# Python bindings for [Reticula][reticula] [![Documentations][docs-badge]][docs-website] [![Paper][paper-badge]][paper-link]
[reticula]: https://github.com/reticula-network/reticula
[paper-badge]: https://img.shields.io/badge/Paper-SoftwareX-informational
[paper-link]: https://www.sciencedirect.com/science/article/pii/S2352711022002199
[docs-badge]: https://img.shields.io/badge/Docs-docs.reticula.network-success
[docs-website]: https://docs.reticula.network
## Installation
The library offers pre-compiled Wheels for x64 Windows, MacOS and Linux. The library
currently supports Python version 3.10 or newer.
```console
$ pip install reticula
```
### Installing from source
Alternatively you can install the library from source:
Clone the library:
```console
$ git clone https://github.com/arashbm/reticula-python.git
```
Build the Wheel:
```console
$ cd reticula-python
$ pip install .
```
Note that compiling from source requires an unbelievable amount (> 40GB) of RAM.
## Basic examples
Generate a random static network and investigate:
```pycon
>>> import reticula as ret
>>> state = ret.mersenne_twister(42) # create a pseudorandom number generator
>>> g = ret.random_gnp_graph[ret.int64](n=100, p=0.02, random_state=state)
>>> g
<undirected_network[int64] with 100 verts and 110 edges>
>>> g.vertices()
[0, 1, 2, 3, .... 99]
>>> g.edges()
[undirected_edge[int64](0, 16), undirected_edge[int64](0, 20),
undirected_edge[int64](0, 31), undirected_edge[int64](0, 51), ...]
>>> ret.connected_components(g)
[<component[int64] of 1 nodes: {9}>, <component[int64] of 1 node {33}>, ...]
>>> lcc = max(ret.connected_components(g), key=len)
>>> lcc
<component[int64] of 93 nodes: {99, 96, 95, 94, ...}>
>>> g2 = ret.vertex_induced_subgraph(g, lcc)
>>> g2
<undirected_network[int64] with 93 verts and 109 edges>
```
A more complete example of static network percolation analysis, running on
multiple threads, can be found in
[`examples/static_network_percolation/`](examples/static_network_percolation/)
Create a random fully-mixed temporal network and calculate simple
(unconstrained) reachability from node 0 at time 0 to all nodes and times.
```pycon
>>> import reticula as ret
>>> state = ret.mersenne_twister(42)
>>> g = ret.random_fully_mixed_temporal_network[ret.int64](\
... size=100, rate=0.01, max_t=1024, random_state=state)
>>> adj = ret.temporal_adjacency.simple[\
... ret.undirected_temporal_edge[ret.int64, ret.double]]()
>>> cluster = ret.out_cluster(\
... temporal_network=g, temporal_adjacency=adj, vertex=0, time=0.0)
>>> cluster
<temporal_cluster[undirected_temporal_edge[int64, double],
simple[undirected_temporal_edge[int64, double]]] with volume 100
and lifetime (0 1.7976931348623157e+308]>
>>> cluster.covers(vertex=12, time=100.0)
True
>>> # Let's see all intervals where vert 15 is reachable from vert 0 at t=0.0:
>>> list(cluster.interval_sets()[15])
[(3.099055278145548, 1.7976931348623157e+308)]
```
Let's now try limited waiting-time (with $dt = 5.0$) reachability:
```pycon
>>> import reticula as ret
>>> state = ret.mersenne_twister(42)
>>> g = ret.random_fully_mixed_temporal_network[int64](\
... size=100, rate=0.01, max_t=1024, random_state=state)
>>> adj = ret.temporal_adjacency.limited_waiting_time[\
... ret.undirected_temporal_edge[ret.int64, ret.double]](dt=5.0)
>>> cluster = ret.out_cluster(\
... temporal_network=g, temporal_adjacency=adj, vertex=0, time=0.0)
>>> cluster
<temporal_cluster[undirected_temporal_edge[int64, double],
limited_waiting_time[undirected_temporal_edge[int64, double]]] with
volume 100 and lifetime (0 1028.9972186553928]>
>>> cluster.covers(vertex=15, time=16.0)
True
>>> list(cluster.interval_sets()[15])
[(3.099055278145548, 200.17866501023616),
(200.39858803326402, 337.96139372380003),
...
