pub struct GlueResult<T> {
    pub glued_log10_probability: Vec<f64, Global>,
    pub borders: Vec<T, Global>,
    pub log10_vec: Vec<Vec<f64, Global>, Global>,
    pub left_list: Vec<usize, Global>,
    pub total_steps: usize,
    pub total_steps_accepted: usize,
    pub total_steps_rejected: usize,
}
Expand description

Glued together probability

Fields

glued_log10_probability: Vec<f64, Global>

probably the result you want, i.e., what you were simulating for

  • this is the log10 of the probability of each bin
  • you get the probability of a bin by 10_f64.powf(glued_log10_probability[i]), i.e., 10^bin_value
  • normed such that the sum of the probabilities is 1.0 (within numerical precision errors)
borders: Vec<T, Global>

This are the bin borders

  • bin_i is defined as the interval [borders[i], borders[i + 1][, i.e. half open interval
  • where the right border is exclusive
log10_vec: Vec<Vec<f64, Global>, Global>

log10 of “probabilities” of the curves, you were glueing together

  • height adjusted, such that the intervals fit together, but not normalized, the sum can be anything
left_list: Vec<usize, Global>

Index map

  • self.left_list[i] is the index of self.borders where the interval self.log10_vec has the first entry
total_steps: usize

How many markov steps were performed in total?

  • includes steps used to find initial valid ensemble
  • for entropic sampling, this includes the steps of the wang landau simulation
total_steps_accepted: usize

How many markov steps were accepted in total?

  • includes steps used to find initial valid ensemble
  • for entropic sampling, this includes the steps of the wang landau simulation
total_steps_rejected: usize

How many markov steps were rejected in total?

  • includes steps used to find initial valid ensemble
  • for entropic sampling, this includes the steps of the wang landau simulation

Implementations

Write the result to a file
  • for plotting with gnuplot etc.

Trait Implementations

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