nadder

nadder

Easy n-dimensional data manipulation with NumPy syntax.

Installation

npm i nadder # or yarn add nadder, or pnpm add nadder

Usage

import { ndarray, array, add, evaluate, arange } from 'nadder';

const dataSource = new Float32Array(1_000_000);
// load data into dataSource manually here...

// Initialize (1000, 1000) ndarray
const t = ndarray(dataSource, [1000, 1000]);

// NumPy slicing fully supported
console.log(t['100:110, 305:300:-1']);

t['..., :100'] = 1.23;

// np.newaxis is now +
console.log(t['0, 0, +']);

const leapYears = Array.from(
    { length: 1000 },
    (_, i) => i % 4 == 0 && (i % 400 == 0 || i % 100 != 0)
);

// nadder.array automatically creates an efficient representation of your
// Array object and translates nested lists into dimensions in the ndarray
const boolIndex = array(leapYears);

// You can even use other ndarrays in the indices!

console.log(t[`..., ${boolIndex}`]);

// You can evaluate things using a Python-esque DSL
console.log(evaluate`${t}[:, 0] * 2 + 1 / ${arange(1000)}`)

// ufuncs also supported through explicit syntax
// broadcasting, typecasting done automatically
console.log(add(t, boolIndex));

The embedded DSL supports a wide variety of common constructs and Python syntax (e.g. @ matrix multiplication, // floor division, keyword arguments). All syntax works both on scalars and ndarrays. The DSL can also use JavaScript values interpolated within the code.

import { parse, argument, arange } from 'nadder';

const calcTrace = parse`
  mat = ${argument('matrix')}.astype(float64);
  n = mat.shape[0];
  trace = 0;

  if ${process.env.DEBUG} {
    ${console.log}(mat);
  }

  for i in arange(n) {
    trace += mat[i, i];
  }

  # Last line with no semicolon is the return value
  trace
`;

// 13
const example = calcTrace({
  matrix: array([
    [1, 4, 5],
    [2, 3, 6],
    [7, 1, 9]
  ])
});

// [154., 158., 162., 166., 170.]
const multiElem = calcTrace({
  matrix: arange(1, 81).reshape(4, 4, 5)
});

Features

• NumPy syntax and evaluation via evaluate
  • Python-esque syntax and ndarray manipulation
  • Support for most numeric operations
  • Conditionals, for/while loops, JavaScript interpolation
• Ergonomic NumPy slicing, broadcasting with or without evaluate
  • All NumPy bracket syntax and indexing routines supported
• Matrix manipulation and matmul
• Tiny: under 20kB gzipped
• Performant view-based manipulation; minimal copying
• Fast bitset-backed boolean ndarrays
• Interleaved complex numbers
• Arithmetic, algebraic, and trigonometric operations for real and complex numbers
• Full TypeScript support
• In progress: support for most NumPy manipulations, more fast paths for higher performance, fast WASM modules

Limitations

• Fortran (column-major) memory layout supported indirectly
• Limited Complex64 support (Complex128 fully supported)

License

MIT