# numpy-ts

> Complete NumPy implementation for TypeScript and JavaScript

## Docs

- [Changelog & Release Notes](https://numpyts.dev/changelog.md)
- [numpy-ts](https://numpyts.dev/index.md)
- [Bitwise Operations](https://numpyts.dev/v1.3.x/api/bitwise/bitwise-ops.md): Element-wise bitwise AND, OR, XOR, NOT, shifts, and bit packing/unpacking.
- [Basic Creation](https://numpyts.dev/v1.3.x/api/creation/basic.md): Create arrays filled with zeros, ones, constants, or from existing data.
- [From Data](https://numpyts.dev/v1.3.x/api/creation/from-data.md): Create arrays from buffers, iterables, strings, functions, and coordinate grids.
- [Special Matrices](https://numpyts.dev/v1.3.x/api/creation/matrices.md): Create identity, diagonal, triangular, and Vandermonde matrices.
- [Ranges & Sequences](https://numpyts.dev/v1.3.x/api/creation/ranges.md): Create arrays with evenly or logarithmically spaced values.
- [Real FFT](https://numpyts.dev/v1.3.x/api/fft/real-fft.md): FFT functions optimized for real-valued input, returning only the positive-frequency half of the spectrum.
- [FFT Transforms](https://numpyts.dev/v1.3.x/api/fft/transforms.md): 1-D, 2-D, and N-dimensional discrete Fourier transforms for complex-valued data.
- [FFT Utilities](https://numpyts.dev/v1.3.x/api/fft/utilities.md): Helper functions for computing frequency bins and shifting zero-frequency components.
- [Gradient & Differences](https://numpyts.dev/v1.3.x/api/gradient/gradient-ops.md): Compute discrete differences, consecutive element differences, and numerical gradients.
- [Advanced Indexing](https://numpyts.dev/v1.3.x/api/indexing/advanced-indexing.md): Take, put, compress, select, broadcast, and other advanced array indexing operations.
- [Diagonal & Index Generators](https://numpyts.dev/v1.3.x/api/indexing/diagonal-indices.md): Generate diagonal, triangular, and mesh index arrays for advanced array manipulation.
- [Index Utilities](https://numpyts.dev/v1.3.x/api/indexing/index-utils.md): Convert between flat and multi-dimensional indices, integer array indexing, and boolean indexing.
- [NPY & NPZ Files](https://numpyts.dev/v1.3.x/api/io/npy-npz.md): Read and write NumPy's binary .npy and .npz file formats in the browser and Node.js.
- [Text I/O](https://numpyts.dev/v1.3.x/api/io/text-io.md): Read and write delimited text formats (CSV, TSV, whitespace-separated) in the browser and Node.js.
- [Decompositions](https://numpyts.dev/v1.3.x/api/linalg/decompositions.md): Eigenvalue, singular value, QR, and Cholesky matrix decompositions.
- [Norms & Determinants](https://numpyts.dev/v1.3.x/api/linalg/norms.md): Matrix and vector norms, condition numbers, determinants, rank, trace, diagonal, and cross products.
- [Products & Contractions](https://numpyts.dev/v1.3.x/api/linalg/products.md): Dot products, matrix multiplication, outer products, Kronecker products, and Einstein summation.
- [Solvers & Inverses](https://numpyts.dev/v1.3.x/api/linalg/solvers.md): Linear system solvers, matrix inverses, pseudo-inverses, and efficient chained products.
- [Comparison](https://numpyts.dev/v1.3.x/api/logic/comparison.md): Element-wise comparison functions that return boolean arrays, plus whole-array equality and closeness tests.
- [Floating-Point Operations](https://numpyts.dev/v1.3.x/api/logic/floating-point.md): Low-level floating-point inspection and manipulation: sign copying, sign-bit testing, ULP stepping, and dtype promotion.
- [Logical Operations](https://numpyts.dev/v1.3.x/api/logic/logical-ops.md): Element-wise logical AND, OR, NOT, and XOR operations that return boolean arrays.
- [Type Testing](https://numpyts.dev/v1.3.x/api/logic/type-testing.md): Element-wise and whole-object tests for NaN, infinity, finiteness, complex type, scalar type, dtype kind, and more.
- [Joining Arrays](https://numpyts.dev/v1.3.x/api/manipulation/joining.md): Concatenate, stack, and assemble arrays together.
- [Rearranging](https://numpyts.dev/v1.3.x/api/manipulation/rearranging.md): Flip, rotate, roll, delete, and promote array dimensions.
- [Shape Operations](https://numpyts.dev/v1.3.x/api/manipulation/shape.md): Reshape, flatten, and modify array dimensions.
- [Splitting Arrays](https://numpyts.dev/v1.3.x/api/manipulation/splitting.md): Split arrays into sub-arrays along one or more axes.
- [Tiling & Repeating](https://numpyts.dev/v1.3.x/api/manipulation/tiling.md): Repeat and pad array contents.
