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pip install graphviz
Requirement already satisfied: graphviz in /opt/conda/lib/python3.12/site-packages (0.20.3) Note: you may need to restart the kernel to use updated packages.
接下来是创建线性图的代码：
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from graphviz import Digraph
创建一个有向图
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dot = Digraph(comment='The Test Table')
添加节点
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dot.node('A', 'A')
dot.node('B', 'B')
dot.node('C', 'C')
添加边
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dot.edges(['AB', 'BC'])
打印生成的DOT源码
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print(dot.source)
// The Test Table
digraph {
A [label=A]
B [label=B]
C [label=C]
A -> B
B -> C
}
保存图像为 test-output/test-table.gv.pdf 文件
dot.render('test-output/test-table.gv', format='pdf', cleanup=True, view = True)
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dot
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from graphviz import Digraph
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dot = Digraph(comment='The While Loop')
添加循环开始节点
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dot.node('A', 'Start Loop')
dot.node('B', 'Do Stuff')
dot.node('C', 'Check Condition')
dot.node('D', 'End Loop')
添加边表示循环结构
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dot.edges(['AB', 'BC', 'CD', 'DA'])
添加条件判断
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dot.edge('C', 'B', constraint='true', label='true')
dot.edge('C', 'D', constraint='false', label='false')
打印生成的DOT源码
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print(dot.source)
// The While Loop
digraph {
A [label="Start Loop"]
B [label="Do Stuff"]
C [label="Check Condition"]
D [label="End Loop"]
A -> B
B -> C
C -> D
D -> A
C -> B [label=true constraint=true]
C -> D [label=false constraint=false]
}
保存图像为 test-output/while-loop.gv.pdf 文件
dot.render('test-output/while-loop.gv', format='pdf', cleanup=True)
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dot
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from graphviz import Digraph
创建一个图形
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dot = Digraph(comment='The Dynamic Graph')
初始添加节点
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dot.node('A', 'Node A')
dot.node('B', 'Node B')
动态添加边和节点
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dot.edge('A', 'B')
根据条件动态删除节点和边
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dot.node('C', 'Node C')
假设这是某些条件判断的结果
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if False:
dot.edge('B', 'C')
dot.edge('B', 'A', style='dotted')
打印生成的DOT源码
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print(dot.source)
// The Dynamic Graph
digraph {
A [label="Node A"]
B [label="Node B"]
A -> B
C [label="Node C"]
B -> A [style=dotted]
}
保存图像为 test-output/dynamic-graph.gv.pdf 文件
dot.render('test-output/dynamic-graph.gv', format='pdf', cleanup=True)
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dot
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from graphviz import Digraph
import os
创建一个基础图形模板
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base_graph = Digraph(comment='Base Graph')
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base_graph.node('A', 'Start')
base_graph.edge('A', 'B', label='Next')
批量处理的函数
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def create_and_render_graph(index):
graph = base_graph.copy()
graph.node('B', f'Node {index}')
# graph.render(f'test-output/graph{index}', format='png', cleanup=True)
return graph.source
处理5个图形
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for i in range(1, 6):
print(f'Creating and rendering graph{i}...')
create_and_render_graph(i)
Creating and rendering graph1... Creating and rendering graph2... Creating and rendering graph3... Creating and rendering graph4... Creating and rendering graph5...
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from graphviz import Digraph
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dot = Digraph(comment='The Example Graph')
添加节点和边
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dot.node('A', 'Start')
dot.node('B', 'End')
dot.edge('A', 'B')
设置渲染格式并保存
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# dot.render('test-output/example-graph', format='png', cleanup=True) # PNG 格式
# dot.render('test-output/example-graph', format='svg', cleanup=True) # SVG 格式
# dot.render('test-output/example-graph', format='pdf', cleanup=True) # PDF 格式
dot
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from graphviz import Digraph
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dot = Digraph(comment='The Colored Graph')
添加节点和边
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dot.node('A', 'Start')
dot.node('B', 'End')
dot.edge('A', 'B', color='red')
使用Neato布局算法渲染图形
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dot.attr(rankdir='LR', splines='ortho', color='blue', fontcolor='red')
dot.edges(['AB'])
dot.attr(size='6,3')
渲染图形为PNG格式
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# dot.render('test-output/colored-graph', format='png', cleanup=True)
dot
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在上述代码中，我们对图形进行了一些高级配置：
rankdir='LR'设置了节点的排列方向，从左到右。splines='ortho'指定了边的形状为直角连接。color='blue'和fontcolor='red'分别设置了图形和字体的颜色。size='6,3'设置了渲染图形的大小。
通过这些高级选项，我们可以对渲染出的图形进行更细致的控制， 使得图形更贴合我们的展示需求。
到目前为止，我们已经详细介绍了如何使用 graphviz 库来创建、编辑和渲染图形， 并展示了具体的示例代码。在接下来的第六章中， 我们将深入探讨Graphviz的自动布局功能以及它支持的多种格式输出。