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

# Changelog & Release Notes

<Update label="v1.4.0" description="2026-05-16">
  ## numpy-ts v1.4.0

  NumPy-compatibility release. A wide set of public functions gain the kwargs / overloads they were missing relative to NumPy, and several signatures accept `ArrayLike` so plain `number[]` no longer needs `np.array(...)` wrapping.

  Small (2-5%) performance regressions due to additional correctness checks which will be optimized in a later release.

  ### Breaking

  * `meshgrid` (`numpy-ts/core` only) - default `indexing` is now `'xy'` (NumPy-compatible). Previously `core/meshgrid` had no options and effectively produced `'ij'` shapes. If you import `meshgrid` from `numpy-ts/core` and relied on the old behavior, pass `{ indexing: 'ij' }` explicitly. Importers from `numpy-ts` (the full API) are unaffected - that wrapper already defaulted to `'xy'`.

  ### New kwargs / overloads

  * Reductions `where` / `initial` / `dtype` - `sum`, `prod`, `max`/`amax`, `min`/`amin`, `all`, `any` now accept a `ReductionOpts` bag as a 4th argument for masked reductions, seeded accumulation, and dtype-controlled accumulation.
  * `argmin` / `argmax` - accept `keepdims?: boolean` (NumPy 1.22+).
  * `ptp` - `axis` widened to `number | number[]`, consistent with the other reductions.
  * `average` - new `returned?: boolean`. When `true`, returns `[avg, sum_of_weights]` matching `np.average(..., returned=True)`.
  * `concatenate` / `concat` - `axis` widened to `number | null`. `axis=null` flattens each input and concatenates along axis 0.
  * `diff` - new `prepend?` and `append?` arguments (`ArrayLike`).
  * `interp` - new `period?: number` for circular/phase data; `xp` is normalized into `[0, period)` and wraps at the boundary.
  * `gradient` - per-axis spacing can now be a 1-D coordinate array (non-uniform second-order central-difference), not just a scalar.
  * `apply_along_axis` - accepts trailing `...args` forwarded to `func1d`, matching `np.apply_along_axis(func1d, axis, arr, *args)`.
  * `pad` - `pad_width` and `constant_values` accept the full NumPy broadcast forms (scalar, `[n]`, `[before, after]`, per-axis scalars, `[[b, a]]`, per-axis pairs, mixed). New exported types `PadWidthArg` and `PadValueArg`.
  * `meshgrid` - `MeshgridOptions { indexing?, sparse?, copy? }` bag. `sparse: true` returns open grids; `copy: false` returns broadcast views.
  * `block` - accepts nested sequences (`NestedNDArrays[]`) with NumPy's innermost-to-outer axis semantics. Flat-list calls behave identically.

  ### `ArrayLike` widening

  These now accept plain `number[]`, nested arrays, and scalars in addition to `NDArrayCore`. Behavior for existing NDArray callers is unchanged.

  * `take` - `indices: ArrayLike` (scalar, 1-D, 2-D, or 3-D). Output preserves the shape of `indices`.
  * `where` - `x` and `y` are `ArrayLike`. Also fixes a falsy-zero bug: `where(cond, x, 0)` now honors the `0` branch instead of falling through to the indices form.
  * `select` - `condlist`, `choicelist`, and `defaultVal` widened to `ArrayLike` / `ArrayLike[]`.
  * `bincount`, `digitize`, `histogram`, `histogram2d`, `histogramdd` - data, weights, and bin arguments are `ArrayLike`.
</Update>

<Update label="v1.3.2" description="2026-04-17">
  ## numpy-ts v1.3.2

  Patch release with a couple of bug fixes and improvements:

  * Fixed issue with `[Symbol.dispose]` not working properly on Safari & old runtimes
  * Added `wasmFreeBytes` to public exports to allow memory usage checks
</Update>

<Update label="v1.3.1" description="2026-04-16">
  ## numpy-ts v1.3.1

  Patch release with new WASM kernels, performance improvements, and export fixes.