- [Transpose & Permutation](https://numpyts.dev/v1.3.x/api/manipulation/transpose.md): Permute, swap, and move array axes.
- [Arithmetic](https://numpyts.dev/v1.3.x/api/math/arithmetic.md): Element-wise arithmetic operations: add, subtract, multiply, divide, power, absolute value, and more.
- [Exponential & Logarithmic](https://numpyts.dev/v1.3.x/api/math/exponential.md): Element-wise exponential and logarithmic functions.
- [Hyperbolic](https://numpyts.dev/v1.3.x/api/math/hyperbolic.md): Element-wise hyperbolic functions and their inverses.
- [Other Math](https://numpyts.dev/v1.3.x/api/math/other-math.md): Clipping, element-wise min/max, NaN handling, interpolation, special functions, and complex number operations.
- [Rounding](https://numpyts.dev/v1.3.x/api/math/rounding.md): Element-wise rounding, floor, ceiling, and truncation functions.
- [Trigonometric](https://numpyts.dev/v1.3.x/api/math/trigonometric.md): Element-wise trigonometric functions and angle conversions.
- [NDArray Class](https://numpyts.dev/v1.3.x/api/ndarray/class.md): The NDArray and NDArrayCore classes -- properties, element access, slicing, conversion, and static methods.
- [NDArray Methods](https://numpyts.dev/v1.3.x/api/ndarray/methods.md): All 100+ chainable instance methods on NDArray, grouped by category.
- [API Reference Overview](https://numpyts.dev/v1.3.x/api/overview.md): Complete API reference for numpy-ts -- 476 of 507 NumPy functions implemented in pure TypeScript.
- [Polynomial Operations](https://numpyts.dev/v1.3.x/api/polynomials/polynomial-ops.md): Polynomial arithmetic, fitting, evaluation, root-finding, and calculus.
- [Basic Random Functions](https://numpyts.dev/v1.3.x/api/random/basic.md): Legacy random sampling functions for uniform, normal, and integer distributions.
- [Distributions](https://numpyts.dev/v1.3.x/api/random/distributions.md): Continuous, discrete, and multivariate probability distributions for random sampling.
- [Generator API](https://numpyts.dev/v1.3.x/api/random/generator.md): Modern random number generator using PCG64 with SeedSequence, matching NumPy's default_rng.
- [Random Module Overview](https://numpyts.dev/v1.3.x/api/random/overview.md): Overview of the numpy-ts random number generation module, including legacy and modern APIs.
- [Basic Reductions](https://numpyts.dev/v1.3.x/api/reductions/basic.md): Core reduction functions: sum, prod, mean, std, variance, min, max, median, average, ptp, all, any.
- [Cumulative Operations](https://numpyts.dev/v1.3.x/api/reductions/cumulative.md): Cumulative sum and product functions: cumsum, cumprod, cumulative_sum, cumulative_prod.
- [NaN-Safe Reductions](https://numpyts.dev/v1.3.x/api/reductions/nan-safe.md): Reduction functions that ignore NaN values: nansum, nanprod, nanmean, nanstd, nanvar, nanmin, nanmax, nanmedian, nancumsum, nancumprod, nanargmin, nanargmax, nanpercentile, nanquantile.
- [Search & Count](https://numpyts.dev/v1.3.x/api/reductions/search-count.md): Functions to find indices, percentiles, quantiles, and count non-zero elements: argmin, argmax, percentile, quantile, count_nonzero.
- [Statistics](https://numpyts.dev/v1.3.x/api/reductions/statistics.md): Statistical functions: histogram, histogram2d, histogramdd, histogram_bin_edges, bincount, digitize, correlate, convolve, cov, corrcoef, trapezoid.
- [Set Operations](https://numpyts.dev/v1.3.x/api/sets/set-ops.md): Unique values, membership testing, set intersection, union, difference, symmetric difference, and zero trimming.
- [Searching](https://numpyts.dev/v1.3.x/api/sorting/search.md): Find elements, indices of non-zero values, insertion points, and count non-zero entries in arrays.
- [Sorting](https://numpyts.dev/v1.3.x/api/sorting/sort.md): Sort arrays, compute sort indices, perform partial sorts, and sort complex-valued arrays.
- [Formatting & Printing](https://numpyts.dev/v1.3.x/api/utilities/formatting.md): Functions for converting arrays and numbers to strings: array2string, array_repr, array_str, base_repr, binary_repr, format_float_positional, format_float_scientific, and print option management.
- [Miscellaneous Utilities](https://numpyts.dev/v1.3.x/api/utilities/misc.md): Utility functions for applying operations along axes, memory inspection, array metadata, and error handling.
- [Type Checking](https://numpyts.dev/v1.3.x/api/utilities/type-checking.md): Functions for inspecting, comparing, and promoting data types: can_cast, common_type, result_type, min_scalar_type, issubdtype, typename, and mintypecode.
