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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.
pip
virtualenv
brew install graphviz
apt-get install graphviz libgraphviz-dev
$ pip install dsplot
from dsplot.tree import BinaryTree
tree = BinaryTree(nodes=[5, 4, 8, 11, None, 13, 4, 7, 2, None, None, 5, 1])
tree.plot()
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()
from dsplot.matrix import Matrix
matrix = Matrix([[1, 2, 3], [4, 5, 6], [1, 2, 6]])
matrix.plot()
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')
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()
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]
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]
FAQs
Visualize Tree, Graph, and Matrix data structures with ease.
We found that dsplot demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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