> ## Documentation Index
> Fetch the complete documentation index at: https://numpyts.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Roadmap

> What's coming next for numpy-ts.

The goal for `numpy-ts` is to be the best possible NumPy implementation for JavaScript and TypeScript. To get there, there's have a long list of features, optimizations, and API improvements to build. Here's a high-level roadmap of what's coming next:

## Async / Worker offloading

Heavy operations like `matmul`, `svd`, `fft`, and `convolve` can block the main thread for tens of milliseconds on large inputs. We're designing an opt-in `np.async.*` namespace that transparently offloads these to a Web Worker pool:

```typescript theme={null}
// Proposed API
const result = await np.async.matmul(A, B);
const { u, s, vt } = await np.async.linalg.svd(largeMatrix);
```

The worker pool will support two transport paths:

* **SharedArrayBuffer** (zero-copy) when COOP/COEP headers are present
* **postMessage** (universal fallback) for environments without cross-origin isolation

## Multi-threaded WASM

Extend the WASM acceleration layer to use multiple threads via `WebAssembly.Memory` with `shared: true` and Web Workers. This would allow large matrix operations to be parallelized across CPU cores without leaving the WASM execution context.

## Ufunc framework

A generalized ufunc (universal function) system that would allow users to define custom element-wise and reduction operations that automatically get broadcasting, dtype promotion, and axis handling:

```typescript theme={null}
// Proposed API
const clampedAdd = np.ufunc((a, b) => Math.min(a + b, 255), 2);
clampedAdd(imageA, imageB); // broadcasts, handles dtypes, etc.
```

## Masked arrays

Support for arrays with a boolean mask that marks invalid or missing entries. Operations would automatically skip masked elements, similar to NumPy's `numpy.ma` module:

```typescript theme={null}
// Proposed API
const a = np.ma.array([1, 2, 3, 4], { mask: [false, false, true, false] });
np.mean(a); // 2.333... (skips index 2)
```

## Structured arrays / record arrays

Arrays with named, heterogeneous fields -- useful for tabular data without pulling in a full DataFrame library:

```typescript theme={null}
// Proposed API
const dt = np.dtype([['name', 'U10'], ['age', 'int32'], ['score', 'float64']]);
const records = np.zeros(100, dt);
```

This is a significant undertaking and may be scoped to a subset of NumPy's structured array features.

## Random distribution parity

While numpy-ts implements all core random distributions, some NumPy random functions have subtle parameter variations and edge-case behaviors that differ. The goal is full behavioral parity with NumPy's `numpy.random.Generator` interface.

<Note>
  This roadmap reflects current thinking, not commitments. Items may be reprioritized, combined, or dropped based on what the community actually needs. The best way to influence the roadmap is to [open an issue](https://github.com/dupontcyborg/numpy-ts/issues) with your use case.
</Note>