Numfit is a JavaScript library for numerical interpolation and extrapolation. It supports tensor-grid interpolation on intervals, rectangles, boxes, and hyperboxes; simplex interpolation on triangles, tetrahedra, and higher-dimensional simplexes; and piecewise meshes that can combine multiple interpolators into one surface or volume.

See docs/API.md for a generated API index from the TypeScript declarations.

Supported Algorithms and Classes

Tensor-Grid Interpolation

These classes interpolate over structured grids: intervals in 1D, rectangles in 2D, boxes in 3D, and their higher-dimensional pattern. They support scalar and vector-valued outputs through the dimension constructor argument.

Class	Order	Variables	Shape
Linear	Linear	1	Interval
Bilinear	Linear	2	Rectangle
Trilinear	Linear	3	Box
Quadratic	Quadratic	1	1D three-point curve
Biquadratic	Quadratic	2	2D quadratic grid
Triquadratic	Quadratic	3	3D quadratic grid
Cubic	Cubic	1	1D four-point curve
Bicubic	Cubic	2	2D cubic grid
Tricubic	Cubic	3	3D cubic grid
Polynomial	Arbitrary	1	1D polynomial curve
Multilinear	Linear	N	N-dimensional hyperbox
Multiquadratic	Quadratic	N	N-dimensional quadratic tensor grid
Multicubic	Cubic	N	N-dimensional cubic tensor grid
Multipolynomial	Arbitrary	N	N-dimensional polynomial tensor grid
CubicHermite	Cubic	1	1D curve with endpoint derivatives
QuinticHermite	Quintic	1	1D curve with endpoint first and second derivatives

All tensor-grid classes provide:

• evaluate(position)
• map(positions)
• mapAsync(positions, options) in Node and modern browsers
• step(start, end, size, handler)
• segment(start, end, amount, handler)
• grid(options, handler)
• slice(options, handler)
• random(count, options, handler)
• latinHypercube(count, options, handler)
• poissonDisk(options, handler)
• adaptive(start, end, options) for 1D interpolators
• stream(options) for chunked grid sampling
• static evaluate(...)
• static coefficients(...)

The fixed 1D/2D/3D classes use optimized coefficient evaluators. The Multi* and Polynomial classes use tensor-product Lagrange interpolation and are useful when you need arbitrary dimensionality or arbitrary polynomial degree. Their mapAsync methods can use the same Node and browser worker path for larger batches.

import { Multipolynomial } from 'numfit';

const polynomial = new Multipolynomial(
	new Float64Array([ 0,0, 1,1, 2,2 ]),
	new Float64Array([
		0, 1, 4,
		1, 2, 5,
		4, 5, 8
	]),
	1,
	2,
	2
);

polynomial.evaluate([ .5, .5 ]);

Simplex Interpolation

Simplex interpolation is for unstructured linear cells. Use it when your domain is made from triangles, tetrahedra, or generic N-dimensional simplexes rather than rectangles or boxes.

Class	Variables	Shape
SimplexLinear	Inferred or supplied	Generic N-dimensional simplex
TriangleLinear	2	Triangle
TetrahedralLinear	3	Tetrahedron

Simplex classes provide:

• evaluate(position)
• map(positions)
• mapAsync(positions)
• grid(options, handler)
• slice(options, handler)
• random(count, options, handler)
• latinHypercube(count, options, handler)
• poissonDisk(options, handler)
• adaptive(start, end, options) for 1D simplexes
• stream(options)
• contains(position)
• getWeights(position)
• static evaluate(...)

Piecewise Meshes

Meshes combine multiple interpolators into one piecewise interpolator. A mesh can mix any interpolator types as long as they share the same variable count and output dimension.

Class	Purpose
InterpolationMesh	Piecewise wrapper for any mix of supported interpolators
Mesh	Alias for InterpolationMesh
Spline	Alias for InterpolationMesh when modeling piecewise curves

Mesh cells can be plain interpolator instances or custom entries:

{
	interpolator,
	contains: position => true,
	weight: position => 1
}

Meshes provide:

• fromIntervals(options)
• fromTriangles(positions, values, indices, options)
• fromTetrahedra(positions, values, indices, options)
• fromSimplexes(positions, values, indices, options)
• add(cell)
• rebuildIndex()
• find(position)
• contains(position)
• evaluate(position)
• map(positions)
• mapAsync(positions)
• grid(options, handler)
• slice(options, handler)
• random(count, options, handler)
• latinHypercube(count, options, handler)
• poissonDisk(options, handler)
• adaptive(start, end, options) for 1D meshes
• stream(options)

Overlap behavior:

• Default: first containing cell wins.
• Weighted mode: set { blending: 'weighted' } and provide per-cell weight(position) functions.

