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- addPseudoAtoms(esim, size)
- analyze(potList)
- perform analysis of EnsWeights terms and return nicely formatted summary
- create_EnsWeights(name, members=None, baseSize=None, derivedSpecs=[], sim=None)
- create an EnsWeights object with the given name to allow optimization of
ensemble weights for specified potential terms.
The optional members argument should be a list of ensemble indices to
optimize.
The baseSize and derivedSpecs arguments are used to specify an
interrelationship between ensemble weights. If baseSize is omitted,
derivedSpecs specifies a set weights dependent on the
previous len(members)-len(derivedSpecs) weights. If baseSize is specified,
the number of independent weights is baseSize, and derivedSpecs should be of
len(members).
derivedSpecs is a list of tuples with three arguments each.The
populations of the dependent (derived) w_k is
w_k = A_k + B_k * w_b + w_b^T * C_k * w_b
where w_b are base weights, A_k is a constant, B_k a vector and C_k a
matrix. The parameters A,B and C are specified by the derivedSpecs entry as a
sequence of three values (A,B,C). The specifications for B and C are in sparse
notation by index of nonzer entries, so the second gives the nonzero members
of the B vector in the equation above in the form [index, value]. The third
entry gives the nonzero values of the C matrix in the form
[index1, index2, value].
- getEnsemblePots(potList)
- getWeights(term)
- optimizeWeights(ensWeights, potList, context=None)
- optimize ensemble weights given by the ensWeights argument using the
supplied potList, and reset target values used during
simulated annealing
- topologySetup(ivm, list=[], options=[])
- configure the given ivm.IVM object's topology setup using the
freedom string for each EnsWeight in list.
This function should be called prior to
ivm.IVM.autoTorsion() or protocol.torsionTopology()
The freedom specifier should be one of the following keywords:
fix
bend
ignore
if list is not specified, then pseudoatoms associated with
all registered EnsWeights objects are configured.
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