- [Data Loading & I/O](https://numpyts.dev/v1.3.x/examples/data-loading.md): Load and save arrays in .npy, .npz, and text formats across Node.js and browser environments.
- [Image Processing](https://numpyts.dev/v1.3.x/examples/image-processing.md): Represent images as arrays, slice regions of interest, apply element-wise transforms, transpose channels, and build convolution filters.
- [Linear Algebra](https://numpyts.dev/v1.3.x/examples/linear-algebra.md): Solve linear systems, decompose matrices, compute inverses, and multiply matrices using numpy-ts.
- [Examples](https://numpyts.dev/v1.3.x/examples/overview.md): Practical examples demonstrating numpy-ts across common domains: linear algebra, signal processing, statistics, image processing, data loading, and performance optimization.
- [Performance Tips](https://numpyts.dev/v1.3.x/examples/performance-tips.md): Optimize numpy-ts usage: avoid unnecessary copies, leverage views, tree-shake bundles, choose efficient dtypes, and prefer vectorized operations.
- [Signal Processing with FFT](https://numpyts.dev/v1.3.x/examples/signal-processing.md): Build signals, compute Fourier transforms, filter frequencies, and apply convolution using numpy-ts.
- [Statistics & Analysis](https://numpyts.dev/v1.3.x/examples/statistics-analysis.md): Descriptive statistics, histograms, correlation matrices, Monte Carlo simulations, and weighted averages with numpy-ts.
- [AI Disclosure](https://numpyts.dev/v1.3.x/guides/ai-disclosure.md)
- [NumPy API Coverage](https://numpyts.dev/v1.3.x/guides/api-coverage.md): Current numpy-ts coverage versus NumPy (auto-generated).
- [Array Basics](https://numpyts.dev/v1.3.x/guides/array-basics.md): Create, inspect, and convert NDArrays -- the core data structure of numpy-ts.
- [Broadcasting](https://numpyts.dev/v1.3.x/guides/broadcasting.md): How numpy-ts combines arrays with different shapes in element-wise operations.
- [Data Types (dtypes)](https://numpyts.dev/v1.3.x/guides/dtypes.md): All 14 supported dtypes, type promotion rules, BigInt handling, and complex numbers.
- [File I/O](https://numpyts.dev/v1.3.x/guides/file-io.md): Save and load arrays in .npy, .npz, and text formats. File-based I/O works on Node.js, Bun, and Deno; buffer-based I/O works everywhere including browsers.
- [Installation](https://numpyts.dev/v1.3.x/guides/installation.md): Install numpy-ts and choose the right entry point for your project.
- [Memory Management](https://numpyts.dev/v1.3.x/guides/memory-management.md)
- [NumPy Migration Guide](https://numpyts.dev/v1.3.x/guides/numpy-migration.md): A side-by-side guide for Python NumPy users moving to numpy-ts in TypeScript/JavaScript.
- [Quickstart](https://numpyts.dev/v1.3.x/guides/quickstart.md): Create arrays, perform operations, and explore numpy-ts in 5 minutes.
- [Roadmap](https://numpyts.dev/v1.3.x/guides/roadmap.md): What's coming next for numpy-ts.
- [Slicing & Indexing](https://numpyts.dev/v1.3.x/guides/slicing-indexing.md): Select sub-arrays, individual elements, rows, and columns using string-based slicing and advanced indexing.
- [Tree-Shaking & Bundle Size](https://numpyts.dev/v1.3.x/guides/tree-shaking.md): Understand the two ways to use numpy-ts and how they affect your bundle.
- [TypeScript Patterns](https://numpyts.dev/v1.3.x/guides/typescript-patterns.md): Type-safe patterns for using numpy-ts in TypeScript projects and libraries.
- [Views & Copies](https://numpyts.dev/v1.3.x/guides/views-copies.md): Understand when numpy-ts shares memory between arrays and when it allocates new data.
- [WASM Acceleration](https://numpyts.dev/v1.3.x/guides/wasm-acceleration.md)
- [Node.js, Deno & Bun](https://numpyts.dev/v1.3.x/performance/deno-bun.md)
- [Benchmark Methodology](https://numpyts.dev/v1.3.x/performance/methodology.md)
- [Performance Overview](https://numpyts.dev/v1.3.x/performance/overview.md)
- [Performance by Array Size](https://numpyts.dev/v1.3.x/performance/size-scaling.md)
- [numpy-ts vs. NumPy (Native)](https://numpyts.dev/v1.3.x/performance/vs-numpy.md)
- [numpy-ts vs. NumPy (Pyodide)](https://numpyts.dev/v1.3.x/performance/vs-pyodide.md)
- [Playground](https://numpyts.dev/v1.3.x/playground.md): Write and run numpy-ts code directly in your browser.

## OpenAPI Specs

- [openapi](https://numpyts.dev/api-reference/openapi.json)