For 1D splines, use the same general wrapper with interval interpolators:

import { CubicHermite, Linear, Spline } from 'numfit';

const spline = Spline.fromIntervals({
	type: CubicHermite,
	positions: [ 0, 1, 2, 3 ],
	values: [ 0, 1, 0, 2 ],
	derivatives: 'finite-difference'
});

spline.evaluate( .5 );
spline.evaluate( 1.5 );

Scattered-Data Interpolation

Scattered-data interpolators work from arbitrary sample points instead of structured grids or explicit mesh cells.

Class	Alias	Purpose
InverseDistanceWeighting	IDW	Weighted scattered interpolation by inverse distance
RadialBasisFunction	RBF	Global scattered interpolation with radial kernels

InverseDistanceWeighting supports scalar and vector-valued outputs, exact sample hits, optional radius cutoffs, and optional nearest-neighbor limits. New scattered-data classes use object-style construction as the preferred API:

import { InverseDistanceWeighting } from 'numfit';

const idw = new InverseDistanceWeighting({
	positions: new Float64Array([
		0, 0,
		1, 0,
		0, 1,
		1, 1
	]),
	values: new Float64Array([ 0, 10, 20, 30 ]),
	variables: 2,
	dimension: 1,
	power: 2,
	neighbors: 8,
	radius: Infinity,
	index: 'grid',
	resolution: 'auto',
	empty: 'throw'
});

idw.evaluate([ .25, .5 ]);

When radius excludes every source sample, empty controls the fallback:

new InverseDistanceWeighting({ positions, values, variables: 2, radius: .1, empty: 'throw' });
new InverseDistanceWeighting({ positions, values, variables: 2, radius: .1, empty: 'nearest' });
new InverseDistanceWeighting({ positions, values, variables: 2, radius: .1, empty: 0 });
new InverseDistanceWeighting({ positions, values, variables: 2, dimension: 3, radius: .1, empty: [ 0, 0, 0 ] });
new InverseDistanceWeighting({ positions, values, variables: 2, radius: .1, empty: 'undefined' });
new InverseDistanceWeighting({ positions, values, variables: 2, radius: .1, empty: 'null' });

empty: 'throw' is the default. In packed outputs from map, grid, or other samplers, undefined and null fallbacks are represented as NaN because numeric typed arrays cannot store those values.

For larger scattered datasets, enable a uniform grid index:

const idw = new InverseDistanceWeighting({
	positions,
	values,
	variables: 2,
	radius: .05,
	neighbors: 16,
	index: 'grid',
	resolution: 'auto'
});

Grid indexing accelerates finite-radius queries and nearest fallback lookup. Use rebuildIndex() after mutating source positions:

idw.positions[ 0 ] = .25;
idw.rebuildIndex();

For consistency with older classes, positional construction is also supported:

const idw = new InverseDistanceWeighting(
	positions,
	values,
	1,
	2,
	{ power: 2 }
);

RadialBasisFunction solves a dense interpolation system and evaluates a weighted radial kernel sum:

import { RadialBasisFunction } from 'numfit';

const rbf = new RadialBasisFunction({
	positions,
	values,
	variables: 2,
	dimension: 1,
	kernel: 'gaussian',
	epsilon: 1,
	smoothing: 0
});

rbf.evaluate([ .25, .5 ]);

Supported RBF kernels:

• gaussian
• multiquadric
• inverse-multiquadric
• linear
• cubic
• thin-plate

The linear, cubic, and thin-plate kernels include a linear polynomial tail so their interpolation systems are well-posed. smoothing adds a non-negative diagonal term when exact interpolation is not desired.

Regression

Regression classes fit approximate models to sampled data instead of requiring exact interpolation through every point.

