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remove some code smell

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This commit is contained in:
Daniel Bauer
2020-03-09 10:05:36 +01:00
parent 133234706e
commit 5390b089ac
5 changed files with 92 additions and 68 deletions
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110
src/lib.rs
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@@ -45,7 +45,9 @@ 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={:?} verbose={}, tolerance={}, iterations={}, temperature={}, cyclic={:?}, bootstrap={:?}, seed={:?}",
write!(f, "Metadata={}, hist_min={:?}, hist_max={:?}, bins={:?},
verbose={}, tolerance={}, iterations={}, temperature={},
cyclic={:?}, bootstrap={:?}, seed={:?}",
self.metadata_file, self.hist_min, self.hist_max, self.num_bins,
self.verbose, self.tolerance, self.max_iterations, self.temperature,
self.cyclic, self.bootstrap, self.bootstrap_seed)
@@ -56,25 +58,34 @@ impl fmt::Display for Config {
// 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 })
// calculates abs diff between every old and new F and checks if any
// is larger than tolerance
!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 given bias offsets (F)
// 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:
// P(x) = \frac {\sum_{i=1}^N{n_i(x)}}
// {\sum_{i=1}^N{ N_i exp(\beta [F_i - U_{bias,i}(x)])}}
fn calc_bin_probability(bin: usize, dataset: &Dataset, F: &[f64]) -> f64 {
let mut denom_sum: f64 = 0.0;
let bin_count: f64 = dataset.get_weighted_bin_count(bin);
for (window, h) in dataset.histograms.iter().enumerate() {
let bias = dataset.get_bias(bin, window);
denom_sum += (dataset.weights[window] * h.num_points as f64) * bias * F[window];
denom_sum += (dataset.weights[window] * h.num_points as f64)
* bias * F[window];
}
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 and returns exp(F/kT)
// This evaluates the second WHAM equation for each window and returns exp(F/kT).
// exp(F/kT) is not required in intermediate steps so we save some time by not
// calculating it for every iteration.
// F_i = - 1/\beta ln[\sum_{X_{bins}}{P(x)exp(-\beta U_{bias,i}(x))}]
fn calc_window_F(window: usize, dataset: &Dataset, P: &[f64]) -> f64 {
let f: f64 = (0..dataset.num_bins).zip(P.iter()) // zip bins and P
.map(|bin_and_prob: (usize, &f64)| {
@@ -84,16 +95,18 @@ fn calc_window_F(window: usize, dataset: &Dataset, P: &[f64]) -> f64 {
1.0/f
}
// 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
// One full WHAM iteration: 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(dataset: &Dataset, F_prev: &[f64], F: &mut Vec<f64>, P: &mut Vec<f64>) {
// evaluate first WHAM equation for each bin to
// estimage probabilities based on previous offsets (F_prev))
(0..dataset.num_bins).into_par_iter()
// Update P
// evaluate first WHAM equation for each bin to
// estimate probabilities based on previous offsets (F_prev))
(0..dataset.num_bins).into_par_iter()
.map(|bin| { calc_bin_probability(bin, dataset, F_prev) })
.collect_into_vec(P);
// Update F
// evaluate second WHAM equation for each window to
// estimate new bias offsets from propabilities
(0..dataset.num_windows).into_par_iter()
@@ -101,12 +114,20 @@ fn perform_wham_iteration(dataset: &Dataset, F_prev: &[f64], F: &mut Vec<f64>, P
.collect_into_vec(F);
}
pub fn perform_wham(cfg: &Config, dataset: &Dataset) -> Result<(Vec<f64>, Vec<f64>, Vec<f64>)> {
// Full WHAM calculation. Calls `perform_wham_iteration` until convergence
// criteria are met or max iterations reached.
pub fn perform_wham(cfg: &Config, dataset: &Dataset)
-> Result<(Vec<f64>, Vec<f64>, Vec<f64>)> {
// allocate required vectors.
let mut P: Vec<f64> = vec![f64::NAN; dataset.num_bins]; // bin probability
let mut F: Vec<f64> = vec![1.0; dataset.num_windows]; // bias offset exp(F/kT)
let mut F_prev: Vec<f64> = vec![f64::NAN; dataset.num_windows]; // previous bias offset
let mut F_tmp: Vec<f64> = vec![f64::NAN; dataset.num_windows]; // temp storage for F
// bin probability
let mut P: Vec<f64> = vec![f64::NAN; dataset.num_bins];
// bias offset exp(F/kT)
let mut F: Vec<f64> = vec![1.0; dataset.num_windows];
// previous bias offset
let mut F_prev: Vec<f64> = vec![f64::NAN; dataset.num_windows];
// temp storage for F
let mut F_tmp: Vec<f64> = vec![f64::NAN; dataset.num_windows];
let mut iteration = 0;
let mut converged = false;
@@ -118,14 +139,15 @@ pub fn perform_wham(cfg: &Config, dataset: &Dataset) -> Result<(Vec<f64>, Vec<f6
// store F values before the next iteration
F_prev.copy_from_slice(&F);
// perform wham iteration (this updates F and P)
// perform wham iteration (this updates F and P).
perform_wham_iteration(&dataset, &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.
