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https://github.com/eliahkagan/codegraph

graphs in software engineering
https://github.com/eliahkagan/codegraph

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graphs in software engineering

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README 

        



# codegraph - graphs in software engineering



[Graphs](https://en.wikipedia.org/wiki/Graph_theory) have many applications in

computer science and software engineering, because they are a general model of

relatedness. This repository is a collection of examples where something in

software engineering has a useful and interesting graph structure that is

straightforward—and fun—to visualize programmatically. Graphs are extremely

versatile, so these examples are necessarily incomplete in the extreme.



These [graphs](https://en.wikipedia.org/wiki/Graph_(discrete_mathematics))

should not be confused with [the other thing called a

graph](https://en.wikipedia.org/wiki/Graph_of_a_function), nor with

[plots](https://en.wikipedia.org/wiki/Plot_(graphics)) (which relate to that

other thing).



## License



[0BSD](https://spdx.org/licenses/0BSD.html). See [**`LICENSE`**](LICENSE).



## The examples



Currently all examples are in Python, with all the code in Jupyter notebooks.



- [`builtin_types.ipynb`](builtin_types.ipynb) - inheritance hierarchy of

  Python builtins

- [`exceptions.ipynb`](exceptions.ipynb) - inheritance hierarchy of Python

  exceptions

- [`fibonacci.ipynb`](fibonacci.ipynb) - relationship of Fibonacci subproblems

- [`git_graph.ipynb`](git_graph.ipynb) - source control commit graphs

- [`nested_sequences.ipynb`](nested_sequences.ipynb) - heterogeneous nested

  collections

- [`nested_tuples.ipynb`](nested_tuples.ipynb) - homogeneous nested collections

- [`yaml.ipynb`](yaml.ipynb) - structured data



## Installation



Clone the repository and go into the cloned directory:



```sh

git clone https://github.com/EliahKagan/codegraph

cd codegraph

```



Then you can install dependencies using `conda` or using `pipenv`.



### `conda`



To use [`conda`](https://en.wikipedia.org/wiki/Conda_(package_manager)), make

sure `conda` is installed (if not, I suggest

[Miniforge](https://github.com/conda-forge/miniforge)). Then run:



```sh

conda env create

```



### `pipenv`



To use [`pipenv`](https://pipenv.pypa.io/en/latest/), make sure `pipenv` is

installed.



Also install [Graphviz](https://graphviz.org/) on your system (`conda` takes

care of that but `pipenv` doesn't). Graphviz 3.0.0 or higher is recommended, to

avoid [scaling problems](https://github.com/EliahKagan/codegraph/issues/5).

(Using `conda` instead of `pipenv` may be the easiest way to get a new version

of Graphviz on some systems.)



Then run:



```sh

pipenv install -d

```



If you already have an application for running code in Jupyter notebooks that

you want to use, such as [VS Code](https://code.visualstudio.com/) with the

[Jupyter

extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter),

then you can omit the `-d`.



As for installing Graphviz, most systems' package managers will install it, and

the package name is usually Graphviz. If you can't find it, look into what

package provides the `dot` command. On Windows, if you want to install Graphviz

via a package manager, you can use [`scoop`](https://scoop.sh/).



## Usage



### Way 1: JupyterLab



First, go to the directory where you ran `conda` or `pipenv` before and

activate the environment you created.



If you used `conda`, run:



```sh

conda activate codegraph

```



If you used `pipenv`, run:



```sh

pipenv shell

```



Then run JupyterLab:



```sh

jupyter lab

```



### Way 2: Another application



If you already have another application you prefer for opening and running code

in Jupyter notebooks, you can use that.



In particular, you can use [VS Code with the Jupyter Notebooks

extension](https://code.visualstudio.com/docs/datascience/jupyter-notebooks).

If you do, make sure the environment you created (with `conda` or `pipenv`) is

selected. If VS Code offers to install `ipykernel` or any other dependency, you

should decline and select the appropriate environment.



## A simpler version



An old, simpler version of some parts of this project is available on the

[**simple**](https://github.com/EliahKagan/codegraph/tree/simple) branch, and

may be of some interest.



## Acknowledgements



Some parts of this project were tested by, and were improved based on input

from, [**David Vassallo**](https://github.com/dmvassallo/cgrep). Furthermore,

significant parts were inspired by code I wrote for our project

[palgoviz](https://github.com/EliahKagan/palgoviz), and some code to create

nested tuple structures is derived from code there.



## See also



- [`object_graph.ipynb`](https://github.com/EliahKagan/palgoviz/blob/main/notebooks/object_graph.ipynb)

  in [palgoviz](https://github.com/EliahKagan/palgoviz).