Class	Purpose
PolynomialRegression	Least-squares, weighted least-squares, and Huber/IRLS polynomial regression for 1D or N-dimensional inputs
LinearRegression	Convenience regression with degree: 1
RidgeRegression	Polynomial regression using the regularization option
RANSACRegression	Robust wrapper that fits a regression model from random inlier consensus

import { PolynomialRegression } from 'numfit';

const fit = new PolynomialRegression({
	positions: new Float64Array([ 0, 1, 2, 3 ]),
	values: new Float64Array([ 1, 3, 5, 7 ]),
	degree: 1,
	regularization: 0,
	weights: new Float64Array([ 1, 1, 1, 1 ])
});

fit.evaluate( 4 );
fit.residuals();
fit.mse();
fit.rmse();
fit.mae();
fit.r2();
fit.summary();

Multivariate polynomial regression uses total-degree polynomial terms:

const surface = new PolynomialRegression({
	positions,
	values,
	variables: 2,
	degree: 2,
	dimension: 1,
	regularization: 1e-6,
	normalize: 'standard'
});

Use normalize: 'standard' to center and scale each input variable before fitting, or normalize: 'minmax' to map each variable by its observed range. Public positions and evaluate() inputs stay in the original coordinate system.

Use weights for weighted least squares. Each sample gets one non-negative weight; zero excludes the sample from the fit:

const weighted = new PolynomialRegression({
	positions,
	values,
	degree: 2,
	weights
});

Use Huber regression when some samples may be outliers but you do not want to discard them entirely. Huber uses iteratively reweighted least squares, starting from the ordinary weighted fit:

const robust = new PolynomialRegression({
	positions,
	values,
	degree: 2,
	robust: 'huber',
	delta: 1,
	maxIterations: 20,
	tolerance: 1e-6
});

delta is the residual size where Huber switches from squared loss to linear loss. iterations reports how many IRLS passes were used.

Use RANSACRegression when a dataset may contain bad samples that should be excluded from the final fit. It wraps PolynomialRegression, LinearRegression, or RidgeRegression, finds the largest inlier set, and optionally refits the final model on those inliers:

const robust = new RANSACRegression({
	model: LinearRegression,
	positions,
	values,
	threshold: .5,
	iterations: 100,
	sampleSize: 2,
	refit: true,
	seed: 7
});

robust.evaluate( 4 );
robust.inliers;
robust.outliers;
robust.bestModel;

Use crossValidateRegression to compare model options with k-fold cross-validation. Array-valued options such as degree, regularization, robust, and normalize are expanded into a grid:

const selected = crossValidateRegression({
	model: PolynomialRegression,
	positions,
	values,
	degree: [ 1, 2, 3 ],
	regularization: [ 0, 1e-6, 1e-3 ],
	normalize: [ false, 'standard' ],
	folds: 5,
	metric: 'mse',
	seed: 7
});

selected.bestModel;
selected.bestOptions;
selected.results;

Supported metrics are mse, rmse, mae, and r2.

Regression instances expose diagnostic helpers for inspecting a fit:

fit.rss();     // residual sum of squares
fit.tss();     // total sum of squares
fit.mse();
fit.rmse();
fit.mae();
fit.r2();
fit.aic();
fit.bic();

const summary = fit.summary();
summary.coefficients;
summary.rmse;

For least-squares-style influence checks, use leverage, studentized residuals, and Cook's distance:

fit.leverage();
fit.studentizedResiduals();
fit.cooksDistance();

const influence = fit.influence();
influence.leverage;
influence.cooksDistance;

These diagnostics respect weighted fits, ridge regularization, and input normalization. RANSACRegression delegates them to its final bestModel.

Hermite Interpolation

Hermite interpolation uses values and derivatives. CubicHermite is a 1D cubic Hermite interpolator for one interval with first derivative constraints. QuinticHermite adds second derivative constraints.

import { CubicHermite } from 'numfit';

const hermite = new CubicHermite({
	positions: new Float64Array([ 0, 1 ]),
	values: new Float64Array([ 0, 1 ]),
	derivatives: new Float64Array([ 0, 0 ])
});

hermite.evaluate( .5 );
hermite.derivative( .5 );

For second derivative constraints, use QuinticHermite:

const quintic = new QuinticHermite({
	positions: [ 0, 1 ],
	values: [ 0, 1 ],
	derivatives: [ 1, 1 ],
	secondDerivatives: [ 0, 0 ]
});

quintic.evaluate( .5 );
quintic.derivative( .5 );
quintic.secondDerivative( .5 );

