mirror of
https://github.com/dnlbauer/WHAM.git
synced 2026-09-10 22:25:31 +00:00
333 lines
9.2 KiB
Rust
333 lines
9.2 KiB
Rust
#![allow(non_snake_case)]
|
|
|
|
#[macro_use]
|
|
extern crate scan_fmt;
|
|
|
|
pub mod io;
|
|
pub mod histogram;
|
|
|
|
use std::error::Error;
|
|
use std::result::Result;
|
|
use histogram::{Dataset,Histogram};
|
|
use std::f64;
|
|
use std::fmt;
|
|
|
|
#[allow(non_upper_case_globals)]
|
|
static k_B: f64 = 0.0083144621; // kJ/mol*K
|
|
|
|
// Application config
|
|
#[derive(Debug)]
|
|
pub struct Config {
|
|
pub metadata_file: String,
|
|
pub hist_min: Vec<f64>,
|
|
pub hist_max: Vec<f64>,
|
|
pub num_bins: Vec<usize>,
|
|
pub dimens: usize,
|
|
pub verbose: bool,
|
|
pub tolerance: f64,
|
|
pub max_iterations: usize,
|
|
pub temperature: f64,
|
|
pub cyclic: bool,
|
|
pub output: String,
|
|
}
|
|
|
|
impl fmt::Display for Config {
|
|
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
|
|
write!(f, "Metadata={}, hist_min={:?}, hist_max={:?}, bins={:?}\nverbose={}, tolerance={}, iterations={}, temperature={}, cyclic={:?}" , self.metadata_file, self.hist_min,
|
|
self.hist_max, self.num_bins, self.verbose, self.tolerance,
|
|
self.max_iterations, self.temperature, self.cyclic)
|
|
}
|
|
}
|
|
|
|
// Checks for convergence between two WHAM iterations. WHAM is considered as
|
|
// converged if the absolute difference for the calculated bias offset is
|
|
// smaller then a tolerance value for every simulation window.
|
|
fn is_converged(old_F: &Vec<f64>, new_F: &Vec<f64>, tolerance: f64) -> bool {
|
|
!new_F.iter().zip(old_F.iter())
|
|
.map(|x| { (x.0-x.1).abs() })
|
|
.any(|diff| { diff > tolerance })
|
|
}
|
|
|
|
// estimate the probability of a bin of the histogram set based on F values
|
|
// This evaluates the first WHAM equation for each bin
|
|
fn calc_bin_probability(bin: usize, ds: &Dataset, F: &Vec<f64>) -> f64 {
|
|
let mut denom_sum: f64 = 0.0;
|
|
let mut bin_count: f64 = 0.0;
|
|
for window in 0..ds.num_windows {
|
|
let h: &Histogram = &ds.histograms[window];
|
|
if let Some(count) = h.get_bin_count(bin) {
|
|
bin_count += count;
|
|
}
|
|
let bias = ds.calc_bias(bin, window);
|
|
let bias_offset = ((F[window] - bias) / ds.kT).exp();
|
|
denom_sum += (h.num_points as f64) * bias_offset;
|
|
}
|
|
bin_count / denom_sum
|
|
}
|
|
|
|
// estimate the bias offset F of the histogram based on given probabilities
|
|
// This evaluates the second WHAM equation for each window
|
|
fn calc_window_F(window: usize, ds: &Dataset, P: &Vec<f64>) -> f64 {
|
|
let bf_sum: f64 = (0..ds.num_bins).zip(P.iter()) // zip bins and P
|
|
.map(|x: (usize, &f64)| {
|
|
x.1 * (-ds.calc_bias(x.0, window)/ds.kT).exp()
|
|
}).sum();
|
|
-ds.kT * bf_sum.ln()
|
|
}
|
|
|
|
// One full WHAM iteration includes calculation of new probabilities P and
|
|
// new bias offsets F based on previous bias offsets F_prev. This updates
|
|
// the values in vectors F and P
|
|
fn perform_wham_iteration(ds: &Dataset, F_prev: &Vec<f64>,F: &mut Vec<f64>, P: &mut Vec<f64>) {
|
|
// reset bias offsets
|
|
for window in 0..ds.num_windows {
|
|
F[window] = 0.0;
|
|
}
|
|
|
|
// for bin in 0..ds.num_bins {
|
|
// let x = get_x_for_bin(bin, ds.hist_min, ds.bin_width);
|
|
// let mut num = 0.0;
|
|
// let mut denom = 0.0;
|
|
|
|
// for window in 0..ds.num_windows {
|
|
// match ds.histograms[window].get_bin_count(bin) {
|
|
// Some(c) => num += c,
|
|
// _ => {}
|
|
// }
|
|
// let bias = calc_bias(
|
|
