- Compatibility with the pandas/numpy/scipy stack
- Intuitive interface. The user can work with nodes, edges, neighbors instead
of directly with CSR matrices.
- Keep the door open for extensions (including with Cython) by providing the
sparse adjacency matrices as part of the public interface.
- Prioritize efficiency in "big" operations (like generating spanning trees)
over "small" and mutable operations (like adding an edge to the graph)
>>> import pandas
>>> from partitions import Graph
>>> graph = Graph.from_edges(
... [(0, 1), (1, 2), (0, 2)],
... data=pandas.DataFrame(
... {"population": [100, 200, 50], "votes": [50, 60, 40]},
... index=[0, 1, 2]
... )
... )
>>> graph
<Graph ['population', 'votes']>
graph
>>> set(graph.nodes) == {0, 1, 2}
True
>>> set(graph.edges) == {(0, 1), (1, 2), (0, 2)}
True
>>> list(graph)
[0, 1, 2]
>>> len(graph.nodes)
3
>>> len(graph.edges)
3
>>> (0, 1) in graph.edges
True
>>> (1, 0) in graph.edges
True
>>> graph.data["population"]
0 100
1 200
2 50
Name: population, dtype: int64
>>> graph.data
population votes
0 100 50
1 200 60
2 50 40
>>> set(graph.neighbors[0]) == {1, 2}
True
>>> set(graph.neighbors[1]) == {0, 2}
True
>>> graph.neighbors[0]
array([1, 2], dtype=int32)
>>> subgraph = graph.subgraph({1, 2})
>>> subgraph.data["population"]
0 200
1 50
Name: population, dtype: int64
>>> list(subgraph.edges)
[(0, 1)]
>>> subgraph
<EmbeddedGraph [2 nodes]>
>>> subgraph.image
array([1, 2])
>>> subgraph.image[0]
1
>>> subgraph.image[[0, 1]]
array([1, 2])
>>> from partitions import Partition
>>> partition = Partition.from_assignment(graph, {0: "a", 1: "b", 2: "b"})
>>> partition
<Partition [2]>
>>> for part in partition:
... print(set(part.image[part.nodes]))
{0}
{1, 2}
>>> partition.data["population"]
a 100
b 250
Name: population, dtype: int64
>>> set(partition["a"].cut_edges)
{(0, 1), (0, 2)}
>>> set(partition["a"].boundary.nodes)
{0}
>>> set(partition["a"].boundary.neighbors)
{1, 2}