The alias Hermite points to CubicHermite. Positional construction is also supported:

const hermite = new CubicHermite(
	[ 0, 1 ],
	[ 0, 1 ],
	[ 0, 0 ]
);

For piecewise Hermite curves, use the same general Spline/InterpolationMesh wrapper used by every other piecewise composition:

import { CubicHermite, Spline } from 'numfit';

const spline = Spline.fromIntervals({
	type: CubicHermite,
	positions: [ 0, 1, 2, 3 ],
	values: [ 0, 1, 0, 2 ],
	derivatives: [ 1, 0, -1, 2 ]
});

spline.evaluate( 1.5 );

Parallel and Browser Support

mapAsync can use worker threads in Node and module workers in modern browsers. Typed-array inputs use the fastest path: position chunks are transferred to workers, and typed-array outputs can be written into a shared output buffer.

Installation

NodeJS Enviroment

Using NPM

npm install numfit

import { Linear } from 'numfit';

Browser Enviroment

There's a couple ways to use Numfit in you browser based projects:

Using CDN (Recommended)

To harness the library without the complexities of local deployment, consider employing a Content Delivery Network (CDN). Simply incorporate the following script tag into your HTML document's <head> section:

<script src="https://cdn.jsdelivr.net/gh/buca/numfit/build/Numfit.min.js"></script>

For modern browser projects, use the ESM build:

<script type="module">
	import { Linear } from 'https://cdn.jsdelivr.net/gh/buca/numfit/build/Numfit.module.js';
</script>

Browser mapAsync uses a module worker when Worker is available. Keep Numfit.worker.js in the same directory as Numfit.module.js, or pass a custom worker URL:

const values = await linear.mapAsync( positions, {
	workers: 4,
	workerUrl: new URL( './Numfit.worker.js', import.meta.url )
});

Mesh mapAsync uses a separate module worker for serializable built-in simplex meshes. Keep Numfit.mesh.worker.js in the same directory as Numfit.module.js, or pass a custom mesh worker URL:

const values = await mesh.mapAsync( positions, {
	workers: 4,
	meshWorkerUrl: new URL( './Numfit.mesh.worker.js', import.meta.url )
});

Using a Local Copy

Alternatively, you can opt for a local deployment of the library. Locate the /build/ directory within this repository and download either the standard Numfit.js file or the minified version Numfit.min.js. Then, incorporate the downloaded file into your HTML document's <head> section using a <script> tag:

<script src="my-directory/Numfit.min.js"></script>

Or import the local ESM build:

import { Linear } from './my-directory/Numfit.module.js';

Replace my-directory with the actual path to the downloaded file.

Basic Usage

const positions = [ 0, 1 ];
const values = [ 0, 0, 0, 255, 255, 255 ];
const dimension = 3;
const linear = new Linear( positions, values, dimension );

linear.evaluate(  0 );
linear.evaluate( .5 ); 
linear.evaluate(  1 );

Parallel Mapping

For larger batches in NodeJS, mapAsync can split map evaluation across worker threads:

const linear = new Linear( [ 0, 1 ], [ 0, 10 ] );
const values = await linear.mapAsync(
	[ 0, .25, .5, .75, 1 ],
	{ workers: 2, threshold: 0 }
);

By default, mapAsync estimates the amount of work from the sample count and interpolation complexity. Small or inexpensive batches fall back to the normal synchronous map implementation; larger batches use the worker pool. The pool size is capped by node:os.availableParallelism() - 1, and auto mode currently uses at most four workers. You can still force worker usage with { threshold: 0, workers: 2 }, up to the available worker limit.

Typed-array inputs and values use the fastest worker path. Position chunks are transferred to workers, and typed-array outputs are written into a shared output buffer so the main thread does not need to copy result chunks back together.