// ds.bias_fc[window],
|
|
// ds.bias_pos[window],
|
|
// x);
|
|
// let bf = ((F_prev[window]-bias) / ds.kT).exp();
|
|
// denom += ds.histograms[window].num_points as f64* bf
|
|
// }
|
|
// P[bin] = num / denom;
|
|
|
|
// for window in 0..ds.num_windows {
|
|
// let bias = calc_bias(
|
|
// ds.bias_fc[window],
|
|
// ds.bias_pos[window],
|
|
// x);
|
|
// let bf = (-bias/ds.kT).exp() * P[bin];
|
|
// F[window] += bf;
|
|
// }
|
|
// }
|
|
|
|
// for window in 0..ds.num_windows {
|
|
// F[window] = -ds.kT * F[window].ln();
|
|
// }
|
|
|
|
// let norm = F[0];
|
|
// for window in 0..ds.num_windows {
|
|
// F[window] = F[window] - norm;
|
|
// }
|
|
|
|
// evaluate first WHAM equation for each bin to
|
|
// estimage probabilities based on previous offsets (F_prev)
|
|
for bin in 0..ds.num_bins {
|
|
P[bin] = calc_bin_probability(bin, ds, F_prev);
|
|
}
|
|
|
|
// evaluate second WHAM equation for each window to
|
|
// estimate new bias offsets from propabilities
|
|
for window in 0..ds.num_windows {
|
|
F[window] = calc_window_F(window, ds, P);
|
|
}
|
|
|
|
// normalize F
|
|
// let norm = F[0];
|
|
// for window in 0..ds.num_windows {
|
|
// F[window] = F[window] - norm;
|
|
// }
|
|
}
|
|
|
|
// get average difference between two bias offset sets
|
|
fn diff_avg(F: &Vec<f64>, F_prev: &Vec<f64>) -> f64 {
|
|
let mut F_sum: f64 = 0.0;
|
|
for i in 0..F.len() {
|
|
F_sum += (F[i]-F_prev[i]).abs()
|
|
}
|
|
F_sum / F.len() as f64
|
|
}
|
|
|
|
|
|
// calculate the normalized free energy from normalized probability values
|
|
fn free_energy(ds: &Dataset, P: &mut Vec<f64>, A: &mut Vec<f64>) {
|
|
let mut bin_min = f64::MAX;
|
|
|
|
// Free energy calculation
|
|
for bin in 0..ds.num_bins {
|
|
A[bin] = -ds.kT*P[bin].ln();
|
|
if A[bin] < bin_min {
|
|
bin_min = A[bin];
|
|
}
|
|
}
|
|
|
|
// Make A relative to minimum
|
|
for bin in 0..ds.num_bins {
|
|
A[bin] -= bin_min;
|
|
}
|
|
|
|
// Normalize P
|
|
// let mut P_sum = 0.0;
|
|
// for bin in 0..ds.num_bins {
|
|
// P_sum += P[bin];
|
|
// }
|
|
// for bin in 0..ds.num_bins {
|
|
// P[bin] /= P_sum;
|
|
// }
|
|
|
|
|
|
}
|
|
|
|
pub fn run(cfg: &Config) -> Result<(), Box<Error>>{
|
|
println!("Supplied WHAM options: {}", &cfg);
|
|
|
|
// read input data into the histograms object
|
|
println!("Reading input files.");
|
|
|
|
let histograms = io::read_data(&cfg) // TODO nicer error handling for this
|
|
.expect("No datapoints in histogram boundaries.");
|
|
println!("{}",&histograms);
|
|
|
|
// allocate only once for better performance
|
|
let mut F_prev: Vec<f64> = vec![f64::INFINITY; histograms.num_windows];
|
|
let mut F: Vec<f64> = vec![0.0; histograms.num_windows];
|
|
let mut P: Vec<f64> = vec![f64::NAN; histograms.num_bins];
|
|
let mut A: Vec<f64> = vec![f64::NAN; histograms.num_bins];
|
|
|
|
// perform WHAM until convergence
|
|
let mut iteration = 0;
|
|
while !is_converged(&F_prev, &F, cfg.tolerance) && iteration < cfg.max_iterations {
|
|
iteration += 1;
|
|
// store F values before the next iteration
|
|
F_prev.copy_from_slice(&F[..]);
|
|
|
|
// perform wham iteration and update F
|
|
perform_wham_iteration(&histograms, &F_prev, &mut F, &mut P);
|
|
|
|
// output some stats during calculation
|
|
if iteration % 10 == 0 {
|
|
println!("Iteration {}: dF={}", &iteration, &diff_avg(&F_prev, &F));
|
|
}
|
|
|
|
// Dump free energy and bias offsets
|
|
if iteration % 100 == 0 {
|
|
free_energy(&histograms, &mut P, &mut A);
|
|
