Scientific Computing Features

SciGrid is built with scientific datasets in mind, offering specialized features for engineering and laboratory applications.

Automatic SI Scaling

Precision is key in scientific data, but readability is equally important. SciGrid automatically scales numeric values using standard SI prefixes (Y, Z, E, P, T, G, M, k, h, da, d, c, m, u, n, p, f, a, z, y).

To enable this, simply set the type of a column to numeric and provide a units string in the header configuration.

const headers = [
  { 
    name: "Precision Voltage", 
    type: "numeric", 
    units: "V" 
  }
];

Actual Value	Rendered Text
0.000001	1μV
1500	1.5kV
0.1	100mV

Complex Headers

Scientific data often requires more context than just a name. SciGrid supports multi-line headers to display Units and Descriptions without taking up valuable cell space.

Set headerSubTextCount in your config to 1 or 2 to enable sub-lines.

Automatic Scientific Scaling

Values are automatically formatted with SI prefixes based on their magnitude.

Performance with Large Sets

SciGrid uses a custom canvas renderer that can handle millions of data points. By using Float32Array or Float64Array in your IDataGridProvider, you can minimize memory overhead and maximize throughput.

Built-in Sparklines

Visualize trends directly in the grid. If a cell returns an array of numbers and the column type is set to sparkline, SciGrid will render a mini-chart.

const provider = {
  getCellData: (r, c) => [10, 20, 15, 30, 25],
  getHeader: (c) => ({ type: 'sparkline', name: 'Trend' })
};