A python module for estimation and analysis of dynamic graphical models to encode transition densities.
In particular, graphtime implements dynamic Markov random fields (dMRF) or "dynamic Ising models", as a particular case of dynamic graphical models (DGM). DGMs represent molecular configurations using multiple features (sub-systems), fx torsion-angles or contacts. This is in contrast to the single global state, used in for example Markov state models. The advantage of this kind of model is that the number of parameters needed to be estimated is only quadratic in the number of sub-system states, rather than being exponential in the number of meta-stable states.
The dMRFs models the interactions between the different sub-systems, or more specifically, how a current configuration of the sub-systems encode the distribution of sub-system states at a time
Although this library was developed with application to molecular systems in mind, there is currently no functionality to analyse and featurize molecular simulation data within graphtime and this is not planned. However, there are several packages that does this including MDTraj, pyEMMA and mdanalysis. graphtime depends on a straj as input for estimation, which is a list of numpy arrays with the dimensions
Further details can be found in the manuscript:
S. Olsson and F. Noé "Dynamic Graphical Models of Molecular Kinetics" in review. pre-print
The graphtime library is minimalisitic and makes extensive use of numpy and sklearn.
- python >= 3.6.1
- numpy >= 1.3
- scikit-learn >= 0.19.0
- scipy >= 1.1.0
- msmtools >= 1.2.1
- pyemma >= 2.5.2
Clone this repository and test
python setup.py test
If succesfull install using
python setup.py install
If you are having problems using this library or discover any bugs please get in touch through the issues section on the graphtime github repository. For bug reports please provide a reproducable example.