dataframe-builder
Version:
A powerful TypeScript/JavaScript library for generating realistic sample data, test data, and mock data. Create dataframes with customizable column types, mathematical series, and random distributions. Perfect for testing, development, and data visualizat
149 lines (148 loc) • 6.1 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.combineSeries = exports.generateTrigonometricSeries = exports.generateExponentialSeries = exports.generateLogSeries = exports.generateQuadraticSeries = exports.generateLinearSeries = void 0;
function addNoise(value, standardDeviation, precision) {
const noise = standardDeviation * (Math.random() * 2 - 1);
const noisyValue = value + noise;
return precision && Number.isInteger(precision) && precision > 0
? Number(noisyValue.toFixed(precision))
: noisyValue;
}
function addOutlier(value, probability, multiplier) {
return Math.random() < probability ? value * multiplier : value;
}
/**
* Generates a linear series of numbers following the equation y = mx + b
* @param options - Configuration options for the series
* @returns Array of numbers following a linear pattern
* @example
* // Generate a simple linear series from 1 to 5
* const series = generateLinearSeries({ start: 1, end: 5, step: 1 });
* // Result: [1, 2, 3, 4, 5]
*
* // Generate a linear series with custom slope and y-intercept
* const series = generateLinearSeries({
* start: 0,
* end: 5,
* step: 1,
* slope: 2,
* yIntercept: 1
* });
* // Result: [1, 3, 5, 7, 9, 11]
*/
function generateLinearSeries({ start, end, step, standardDeviation = 0, precision = 3, yIntercept = 0, slope = 1, outlierProbability = 0, outlierMultiplier = 3, }) {
const series = [];
for (let i = start; i <= end; i += step) {
let value = yIntercept + slope * i;
value = addNoise(value, standardDeviation, precision);
value = addOutlier(value, outlierProbability, outlierMultiplier);
series.push(value);
}
return series;
}
exports.generateLinearSeries = generateLinearSeries;
/**
* Generates a quadratic series of numbers following the equation y = x²
* @param options - Configuration options for the series
* @returns Array of numbers following a quadratic pattern
* @example
* // Generate a quadratic series from 1 to 5
* const series = generateQuadraticSeries({ start: 1, end: 5, step: 1 });
* // Result: [1, 4, 9, 16, 25]
*/
function generateQuadraticSeries({ start, end, step, standardDeviation = 0, precision = 3, a = 1, b = 0, c = 0, outlierProbability = 0, outlierMultiplier = 3, }) {
const series = [];
for (let i = start; i <= end; i += step) {
let value = a * i * i + b * i + c;
value = addNoise(value, standardDeviation, precision);
value = addOutlier(value, outlierProbability, outlierMultiplier);
series.push(value);
}
return series;
}
exports.generateQuadraticSeries = generateQuadraticSeries;
/**
* Generates a logarithmic series of numbers following the equation y = log(x)
* @param options - Configuration options for the series
* @returns Array of numbers following a logarithmic pattern
* @example
* // Generate a logarithmic series from 1 to 5
* const series = generateLogSeries({ start: 1, end: 5, step: 1 });
* // Result: [0, 0.693, 1.099, 1.386, 1.609]
*/
function generateLogSeries({ start, end, step, standardDeviation = 0, precision = 3, a = 1, b = 0, base = Math.E, outlierProbability = 0, outlierMultiplier = 3, }) {
const series = [];
for (let i = start; i <= end; i += step) {
let value = (a * Math.log(i)) / Math.log(base) + b;
value = addNoise(value, standardDeviation, precision);
value = addOutlier(value, outlierProbability, outlierMultiplier);
series.push(value);
}
return series;
}
exports.generateLogSeries = generateLogSeries;
function generateExponentialSeries({ start, end, step, standardDeviation = 0, precision = 3, a = 1, b = 2, c = 0, outlierProbability = 0, outlierMultiplier = 3, }) {
const series = [];
for (let i = start; i <= end; i += step) {
let value = a * Math.pow(b, i) + c;
value = addNoise(value, standardDeviation, precision);
value = addOutlier(value, outlierProbability, outlierMultiplier);
series.push(value);
}
return series;
}
exports.generateExponentialSeries = generateExponentialSeries;
function generateTrigonometricSeries({ start, end, step, standardDeviation = 0, precision = 3, function: func, amplitude = 1, frequency = 1, phase = 0, outlierProbability = 0, outlierMultiplier = 3, }) {
const series = [];
for (let i = start; i <= end; i += step) {
let value;
const x = frequency * i + phase;
switch (func) {
case 'sin':
value = amplitude * Math.sin(x);
break;
case 'cos':
value = amplitude * Math.cos(x);
break;
case 'tan':
value = amplitude * Math.tan(x);
break;
default:
throw new Error(`Unsupported trigonometric function: ${func}`);
}
value = addNoise(value, standardDeviation, precision);
value = addOutlier(value, outlierProbability, outlierMultiplier);
series.push(value);
}
return series;
}
exports.generateTrigonometricSeries = generateTrigonometricSeries;
function combineSeries(series, operation) {
if (series.length === 0)
return [];
const maxLength = Math.max(...series.map((s) => s.length));
const result = [];
for (let i = 0; i < maxLength; i++) {
let value = series[0][i] || 0;
for (let j = 1; j < series.length; j++) {
const nextValue = series[j][i] || 0;
switch (operation) {
case 'add':
value += nextValue;
break;
case 'subtract':
value -= nextValue;
break;
case 'multiply':
value *= nextValue;
break;
case 'divide':
value /= nextValue || 1; // Avoid division by zero
break;
}
}
result.push(value);
}
return result;
}
exports.combineSeries = combineSeries;