normalizedim

Archetype: Node.js package

normalizeDim normalizes an N-dimensional array by setting one specific dimension (axis) to an exact value, while maintaining proportions for all other dimensions. It's an atomic, pure, and performant.

Details

• Works with any number of dimensions 2D, 3D, 4D, and beyond.
• Locks one dimension and rescales all others proportionally.
• Pure & atomic function ideal for composable utility pipelines.

Install

npm install normalizedim

Usage

import { normalizeDim } from 'normalizedim';

const dims = [1920, 1080, 400];

// ✅ Normalize width (index 0) to 1280
console.log(normalizeDim(dims, 0, 1280));
// [1280, 720, 266.67]

// ✅ Normalize height (index 1) to 800
console.log(normalizeDim(dims, 1, 800));
// [1422.22, 800, 296.30]

// ✅ Normalize depth (index 2) to 500
console.log(normalizeDim(dims, 2, 500));
// [2400, 1350, 500]

🔧 API

normalizeDim(dims: number[], axis: number, value: number): number[]

Scales an N-dimensional array such that the selected axis (axis) is exactly value, while maintaining the original proportions for all other dimensions.

• dims – Array of dimensions (e.g., [width, height, depth]).
• axis – The index of the dimension to normalize (0-based).
• value – The target size for the chosen dimension.
• Returns: A new array with adjusted dimensions.

⚠️ Errors

Throws an error if:

• axis is out of bounds (< 0 or >= dims.length).

Cheatsheet Patterns

These patterns help apply common transformations using normalizeDim! They are excluded to preserve the atomicness of the package.

import normalizeDim from 'normalizedim';

// 🔢 Round the result (Ceil, Floor, Nearest)
const dims = normalizeDim([1920, 1080], 0, 1280);
const roundedDims = dims.map(Math.round);
//or Math.floor or Math.ceil

// ❓ Check if normalization occurred (detect changes)
const originalDims = [1920, 1080];
const normalizedDims = normalizeDim(originalDims, 0, 1920);
const isNormalized = !originalDims.every((dim, i) => dim === normalizedDims[i]);

console.log(isNormalized); // false (already at target size)

Example Use Cases

• Image Processing – Prevent excessive pixel count while keeping aspect ratio.
• 3D Modeling – Ensure objects stay within a total volume constraint.
• Physics Simulations – Rescale multi-dimensional datasets.
• Grid/Matrix Operations – Adjust data structures without exceeding capacity.

Related Packages

• https://github.com/alexstevovich/capdim – Caps total volume while keeping proportions.
• https://github.com/alexstevovich/fitdim – Ensures dimensions fit within a bounding box.

These link might be suffixed with "-node" in the future if conflicts arise.

Links

Development Homepage

https://github.com/alexstevovich/normalizedim

This link might be suffixed with "-node" in the future if conflicts arise.

License

Licensed under the Apache License 2.0.