Rosmontis

Rosmontis is a lightweight library that outputs a graph image using graphviz based on the adjacency list, dictionary, or matrix. This module simplified the steps of creating a graph, where it adds the nodes and edges automatically from the input data to graphviz, creating the graph in one function call.

Installation

1. Currently, rosmontis only supports Python 3. Use pip to install:

$ pip install --upgrade rosmontis

2. You also need to have graphviz installed in order to generate the image based on the .dot files. See the official website for the installation process: Graphviz.org.

Usage & Examples

Unweighted Undirected Graph in Adjacency List

import rosmontis

g = [['A', ['B', 'E']],   # node A is connected to node B and node E
     ['B', ['E', 'C']],   # node B is connected to node E and node C
     ['C', ['D']], 
     ['D', ['E', 'F']]]

# output a png image representing the graph in the same
# directory of this file.
rosmontis.renderGraphList(graph=g, graphName="example1", weighted=False, directed=False)

Weighted Undirected Graph in Adjacency Dictionary

import rosmontis

# node A is connected to node B with weight of 2, and node E with weight of 0.5
# node B is connected to node E with weight of 0.2, and node C with weight of 3
# ... etc
g = {'A': [['B', 2], ['E', 0.5]], 
     'B': [['E', 0.2], ['C', 3]], 
     'C': [['D', 7]], 
     'D': [['E', 0.15], ['F', 1.6]]}

rosmontis.renderGraphDict(graph=g, graphName="example2", weighted=True, directed=False)

Unweighted Undirected Graph in Adjacency Matrix

import rosmontis

# Note column 0 and row 0 are the headers/labels of each node. The actual weight
# starts from row 1 column 1
# 1 indicates there is an edge, 0 indicates no connection
g = [[None, "A", "B", "C", "D", "E", "F"],
     ["A",   0,   0,   0,   0,   1,   1 ],
     ["B",   0,   0,   1,   0,   0,   1 ],
     ["C",   0,   1,   0,   1,   0,   0 ],
     ["D",   0,   0,   1,   0,   1,   1 ],
     ["E",   1,   0,   0,   1,   0,   1 ],
     ["F",   1,   1,   0,   1,   1,   0 ]]

rosmontis.renderGraphMatrix(graph=g, graphName="example3", weighted=False, directed=False)

Weighted Directed Graph in Adjacency Matrix

import rosmontis

# Change the numbers from 1 to the weight value. Numbers other than 0 represents
# a connection, vice versa.
g = [[None, "A",   "B",   "C",   "D",   "E",   "F"  ],
     ["A",   0,     2,     0,     0,     0,     3   ],
     ["B",   0,     0,     0,     0,     0.2,   0   ],
     ["C",   0,     0,     0,     7.5,   0,     13  ],
     ["D",   0,     0,     0,     0,     0,     1.6 ],
     ["E",   0,     0,     0,     0,     0,     -4  ],
     ["F",   0,     0,     0,     0,     0,     0   ]]

rosmontis.renderGraphMatrix(graph=g, graphName="example4", weighted=True, directed=True)

See more examples in the examples/ folder.