Samplers

step and segment remain the low-level structured samplers. grid wraps that idea with plotting-friendly metadata:

const sampled = bilinear.grid({
	start: [ 0, 0 ],
	end: [ 1, 1 ],
	shape: [ 128, 128 ]
});

sampled.positions;
sampled.values;
sampled.shape;

Use slice to sample a lower-dimensional cut through a higher-dimensional interpolator:

const zHalf = tricubic.slice({
	axes: [ 0, 1 ],
	fixed: { 2: .5 },
	start: [ 0, 0 ],
	end: [ 1, 1 ],
	shape: [ 128, 128 ]
});

For scattered sampling, use random, latinHypercube, or poissonDisk. They accept explicit bounds, or infer bounds from the interpolator:

const random = mesh.random( 1000, {
	min: [ 0, 0 ],
	max: [ 1, 1 ],
	seed: 123
});

const stratified = mesh.latinHypercube( 1000, {
	min: [ 0, 0 ],
	max: [ 1, 1 ],
	seed: 123
});

const even = mesh.poissonDisk({
	min: [ 0, 0 ],
	max: [ 1, 1 ],
	radius: .05,
	seed: 123
});

poissonDisk can also estimate a radius from a target count. The result may contain fewer points than the requested count when the inferred radius fills the domain first:

const even = mesh.poissonDisk({
	min: [ 0, 0 ],
	max: [ 1, 1 ],
	count: 1000,
	seed: 123
});

For 1D curves, adaptive recursively adds samples where midpoint error is above a tolerance:

const curve = cubic.adaptive( 0, 1, {
	tolerance: 1e-4,
	maxDepth: 16
});

For very large grids, stream yields chunks without allocating the full grid at once:

for await ( const chunk of tricubic.stream({
	start: [ 0, 0, 0 ],
	end: [ 1, 1, 1 ],
	shape: [ 256, 256, 128 ],
	chunkSize: 4096
}) ) {
	chunk.positions;
	chunk.values;
}

Simplex Interpolation

For unstructured linear interpolation, Numfit also supports simplexes: line segments in 1D, triangles in 2D, tetrahedra in 3D, and higher-dimensional simplexes.

import { TriangleLinear } from 'numfit';

const triangle = new TriangleLinear(
	new Float64Array([
		0, 0,
		1, 0,
		0, 1
	]),
	new Float64Array([ 10, 20, 30 ])
);

triangle.evaluate([ .25, .25 ]); // 17.5
triangle.contains([ .25, .25 ]); // true

Use SimplexLinear directly for generic N-dimensional simplexes, or TriangleLinear and TetrahedralLinear for the common 2D and 3D cases.

Interpolation Meshes

InterpolationMesh is a piecewise wrapper that can combine any mix of interpolators with the same input variable count and output dimension. Simplex interpolators use their barycentric contains checks; grid interpolators use inferred hyperbox bounds. Earlier cells win when regions overlap.

import { Bilinear, InterpolationMesh, TriangleLinear } from 'numfit';

const mesh = new InterpolationMesh([
	new TriangleLinear(
		new Float64Array([ 0,0, 1,0, 0,1 ]),
		new Float64Array([ 0, 10, 20 ])
	),
	new Bilinear(
		new Float64Array([ 1,0, 2,1 ]),
		new Float64Array([ 100, 200, 300, 400 ])
	)
]);

mesh.evaluate([ .25, .25 ]);
mesh.evaluate([ 1.5, .5 ]);

For indexed triangle or tetrahedron data, build cells directly from shared vertex buffers:

const mesh = InterpolationMesh.fromTriangles(
	new Float64Array([
		0, 0,
		1, 0,
		0, 1,
		1, 1
	]),
	new Float64Array([ 0, 10, 20, 30 ]),
	new Uint16Array([ 0, 1, 2, 1, 3, 2 ]),
	{
		index: 'grid',
		resolution: [ 32, 32 ]
	}
);

Use fromTetrahedra for 3D tetrahedral meshes, or fromSimplexes with { cellSize, variables } for generic simplex meshes.

