Ensemble

class Ensemble

Perform reweighting and ensemble averages using macrostate distributions

Subclassed by feasst::GrandCanonicalEnsemble

Public Functions

Ensemble(const Histogram &macrostates, const LnProbability &ln_prob, const double conjugate = 0.)

Store the original conjugate, macrostate and distribution.

Ensemble(const FlatHistogram &flat_hist)

Same as above, but taken from FlatHistogram.

Ensemble(const Clones &clones)

Same as above, but taken from spliced Clones.

const LnProbability &ln_prob_original() const

Return the stored macrostate distribution from the original simulation.

const LnProbability &ln_prob() const

Return the LnProbability (that may have been reweighted).

const Histogram &macrostates() const

Return the stored Histogram representing the macrostates of the original simulation.

void phase_boundary(const int phase, int *min, int *max) const

Determine min and max macrostate indices for a given phase Return 1 if no phase boundary, or 0 for success.

bool is_phase_boundary() const

Return true if phase boundary is found.

double original_conjugate() const

Return the conjugate thermodynamic variable of the macrostate from the original simulation.

class GrandCanonicalEnsemble : public feasst::Ensemble

Grand canonical ensemble currently implemented for single component systems.

Subclassed by feasst::ExtrapolateBetaGCE

class ExtrapolateBetaGCE : public feasst::GrandCanonicalEnsemble

Public Functions

ExtrapolateBetaGCE(const MonteCarlo &monte_carlo, const FlatHistogram &flat_histogram, const argtype &args = argtype())

Extrapolate the original simulation data to a different \(\beta\). See: https://doi.org/10.1063/1.4975331 Assumes a single component. In this implementation, extrapolation should be done before reweighting because this function changes ln_prob_original, not ln_prob.

args:

• beta_new: \(\beta\) to extrapolate toward.

• beta_original: original \(\beta\) of the simulation.

• order: truncate Taylor series at this order (default: 2).