TemporAl COntact Modeling and Analysis. Provides fast tools to analyze temporal contact networks, produce surrogate networks using qualitative models and simulate Gillespie processes on them. Currently only working on OSX and Linux distributions. No Windows support yet!
In order to download the SocioPatterns 'Hypertext 2009'-dataset and visualize it interactively, do the following.
>>> import tacoma as tc
>>> from tacoma.interactive import visualize
>>> temporal_network = tc.download_and_convert_sociopatterns_hypertext_2009()
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>>> visualize(temporal_network, frame_dt = 20)
tacoma is a joint C++/Python-package for the modeling and analysis of undirected and
unweighted temporal networks, with a focus on (but not limited to) human face-to-face contact networks.
- networks are natively described in continuous time (which includes descriptions in discrete time
- two main native formats to describe temporal networks (
tc.edge_listsandtc.edge_changes), a third way, a sorted list ofon-intervals for each edge calledtc.edge_trajectoriesis available, but algorithms work on the two native formats only - the simple portable file-format
.tacoas a standardized way to share temporal network data (which is just the data dumped to a.json-file, a simple file format readable from a variety of languages) - easy functions to produce surrogate temporal networks from four different models
- easy way to simulate Gillespie (here, epidemic spreading) processes on temporal networks with an implementation of Vestergaard's and Génois's adapted algortihm
- easy framework to develop new Gillespie-simulations algorithms on temporal networks
- multiple and simple ways to interactively visualize temporal networks
- simple functions to manipulate temporal networks (slice, concatenate, rescale time, sample, bin, convert)
- simple functions to analyze structural and statistical properties of temporal networks (mean degree, degree distribution, group size distribution, group life time distributions, etc.)
- fast algorithms due to C++-core (fast as in faster than pure Python)
- relatively fast and easy to compile since it only depends on the C++11-stdlib
and pybind11 without the large overhead of
Boost
- no support for directed temporal networks yet
- no support for weighted temporal networks yet
If you get compiling errors, make sure that pybind11 is installed.
$ git clone https://github.com/benmaier/tacoma
$ pip install ./tacoma
Note that a C++11-compiler has to be installed on the system before installing tacoma. On OS X
it might happen that even though pip installed pybind11 it's not available during installation.
If that happens please open a detailed issue here. You might want to try
$ brew install pybind11
as a work-around.
The following packages are not installed during installation with pip since they're only required
for drawing and drawing is not essential. If you want to use tacoma.drawing, please install
matplotlib
networkx
python-louvain (community)
The documentation is currently available at http://tacoma.benmaier.org . It is full of typos and non-exhaustive but I think the important points are in there.
Check out the sandbox directory.
Here is an example for the temporal network format tc.edge_changes.
import tacoma as tc
from tacoma.interactive import visualize
# define temporal network as a list of edge changes
temporal_network = tc.edge_changes()
temporal_network.N = 10
temporal_network.edges_initial = [ (0,1), (2,3), (1,7), (3,5), (1,9), (7,2) ]
temporal_network.t0 = 0.0
temporal_network.t = [ 0.8, 2.4 ]
temporal_network.tmax = 3.1
temporal_network.edges_in = [
[ (0, 5), (3, 6) ],
[ (3, 7), (4, 9), (7, 8) ],
]
temporal_network.edges_out = [
[ (0, 1) ],
[ (2, 3), (3, 6) ],
]
visualize(temporal_network, frame_dt = 0.05)
The whole software is published under the MIT software license. The documentation and all figures are copyrighted by Benjamin F. Maier. Ask for permission if you want to distribute parts.