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pyGPCCA - Generalized Perron Cluster Cluster Analysis

Generalized Perron Cluster Cluster Analysis program to coarse-grain reversible and non-reversible Markov state models.

Markov state models (MSM) enable the identification and analysis of metastable states and related kinetics in a very instructive manner. They are widely used, e.g., to model molecular or cellular kinetics.
Common state-of-the-art Markov state modeling methods and tools are very well suited to model reversible processes in closed equilibrium systems. However, most are not well suited to deal with non-reversible or even non-autonomous processes of non-equilibrium systems.
To overcome this limitation, the Generalized Robust Perron Cluster Cluster Analysis (GPCCA or G-PCCA) was developed. The GPCCA method implemented in the pyGPCCA program readily handles equilibrium as well as non-equilibrium data by utilizing real Schur vectors instead of eigenvectors.
pyGPCCA enables the semiautomatic coarse-graining of transition matrices representing the dynamics of the system under study. Utilizing pyGPCCA, metastable states as well as cyclic kinetics can be identified and modeled.