Sample data for any heatmap chart
The Heatmap Data Generator produces matrix datasets shaped for heatmap visualizations — color-coded grids where each cell’s value maps to a shade. It covers the two most common layouts: a dense numeric grid like a correlation matrix, and a GitHub contribution calendar where weeks run across and weekdays run down. Configure the size, value range and structure, then export ready-to-plot JSON.
How it works
The tool builds an n × m grid using a seeded pseudo-random generator so the same settings always produce the same data, while the Regenerate button advances the seed for a fresh set. Each cell starts as a value t between 0 and 1. When clustering is enabled, the generator chooses a random hot-spot and computes each cell’s normalized distance from it; the final t becomes a blend of the raw noise and a 1 − distance bump, so cells near the hot-spot trend high and distant cells trend low. The t value is then scaled into your chosen min-to-max range with value = min + t × (max − min) and rounded to two decimals.
Serialization offers three shapes. The matrix format emits a 2D array. The row/col/value format flattens the grid into one object per cell — convenient for libraries that expect long-form data. The GitHub format rounds each value to an integer count and labels columns as x and rows as y, matching contribution-calendar renderers.
Tips and example
- For a correlation-matrix look, use an equal row and column count with clustering off and a range of
0to1. - For a GitHub contribution calendar, set rows to
7(weekdays), columns to about52(weeks), and pick the GitHub format. - Turn clustering on when you want the demo heatmap to clearly show a high-density region rather than uniform noise — useful for testing color legends.
- A single matrix cell maps directly to one colored square, so the preview grid above mirrors exactly what your chart will render.