For 1D piecewise curves, fromIntervals builds interval cells from shared point arrays:

const linearSpline = Spline.fromIntervals({
	type: Linear,
	positions: [ 0, 1, 2, 3 ],
	values: [ 0, 1, 0, 2 ]
});

const hermiteSpline = Spline.fromIntervals({
	type: CubicHermite,
	positions: [ 0, 1, 2, 3 ],
	values: [ 0, 1, 0, 2 ],
	derivatives: [ 1, 0, -1, 2 ]
});

For Hermite intervals, derivatives can be an explicit array or an inference mode:

Spline.fromIntervals({ type: CubicHermite, positions, values, derivatives: 'finite-difference' });
Spline.fromIntervals({ type: CubicHermite, positions, values, derivatives: 'catmull-rom' });
Spline.fromIntervals({ type: CubicHermite, positions, values, derivatives: 'zero' });
Spline.fromIntervals({ type: CubicHermite, positions, values, derivatives: 'monotone' });
Spline.fromIntervals({ type: CubicHermite, positions, values, derivatives: 'natural' });
Spline.fromIntervals({ type: CubicHermite, positions, values, derivatives: 'akima' });
Spline.fromIntervals({ type: CubicHermite, positions, values, derivatives: { mode: 'cardinal', tension: .5 } });
Spline.fromIntervals({ type: CubicHermite, positions, values, derivatives: { mode: 'clamped', start: 0, end: 0 } });

monotone is useful for shape-preserving curves, natural solves a global natural cubic derivative system, akima is useful for uneven or noisy data, cardinal adds tension control, and clamped lets you specify endpoint derivatives while inferring interior derivatives.

QuinticHermite intervals also accept secondDerivatives:

Spline.fromIntervals({
	type: QuinticHermite,
	positions,
	values,
	derivatives: 'finite-difference',
	secondDerivatives: 'finite-difference'
});

Spline.fromIntervals({
	type: QuinticHermite,
	positions,
	values,
	derivatives: 'finite-difference',
	secondDerivatives: 'zero'
});

For larger meshes, enable a uniform grid index to avoid checking every cell on each lookup:

const mesh = new InterpolationMesh(cells, {
	index: 'grid',
	resolution: 'auto'
});

When index: 'grid' is enabled, omitted resolution values use the same auto heuristic. You can still pass a fixed number for every axis, or an array for per-axis control:

new InterpolationMesh(cells, { index: 'grid', resolution: 32 });
new InterpolationMesh(cells, { index: 'grid', resolution: [ 64, 32 ] });

Grid indexing uses each cell's bounds. Built-in interpolators infer bounds from their positions. Custom cells can provide bounds explicitly:

mesh.add({
	interpolator,
	bounds: [
		{ min: 0, max: 1 },
		{ min: 0, max: 1 }
	],
	contains: position => position[ 0 ] >= 0 && position[ 1 ] >= 0
});

For custom regions, pass { interpolator, contains } cells:

mesh.add({
	interpolator,
	contains: position => position[ 0 ] >= 0 && position[ 1 ] >= 0
});

By default, the first containing cell wins. For intentional overlaps, enable weighted blending and provide per-cell weights:

const mesh = new InterpolationMesh([
	{
		interpolator: left,
		contains: position => position >= 0 && position <= 1,
		weight: position => 1 - position
	},
	{
		interpolator: right,
		contains: position => position >= 0 && position <= 1,
		weight: position => position
	}
], {
	blending: 'weighted'
});

For serializable built-in simplex meshes, mapAsync can evaluate larger batches in worker threads or browser module workers:

const values = await mesh.mapAsync( positions, {
	workers: 4,
	threshold: 0
});

Custom mesh cells with user-defined contains or weight functions fall back to synchronous mapping because those functions cannot be serialized into workers.

const positions = [ 0,0, 1,1, 2,2, 3,3 ];
const values = [ 
	0,0,0, 255,0,0, 0,255,0, 0,0,255,
	255,0,0, 0,255,0, 0,0,255, 0,0,0, 
	0,255,0, 0,0,255, 0,0,0, 255,0,0,
	0,0,255, 0,0,0, 255,0,0, 0,255,0
];
const dimension = 3;
const bicubic = new Bicubic( positions, values, dimension );

bicubic.step( [0,0], [1,1], [.1,.1] ( position, value ) => {
	// Do something with positions and the values.
});

Building and Testing

Setup

Running the build and testing procedures is easy. First make sure you have npm installed. Then navigate to the root directory / in your CLI and run:

npm install

This installs the development dependencies used for building, unit tests, benchmarks, and optional browser smoke tests.

Building

To run the build procedure, you simply navigate to the root directory / in your CLI and run npm run build which will produce five files in the /build/ directory: Numfit.js, Numfit.min.js, Numfit.module.js, Numfit.worker.js, and Numfit.mesh.worker.js.

