Improving Layout Aspect Ratio of Directed Graphs with Unflatten (with examples)

Improving Layout Aspect Ratio of Directed Graphs with Unflatten (with examples)

Graph visualization is an essential part of data analysis and representation. However, when dealing with directed graphs, the aspect ratio of the layout can sometimes be less than optimal. This is where the unflatten command comes in handy. unflatten is a Graphviz tool that adjusts directed graphs to improve the layout aspect ratio.

In this article, we will explore three different use cases of the unflatten command, along with their corresponding code examples. We will also provide motivations for using each example, explanations for every argument, and example outputs.

Use Case 1: Adjusting Directed Graphs to Improve Layout Aspect Ratio

Code Example:

unflatten path/to/input1.gv path/to/input2.gv ... > path/to/output.gv

Motivation:

When working with directed graphs, it is crucial to have a visually pleasing and easily understandable layout. However, sometimes the layout generated by Graphviz may have a suboptimal aspect ratio, making the graph less appealing or challenging to comprehend. By using the unflatten command, we can optimize the layout aspect ratio of directed graphs, leading to a more visually appealing and comprehensible representation.

Explanation for Every Argument:

  • path/to/input1.gv, path/to/input2.gv, …: These arguments represent the path(s) to the input Graphviz files containing the directed graphs that need to be adjusted.

  • path/to/output.gv: This argument represents the path to the output Graphviz file where the adjusted directed graph will be saved.

Example Output:

After executing the unflatten command, you will find the adjusted directed graph saved in the specified output Graphviz file (path/to/output.gv). The resulting graph will have an improved layout aspect ratio compared to the original input graph(s).

Use Case 2: Using Unflatten as a Preprocessor for Dot Layout

Code Example:

unflatten path/to/input.gv | dot -T png path/to/output.png

Motivation:

The unflatten command can be used as a preprocessor for the dot layout to further enhance the aspect ratio of directed graphs. By incorporating unflatten before using the dot layout algorithm, we can achieve even better visualization results.

Explanation for Every Argument:

  • path/to/input.gv: This argument represents the path to the input Graphviz file containing the directed graph that needs to be adjusted.

  • -T png: This argument specifies the desired output format for the resulting visualization. In this example, we have chosen PNG as the output format, but other valid options include SVG, PDF, and more.

  • path/to/output.png: This argument represents the path to the output PNG file where the visualization will be saved.

Example Output:

Executing the command will generate a PNG image file (path/to/output.png) that visualizes the directed graph with an improved layout aspect ratio. The resulting visualization will have been adjusted by both the unflatten command and the dot layout algorithm.

Use Case 3: Displaying Help for Unflatten

Code Example:

unflatten -?

Motivation:

When working with unfamiliar command-line tools, it is crucial to have access to documentation and help materials. By using the -? option with the unflatten command, we can quickly obtain the help documentation, which provides detailed information about the command’s usage, supported arguments, and other useful tips.

Explanation for Every Argument:

  • -?: This argument triggers the command to display the help documentation for the unflatten command.

Example Output:

Executing the command will display the help documentation for the unflatten command. This documentation will provide detailed information about the command’s usage, supported arguments, and any additional tips or instructions that may be useful for working with unflatten.


In conclusion, the unflatten command is an invaluable tool for adjusting the layout aspect ratio of directed graphs. By exploring these various use cases, we can enhance the visual representation and comprehensibility of our graphs, making them more impactful and informative.

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