Hetnetpy

This release provides some bugfixes as well as many new features. Development for this release occurred during work on [Project Rephetio](https://doi.org/10.15363/thinklab.4). This is the first release that has been uploaded to PyPI.

New modules
- The new `neo4j` module provides support for exporting a `hetio.graph` into Neo4j. The module also supports creating DWPC Cypher queries from a metagraph. See https://doi.org/10.15363/thinklab.d112 for more information on our adoption of Neo4j.
- The new `permute` module provides hetnet permutation functionality. For an example use, see https://doi.org/10.15363/thinklab.d178.
- The new `stats` module produces user-friendly TSVs and visualizations with hetnet summary stats.

Enhancements
- MetaGraphs now have a `metapath_from_abbrev` method which takes a metapath abbreviation str and returns the corresponding metapath object.
- The `hetio.readwrite` module now supports reading metagraphs and graphs from a (compressed) URL.
- Pretty formatting of (meta)edges and (meta)paths using the `get_unicode_str` method.
- Support for Graph Modeling Language (GML) was removed to do the poor documentation and esoterism of this format.
- Improved `abbreviation` module.

[](https://doi.org/10.5281/zenodo.31763)

This is the first release of `hetio` for hetnets in Python.

Currently, the following features are supported:
- creating a hetnet consisting of a metagraph (types) and a graph (nodes and edges)
- metanode and metaedge abbreviations
- node and edge properties using the `data` attribute
- node and edge masking
- writing and reading hetnets
- summary statistics
- summary visualizations
- metapath extraction from the metagraph
- graph traversal to extract paths
- feature computation, specifically DWPCs

The following features are not currently supported:
- node or edge deletion
- network permutation (edge swaps)

Documentation is lacking, but see these examples of [network creation](https://github.com/dhimmel/integrate/blob/bab40969df989d3446bacc97a5532cf3d3cab4b5/integrate.ipynb) and [feature extraction](https://github.com/dhimmel/learn/blob/eabff6bdbe777cb21ad731a7b788720eb1d622f8/learn.ipynb).