<img src="https://github.com/billtrn/dsplot/blob/master/img/logo.png?raw=true" alt="dsplot-logo"> <h1 align="center"> DSPlot - Data Structure Visualization </h1> <a href="https://badge.fury.io/py/dsplot"> <img src="https://badge.fury.io/py/dsplot.svg" alt="PyPI version" height="18"> </a> <a href="https://dl.circleci.com/status-badge/redirect/circleci/5AFmWEafY2avg6nRh9G3Rw/7QZG8vWJDWRJPjULgXxNSc/tree/master"> <img src="https://dl.circleci.com/status-badge/img/circleci/5AFmWEafY2avg6nRh9G3Rw/7QZG8vWJDWRJPjULgXxNSc/tree/master.svg?style=svg&circle-token=5155d812aff1e1b13de2a0313ba1a3e6c600fa85" alt="Build Status" height="18"> </a> <a href="https://coveralls.io/github/billtrn/dsplot?branch=master"> <img src="https://coveralls.io/repos/github/billtrn/dsplot/badge.svg?branch=master" alt="Coverage Status" height="18"> </a> <a href="https://github.com/billtrn/dsplot/blob/master/LICENSE"> <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License" height="18"> </a> <hr /> Graph Pythonically. 📏📐🖍️🐍

DSPlot is a tool to simply visualize tree and graph data structures by serving as a Pythonic interface to the Graphviz layout. DSPlot allows you to easily draw trees, graphs (both directed and undirected), and matrices by passing data in primitive form and directly output an image.

⬇ Installation

0. Prerequisites

Python 3.9 or later
pip
virtualenv

1. Install Graphviz

MacOS:

brew install graphviz

Linux:

apt-get install graphviz libgraphviz-dev

Other OS(s): https://graphviz.org/download/

2. Install package

$ pip install dsplot

📦 Features

Binary Tree:

from dsplot.tree import BinaryTree

tree = BinaryTree(nodes=[5, 4, 8, 11, None, 13, 4, 7, 2, None, None, 5, 1])
tree.plot()

Graph:

from dsplot.graph import Graph

graph = Graph(
    {0: [1, 4, 5], 1: [3, 4], 2: [1], 3: [2, 4], 4: [], 5: []}, directed=True
)
graph.plot()

from dsplot.graph import Graph

graph = Graph(
    {1: [2, 4], 2: [1, 3], 3: [2, 4, 5], 4: [1, 3], 5: [3, 6, 7], 6: [5], 7: [5]}, directed=False
)
graph.plot()

Matrix:

from dsplot.matrix import Matrix

matrix = Matrix([[1, 2, 3], [4, 5, 6], [1, 2, 6]])
matrix.plot()

Customization: You can customize the border color, shape, style, and fill color of the nodes, and the orientation (left to right - LR, top to bottom - TB) of the graph.

from dsplot.graph import Graph

graph = Graph(
    {0: [1, 4, 5], 1: [3, 4], 2: [1], 3: [2, 4], 4: [], 5: []}, directed=True
)
graph.plot(fill_color='#aec6cf')

from dsplot.tree import BinaryTree

tree = BinaryTree(nodes=[5, 4, 8, 11, None, 13, 4, 7, 2, None, None, 5, 1])
tree.plot(orientation='LR', border_color='#FFCE30', fill_color='#aec6cf')

Edge values for Graphs: For edge values, str and int data types are supported at the moment.

from dsplot.graph import Graph

graph = Graph(
    {0: [1, 4, 5], 1: [3, 4], 2: [1], 3: [2, 4], 4: [], 5: []},
    directed=True,
    edges={'01': 1, '04': 4, '05': 5, '13': 3, '14': 4, '21': 2, '32': 3, '34': 4},
)
graph.plot()

🎁 Bonus

1. Tree traversals:

from dsplot.tree import BinaryTree

tree = BinaryTree(nodes=[5, 4, 8, 11, None, 13, 4, 7, 2, None, None, 5, 1])

print(tree.preorder())
# [5, 4, 11, 7, 2, 8, 13, 4, 5, 1]

print(tree.inorder())
# [7, 11, 2, 4, 5, 13, 8, 5, 4, 1]

print(tree.postorder())
# [7, 2, 11, 4, 13, 5, 1, 4, 8, 5]

2. Graph traversals:

from dsplot.graph import Graph

graph = Graph(
    {0: [1, 4, 5], 1: [3, 4], 2: [1], 3: [2, 4], 4: [], 5: []}, directed=True
)

print(graph.bfs())
# [0, 1, 4, 5, 3, 2]

print(graph.dfs())
# [0, 1, 3, 2, 4, 5]

🤝 Contributing

Contributions, issues and feature requests are welcome!

Development setup

DSPlot uses poetry for dependency management. For guidance on how to install poetry, please refer to the official documentation.
After cloning the repo, run poetry install to install the dependencies.

Running tests

DSPlot uses pytest for testing. To run the tests, run poetry run pytest in the root directory.

📄 License

MIT

Dsplot

Install / Use

README