dump_state(&histograms, &F, &F_prev, &P, &A);
|
|
}
|
|
}
|
|
|
|
// Normalize P
|
|
// let mut P_sum = 0.0;
|
|
// for bin in 0..histograms.num_bins {
|
|
// P_sum += P[bin];
|
|
// }
|
|
// for bin in 0..histograms.num_bins {
|
|
// P[bin] /= P_sum;
|
|
// }
|
|
|
|
// final free energy calculation and state dump
|
|
println!("Finished. Dumping final PMF");
|
|
free_energy(&histograms, &mut P, &mut A);
|
|
dump_state(&histograms, &F, &F_prev, &P, &A);
|
|
|
|
if iteration == cfg.max_iterations {
|
|
println!("!!!!! WHAM not converged! (max iterations reached) !!!!!");
|
|
}
|
|
|
|
io::write_results(&cfg.output, &histograms, &A, &P)?;
|
|
|
|
Ok(())
|
|
}
|
|
|
|
fn dump_state(ds: &Dataset, F: &Vec<f64>, F_prev: &Vec<f64>, P: &Vec<f64>, A: &Vec<f64>) {
|
|
println!("# PMF");
|
|
println!("#x\t\tFree Energy\t\tP(x)");
|
|
for bin in 0..ds.num_bins {
|
|
let x = ds.get_x_for_bin(bin);
|
|
println!("{:9.5}\t{:9.5}\t{:9.5}", x, A[bin], P[bin]);
|
|
}
|
|
println!("# Bias offsets");
|
|
println!("#Window\t\tF\t\tdF");
|
|
for window in 0..ds.num_windows {
|
|
println!("{}\t{:9.5}\t{:8.8}", window, F[window], (F[window]-F_prev[window]).abs());
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::histogram::{Dataset,Histogram};
|
|
use std::f64;
|
|
|
|
#[test]
|
|
fn is_converged() {
|
|
let new = vec![1.0,1.0];
|
|
let old = vec![0.95, 1.0];
|
|
let tolerance = 0.1;
|
|
let converged = super::is_converged(&old, &new, tolerance);
|
|
assert!(converged);
|
|
|
|
let old = vec![0.8, 1.0];
|
|
let converged = super::is_converged(&old, &new, tolerance);
|
|
assert!(!converged);
|
|
}
|
|
|
|
fn create_test_ds() -> Dataset {
|
|
let h1 = Histogram::new(0, 2, 10, vec![3.0, 4.0, 3.0]);
|
|
let h2 = Histogram::new(0, 3, 20, vec![3.0, 2.0, 5.0, 10.0]);
|
|
Dataset::new(4, 1.0, 0.0, 4.0, vec![1.0, 2.0], vec![10.0, 10.0], 2.479, vec![h1, h2], false)
|
|
}
|
|
|
|
fn assert_near(a: f64, b: f64, tolerance: f64) {
|
|
let d = (a-b).abs();
|
|
assert!(d <= tolerance, "Values are not close: {}, {}, d={}", &a, &b, &d);
|
|
}
|
|
|
|
#[test]
|
|
fn calc_bias_offset() {
|
|
let ds = create_test_ds();
|
|
let probability = vec!(0.959, 0.331, 0.656, 46.750);
|
|
let expected = vec!(0.596, -0.250);
|
|
for window in 0..ds.num_windows {
|
|
let F = super::calc_window_F(window, &ds, &probability);
|
|
assert_near(expected[window], F, 0.001);
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn calc_bin_probability() {
|
|
let ds = create_test_ds();
|
|
let F = vec!(0.0, 0.0);
|
|
let expected = vec!(0.959, 0.331, 0.656, 46.750);
|
|
for b in 0..4 {
|
|
let p = super::calc_bin_probability(b, &ds, &F);
|
|
assert_near(expected[b], p, 0.001);
|
|
}
|
|
|
|
let F = vec!(1.0, 1.0);
|
|
let expected = vec!(0.641, 0.221, 0.439, 31.232);
|
|
for b in 0..4 {
|
|
let p = super::calc_bin_probability(b, &ds, &F);
|
|
assert_near(expected[b], p, 0.001);
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn perform_wham_iteration() {
|
|
let ds = create_test_ds();
|
|
let prev_F = vec![0.0; ds.num_windows];
|
|
let mut F = vec![0.0; ds.num_windows];
|
|
let mut P = vec![f64::NAN; ds.num_bins];
|
|
super::perform_wham_iteration(&ds, &prev_F, &mut F, &mut P);
|
|
let expected_F = vec!(0.5948, -0.2513);
|
|
let expected_P = vec!(0.959, 0.331, 0.656, 46.750);
|
|
for bin in 0..ds.num_bins {
|
|
assert_near(expected_P[bin], P[bin], 0.01)
|
|
}
|
|
for window in 0..ds.num_windows {
|
|
assert_near(expected_F[window], F[window], 0.01)
|
|
}
|
|
|
|
}
|
|
} |