benmaier/tacoma - Githubissues

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!

Quick example

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)

visualization example

What is tacoma?

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.

Pros of using tacoma

networks are natively described in continuous time (which includes descriptions in discrete time
two main native formats to describe temporal networks (tc.edge_lists and tc.edge_changes), a third way, a sorted list of on-intervals for each edge called tc.edge_trajectories is available, but algorithms work on the two native formats only
the simple portable file-format .taco as 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

Cons of using tacoma

no support for directed temporal networks yet
no support for weighted temporal networks yet

Install

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.

Packages not automatically installed during installation

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)

Documentation

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.

Examples

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)