n getProperty: (filename) => {\n const extension = getExtension(filename);\n if ((extension === 'jdx' || extension === 'dx' || extension === 'jcamp') &&\n isFid.test(filename)) {\n return 'jcampFID';\n }\n return getTargetProperty(filename);\n },\n process: (filename, content) => {\n const extension = getExtension(filename);\n let metaData = {};\n if (extension === 'jdx' || extension === 'dx' || extension === 'jcamp') {\n const textContent = getTextContent(content);\n metaData = fromJcamp(textContent);\n }\n return metaData;\n },\n jpath: ['spectra', 'nmr'],\n};\nexport default nmr;\n/**\n * An experiment is delivered as a FID and its Fourier transform, so the `fid`\n * marker is not part of its identity.\n * @param filename - The file path.\n * @returns The reference string.\n */\nfunction getNmrReference(filename) {\n const reference = getReference(filename);\n if (isFid.test(filename)) {\n return reference.replace(replaceFid, '');\n }\n return reference;\n}\n//# sourceMappingURL=nmr.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst oan = {\n jpath: ['spectra', 'oan'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default oan;\n//# sourceMappingURL=oan.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst pelletHardness = {\n jpath: ['spectra', 'pelletHardness'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default pelletHardness;\n//# sourceMappingURL=pelletHardness.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst permeability = {\n jpath: ['spectra', 'permeability'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default permeability;\n//# sourceMappingURL=permeability.js.map","const physical = {\n jpath: ['physical'],\n getEmpty() {\n return {};\n },\n};\nexport default physical;\n//# sourceMappingURL=physical.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst raman = {\n jpath: ['spectra', 'raman'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default raman;\n//# sourceMappingURL=raman.js.map","const stock = {\n jpath: ['stock'],\n getEmpty() {\n return {};\n },\n};\nexport default stock;\n//# sourceMappingURL=stock.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst thermogravimetricAnalysis = {\n jpath: ['spectra', 'thermogravimetricAnalysis'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default thermogravimetricAnalysis;\n//# sourceMappingURL=thermogravimetricAnalysis.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst uv = {\n jpath: ['spectra', 'uv'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default uv;\n//# sourceMappingURL=uv.js.map","import { basenameFind, getTargetProperty } from \"../common.js\";\nconst video = {\n jpath: ['video'],\n find: basenameFind,\n getProperty: getTargetProperty,\n};\nexport default video;\n//# sourceMappingURL=video.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst voidVolume = {\n jpath: ['spectra', 'voidVolume'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default voidVolume;\n//# sourceMappingURL=voidVolume.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst xps = {\n jpath: ['spectra', 'xps'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default xps;\n//# sourceMappingURL=xps.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst xray = {\n jpath: ['spectra', 'xray'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default xray;\n//# sourceMappingURL=xray.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst xrd = {\n jpath: ['spectra', 'xrd'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default xrd;\n//# sourceMappingURL=xrd.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst xrf = {\n jpath: ['spectra', 'xrf'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default xrf;\n//# sourceMappingURL=xrf.js.map","import { getMetaFromJcamp, getTargetProperty, referenceFind, } from \"../common.js\";\nconst zetaPotential = {\n jpath: ['spectra', 'zetaPotential'],\n find: referenceFind,\n getProperty: getTargetProperty,\n process: getMetaFromJcamp,\n};\nexport default zetaPotential;\n//# sourceMappingURL=zetaPotential.js.map","import reactionGeneral from \"./reaction/general.js\";\nimport sampleChromatogram from \"./sample/chromatogram.js\";\nimport sampleCyclicVoltammetry from \"./sample/cyclicVoltammetry.js\";\nimport sampleDifferentialCentrifugalSedimentation from \"./sample/differentialCentrifugalSedimentation.js\";\nimport sampleDifferentialScanningCalorimetry from \"./sample/differentialScanningCalorimetry.js\";\nimport sampleDls from \"./sample/dls.js\";\nimport sampleElementAnalysis from \"./sample/elementAnalysis.js\";\nimport sampleGenbank from \"./sample/genbank.js\";\nimport sampleGeneral from \"./sample/general.js\";\nimport sampleHgPorosimetry from \"./sample/hgPorosimetry.js\";\nimport sampleIcp from \"./sample/icp.js\";\nimport sampleImage from \"./sample/image.js\";\nimport