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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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228
example/1d/wham.out
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@@ -1,127 +1,101 @@
#Coor Free +/- Prob +/-
-3.108600 7.109190 -nan 0.003556 -nan
-3.045800 5.311412 -nan 0.007311 -nan
-2.983000 3.849095 -nan 0.013139 -nan
-2.920200 2.933168 -nan 0.018968 -nan
-2.857400 1.907181 -nan 0.028620 -nan
-2.794600 1.385773 -nan 0.035274 -nan
-2.731800 1.139312 -nan 0.038937 -nan
-2.669000 0.814751 -nan 0.044348 -nan
-2.606200 0.609195 -nan 0.048157 -nan
-2.543400 0.694709 -nan 0.046534 -nan
-2.480600 1.042492 -nan 0.040478 -nan
-2.417800 1.487426 -nan 0.033865 -nan
-2.355000 1.994612 -nan 0.027634 -nan
-2.292200 2.207453 -nan 0.025374 -nan
-2.229400 2.555138 -nan 0.022072 -nan
-2.166600 2.575426 -nan 0.021894 -nan
-2.103800 2.461877 -nan 0.022913 -nan
-2.041000 2.500517 -nan 0.022561 -nan
-1.978200 2.458051 -nan 0.022949 -nan
-1.915400 2.217054 -nan 0.025276 -nan
-1.852600 2.096290 -nan 0.026530 -nan
-1.789800 1.771307 -nan 0.030222 -nan
-1.727000 1.441175 -nan 0.034499 -nan
-1.664200 0.901911 -nan 0.042825 -nan
-1.601400 0.301267 -nan 0.054485 -nan
-1.538600 0.245895 -nan 0.055708 -nan
-1.475800 0.000000 -nan 0.061480 -nan
-1.413000 0.537640 -nan 0.049559 -nan
-1.350200 1.431041 -nan 0.034639 -nan
-1.287400 2.402640 -nan 0.023464 -nan
-1.224600 3.790635 -nan 0.013450 -nan
-1.161800 5.704072 -nan 0.006246 -nan
-1.099000 7.665152 -nan 0.002845 -nan
-1.036200 9.965495 -nan 0.001131 -nan
-0.973400 12.369366 -nan 0.000432 -nan
-0.910600 14.967624 -nan 0.000152 -nan
-0.847800 17.760111 -nan 0.000050 -nan
-0.785000 20.575788 -nan 0.000016 -nan
-0.722200 22.801620 -nan 0.000007 -nan
-0.659400 25.195750 -nan 0.000003 -nan
-0.596600 26.441571 -nan 0.000002 -nan
-0.533800 27.909051 -nan 0.000001 -nan
-0.471000 29.053664 -nan 0.000001 -nan
-0.408200 30.392176 -nan 0.000000 -nan
-0.345400 31.642366 -nan 0.000000 -nan
-0.282600 32.810590 -nan 0.000000 -nan
-0.219800 33.762125 -nan 0.000000 -nan
-0.157000 34.508470 -nan 0.000000 -nan
-0.094200 35.421120 -nan 0.000000 -nan
-0.031400 35.614610 -nan 0.000000 -nan
0.031400 35.555913 -nan 0.000000 -nan
0.094200 35.381388 -nan 0.000000 -nan
0.157000 34.924243 -nan 0.000000 -nan
0.219800 33.673332 -nan 0.000000 -nan
0.282600 32.727755 -nan 0.000000 -nan
0.345400 31.255053 -nan 0.000000 -nan
0.408200 29.725589 -nan 0.000000 -nan
0.471000 28.078949 -nan 0.000001 -nan
0.533800 26.487669 -nan 0.000002 -nan
0.596600 24.481873 -nan 0.000003 -nan
0.659400 22.360728 -nan 0.000008 -nan
0.722200 20.238391 -nan 0.000018 -nan
0.785000 18.348925 -nan 0.000039 -nan
0.847800 16.276613 -nan 0.000090 -nan
0.910600 14.308785 -nan 0.000198 -nan
0.973400 12.620723 -nan 0.000390 -nan
1.036200 11.243896 -nan 0.000678 -nan
1.099000 10.102041 -nan 0.001071 -nan
1.161800 9.431925 -nan 0.001401 -nan
1.224600 9.159750 -nan 0.001563 -nan
1.287400 9.321889 -nan 0.001464 -nan
1.350200 9.881419 -nan 0.001170 -nan
1.413000 11.014057 -nan 0.000743 -nan
1.475800 12.589255 -nan 0.000395 -nan
1.538600 14.481607 -nan 0.000185 -nan
1.601400 16.525295 -nan 0.000082 -nan
1.664200 18.663885 -nan 0.000035 -nan
1.727000 20.745586 -nan 0.000015 -nan
1.789800 22.882359 -nan 0.000006 -nan
1.852600 24.611022 -nan 0.000003 -nan
1.915400 26.263981 -nan 0.000002 -nan
1.978200 27.362886 -nan 0.000001 -nan
2.041000 28.656631 -nan 0.000001 -nan
2.103800 29.415584 -nan 0.000000 -nan
2.166600 29.985317 -nan 0.000000 -nan
2.229400 30.409211 -nan 0.000000 -nan
2.292200 30.154379 -nan 0.000000 -nan
2.355000 29.898976 -nan 0.000000 -nan
2.417800 29.431664 -nan 0.000000 -nan
2.480600 28.566154 -nan 0.000001 -nan
2.543400 27.774399 -nan 0.000001 -nan
2.606200 26.550657 -nan 0.000001 -nan
2.669000 24.524409 -nan 0.000003 -nan
2.731800 22.387526 -nan 0.000008 -nan
2.794600 20.061389 -nan 0.000020 -nan
2.857400 17.710243 -nan 0.000051 -nan
2.920200 15.524401 -nan 0.000122 -nan
2.983000 13.172725 -nan 0.000313 -nan
3.045800 11.135246 -nan 0.000708 -nan
3.108600 9.106349 -nan 0.001597 -nan
