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comments and code cleanup

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This commit is contained in:
Daniel Bauer
2018-10-18 01:03:29 +02:00
parent 6b9661bd13
commit c835520bf4
3 changed files with 198 additions and 262 deletions
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7
src/io.rs
View File

@@ -236,13 +236,6 @@ mod tests {
println!("{:?}", ds);
assert_eq!(2, ds.num_windows);
assert_eq!(cfg.num_bins[0], ds.dimens_lengths[0]);
// fields are private
// assert_eq!(cfg.hist_min[0], ds.hist_min[0]);
// assert_eq!(cfg.hist_max[0], ds.hist_max[0]);
// let expected_bin_width = (cfg.hist_max[0] - cfg.hist_min[0])/cfg.num_bins[0] as f64;
// assert_eq!(expected_bin_width, ds.bin_width);
// assert_eq!(vec![0.0, 1.0], ds.bias_pos);
// assert_eq!(vec![100.0, 200.0], ds.bias_fc);
assert_eq!(cfg.temperature * k_B, ds.kT);
assert_eq!(2, ds.histograms.len())
}

225
src/lib.rs
View File

@@ -31,23 +31,22 @@ pub struct Config {
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)
write!(f, "Metadata={}, hist_min={:?}, hist_max={:?}, bins={:?} verbose={}, 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.
// converged if the maximal difference for the calculated bias offsets is
// smaller then a tolerance value.
fn is_converged(old_F: &[f64], new_F: &[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
// estimate the probability of a bin of the histogram set based on given bias offsets (F)
// This evaluates the first WHAM equation for each bin.
fn calc_bin_probability(bin: usize, ds: &Dataset, F: &[f64]) -> f64 {
let mut denom_sum: f64 = 0.0;
let mut bin_count: f64 = 0.0;
@@ -59,8 +58,8 @@ fn calc_bin_probability(bin: usize, ds: &Dataset, F: &[f64]) -> f64 {
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
// estimate the bias offset F of the histogram based on given probabilities.
// This evaluates the second WHAM equation for each window and returns exp(F/kT)
fn calc_window_F(window: usize, ds: &Dataset, P: &[f64]) -> f64 {
let f: f64 = (0..ds.num_bins).zip(P.iter()) // zip bins and P
.filter_map(|bin_and_prob: (usize, &f64)| {
@@ -78,44 +77,6 @@ fn calc_window_F(window: usize, ds: &Dataset, P: &[f64]) -> f64 {
// 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: &[f64], F: &mut [f64], P: &mut [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();
// }
// evaluate first WHAM equation for each bin to
// estimage probabilities based on previous offsets (F_prev)
for bin in 0..ds.num_bins {
@@ -127,9 +88,78 @@ fn perform_wham_iteration(ds: &Dataset, F_prev: &[f64], F: &mut [f64], P: &mut [
for window in 0..ds.num_windows {
F[window] = calc_window_F(window, ds, P);
}
}
pub fn run(cfg: &Config) -> Result<(), Box<Error>>{
println!("Supplied WHAM options: {}", &cfg);
println!("Reading input files.");
// TODO Better error handling with nice error messages instead of a panic!
let histograms = io::read_data(&cfg)
.expect("No datapoints in histogram boundaries.");
println!("{}",&histograms);
// allocate required vectors.
let mut P: Vec<f64> = vec![f64::NAN; histograms.num_bins]; // bin probability
let mut F: Vec<f64> = vec![1.0; histograms.num_windows]; // bias offset exp(F/kT)
let mut F_prev: Vec<f64> = vec![f64::NAN; histograms.num_windows]; // previous bias offset
let mut F_tmp: Vec<f64> = vec![f64::NAN; histograms.num_windows]; // temp storage for F
let mut iteration = 0;
let mut converged = false;
// perform WHAM until convergence
while !converged && iteration < cfg.max_iterations {
iteration += 1;
// store F values before the next iteration
F_prev.copy_from_slice(&F);
// perform wham iteration (this updates F and P)
perform_wham_iteration(&histograms, &F_prev, &mut F, &mut P);
// convergence check
if iteration % 10 == 0 {
// This backups exp(F/kT) in a temporary vector and calculates true F and F_prev for
// convergence. Finally, F is restored. F_prev does not need to be restored because
// its overwritten for the next iteration.
F_tmp.copy_from_slice(&F);
for window in 0..histograms.num_windows {
F[window] = -histograms.kT * F[window].ln();
F_prev[window] = -histograms.kT * F_prev[window].ln();
}
converged = is_converged(&F_prev, &F, cfg.tolerance);
println!("Iteration {}: dF={}", &iteration, &diff_avg(&F_prev, &F));
F.copy_from_slice(&F_tmp);
}
// 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 to sum(P) = 1.0
let P_sum: f64 = P.iter().sum();
P.iter_mut().map(|p| *p /= P_sum).count();
// calculate free energy and dump state
println!("Finished. Dumping final PMF");
let free_energy = calc_free_energy(&histograms, &P);
dump_state(&histograms, &F, &F_prev, &P, &free_energy);
if iteration == cfg.max_iterations {
println!("!!!!! WHAM not converged! (max iterations reached) !!!!!");
}
io::write_results(&cfg.output, &histograms, &free_energy, &P)?;
Ok(())
}
// get average difference between two bias offset sets
fn diff_avg(F: &[f64], F_prev: &[f64]) -> f64 {
let mut F_sum: f64 = 0.0;
@@ -137,97 +167,36 @@ fn diff_avg(F: &[f64], F_prev: &[f64]) -> f64 {
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: &[f64], A: &mut [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;
}
}
pub fn run(cfg: &Config) -> Result<(), Box<Error>>{
println!("Supplied WHAM options: {}", &cfg);
// calculate the normalized free energy from probability values
fn calc_free_energy(ds: &Dataset, P: &[f64]) -> Vec<f64> {
let mut minimum = f64::MAX;
let mut free_energy: Vec<f64> = P.iter()
.map(|p| {
-ds.kT * p.ln()
})
.inspect(|free_e| {
if free_e < &minimum {
minimum = *free_e;
}
})
.collect();
// 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::NAN; histograms.num_windows];
let mut F: Vec<f64> = vec![1.0; histograms.num_windows];
let mut F_tmp: Vec<f64> = vec![f64::NAN; 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;
let mut converged = false;
while !converged && 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 {
F_tmp.copy_from_slice(&F);
F.iter_mut().map(|f| *f=-histograms.kT*f.ln()).count();
F_prev.iter_mut().map(|f| *f=-histograms.kT*f.ln()).count();
converged = is_converged(&F_prev, &F, cfg.tolerance);
println!("Iteration {}: dF={}", &iteration, &diff_avg(&F_prev, &F));
F.copy_from_slice(&F_tmp);
}
// 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 P_sum: f64 = P.iter().sum();
P.iter_mut().map(|p| *p /= P_sum).count();
// 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(())
for e in free_energy.iter_mut() {
*e -= minimum
}
free_energy
}
// TODO print nice headers for N dimensions
fn dump_state(ds: &Dataset, F: &[f64], F_prev: &[f64], P: &[f64], A: &[f64]) {
let out = std::io::stdout();
let mut lock = out.lock();
writeln!(lock, "# PMF");
writeln!(lock, "#x\t\tFree Energy\t\tP(x)");
for bin in 0..ds.num_bins {
let x = ds.get_coords_for_bin(bin)[0]; // TODO
let x = ds.get_coords_for_bin(bin)[0];
writeln!(lock, "{:9.5}\t{:9.5}\t{:9.5}", x, A[bin], P[bin]);
}
writeln!(lock, "# Bias offsets");