  * **New WASM kernels**: `conj`, `deg2rad`, `modf`, `packbits`, `nanquantile`, `argwhere`, and `float32` SVD
  * Optimized WASM SIMD for `argmin`/`argmax` int16/uint16 paths
  * Reduced WASM base threshold for earlier WASM dispatch on smaller arrays
  * **Export fixes**: `configureWasm`, `wasmConfig`, and `hasFloat16` now exported from `core` and `full` entrypoints
  * Benchmark runner refactored from if-else chain to dictionary lookup
</Update>

<Update label="v1.3.0" description="2026-04-13">
  ## numpy-ts v1.3.0

  **This is the first release where numpy-ts is faster than native NumPy on average** — **1.13x** across 7,200 benchmarks, leading in 12 of 18 categories. See the [Performance Overview](./v1.3.x/performance/overview) for the full breakdown.

  v1.3.0 gets there by moving array storage into WebAssembly linear memory for true zero-copy WASM kernel execution, ships a new memory-management API, and brings a stack of NumPy compatibility fixes.

  * **WASM-backed array storage**: Arrays now live directly in a shared WebAssembly memory pool (default 256 MiB). WASM kernels operate on these pointers with **zero copy-in/copy-out overhead**.
    * Bandwidth-bound operations (`add`, `multiply`, bitwise) see up to **2.6x improvement**
    * Compute-bound operations (`matmul`, `svd`) see minimal change (already kernel-dominated)
    * Graceful fallback to JS `TypedArray`s when the pool is full
    * Comes with new `dispose()` and `configureWasm()` methods for manual memory management and pool configuration
  * **Browser bundle is ESM**. Load via `<script type="module">` or `import()`. See the [CDN section](./v1.3.x/guides/installation#cdn-usage-browsers).
  * Default dtype changes:
    * `arange()` now defaults to `float64` (was `int32` for integer args), matching `zeros`/`ones`/`linspace`. Pass `dtype` explicitly to opt out.
    * `full()` integer inference: out-of-range integers now infer to `int64` instead of `float64`. In-range integers still infer to `int32`.
    * Index-returning APIs return `float64` instead of `int64` (`argsort`, `argmax`/`min`, `argwhere`, `nonzero`, `unravel_index`, `indices`, …) — deliberate NumPy divergence to avoid `BigInt` friction.
  * `array()` now accepts `TypedArray` inputs.
  * Various slice correctness fixes (thanks [@BorisTheBrave](https://github.com/BorisTheBrave)):
    * Added \`vindex: vectorized multi-dimensional indexing matching NumPy's integer array indexing semantics. See [Advanced Indexing](./v1.3.x/api/indexing/advanced-indexing#vindex). ([#94](https://github.com/dupontcyborg/numpy-ts/pull/94))
    * Slice now supports `'...'` (ellipsis) and `'newaxis'` tokens for compact multi-dim slicing. See [Slicing & Indexing](./v1.3.x/guides/slicing-indexing#ellipses). ([#84](https://github.com/dupontcyborg/numpy-ts/pull/84))
  * `in1d` deprecated (matches NumPy 2.4) — use `isin`.
</Update>

<Update label="v1.2.0" description="2026-03-27">
  ## numpy-ts v1.2.0

  The DX release! v1.2.0 brings some quality-of-life upgrades:

  * Added `float16` dtype support:
    * New half-precision dtype with native `Float16Array` on modern runtimes (Node 23+, Chrome 127+) and `Float32Array` fallback on older runtimes. Full NPY round-trip support.
  * Complete `random` module rewrite:
    * Entire `np.random` module rewritten as Zig-compiled WASM. Now **matches NumPy's random output bit-for-bit** for all distributions with both legacy (MT19937) and modern (PCG64) APIs.
    * Random functions are now **\~6x faster on average**.
  * Simplified bundles:
    * Removed CJS bundle. The package is now ESM-only (`dist/numpy-ts.node.cjs` removed).
    * All 22 file I/O functions now available from `import from 'numpy-ts'` — works on Node, Bun, and Deno. `numpy-ts/node` is deprecated (still works as an alias).
    * 2 build outputs (down from 5) — tree-shakeable ESM + browser IIFE
  * Performance optimizations across the board - yielding an **average 20% speedup**.
  * Cross-runtime test coverage: all 10,000+ tests now run on Node, Bun, Deno, and Chromium
</Update>