sampleIr from \"./sample/ir.js\";\nimport sampleIsotherm from \"./sample/isotherm.js\";\nimport sampleIv from \"./sample/iv.js\";\nimport sampleMass from \"./sample/mass.js\";\nimport sampleNmr from \"./sample/nmr.js\";\nimport sampleOan from \"./sample/oan.js\";\nimport samplePelletHardness from \"./sample/pelletHardness.js\";\nimport samplePermeability from \"./sample/permeability.js\";\nimport samplePhysical from \"./sample/physical.js\";\nimport sampleRaman from \"./sample/raman.js\";\nimport sampleStock from \"./sample/stock.js\";\nimport sampleThermogravimetricAnalysis from \"./sample/thermogravimetricAnalysis.js\";\nimport sampleUv from \"./sample/uv.js\";\nimport sampleVideo from \"./sample/video.js\";\nimport sampleVoidVolume from \"./sample/voidVolume.js\";\nimport sampleXps from \"./sample/xps.js\";\nimport sampleXray from \"./sample/xray.js\";\nimport sampleXrd from \"./sample/xrd.js\";\nimport sampleXrf from \"./sample/xrf.js\";\nimport sampleZetaPotential from \"./sample/zetaPotential.js\";\nconst lib = {\n reaction: {\n general: reactionGeneral,\n },\n sample: {\n chromatogram: sampleChromatogram,\n cyclicVoltammetry: sampleCyclicVoltammetry,\n differentialCentrifugalSedimentation: sampleDifferentialCentrifugalSedimentation,\n differentialScanningCalorimetry: sampleDifferentialScanningCalorimetry,\n dls: sampleDls,\n elementAnalysis: sampleElementAnalysis,\n genbank: sampleGenbank,\n general: sampleGeneral,\n hgPorosimetry: sampleHgPorosimetry,\n icp: sampleIcp,\n image: sampleImage,\n ir: sampleIr,\n isotherm: sampleIsotherm,\n iv: sampleIv,\n mass: sampleMass,\n nmr: sampleNmr,\n oan: sampleOan,\n pelletHardness: samplePelletHardness,\n permeability: samplePermeability,\n physical: samplePhysical,\n raman: sampleRaman,\n stock: sampleStock,\n thermogravimetricAnalysis: sampleThermogravimetricAnalysis,\n uv: sampleUv,\n video: sampleVideo,\n voidVolume: sampleVoidVolume,\n xps: sampleXps,\n xray: sampleXray,\n xrd: sampleXrd,\n xrf: sampleXrf,\n zetaPotential: sampleZetaPotential,\n },\n};\nexport default lib;\n//# sourceMappingURL=index.js.map","import defaultType from \"./types/default.js\";\nimport lib from \"./types/index.js\";\n/**\n * Get a type processor by type name, optionally scoped to a kind.\n * @param type - The type name.\n * @param kind - Optional kind to scope the search.\n * @param custom - Optional custom overrides.\n * @returns The type processor.\n */\nexport function getType(type, kind, custom) {\n if (kind) {\n if (lib[kind]?.[type]) {\n return { ...defaultType, ...lib[kind][type], ...custom };\n }\n }\n else {\n for (const kindKey in lib) {\n if (lib[kindKey]?.[type]) {\n return {\n ...defaultType,\n ...lib[kindKey].default,\n ...lib[kindKey][type],\n ...custom,\n };\n }\n }\n }\n return { ...defaultType };\n}\n/**\n * Get all type processors for a given kind.\n * @param kind - The kind name.\n * @param custom - Optional custom overrides.\n * @returns Array of type processors.\n */\nexport function getAllTypes(kind, custom) {\n const all = [];\n for (const type in lib[kind]) {\n if (type !== 'default') {\n all.push(getType(type, kind, custom));\n }\n }\n return all;\n}\n//# sourceMappingURL=typeUtils.js.map","/*\n Modified from https://github.com/justmoon/node-extend\n Copyright (c) 2014 Stefan Thomas\n */\nfunction isPlainObject(obj) {\n if (!obj || Object.prototype.toString.call(obj) !== '[object Object]') {\n return false;\n }\n const typedObj = obj;\n const hasOwnConstructor = Object.hasOwn(typedObj, 'constructor');\n const hasIsPrototypeOf = typedObj.constructor?.prototype &&\n Object.hasOwn(typedObj.constructor.prototype, 'isPrototypeOf');\n // Not own constructor property must be Object\n if (typedObj.constructor && !hasOwnConstructor && !hasIsPrototypeOf) {\n return false;\n }\n // Own properties are enumerated firstly, so to speed up,\n // if last one is own, then all properties are own.\n let key;\n for (key in typedObj) {\n /**/\n }\n return key === undefined || Object.hasOwn(typedObj, key);\n}\n/**\n * Deep merge with defaults behavior: only set properties that are undefined on the target.\n * @param args - Arguments: optional boolean (deep), target, and sources.\n * @returns The merged target.\n */\nexport default function defaults(...args) {\n let target;\n let i = 1;\n const length = args.length;\n let deep = false;\n // Handle a deep copy situation\n if (typeof args[0] === 'boolean') {\n deep = args[0];\n target = (args[1] || {});\n // skip the boolean and the target\n i = 2;\n }\n else if ((typeof args[0] !== 'object' && typeof args[0] !== 'function') ||\n args[0] == null) {\n target = {};\n }\n else {\n target = args[0];\n }\n for (; i < length; ++i) {\n const options = args[i];\n // Only deal with non-null/undefined values\n if (options != null) {\n // Extend the base object\n for (const name in options) {\n const copy = options[name];\n // Prevent never-ending loop\n if (target !