Testing

To run the tests, you again navigate to the root directory / in your CLI and run npm run test which will produce the test results.

To verify the shipped TypeScript declarations, run:

npm run test:types

To verify browser worker support, install the Playwright Chromium browser once and run the browser smoke test:

npm run test:browser:install
npm run test:browser

The browser test builds the browser artifacts, serves the project locally, opens Chromium, and verifies that mapAsync matches synchronous map output with maxDiff: 0.

Benchmarking

To compare synchronous mapping with worker-thread mapping, run:

npm run benchmark

You can tune the run with environment variables:

NUMFIT_BENCH_SIZES=10000,100000 NUMFIT_BENCH_WORKERS=2,4 npm run benchmark

NUMFIT_BENCH_WORKERS accepts a comma-separated list of worker counts. Counts larger than node:os.availableParallelism() - 1 are ignored by the benchmark and clamped by mapAsync. The benchmark includes fixed 1D/2D/3D evaluators and tensor-polynomial variants such as Multicubic and Multipolynomial.

To compare linear mesh lookup with grid-indexed and worker-backed mesh lookup, run:

npm run benchmark:mesh

You can tune the mesh benchmark with environment variables:

NUMFIT_MESH_CELLS=200,2000 NUMFIT_MESH_SAMPLES=1000,10000 NUMFIT_MESH_RESOLUTION=auto,16,32 NUMFIT_MESH_WORKERS=2,4 npm run benchmark:mesh

The mesh benchmark generates indexed triangle grids, validates indexed and worker-backed results against linear lookup, and reports speedups for each grid resolution and worker count.

To compare linear IDW lookup with grid-indexed IDW lookup, run:

npm run benchmark:idw

You can tune the IDW benchmark with environment variables:

NUMFIT_IDW_SOURCES=1000,10000 NUMFIT_IDW_QUERIES=1000,10000 NUMFIT_IDW_RADIUS=.08 NUMFIT_IDW_NEIGHBORS=16 npm run benchmark:idw

To compare regression fit costs, run:

npm run benchmark:regression

You can tune the regression benchmark with environment variables:

NUMFIT_REGRESSION_SAMPLES=1000,10000 NUMFIT_REGRESSION_DEGREE=3 NUMFIT_RANSAC_ITERATIONS=50 npm run benchmark:regression

Roadmap

Recently completed:

• Modern ESM imports and browser ESM build
• Node worker-thread mapAsync
• Browser module-worker mapAsync
• Transfer/shared-buffer worker fast path for typed arrays
• Worker acceleration for polynomial multi-variants
• Validation for constructor, evaluation, and sampling inputs
• Benchmark suite with correctness checks
• Browser worker smoke test
• Polynomial
• Multilinear
• Multiquadratic
• Multicubic
• Multipolynomial
• Simplex interpolation with SimplexLinear, TriangleLinear, and TetrahedralLinear
• Piecewise interpolation meshes with mixed interpolator support
• Optional weighted blending for overlapping mesh cells
• Uniform-grid spatial acceleration for large meshes
• Mesh construction from triangle/tetrahedron index buffers
• Mesh benchmark suite for linear scan vs grid-indexed lookup
• Adaptive mesh index resolution heuristics
• Worker acceleration for serializable InterpolationMesh simplex meshes
• Plotting-friendly grid sampler
• Lower-dimensional slice sampler
• Random and Latin-hypercube scattered samplers
• Poisson disk scattered sampler
• 1D adaptive sampler
• Chunked async stream sampler
• Inverse distance weighting for scattered data
• Uniform-grid spatial acceleration for IDW
• IDW benchmark suite for linear scan vs grid-indexed lookup
• Radial basis function interpolation for scattered data
• Polynomial, linear, and ridge regression
• Regression metrics: residuals, MSE, and R-squared
• Weighted least-squares regression
• Huber robust regression with IRLS
• RANSAC regression wrapper
• Regression input normalization
• Cross-validation and model selection helpers
• Cubic Hermite interpolation with endpoint derivative constraints
• Quintic Hermite interpolation with endpoint first and second derivative constraints
• Unified Spline alias for InterpolationMesh
• Spline.fromIntervals for 1D piecewise curves
• TypeScript declarations and type smoke test

Planned:

• Regression diagnostics and confidence intervals