<Update label="v1.1.0" description="2026-03-19">
  ## numpy-ts v1.1.0

  The performance release! v1.1.0 introduces Zig-compiled WASM microkernels that transparently accelerate compute- and memory-bound operations across the library. numpy-ts remains lightweight, tree-shakeable, and zero-config - just faster.

  * Implemented **97 WASM-accelerated operations** compiled from Zig with 128-bit SIMD
    * WASM kernels are embedded as base64, synchronously initialized, and individually tree-shakeable
    * Acceleration is fully transparent: same API, same results, no configuration needed
  * Generally sped up the entire library by \~8x:
    * **Arithmetic**: \~23x faster (65x slower than NumPy → 2.75x)
    * **Linear Algebra**: \~19x faster (61x → 3.2x)
    * **Logic**: \~27x faster (48x → 1.8x)
    * **Manipulation**: \~9x faster (15x → 1.6x)
    * **Gradient**: \~60x faster (30x slower → 2x *faster* than NumPy)
    * **FFT**: \~3x faster (22x → 8x)
    * **Random**: \~6x faster (11x → 1.9x)
    * **Indexing**: \~5.5x faster (12x → 2.2x)
  * Added bracket indexing (`arr[i]`, `arr[i][j]`, `arr[i] = val`) on all NDArray instances via Proxy
  * Added multi-axis reductions: `amin`, `amax`, `median`, `all`, `any`, `nanmin`, `nanmax` now accept `axis: number[]`
  * Added batched linear algebra: `eig`, `eigh`, `eigvals`, `eigvalsh`, `cholesky`, `pinv`, `slogdet`, `matrix_norm` support `[..., n, n]` batch inputs
  * Added `unique` axis parameter: find unique rows, columns, or slices along any axis
  * Added `apply_along_axis` ND support: now works with arrays of any dimensionality
  * Unified `slice()` implementation: moved from duplicated logic in `NDArrayCore`/`NDArray` to a single `shapeOps.slice()` function, and added `sliceKeepDim()` for rank-preserving slicing (thanks [@BorisTheBrave](https://github.com/dupontcyborg/numpy-ts/pull/62))
  * Revamped benchmark suite to thoroughly test all functions and dtype combinations
  * Added [WASM Acceleration](/v1.1.x/guides/wasm-acceleration) guide
  * Added [AI Disclosure](/v1.1.x/guides/ai-disclosure) page
</Update>

<Update label="v1.0.0" description="2026-02-20">
  ## numpy-ts v1.0.0

  The first stable release of numpy-ts, providing a comprehensive NumPy implementation for TypeScript and JavaScript.

  * **476 of 507 NumPy functions** implemented (94% API coverage)
  * **Full test suite** with 6,000+ tests validated against NumPy
  * **Zero dependencies**: pure TypeScript, no native modules or WebAssembly
  * **Universal runtime** support for Node.js, Bun, Deno, and browsers
  * **Tree-shakeable** ESM build for minimal bundle sizes

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.13.0...v1.0.0)
</Update>

<Update label="v0.13.1" description="2026-02-17">
  ## numpy-ts v0.13.1

  Final `v0.x` release before `v1.0`!