#Window Free +/-
#0 0.000000 -nan
#1 -4.304935 -nan
#2 -11.772646 -nan
#3 -18.486638 -nan
#4 -22.834670 -nan
#5 -24.172759 -nan
#6 -22.273996 -nan
#7 -17.239462 -nan
#8 -10.107963 -nan
#9 -5.386748 -nan
#10 -10.219397 -nan
#11 -18.470489 -nan
#12 -25.454944 -nan
#13 -3.099544 -nan
#14 -10.785962 -nan
#15 -19.937669 -nan
#16 -27.163650 -nan
#17 -31.721722 -nan
#18 -33.474006 -nan
#19 -32.952581 -nan
#20 -32.029659 -nan
#21 -32.180907 -nan
#22 -33.041061 -nan
#23 -32.813126 -nan
#24 -30.673600 -nan
#x Free Energy Probability
-3.108600 7.118166 0.003561
-3.045800 5.331290 0.007290
-2.983000 3.879241 0.013048
-2.920200 2.968659 0.018796
-2.857400 1.942494 0.028362
-2.794600 1.418408 0.034994
-2.731800 1.169398 0.038668
-2.669000 0.843024 0.044073
-2.606200 0.636021 0.047887
-2.543400 0.720123 0.046299
-2.480600 1.066434 0.040297
-2.417800 1.509855 0.033734
-2.355000 2.015549 0.027544
-2.292200 2.226942 0.025306
-2.229400 2.573176 0.022026
-2.166600 2.591946 0.021861
-2.103800 2.476835 0.022893
-2.041000 2.513953 0.022555
-1.978200 2.470032 0.022956
-1.915400 2.227585 0.025299
-1.852600 2.105310 0.026570
-1.789800 1.778779 0.030286
-1.727000 1.447127 0.034593
-1.664200 0.906384 0.042968
-1.601400 0.304263 0.054699
-1.538600 0.247397 0.055960
-1.475800 0.000000 0.061795
-1.413000 0.536142 0.049843
-1.350200 1.428046 0.034859
-1.287400 2.398149 0.023627
-1.224600 3.784647 0.013552
-1.161800 5.696583 0.006297
-1.099000 7.656158 0.002870
-1.036200 9.955000 0.001142
-0.973400 12.357407 0.000436
-0.910600 14.954254 0.000154
-0.847800 17.745327 0.000050
-0.785000 20.559461 0.000016
-0.722200 22.783560 0.000007
-0.659400 25.175865 0.000003
-0.596600 26.419980 0.000002
-0.533800 27.885999 0.000001
-0.471000 29.029334 0.000001
-0.408200 30.366556 0.000000
-0.345400 31.615252 0.000000
-0.282600 32.781716 0.000000
-0.219800 33.731378 0.000000
-0.157000 34.476014 0.000000
-0.094200 35.387300 0.000000
-0.031400 35.579756 0.000000
0.031400 35.520194 0.000000
0.094200 35.344763 0.000000
0.157000 34.886460 0.000000
0.219800 33.633998 0.000000
0.282600 32.686511 0.000000
0.345400 31.211777 0.000000
0.408200 29.680451 0.000000
0.471000 28.032242 0.000001
0.533800 26.439599 0.000002
0.596600 24.432467 0.000003
0.659400 22.309893 0.000008
0.722200 20.186040 0.000019
0.785000 18.295046 0.000040
0.847800 16.221232 0.000093
0.910600 14.251911 0.000204
0.973400 12.562353 0.000402
1.036200 11.184029 0.000698
1.099000 10.040677 0.001103
1.161800 9.369064 0.001444
1.224600 9.095392 0.001612
1.287400 9.256035 0.001511
1.350200 9.814068 0.001208
1.413000 10.945207 0.000768
1.475800 12.518898 0.000409
1.538600 14.409744 0.000191
1.601400 16.451965 0.000084
1.664200 18.589166 0.000036
1.727000 20.669504 0.000016
1.789800 22.804791 0.000007
1.852600 24.531690 0.000003
1.915400 26.182612 0.000002
1.978200 27.279439 0.000001
2.041000 28.571380 0.000001
2.103800 29.328969 0.000000
2.166600 29.897739 0.000000
2.229400 30.320908 0.000000
2.292200 30.065391 0.000000
2.355000 29.809137 0.000000
2.417800 29.340601 0.000000
2.480600 28.473346 0.000001
2.543400 27.679353 0.000001
2.606200 26.453160 0.000002
2.669000 24.424649 0.000003
2.731800 22.285933 0.000008
2.794600 19.958407 0.000021
2.857400 17.606291 0.000053
2.920200 15.420138 0.000128
2.983000 13.069599 0.000328
3.045800 11.036085 0.000740
3.108600 9.015657 0.001664

7
src/io.rs
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@@ -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())
}

221
src/lib.rs
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@@ -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;
@@ -139,95 +169,34 @@ fn diff_avg(F: &[f64], F_prev: &[f64]) -> f64 {
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];
}
// 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();
// Make A relative to minimum
for bin in 0..ds.num_bins {
A[bin] -= bin_min;
for e in free_energy.iter_mut() {
*e -= minimum
}
free_energy
}
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::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(())
}
// 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");