| Name | graphrs-python JSON |
| Version |
0.11.4
JSON |
| download |
| home_page | None |
| Summary | A Python module that wraps the high-performance `graphrs` Rust library. |
| upload_time | 2024-12-11 15:07:07 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | MIT License Copyright (c) 2024 Malcolm van Raalte Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
graph
network
graph theory
network theory
graph algorithms
network algorithms
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# graphrs_python
`graphrs_python` is a Python wrapper around the high-performance [graphrs](<https://crates.io/crates/graphrs>)
Rust crate. See the [performance documentation](<https://github.com/malcolmvr/graphrs/blob/main/performance.md>)
for a comparison of the performance of `graphrs` to other graph libraries.
## Example Usage
```python
import graphrs_python as grs
nodes = ["n1", "n2", "n3", "n4"]
edges = [("n1", "n2", 1.0), ("n2", "n3", 1.0), ("n3", "n4", 1.0), ("n4", "n2", 1.0)]
graph = grs.create_graph_from_nodes_and_edges(nodes, edges, directed=True)
print(grs.betweenness_centrality(graph, weighted=True, normalized=True))
print(grs.closeness_centrality(graph, weighted=True, wf_improved=True))
print(grs.eigenvector_centrality(graph, weighted=True))
```
Graphs can also be created from just edges:
```python
import graphrs_python as grs
edges = [("n1", "n2", 1.0), ("n2", "n3", 1.0), ("n3", "n4", 1.0), ("n4", "n2", 1.0)]
graph = grs.create_graph_from_edges(edges, directed=True)
```
And graphs can also be created from NetworkX `Graph` objects:
```python
import graphrs_python as grs
import networkx
graph = nx.DiGraph()
graph.add_edges_from(
[
("n1", "n2", {"weight": 1.0}),
("n2", "n3", {"weight": 1.0}),
("n3", "n4", {"weight": 1.0}),
("n4", "n2", {"weight": 1.0}),
]
)
graph = grs.create_graph_from_networkx(graph, weight="weight")
```
## License
MIT
Raw data
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"description": "# graphrs_python\n\n`graphrs_python` is a Python wrapper around the high-performance [graphrs](<https://crates.io/crates/graphrs>)\nRust crate. See the [performance documentation](<https://github.com/malcolmvr/graphrs/blob/main/performance.md>)\nfor a comparison of the performance of `graphrs` to other graph libraries.\n\n## Example Usage\n\n```python\nimport graphrs_python as grs\n\nnodes = [\"n1\", \"n2\", \"n3\", \"n4\"]\nedges = [(\"n1\", \"n2\", 1.0), (\"n2\", \"n3\", 1.0), (\"n3\", \"n4\", 1.0), (\"n4\", \"n2\", 1.0)]\ngraph = grs.create_graph_from_nodes_and_edges(nodes, edges, directed=True)\n\nprint(grs.betweenness_centrality(graph, weighted=True, normalized=True))\nprint(grs.closeness_centrality(graph, weighted=True, wf_improved=True))\nprint(grs.eigenvector_centrality(graph, weighted=True))\n```\n\nGraphs can also be created from just edges:\n\n```python\nimport graphrs_python as grs\n\nedges = [(\"n1\", \"n2\", 1.0), (\"n2\", \"n3\", 1.0), (\"n3\", \"n4\", 1.0), (\"n4\", \"n2\", 1.0)]\ngraph = grs.create_graph_from_edges(edges, directed=True)\n```\n\nAnd graphs can also be created from NetworkX `Graph` objects:\n\n```python\nimport graphrs_python as grs\nimport networkx\n\ngraph = nx.DiGraph()\ngraph.add_edges_from(\n [\n (\"n1\", \"n2\", {\"weight\": 1.0}),\n (\"n2\", \"n3\", {\"weight\": 1.0}),\n (\"n3\", \"n4\", {\"weight\": 1.0}),\n (\"n4\", \"n2\", {\"weight\": 1.0}),\n ]\n)\ngraph = grs.create_graph_from_networkx(graph, weight=\"weight\")\n```\n\n## License\n\nMIT\n",
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