Thermo package (pyemma.thermo)

The thermo package provides functions to analyze data originating from potentially biased multi-ensemble MD-Simulations.

User-Functions

For most users, the following high-level functions are sufficient to estimate models from data.

estimate_umbrella_sampling(us_trajs, ...[, ...])	This function acts as a wrapper for tram(), dtram(), mbar, and wham() and handles the calculation of bias energies (bias) and thermodynamic state trajectories (ttrajs) when the data comes from umbrella sampling and (optional) unbiased simulations.
estimate_multi_temperature(energy_trajs, ...)	This function acts as a wrapper for tram(), dtram(), mbar, and wham() and handles the calculation of bias energies (bias) and thermodynamic state trajectories (ttrajs) when the data comes from multi-temperature simulations.
tram(ttrajs, dtrajs, bias, lag[, ...])	Transition-based reweighting analysis method
dtram(ttrajs, dtrajs, bias, lag[, ...])	Discrete transition-based reweighting analysis method
wham(ttrajs, dtrajs, bias[, maxiter, ...])	Weighted histogram analysis method
mbar(ttrajs, dtrajs, bias[, maxiter, ...])	Multi-state Bennet acceptance ratio

Thermo classes

Estimators to generate models from data. If you are not an expert user, use the API functions above.

StationaryModel([pi, f, normalize_energy, label])	StationaryModel combines a stationary vector with discrete-state free energies.
MultiThermModel(models, f_therm[, pi, f, label])	Coupled set of stationary models at multiple thermodynamic states
MEMM(models, f_therm[, pi, f, label])	Coupled set of Markov state models at multiple thermodynamic states
WHAM(bias_energies_full[, maxiter, maxerr, ...])	Weighted Histogram Analysis Method.
MBAR([maxiter, maxerr, ...])	Multi-state Bennet Acceptance Ratio Method.
DTRAM(bias_energies_full, lag[, count_mode, ...])	Discrete Transition(-based) Reweighting Analysis Method.
TRAM(lag[, count_mode, connectivity, ...])	Transition(-based) Reweighting Analysis Method.