- class openfe.protocols.openmm_rfe.RelativeHybridTopologyProtocolResult(**data)#
Dict-like container for the output of a RelativeHybridTopologyProtocol
- get_estimate() Quantity #
Average free energy difference of this transformation
- Returns:
dG – The free energy difference between the first and last states. This is a Quantity defined with units.
- Return type:
unit.Quantity
- get_uncertainty() Quantity #
The uncertainty/error in the dG value: The std of the estimates of each independent repeat
- get_individual_estimates() list[tuple[pint.Quantity, pint.Quantity]] #
Return a list of tuples containing the individual free energy estimates and associated MBAR errors for each repeat.
- get_forward_and_reverse_energy_analysis() list[typing.Optional[dict[str, typing.Union[numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]], pint.Quantity]]]] #
Get a list of forward and reverse analysis of the free energies for each repeat using uncorrelated production samples.
The returned dicts have keys: ‘fractions’ - the fraction of data used for this estimate ‘forward_DGs’, ‘reverse_DGs’ - for each fraction of data, the estimate ‘forward_dDGs’, ‘reverse_dDGs’ - for each estimate, the uncertainty
The ‘fractions’ values are a numpy array, while the other arrays are Quantity arrays, with units attached.
If the list entry is
None
instead of a dictionary, this indicates that the analysis could not be carried out for that repeat. This is most likely caused by MBAR convergence issues when attempting to calculate free energies from too few samples.- Returns:
forward_reverse
- Return type:
list[Optional[dict[str, Union[npt.NDArray, unit.Quantity]]]]
- Raises:
UserWarning – If any of the forward and reverse entries are
None
.
- get_overlap_matrices() list[dict[str, numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]]]] #
Return a list of dictionary containing the MBAR overlap estimates calculated for each repeat.
- Returns:
overlap_stats –
- A list of dictionaries containing the following keys:
scalar
: One minus the largest nontrivial eigenvalueeigenvalues
: The sorted (descending) eigenvalues of the overlap matrixmatrix
: Estimated overlap matrix of observing a sample from state i in state j
- Return type:
- get_replica_transition_statistics() list[dict[str, numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]]]] #
The replica lambda state transition statistics for each repeat.
Note
This is currently only available in cases where a replica exchange simulation was run.
- get_replica_states() list[numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]]] #
Returns the timeseries of replica states for each repeat.
- Returns:
replica_states – List of replica states for each repeat
- Return type:
List[npt.NDArray]
- equilibration_iterations() list[float] #
Returns the number of equilibration iterations for each repeat of the calculation.