Skip to main content

A Hypothesis strategy for generating NetworkX graphs

Project description

# Hypothesis-networkx

This module provides a Hypothesis strategy for generating networkx graphs.
This can be used to efficiently and thoroughly test your code.

## Installation

This module can be installed via `pip`:
```
pip install hypothesis-networkx
```

## User guide

The module exposes a single function: `graph_builder`. This function is a
hypothesis composite strategy for building graphs. You can use it as follows:

```python3
from hypothesis_networkx import graph_builder
from hypothesis import strategies as st
import networkx as nx

node_data = st.fixed_dictionaries({'name': st.text(),
'number': st.integers()})
edge_data = st.fixed_dictionaries({'weight': st.floats(allow_nan=False,
allow_infinity=False)})


builder = graph_builder(graph_type=nx.Graph,
node_keys=st.integers(),
node_data=node_data,
edge_data=edge_data,
min_nodes=2, max_nodes=10,
min_edges=1, max_edges=None,
self_loops=False,
connected=True)

graph = builder.example()
print(graph.nodes(data=True))
print(graph.edges(data=True))
```

Of course this builder is a valid hypothesis strategy, and using it to just
make examples is not super usefull. Instead, you can (and should) use it in
your testing framework:

```python3
from hypothesis import given

@given(graph=builder)
def test_my_function(graph):
assert my_function(graph) == known_function(graph)

```

The meaning of the arguments given to `graph_builder` are pretty
self-explanatory, but they *must* be given as keyword arguments. Of particular
note are the following arguments:

- `graph_type`: This function (or class) will be called to create an empty
initial graph.
- `connected`: If True, the generated graph is garuanteed to be a single
connected component.
- `self_loops`: If False, there will be no self-loops in the generated graph.
Self-loops are edges between a node and itself.

## Known limitations

There are a few (minor) outstanding issues with this module:

- Graph generation may be slow for large graphs.
- The `min_edges` argument is not always respected when the produced graph
is too small.
- It currently works for Python 2.7, but this is considered deprecated and
may stop working without notice.

## See also

[Networkx](https://networkx.github.io/documentation/stable/index.html)
[Hypothesis](https://hypothesis.readthedocs.io/en/latest/index.html)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for hypothesis-networkx, version 0.1.3
Filename, size File type Python version Upload date Hashes
Filename, size hypothesis_networkx-0.1.3.tar.gz (11.6 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page