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(), 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(), 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 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 |