// 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 f in F.iter_mut() { *f = -dataset.kT * f.ln() }
for f in F_prev.iter_mut() { *f = -dataset.kT * f.ln() }
@@ -134,17 +156,13 @@ pub fn perform_wham(cfg: &Config, dataset: &Dataset) -> Result<(Vec<f64>, Vec<f6
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();
for p in P.iter_mut() {
*p /= P_sum;
}
if iteration == cfg.max_iterations {
bail!("WHAM not converged! (max iterations reached)");
@@ -214,19 +232,23 @@ fn calc_free_energy(dataset: &Dataset, P: &[f64]) -> Vec<f64> {
free_energy
}
fn dump_state(dataset: &Dataset, F: &[f64], F_prev: &[f64], P: &[f64], P_std: &[f64], A: &[f64], A_std: &[f64]) {
// Print the current WHAM iteration state. Dumps the PMF and associated vectors
fn dump_state(dataset: &Dataset, F: &[f64], F_prev: &[f64], P: &[f64],
P_std: &[f64], A: &[f64], A_std: &[f64]) {
// TODO fix output of F/F_prev
let out = std::io::stdout();
let mut lock = out.lock();
writeln!(lock, "# PMF");
writeln!(lock, "#bin\t\tFree Energy\t\t+/-\t\tP(x)\t\t+/-");
writeln!(lock, "# PMF").unwrap();
writeln!(lock, "#bin\t\tFree Energy\t\t+/-\t\tP(x)\t\t+/-").unwrap();
for bin in 0..dataset.num_bins {
writeln!(lock, "{:9.5}\t{:9.5}\t{:9.5}\t{:9.5}\t{:9.5}", bin, A[bin], A_std[bin], P[bin], P_std[bin]);
writeln!(lock, "{:9.5}\t{:9.5}\t{:9.5}\t{:9.5}\t{:9.5}",
bin, A[bin], A_std[bin], P[bin], P_std[bin]).unwrap();
}
writeln!(lock, "# Bias offsets");
writeln!(lock, "#Window\t\tF\t\tF_prev");
writeln!(lock, "# Bias offsets").unwrap();
writeln!(lock, "#Window\t\tF\t\tF_prev").unwrap();
for window in 0..dataset.num_windows {
writeln!(lock, "{}\t{:9.5}\t{:8.8}", window, F[window], (F[window]-F_prev[window]).abs());
writeln!(lock, "{}\t{:9.5}\t{:8.8}",
window, F[window], (F[window]-F_prev[window]).abs()).unwrap();
}
}
@@ -268,11 +290,11 @@ mod tests {
fn calc_bin_probability() {
let dataset = create_test_dataset();
let F = vec![1.0; dataset.num_bins] ;
let expected = vec!(0.0, 0.0825296687031316, 40.92355847097493,
124226.70003377, 2308526035.5283747);
let expected = vec!(0.0, 0.082_529_668_703_131_6, 40.923_558_470_974_93,
124_226.700_033_77, 2_308_526_035.528_374_7);
for b in 0..dataset.num_bins {
let p = super::calc_bin_probability(b, &dataset, &F);
assert_delta!(expected[b], p, 0.0000001);
assert_delta!(expected[b], p, 0.000_000_1);
}
}
@@ -280,10 +302,10 @@ mod tests {
fn calc_bias_offset() {
let dataset = create_test_dataset();
let probability = vec!(0.0, 0.1, 0.2, 0.3, 0.4);
let expected = vec!(15.927477169990633, 15.927477169990633);
let expected = vec!(15.927_477_169_990_633, 15.927_477_169_990_633);
for window in 0..dataset.num_windows {
let F = super::calc_window_F(window, &dataset, &probability);
assert_delta!(expected[window], F, 0.0000001);
assert_delta!(expected[window], F, 0.000_000_1);
}
}
@@ -295,8 +317,8 @@ mod tests {
let mut P = vec![f64::NAN; dataset.num_bins];
super::perform_wham_iteration(&dataset, &prev_F, &mut F, &mut P);
let expected_F = vec!(1.0, 1.0);
let expected_P = vec!(0.0, 0.0825296687031316, 40.92355847097493,
124226.70003377, 2308526035.5283747);
let expected_P = vec!(0.0, 0.082_529_668_703_131_6, 40.923_558_470_974_93,
124_226.700_033_77, 2_308_526_035.528_374_7);
for bin in 0..dataset.num_bins {
assert_delta!(expected_P[bin], P[bin], 0.01)
}