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Read .npy arrays saved with NumPy directly in modern JavaScript runtimes.


Installation

npm install npyjs
# or
yarn add npyjs

Supports Node ≥18, modern browsers, and Deno/Bun.


Import

// Modern named export (recommended)
import { load } from "npyjs";

// Back-compatibility class (matches legacy docs/tests)
import npyjs from "npyjs";

Usage

1. Functional API (preferred)

import { load } from "npyjs";

const arr = await load("my-array.npy");
// arr has { data, shape, dtype, fortranOrder }
console.log(arr.shape); // e.g., [100, 784]

2. Legacy Class API (still supported)

import npyjs from "npyjs";

// Default options
const n = new npyjs();

// Disable float16→float32 conversion
const n2 = new npyjs({ convertFloat16: false });

const arr = await n.load("my-array.npy");

Accessing multidimensional elements

npyjs returns flat typed arrays with a shape. npyjs also ships a small helper to turn the flat data + shape into nested JS arrays.

import { load } from "npyjs";
import { reshape } from "npyjs/reshape";

const { data, shape, fortranOrder } = await load("my-array.npy");
const nested = reshape(data, shape, fortranOrder); // -> arrays nested by dims

For C-order arrays (the NumPy default), pass fortranOrder = false (default).

For Fortran-order arrays, pass true and the helper will return the natural row-major nested structure.

Or pair it with ndarray or TensorFlow.js:

import ndarray from "ndarray";
import { load } from "npyjs";

const { data, shape } = await load("my-array.npy");
const tensor = ndarray(data, shape);

console.log(tensor.get(10, 15));

Supported Data Types

Float16 Control

// Default: converts float16 → float32
const n1 = new npyjs();

// Keep raw Uint16Array
const n2 = new npyjs({ convertFloat16: false });

Development

Commands

npm run build       # Build to dist/
npm test            # Run Vitest
npm run typecheck   # TypeScript type checking

License

Apache-2.0 © JHU APL


Made with ♥ at JHU APL

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