squarify

Pure Python implementation of the squarify treemap layout algorithm.

Based on algorithm from Bruls, Huizing, van Wijk, "Squarified Treemaps", but implements it differently.

Installation

Compatible with Python 2 and Python 3.

pip install squarify

Usage

The main function is squarify and it requires two things:

• A coordinate system comprising values for the origin (x and y) and the width/height (dx and dy).
• A list of positive values sorted from largest to smallest and normalized to the total area, i.e., dx * dy).

The function returns a list of dicts (i.e., JSON objects), each one a rectangle with coordinates corresponding to the given coordinate system and area proportional to the corresponding value. Here's an example rectangle:

{
    "x": 0.0,
    "y": 0.0,
    "dx": 327.7,
    "dy": 433.0
}

The rectangles can be easily plotted using, for example, d3.js.

There is also a version of squarify called padded_squarify that returns rectangles that, when laid out, have a bit of padding to show their borders.

The helper function normalize_sizes will compute the normalized values, and the helper function plot will generate a Matplotlib-based treemap visualization of your data (see docstring).

Example

import squarify

# these values define the coordinate system for the returned rectangles
# the values will range from x to x + width and y to y + height
x = 0.
y = 0.
width = 700.
height = 433.

values = [500, 433, 78, 25, 25, 7]

# values must be sorted descending (and positive, obviously)
values.sort(reverse=True)

# the sum of the values must equal the total area to be laid out
# i.e., sum(values) == width * height
values = squarify.normalize_sizes(values, width, height)

# returns a list of rectangles
rects = squarify.squarify(values, x, y, width, height)

# padded rectangles will probably visualize better for certain cases
padded_rects = squarify.padded_squarify(values, x, y, width, height)

The variable rects contains

[
  {
    "dy": 433,
    "dx": 327.7153558052434,
    "x": 0,
    "y": 0
  },
  {
    "dy": 330.0862676056338,
    "dx": 372.2846441947566,
    "x": 327.7153558052434,
    "y": 0
  },
  {
    "dy": 102.9137323943662,
    "dx": 215.0977944236371,
    "x": 327.7153558052434,
    "y": 330.0862676056338
  },
  {
    "dy": 102.9137323943662,
    "dx": 68.94160077680677,
    "x": 542.8131502288805,
    "y": 330.0862676056338
  },
  {
    "dy": 80.40135343309854,
    "dx": 88.24524899431273,
    "x": 611.7547510056874,
    "y": 330.0862676056338
  },
  {
    "dy": 22.51237896126767,
    "dx": 88.2452489943124,
    "x": 611.7547510056874,
    "y": 410.4876210387323
  }
]