Analyse temporal network and hypergraphs efficiently.
Project description
Python bindings for Reticula
Installation
The library offers pre-compiled Wheels for manylinux2014
compatible systems.
That is, Linux systems with GNU C Library (glibc) version 2.17 and newer. The
library currently supports Python version 3.8, 3.9 and 3.10.
$ pip install reticula
Installing from source
Alternatively you can install the library from source:
Clone the library:
$ git clone https://github.com/arashbm/reticula-python.git
Build the Wheel:
$ cd reticula-python
$ pip install .
Note that compiling source requires an unbelievable amount (> 40GB) of RAM.
Basic examples
Generate a random static network and investigate:
>>> 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/
Create a random fully-mixed temporal network and calculate simple (unconstrained) reachability from node 0 at time 0 to all nodes and times.
>>> 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:
>>> 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)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Hashes for reticula-0.2.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | de960456035f1da17a9b57f792ce9d4ec54de1ade1ed490952b2e30f7e6fc147 |
|
MD5 | 812600b876ee65369de2030cb812ddd2 |
|
BLAKE2b-256 | 50d8cd1f9b6913739eb882460ecc0ae5b23f5f2342ead46c8d332b2937b501e5 |
Hashes for reticula-0.2.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 745899f7340a2d449a2cd276a83f79e5abcded23d3327aa910f4b95e3f23807a |
|
MD5 | f4b56d589b07f3b76a3d4d6e1841acd2 |
|
BLAKE2b-256 | f84703ca82db67f4d391ee3de5203119d6018e54fff5caef0b08423d25765b6b |
Hashes for reticula-0.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a74635f0b3810ca99a3355f8b6654d957dfb2dc34a2435e0df9390eb8dc86341 |
|
MD5 | 0d5d8f1b3fb72e8d904f86b05501e39f |
|
BLAKE2b-256 | be2d78f4dd2a93bec212a2ba0c8d701881c9edc65e5bfa4a03238002b5d27735 |
Hashes for reticula-0.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5761cec42b32980eecc9ecc1ba4fcfe87dbcb291e93c79d7f75a2f2acefde9de |
|
MD5 | fee840f6bd09fc4ef09bbe0dd21c80ff |
|
BLAKE2b-256 | 1b1485c65e908b5c3b005acf7fb2a48d0473691b2dfa03cb30d09da6a99c6c37 |
Hashes for reticula-0.2.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ba35d71726f4e51b214d34e49e31f8b0dfc4fe9ffc3d623b67f61376213fc290 |
|
MD5 | eb05a8be03d018beed7bdcfbee1e65b0 |
|
BLAKE2b-256 | 17b9751640f8d28bd85ab36eff774c0366f771415b36b1c0d8ffdfd6694bd0e0 |