TI¶
The TI
estimator is a simple implementation of thermodynamic integration that uses the trapezoid rule for integrating the space between \(\left<\frac{dH}{d\lambda}\right>\) values for each \(\lambda\) sampled.
API Reference¶
- class alchemlyb.estimators.TI(verbose=False)¶
Thermodynamic integration (TI).
- Parameters
verbose (bool, optional) – Set to True if verbose debug output is desired.
- delta_f_¶
The estimated dimensionless free energy difference between each state.
- Type
DataFrame
- d_delta_f_¶
The estimated statistical uncertainty (one standard deviation) in dimensionless free energy differences.
- Type
DataFrame
- dhdl¶
The estimated dhdl of each state.
- Type
DataFrame
Changed in version 1.0.0: delta_f_, d_delta_f_, states_ are view of the original object.
- fit(dHdl)¶
Compute free energy differences between each state by integrating dHdl across lambda values.
- Parameters
dHdl (DataFrame) – dHdl[n,k] is the potential energy gradient with respect to lambda for each configuration n and lambda k.
- separate_dhdl()¶
For transitions with multiple lambda, the attr:dhdl would return a
DataFrame
which gives the dHdl for all the lambda states, regardless of whether it is perturbed or not. This function creates a list ofpandas.Series
for each lambda, where eachpandas.Series
describes the potential energy gradient for the lambdas state that is perturbed.- Returns
dHdl_list – A list of
pandas.Series
such thatdHdl_list[k]
is the potential energy gradient with respect to lambda for each configuration that lambda k is perturbed.- Return type
- get_params(deep=True)¶
Get parameters for this estimator.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance