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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:

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,

builder = graph_builder(graph_type=nx.Graph,
min_nodes=2, max_nodes=10,
min_edges=1, max_edges=None,

graph = builder.example()

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:

from hypothesis import given

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


Project details

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