bauer/WHAM
1
0
Fork 0
mirror of https://github.com/dnlbauer/WHAM.git synced 2026-09-11 06:35:30 +00:00
Code Issues Packages Projects Releases Wiki Activity

remove todo

Browse Source
This commit is contained in:
Daniel Bauer
2018-10-25 11:28:57 +02:00
parent 8e9e06a644
commit 7773ec6c77
1 changed files with 7 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
src/histogram.rs
View File

@@ -106,18 +106,20 @@ impl Dataset {
// Harmonic bias calculation: bias = 0.5*k(dx)^2 // Harmonic bias calculation: bias = 0.5*k(dx)^2
// if cyclic is true, lowest and highest bins are assumed to be // if cyclic is true, lowest and highest bins are assumed to be
// neighbors // neighbors. This returns exp(U/kT) instead of U for better performance.
pub fn calc_bias(&self, bin: usize, window: usize) -> f64 { pub fn calc_bias(&self, bin: usize, window: usize) -> f64 {
let ndx = window * self.num_bins + bin; let ndx = window * self.num_bins + bin;
let mut cache = self.bias.borrow_mut(); let mut cache = self.bias.borrow_mut();
match cache[ndx] { match cache[ndx] {
Some(val) => val, Some(val) => val,
None => { None => {
// TODO optimize this part! let dimens = self.dimens_lengths.len();
let dimens = self.hist_min.len();
// index of the bias value depends on the window und dimension
let bias_ndx: Vec<usize> = (0..dimens) let bias_ndx: Vec<usize> = (0..dimens)
.map(|dimen| { window * dimens + dimen }).collect(); .map(|dimen| { window * dimens + dimen }).collect();
// find the N coords, force constants and bias coords
let coord = self.get_coords_for_bin(bin); let coord = self.get_coords_for_bin(bin);
let bias_fc: Vec<f64> = bias_ndx.iter().map(|ndx| { self.bias_fc[*ndx] }).collect(); let bias_fc: Vec<f64> = bias_ndx.iter().map(|ndx| { self.bias_fc[*ndx] }).collect();
let bias_pos: Vec<f64> = bias_ndx.iter().map(|ndx| { self.bias_pos[*ndx] }).collect(); let bias_pos: Vec<f64> = bias_ndx.iter().map(|ndx| { self.bias_pos[*ndx] }).collect();
@@ -125,12 +127,13 @@ impl Dataset {
let mut bias_sum = 0.0; let mut bias_sum = 0.0;
for i in 0..dimens { for i in 0..dimens {
let mut dist = (coord[i] - bias_pos[i]).abs(); let mut dist = (coord[i] - bias_pos[i]).abs();
if self.cyclic { if self.cyclic { // periodic conditions
let hist_len = self.hist_max[i] - self.hist_min[i]; let hist_len = self.hist_max[i] - self.hist_min[i];
if dist > 0.5 * hist_len { if dist > 0.5 * hist_len {
dist -= hist_len; dist -= hist_len;
} }
} }
// store exp(U/kT) for better performance
bias_sum += 0.5 * bias_fc[i] * dist * dist bias_sum += 0.5 * bias_fc[i] * dist * dist
} }
let bias_sum = (-bias_sum/self.kT).exp(); let bias_sum = (-bias_sum/self.kT).exp();