  * String repr fixes for 1.0 by @dupontcyborg in [#48](https://github.com/dupontcyborg/numpy-ts/pull/48)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.13.0...v0.13.1)
</Update>

<Update label="v0.13.0" description="2026-02-16">
  ## numpy-ts v0.13.0

  * Implemented fft functions by @dupontcyborg in [#42](https://github.com/dupontcyborg/numpy-ts/pull/42)
  * Implemented all missing random distribution functions (36 functions) by @dupontcyborg in [#43](https://github.com/dupontcyborg/numpy-ts/pull/43)
  * Pre-1.0.0 improvements by @dupontcyborg in [#44](https://github.com/dupontcyborg/numpy-ts/pull/44)
  * Refactored library to be tree-shakeable by @dupontcyborg in [#46](https://github.com/dupontcyborg/numpy-ts/pull/46)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.12.0...v0.13.0)
</Update>

<Update label="v0.12.0" description="2025-12-22">
  ## numpy-ts v0.12.0

  * Added missing functions for reductions, array creation, and array manipulation by @dupontcyborg in [#37](https://github.com/dupontcyborg/numpy-ts/pull/37)
  * Added missing functions for statistics, bit operations, and set operations by @dupontcyborg in [#38](https://github.com/dupontcyborg/numpy-ts/pull/38)
  * Implemented complete linear algebra coverage (100%) by @dupontcyborg in [#39](https://github.com/dupontcyborg/numpy-ts/pull/39)
  * Implemented other math and utilities functions by @dupontcyborg in [#40](https://github.com/dupontcyborg/numpy-ts/pull/40)
  * Implemented polynomial and type checking functions by @dupontcyborg in [#41](https://github.com/dupontcyborg/numpy-ts/pull/41)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.11.0...v0.12.0)
</Update>

<Update label="v0.11.0" description="2025-12-17">
  ## numpy-ts v0.11.0

  * Added fancy indexing (iindex/bindex) and document NumPy divergences by @dupontcyborg in [#34](https://github.com/dupontcyborg/numpy-ts/pull/34)
  * Added complex number support (complex64, complex128) by @dupontcyborg in [#35](https://github.com/dupontcyborg/numpy-ts/pull/35) & [#36](https://github.com/dupontcyborg/numpy-ts/pull/36)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.10.0...v0.11.0)
</Update>

<Update label="v0.10.0" description="2025-12-12">
  ## numpy-ts v0.10.0

  * Implemented statistics functions (bincount, digitize, histogram, histogram2d, histogramdd, correlate, convolve, cov, corrcoef) by @dupontcyborg in [#27](https://github.com/dupontcyborg/numpy-ts/pull/27)
  * Implemented np.random module with 17 functions by @dupontcyborg in [#28](https://github.com/dupontcyborg/numpy-ts/pull/28)
  * Implemented logic functions for 100% category coverage by @dupontcyborg in [#29](https://github.com/dupontcyborg/numpy-ts/pull/29)
  * Full API tracking by @dupontcyborg in [#30](https://github.com/dupontcyborg/numpy-ts/pull/30)
  * Implemented feature gap analysis and missing NDArray properties by @dupontcyborg in [#31](https://github.com/dupontcyborg/numpy-ts/pull/31)
  * Implemented miscellaneous missing methods by @dupontcyborg in [#32](https://github.com/dupontcyborg/numpy-ts/pull/32)
  * Bumped workflow Node versions to 24 by @dupontcyborg in [#33](https://github.com/dupontcyborg/numpy-ts/pull/33)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.9.0...v0.10.0)
</Update>

<Update label="v0.9.0" description="2025-12-09">
  ## numpy-ts v0.9.0

  * Implemented numpy.linalg module (19 functions) by @dupontcyborg in [#24](https://github.com/dupontcyborg/numpy-ts/pull/24)
  * Implemented exponential and gradient functions by @dupontcyborg in [#25](https://github.com/dupontcyborg/numpy-ts/pull/25)
  * Implemented rounding and set operations with full NumPy compatibility by @dupontcyborg in [#26](https://github.com/dupontcyborg/numpy-ts/pull/26)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.8.0...v0.9.0)
</Update>

<Update label="v0.8.0" description="2025-12-05">
  ## numpy-ts v0.8.0

  * Added missing I/O functions by @dupontcyborg in [#22](https://github.com/dupontcyborg/numpy-ts/pull/22)
  * Implemented sorting and searching functions by @dupontcyborg in [#23](https://github.com/dupontcyborg/numpy-ts/pull/23)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.7.0...v0.8.0)
</Update>

