bauer/WHAM
1
0
Fork 0
mirror of https://github.com/dnlbauer/WHAM.git synced 2026-09-10 22:25:31 +00:00
Code Issues Packages Projects Releases Wiki Activity

small performance improvments?

Browse Source
This commit is contained in:
Daniel Bauer
2018-10-14 22:46:15 +02:00
parent 1da5bc7000
commit 7a371b9bb2
1 changed files with 10 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
src/lib.rs
View File

@@ -5,7 +5,7 @@ pub mod histogram;
use std::error::Error; use std::error::Error;
use std::result::Result; use std::result::Result;
use histogram::{Dataset,Histogram}; use histogram::Dataset;
use std::f64; use std::f64;
use std::fmt; use std::fmt;
use std::io::prelude::*; use std::io::prelude::*;
@@ -49,15 +49,15 @@ fn is_converged(old_F: &[f64], new_F: &[f64], tolerance: f64) -> bool {
// estimate the probability of a bin of the histogram set based on F values // estimate the probability of a bin of the histogram set based on F values
// This evaluates the first WHAM equation for each bin // This evaluates the first WHAM equation for each bin
fn calc_bin_probability(bin: usize, ds: &Dataset, F: &[f64]) -> f64 { fn calc_bin_probability(bin: usize, ds: &Dataset, F: &[f64]) -> f64 {
let mut denom_sum: f64 = 0.0; let mut denom_sum: f64 = 0.0;
let mut bin_count: f64 = 0.0; let mut bin_count: f64 = 0.0;
for window in 0..ds.num_windows { for (window, h) in ds.histograms.iter().enumerate() {
let h: &Histogram = &ds.histograms[window];
bin_count += h.bins[bin]; bin_count += h.bins[bin];
let bias = ds.calc_bias(bin, window); let bias = ds.calc_bias(bin, window);
let bias_offset = ((F[window] - bias) / ds.kT).exp(); let bias_offset = ((F[window] - bias) / ds.kT).exp();
denom_sum += (h.num_points as f64) * bias_offset; denom_sum += (h.num_points as f64) * bias_offset;
} }
bin_count / denom_sum bin_count / denom_sum
} }
@@ -65,8 +65,12 @@ fn calc_bin_probability(bin: usize, ds: &Dataset, F: &[f64]) -> f64 {
// This evaluates the second WHAM equation for each window // This evaluates the second WHAM equation for each window
fn calc_window_F(window: usize, ds: &Dataset, P: &[f64]) -> f64 { fn calc_window_F(window: usize, ds: &Dataset, P: &[f64]) -> f64 {
let bf_sum: f64 = (0..ds.num_bins).zip(P.iter()) // zip bins and P let bf_sum: f64 = (0..ds.num_bins).zip(P.iter()) // zip bins and P
.map(|x: (usize, &f64)| { .filter_map(|count_and_prob: (usize, &f64)| {
x.1 * (-ds.calc_bias(x.0, window)/ds.kT).exp() if count_and_prob.1 == &0.0 { // skip zeros for speed
None
} else {
Some(count_and_prob.1 * (-ds.calc_bias(count_and_prob.0, window) / ds.kT).exp())
}
}).sum(); }).sum();
-ds.kT * bf_sum.ln() -ds.kT * bf_sum.ln()
} }