styfle/nstr

number → string, but looks good

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nstr

number → string, but looks good

Automatically detects and fixes floating-point precision issues. No more 0.30000000000000004 in your UI.

The Problem

Floating-point arithmetic creates ugly precision artifacts that show up in your UI:

0.1 + 0.2                 // => 0.30000000000000004
12.2 / 0.1                // => 121.99999999999999
19.9 * 100                // => 1989.9999999999998
0.14499999582767487       // => 0.14499999582767487
-0.0000001                // => -1e-7

Real-world impact:

// Your draggable component
<div style={{
  transform: `translateX(${currentMouseX - startMouseX}px)`
}}>
// 😱 Results in: translateX(146.23999999999998px)

// Your price display
<span>${(price * rate).toString()}</span>
// 😱 Shows: $1989.9999999999998 instead of $1990

Why traditional solutions fall short:

  • toString() → Shows the ugly decimals
  • toFixed(4) → Turns 0.0000001 into "0.0000"
  • toPrecision(4) → Converts 12345.6 to "1.235e+4"

Native APIs force you to pick fixed precision parameters, but nstr() automatically detects the best precision for each number.

Solution

import nstr from 'nstr'

// ✨ Smart precision detection
nstr(0.1 + 0.2)                 // "0.3"
nstr(12.2 / 0.1)                // "122"
nstr(19.9 * 100)                // "1990"
nstr(0.14499999582767487)       // "0.145"  
nstr(1.9999999999)              // "2"
nstr(9999999.123000001)         // "9999999.123"
nstr(-0.0000001)                // "0"

// ✨ Preserves intentional precision  
nstr(42)                        // "42"
nstr(3.1415926)                 // "3.1415926"
nstr(9999999.12345)             // "9999999.12345"

Perfect for UI components:

// ✨ Clean CSS transforms
<div style={{
  transform: `translateX(${nstr(currentMouseX - startMouseX)}px)`
}}>

// ✨ Clean price displays
<span>${nstr(price * rate)}</span>

Installation

npm install nstr
# or
pnpm add nstr
# or
yarn add nstr

Usage

Basic Usage

import nstr from 'nstr'

// Just wrap any number
const result = nstr(0.1 + 0.2)  // "0.3"

// Works with any arithmetic
nstr(price * rate * taxRate)    // Clean decimals
nstr(mouseX - startX)           // Perfect for transforms  
nstr(Math.random() * 100)       // Clean random numbers

Advanced Options

// Customize precision detection sensitivity
nstr(0.1239991, { threshold: 2 })     // "0.123" (detects shorter patterns)
nstr(0.1239991, { threshold: 5 })     // "0.1239991" (more precise)

// Limit maximum decimal places  
nstr(Math.PI, { maxDecimals: 4 })     // "3.1416"
nstr(1/3, { maxDecimals: 6 })         // "0.333333"

Options:

  • threshold (default: 4) - Minimum consecutive 0s/9s to trigger cleanup
  • maxDecimals (default: 10) - Maximum decimal places to preserve

How It Works

Let's trace through the algorithm using 0.14499999582767487 as an example:

Step 1: Convert to fixed decimal

0.14499999582767487.toFixed(10)  // "0.1449999958"

Step 2: Detect consecutive patterns

"0.1449999958"
//    ^^^^^
//    5 consecutive "9"s detected (≥ threshold of 4)

Step 3: Truncate and clean up

"0.1449999958" → "0.145"

The algorithm detects floating-point artifacts by looking for consecutive identical digits (0s or 9s) longer than the threshold. When found, it intelligently truncates or rounds to produce clean results.

License

MIT

Contributors

shudingLukeberryPi

Issues