<Update label="v0.7.0" description="2025-12-03">
  ## numpy-ts v0.7.0

  * Added indexing functions to achieve 100% Indexing API coverage by @dupontcyborg in [#20](https://github.com/dupontcyborg/numpy-ts/pull/20)
  * Added bitwise operations (bitwise\_and, bitwise\_or, bitwise\_xor, bitwise\_not, invert, left\_shift, right\_shift, packbits, unpackbits) by @dupontcyborg in [#21](https://github.com/dupontcyborg/numpy-ts/pull/21)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.6.0...v0.7.0)
</Update>

<Update label="v0.6.0" description="2025-12-02">
  ## numpy-ts v0.6.0

  * Added missing minor functions by @dupontcyborg in [#16](https://github.com/dupontcyborg/numpy-ts/pull/16)
  * Implemented missing arithmetic and linear algebra functions by @dupontcyborg in [#17](https://github.com/dupontcyborg/numpy-ts/pull/17)
  * Implemented complete array creation and manipulation functions by @dupontcyborg in [#18](https://github.com/dupontcyborg/numpy-ts/pull/18)
  * Implemented remaining reductions by @dupontcyborg in [#19](https://github.com/dupontcyborg/numpy-ts/pull/19)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.5.0...v0.6.0)
</Update>

<Update label="v0.5.0" description="2025-12-01">
  ## numpy-ts v0.5.0

  * Add NPY and NPZ file format support by @dupontcyborg in [#12](https://github.com/dupontcyborg/numpy-ts/pull/12)
  * Add array manipulation and advanced functions for NumPy parity by @dupontcyborg in [#13](https://github.com/dupontcyborg/numpy-ts/pull/13)
  * Api + README cleanup by @dupontcyborg in [#14](https://github.com/dupontcyborg/numpy-ts/pull/14)
  * Trigonometric & hyperbolic functions by @dupontcyborg in [#15](https://github.com/dupontcyborg/numpy-ts/pull/15)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.4.0...v0.5.0)
</Update>

<Update label="v0.4.0" description="2025-11-26">
  ## numpy-ts v0.4.0

  * Custom BLAS by @dupontcyborg in [#11](https://github.com/dupontcyborg/numpy-ts/pull/11)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.3.0...v0.4.0)
</Update>

<Update label="v0.3.0" description="2025-11-25">
  ## numpy-ts v0.3.0

  * Arithmetic improvements by @dupontcyborg in [#7](https://github.com/dupontcyborg/numpy-ts/pull/7)
  * Removed stdlib ndarray and implemented our own by @dupontcyborg in [#9](https://github.com/dupontcyborg/numpy-ts/pull/9)
  * Updated README by @dupontcyborg in [#10](https://github.com/dupontcyborg/numpy-ts/pull/10)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.2.0...v0.3.0)
</Update>

<Update label="v0.2.0" description="2025-10-27">
  ## numpy-ts v0.2.0

  * Remaining arithmetic by @dupontcyborg in [#3](https://github.com/dupontcyborg/numpy-ts/pull/3)
  * Improved unit test coverage by @dupontcyborg in [#4](https://github.com/dupontcyborg/numpy-ts/pull/4)
  * Potential fix for code scanning alert no. 10: Workflow does not contain permissions by @dupontcyborg in [#5](https://github.com/dupontcyborg/numpy-ts/pull/5)
  * v0.2.0 by @dupontcyborg in [#6](https://github.com/dupontcyborg/numpy-ts/pull/6)

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/v0.1.0...v0.2.0)
</Update>

<Update label="v0.1.0" description="2025-10-27">
  ## numpy-ts v0.1.0

  First release of `numpy-ts`!

  Full changelog available [here](https://github.com/dupontcyborg/numpy-ts/compare/...v0.1.0)
</Update>