(1017.5258263596586, 1028.9149586273347)]
>>> # Total "human-hours" of reachability cluster
>>> cluster.mass()
101747.97444555275
>>> # Survival time of the reachability cluster
>>> cluster.lifetime()
(0.0, 1028.9972186553928)
```
Raw data
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"description": "# Python bindings for [Reticula][reticula] [![Documentations][docs-badge]][docs-website] [![Paper][paper-badge]][paper-link]\n\n[reticula]: https://github.com/reticula-network/reticula\n[paper-badge]: https://img.shields.io/badge/Paper-SoftwareX-informational\n[paper-link]: https://www.sciencedirect.com/science/article/pii/S2352711022002199\n[docs-badge]: https://img.shields.io/badge/Docs-docs.reticula.network-success\n[docs-website]: https://docs.reticula.network\n\n## Installation\n\nThe library offers pre-compiled Wheels for x64 Windows, MacOS and Linux. The library\ncurrently supports Python version 3.10 or newer.\n\n```console\n$ pip install reticula\n```\n\n### Installing from source\nAlternatively you can install the library from source:\n\nClone the library:\n```console\n$ git clone https://github.com/arashbm/reticula-python.git\n```\n\nBuild the Wheel:\n```console\n$ cd reticula-python\n$ pip install .\n```\n\nNote that compiling from source requires an unbelievable amount (> 40GB) of RAM.\n\n## Basic examples\n\nGenerate a random static network and investigate:\n```pycon\n>>> import reticula as ret\n>>> state = ret.mersenne_twister(42) # create a pseudorandom number generator\n>>> g = ret.random_gnp_graph[ret.int64](n=100, p=0.02, random_state=state)\n>>> g\n<undirected_network[int64] with 100 verts and 110 edges>\n>>> g.vertices()\n[0, 1, 2, 3, .... 99]\n>>> g.edges()\n[undirected_edge[int64](0, 16), undirected_edge[int64](0, 20),\n undirected_edge[int64](0, 31), undirected_edge[int64](0, 51), ...]\n>>> ret.connected_components(g)\n[<component[int64] of 1 nodes: {9}>, <component[int64] of 1 node {33}>, ...]\n>>> lcc = max(ret.connected_components(g), key=len)\n>>> lcc\n<component[int64] of 93 nodes: {99, 96, 95, 94, ...}>\n>>> g2 = ret.vertex_induced_subgraph(g, lcc)\n>>> g2\n<undirected_network[int64] with 93 verts and 109 edges>\n```\nA more complete example of static network percolation analysis, running on\nmultiple threads, can be found in\n[`examples/static_network_percolation/`](examples/static_network_percolation/)\n\nCreate a random fully-mixed temporal network and calculate simple\n(unconstrained) reachability from node 0 at time 0 to all nodes and times.\n```pycon\n>>> import reticula as ret\n>>> state = ret.mersenne_twister(42)\n>>> g = ret.random_fully_mixed_temporal_network[ret.int64](\\\n... size=100, rate=0.01, max_t=1024, random_state=state)\n>>> adj = ret.temporal_adjacency.simple[\\\n... ret.undirected_temporal_edge[ret.int64, ret.double]]()\n>>> cluster = ret.out_cluster(\\\n... temporal_network=g, temporal_adjacency=adj, vertex=0, time=0.0)\n>>> cluster\n<temporal_cluster[undirected_temporal_edge[int64, double],\n simple[undirected_temporal_edge[int64, double]]] with volume 100\n and lifetime (0 1.7976931348623157e+308]>\n>>> cluster.covers(vertex=12, time=100.0)\nTrue\n\n>>> # Let's see all intervals where vert 15 is reachable from vert 0 at t=0.0:\n>>> list(cluster.interval_sets()[15])\n[(3.099055278145548, 1.7976931348623157e+308)]\n```\n\nLet's now try limited waiting-time (with $dt = 5.0$) reachability:\n```pycon\n>>> import reticula as ret\n>>> state = ret.mersenne_twister(42)\n>>> g = ret.random_fully_mixed_temporal_network[int64](\\\n... size=100, rate=0.01, max_t=1024, random_state=state)\n>>> adj = ret.temporal_adjacency.limited_waiting_time[\\\n... ret.undirected_temporal_edge[ret.int64, ret.double]](dt=5.0)\n>>> cluster = ret.out_cluster(\\\n... temporal_network=g, temporal_adjacency=adj, vertex=0, time=0.0)\n>>> cluster\n<temporal_cluster[undirected_temporal_edge[int64, double],\n limited_waiting_time[undirected_temporal_edge[int64, double]]] with\n volume 100 and lifetime (0 1028.9972186553928]>\n>>> cluster.covers(vertex=15, time=16.0)\nTrue\n>>> list(cluster.interval_sets()[15])\n[(3.099055278145548, 200.17866501023616),\n (200.39858803326402, 337.96139372380003),\n ...\n (1017.5258263596586, 1028.9149586273347)]\n\n>>> # Total \"human-hours\" of reachability cluster\n>>> cluster.mass()\n101747.97444555275\n\n>>> # Survival time of the reachability cluster\n>>> cluster.lifetime()\n(0.0, 1028.9972186553928)\n```\n",
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