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
Usage
Example
Documentation

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 documentation).

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
  }
]

Documentation for Squarify

normalize_sizes(sizes, dx, dy) : Normalize list of values.

Normalizes a list of numeric values so that sum(sizes) == dx * dy.

Parameters

sizes : list-like of numeric values
    Input list of numeric values to normalize.
dx, dy : numeric
    The dimensions of the full rectangle to normalize total values to.

Returns
-------
list[numeric]
    The normalized values.

padded_squarify(sizes, x, y, dx, dy) : Compute padded treemap rectangles.

See squarify docstring for details. The only difference is that the returned rectangles have been "padded" to allow for a visible border.

plot(sizes, norm_x=100, norm_y=100, color=None, label=None, value=None, ax=None, pad=False, bar_kwargs=None, text_kwargs=None, **kwargs) : Plotting with Matplotlib.

Parameters

sizes
    input for squarify
norm_x, norm_y
    x and y values for normalization
color
    color string or list-like (see Matplotlib documentation for details)
label
    list-like used as label text
value
    list-like used as value text (in most cases identical with sizes argument)
ax
    Matplotlib Axes instance
pad
    draw rectangles with a small gap between them
bar_kwargs : dict
    keyword arguments passed to matplotlib.Axes.bar
text_kwargs : dict
    keyword arguments passed to matplotlib.Axes.text
**kwargs
    Any additional kwargs are merged into `bar_kwargs`. Explicitly provided
    kwargs here will take precedence.

Returns

matplotlib.axes.Axes
    Matplotlib Axes

squarify(sizes, x, y, dx, dy) : Compute treemap rectangles.

Given a set of values, computes a treemap layout in the specified geometry using an algorithm based on Bruls, Huizing, van Wijk, "Squarified Treemaps". See README for example usage.

Parameters

sizes : list-like of numeric values
    The set of values to compute a treemap for. `sizes` must be positive
    values sorted in descending order and they should be normalized to the
    total area (i.e., `dx * dy == sum(sizes)`)
x, y : numeric
    The coordinates of the "origin".
dx, dy : numeric
    The full width (`dx`) and height (`dy`) of the treemap.

Returns

list[dict]
    Each dict in the returned list represents a single rectangle in the
    treemap. The order corresponds to the input order.

laserson/squarify

squarify

Contents

Installation

Usage

Example

Documentation for Squarify