Changelog & Release Notes

​

numpy-ts v1.4.0

​

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

• 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.

​

numpy-ts v1.3.2

• Fixed issue with [Symbol.dispose] not working properly on Safari & old runtimes
• Added wasmFreeBytes to public exports to allow memory usage checks

​

numpy-ts v1.3.1

• 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

​

numpy-ts v1.3.0

• 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 TypedArrays 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.
• 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):
  • Added `vindex: vectorized multi-dimensional indexing matching NumPy’s integer array indexing semantics. See Advanced Indexing. (#94)
  • Slice now supports '...' (ellipsis) and 'newaxis' tokens for compact multi-dim slicing. See Slicing & Indexing. (#84)
• in1d deprecated (matches NumPy 2.4) — use isin.

​

numpy-ts v1.2.0

• 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

​

numpy-ts v1.1.0

• 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)
• Revamped benchmark suite to thoroughly test all functions and dtype combinations
• Added WASM Acceleration guide
• Added AI Disclosure page

​

numpy-ts v1.0.0

• 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

​

numpy-ts v0.13.1

• String repr fixes for 1.0 by @dupontcyborg in #48

​

numpy-ts v0.13.0

• Implemented fft functions by @dupontcyborg in #42
• Implemented all missing random distribution functions (36 functions) by @dupontcyborg in #43
• Pre-1.0.0 improvements by @dupontcyborg in #44
• Refactored library to be tree-shakeable by @dupontcyborg in #46

​

numpy-ts v0.12.0

• Added missing functions for reductions, array creation, and array manipulation by @dupontcyborg in #37
• Added missing functions for statistics, bit operations, and set operations by @dupontcyborg in #38
• Implemented complete linear algebra coverage (100%) by @dupontcyborg in #39
• Implemented other math and utilities functions by @dupontcyborg in #40
• Implemented polynomial and type checking functions by @dupontcyborg in #41

​

numpy-ts v0.11.0

• Added fancy indexing (iindex/bindex) and document NumPy divergences by @dupontcyborg in #34
• Added complex number support (complex64, complex128) by @dupontcyborg in #35 & #36

​

numpy-ts v0.10.0

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

​

numpy-ts v0.9.0

• Implemented numpy.linalg module (19 functions) by @dupontcyborg in #24
• Implemented exponential and gradient functions by @dupontcyborg in #25
• Implemented rounding and set operations with full NumPy compatibility by @dupontcyborg in #26

​

numpy-ts v0.8.0

• Added missing I/O functions by @dupontcyborg in #22
• Implemented sorting and searching functions by @dupontcyborg in #23

​

numpy-ts v0.7.0

• Added indexing functions to achieve 100% Indexing API coverage by @dupontcyborg in #20
• Added bitwise operations (bitwise_and, bitwise_or, bitwise_xor, bitwise_not, invert, left_shift, right_shift, packbits, unpackbits) by @dupontcyborg in #21

​

numpy-ts v0.6.0

• Added missing minor functions by @dupontcyborg in #16
• Implemented missing arithmetic and linear algebra functions by @dupontcyborg in #17
• Implemented complete array creation and manipulation functions by @dupontcyborg in #18
• Implemented remaining reductions by @dupontcyborg in #19

​

numpy-ts v0.5.0

• Add NPY and NPZ file format support by @dupontcyborg in #12
• Add array manipulation and advanced functions for NumPy parity by @dupontcyborg in #13
• Api + README cleanup by @dupontcyborg in #14
• Trigonometric & hyperbolic functions by @dupontcyborg in #15

​

numpy-ts v0.4.0

• Custom BLAS by @dupontcyborg in #11

​

numpy-ts v0.3.0

• Arithmetic improvements by @dupontcyborg in #7
• Removed stdlib ndarray and implemented our own by @dupontcyborg in #9
• Updated README by @dupontcyborg in #10

​

numpy-ts v0.2.0

• Remaining arithmetic by @dupontcyborg in #3
• Improved unit test coverage by @dupontcyborg in #4
• Potential fix for code scanning alert no. 10: Workflow does not contain permissions by @dupontcyborg in #5
• v0.2.0 by @dupontcyborg in #6

​

numpy-ts v0.1.0