== copy) {\n // Recurse if we're merging plain objects or arrays\n let copyIsArray = false;\n if (deep &&\n copy &&\n (isPlainObject(copy) || (copyIsArray = Array.isArray(copy)))) {\n const src = target[name];\n let clone;\n if (copyIsArray) {\n clone = src && Array.isArray(src) ? src : [];\n }\n else {\n clone = src && isPlainObject(src) ? src : {};\n }\n // Never move original objects, clone them\n if (target[name] === undefined) {\n target[name] = defaults(deep, clone, copy);\n }\n else {\n defaults(deep, clone, copy);\n }\n // Don't bring in undefined values\n }\n else if (copy !== undefined && target[name] === undefined) {\n target[name] = copy;\n }\n }\n }\n }\n }\n // Return the modified object\n return target;\n}\n//# sourceMappingURL=defaults.js.map","import { getAllTypes, getType } from \"./typeUtils.js\";\nimport common, { getContent } from \"./types/common.js\";\nimport defaults from \"./util/defaults.js\";\nconst elnPlugin = {\n util: common,\n /**\n * Process a file and add its metadata to the document.\n * @param type - The type name.\n * @param doc - The document object to update.\n * @param content - The content data.\n * @param customMetadata - Optional custom metadata to merge.\n * @param options - Processing options.\n * @returns The updated document.\n */\n process(type, doc, content, customMetadata, options = {}) {\n const filename = content.filename;\n if (!filename) {\n throw new Error('Content must have a filename');\n }\n const typeProcessor = getType(type);\n const arr = createFromJpath(doc, typeProcessor);\n const entry = typeProcessor.find?.(arr, filename);\n const property = typeProcessor.getProperty?.(filename, content);\n if (property === undefined) {\n throw new Error(`Could not get property of ${filename} (type ${type}`);\n }\n const metadata = typeProcessor.process?.(filename, content) ?? {};\n metadata[property] = {\n filename: elnPlugin.getFilename(type, filename),\n };\n if (options.keepContent) {\n metadata[property].data = getContent(content, property);\n }\n if (entry) {\n Object.assign(entry, metadata, customMetadata);\n }\n else {\n Object.assign(metadata, customMetadata);\n arr.push(metadata);\n }\n return doc;\n },\n /**\n * Get the type data from a document.\n * @param type - The type name.\n * @param doc - The document object.\n * @param kind - Optional kind scope.\n * @returns The type data or undefined.\n */\n getType(type, doc, kind) {\n const typeProcessor = getType(type, kind);\n return getFromJpath(doc, typeProcessor);\n },\n /**\n * Get the full filename including jpath prefix.\n * @param type - The type name.\n * @param filename - The base filename.\n * @returns The full path.\n */\n getFilename(type, filename) {\n const match = /[^/]*$/.exec(filename);\n if (match)\n filename = match[0];\n const typeProcessor = getType(type);\n const jpath = typeProcessor.jpath;\n if (!jpath)\n throw new Error('No such type or no jpath');\n return jpath.concat(filename).join('/');\n },\n /**\n * Get an empty document structure for a given kind.\n * @param kind - The kind name.\n * @param content - Optional existing content to populate.\n * @returns The empty document.\n */\n getEmpty(kind, content) {\n const typeProcessors = getAllTypes(kind);\n if (!content)\n content = {};\n for (const typeProcessor of typeProcessors) {\n createFromJpath(content, typeProcessor);\n }\n return content;\n },\n /**\n * Apply default values to a document.\n * @param kind - The kind name.\n * @param content - The document to apply defaults to.\n * @returns The document with defaults applied.\n */\n defaults(kind, content) {\n const empty = elnPlugin.getEmpty(kind);\n defaults(true, content, empty);\n return content;\n },\n};\nfunction createFromJpath(doc, typeProcessor) {\n const jpath = typeProcessor.jpath;\n if (!jpath) {\n throw new Error('createFromJpath: undefined jpath argument');\n }\n let current = doc;\n for (const [index, key] of jpath.entries()) {\n if (current[key] === undefined) {\n if (index !== jpath.length - 1) {\n current[key] = {};\n }\n else {\n current[key] = typeProcessor.getEmpty?.() ?? [];\n }\n }\n current = current[key];\n }\n if (jpath.length === 0) {\n Object.assign(current, typeProcessor.getEmpty?.() ?? []);\n }\n return current;\n}\nfunction getFromJpath(doc, typeProcessor) {\n if (!doc)\n return undefined;\n const jpath = typeProcessor.jpath;\n if (!jpath)\n throw new Error('getFromJpath: undefined jpath argument');\n let current = doc;\n for (const key of jpath) {\n if (current[key] === undefined) {\n return undefined;\n }\n current = current[key];\n }\n return current;\n}\nexport default elnPlugin;\n//# 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