dsplot

Data Structure Plot (DSPlot)

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 3.7 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

🤟 Usage

• 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()

🎁 Additional features

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]

📄 License

MIT