gitnet is a Python 3 package with tools for collecting, cleaning, and exporting datasets from local Git repositories, as well as creating network datasets and visualizations. The primary purpose of gitnet is to provide scholarly tools to study the collaboration structure of free and open source software development projects, but may also be of use to organizations, project managers, and curious coders.
gitnet is not under active development right now, but we are happy to respond to any questions you might have about using it.
gitnet makes it easy to collect, clean, and visualize local Git repositories. Here, we used it to create a network visualization of contributions to .py files in our Git repository.
import gitnet as gn
gn_log = gn.get_log("Users/localpath/gitnet")
gn_log = gn_log.ignore("\.py$", ignoreif="no match")
gn_net = gn_log.network("author", "file", colours="simple")
gn_net.quickplot("plot.pdf", layout = "spring")
This snippet imports gitnet, creates a CommitLog from our local repository, uses a regular expression to ignore files with names that do not end with .py, creates a MultiGraphPlus object using presets for a bipartite author/file network, and saves a basic visualization of the network. (By default, author nodes are coloured white and python files are coloured light red.) The result looks like this:
Additionally, you can export data retrieved by gitnet in either grapml or plaintext edgelist format. This data can then be used in the statistical programming language R, to create visualizations like this one:
Currently, only local Git retrieval is supported. Use the get_log() function to create a CommitLog object, by passing a file path for the Git repository.
my_log = gn.get_log("Users/localpath/my_repository")
The core data class for all data collected by gitnet is a Log. Logs contain a core dataset of records, attributes documenting its retrieval, and a number of methods to explore, clean, and export the data it contains. In practice, users will generally use a subclass of the Log class, with extra features appropriate for the source of their data (e.g. the Log subclass for Git commit data is called CommitLog, and has methods for generating author-file networks, ignoring files by extension, and so on.)
The core dataset is a dictionary of dictionaries, and held in log.collection. All Logs are subscriptable, so you can access individual records directly by their identifiers (e.g. their commit hash).
The basic methods available for Log and all its subclasses are as follows:
| Method | Purpose |
|---|---|
| .attributes() | Produces a list of all the tags in the collection. |
| .describe() | Prints a detailed, subclass-specific summary of the Log |
| .browse() | Interactively prints the content of each record in the collection. |
| .filter() | Selectively remove records using some matching criteria. |
| .tsv() | Export a tab delimited spreadsheet containing the collected data. |
| .df() | Create a Pandas dataframe object using the collected data. |
| .vector() | Create a list of all values with a specified tag. |
| .replace_val() | Replace a specified tag value. |
| .generate_edges() | Creates network edges by record. |
| .write_edges() | Writes an edgelist (with attributes) to a file. |
| .generate_nodes() | Creates a dictionary of network nodes. |
| .write_nodes() | Writes a list of nodes (with attributes) to a file. |
| .generate_network() | Creates a network, producing a MultiGraphPlus object. |
Git commit log datasets are stored as a CommitLog, which inherits all the features of a Log as well as the following methods:
| Method | Purpose |
|---|---|
| .describe() | A CommitLog specific summary, which overrides Log describe. |
| .ignore() | Removes files matching a regular expression from all records. |
| .network() | Contains preset options for generating networks from a CommitLog. |
When you create a network using gitnet, it is represented as a MultiGraphPlus object, which is a subclass of the networkx class for undirected graphs with duplicate edges, the MultiGraph. MultiGraphPlus inherits all the features of a MultiGraph, and so can be used with all networkx functions that have MultiGraph support. However, MultiGraphPlus defines a number of new methods to make working with gitnet networks more convenient. The methods unique to MultiGraph are:
| Method | Purpose |
|---|---|
| .describe() | A description of the network. |
| .quickplot() | Presets for plotting networks in one line of code. |
| .node_attributes() | Adds node attributes, with prebuilt or custom helper functions. |
| .node_merge() | Merges two nodes. |
| .collapse_edges() | Simplifies a network by merging edges which occur between node pairs. |
| .write_graphml() | Exports the network as a GraphML file. |
| .write_tnet() | Exports the network as tnet edgelist for use in R. |
If you want to use the features of gitnet for an unsupported data source, it is easy to initialize a Log object with a custom dataset. First, convert your data into a dictionary of dictionaries, for example:
data = {"id1":{"attr1":val1,...,"attrn":valn},
:"idm":{"attr1":val1,...,"attrn":valn}}
Then, initialize a Log with the dictionary of dictionaries.
my_log = Log(data)
If you wish to request or contribute support for a new data source, please contact the developers. Further documentation can be found here.
- Gitnet is currently beta-0.1.1.
As a project in development, Gitnet will have a list of potential issues, updates, and features. Any external requests and issue reports can be made on our GitHub project page. We appreciate any comments from developers and researchers who stumble upon our work.
- Solve problems related to the pygraphviz dependency on Windows. Some users may encounter difficulty running graph.quickplot() as a result. - May not be possible given the general inaccessibility of the graphviz software interface.
- Increase efficiency of internal log parsing. Some large projects can take up to several minutes to process. - Currently in progress, some significant improvements have been made, although ahead of any official release.
- Include remote log extraction. One of the biggest caveats of gitnet is that you have to spend a significant amount of time downloading large projects.
- Include additional export options for users of additional visualization packages, and who want to export dynamic network data.
- Include additional custom classes for more VCS types and mailing lists.