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uncorrelate data
This commit is contained in:
Daniel Bauer
101
example/1d_cyclic/wham_uncorrelated.out
Normal file
101
example/1d_cyclic/wham_uncorrelated.out
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@@ -0,0 +1,101 @@
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#coord1 Free Energy +/- Probability +/-
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-3.110177 7.531315 0.000000 0.003080 0.000000
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-3.047345 5.690157 0.000000 0.006443 0.000000
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-2.984513 4.243063 0.000000 0.011509 0.000000
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-2.921681 3.334686 0.000000 0.016564 0.000000
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-2.858849 2.277349 0.000000 0.025309 0.000000
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-2.796017 1.723296 0.000000 0.031604 0.000000
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-2.733186 1.246264 0.000000 0.038265 0.000000
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-2.670354 1.099867 0.000000 0.040578 0.000000
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-2.607522 0.771910 0.000000 0.046279 0.000000
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-2.544690 0.770616 0.000000 0.046303 0.000000
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-2.481858 1.265507 0.000000 0.037971 0.000000
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-2.419026 1.562335 0.000000 0.033711 0.000000
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-2.356194 1.891577 0.000000 0.029542 0.000000
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-2.293363 2.227858 0.000000 0.025816 0.000000
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-2.230531 2.488355 0.000000 0.023256 0.000000
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-2.167699 2.502265 0.000000 0.023127 0.000000
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-2.104867 2.358037 0.000000 0.024503 0.000000
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-2.042035 2.278147 0.000000 0.025301 0.000000
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-1.979203 2.974067 0.000000 0.019141 0.000000
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-1.916372 2.696600 0.000000 0.021393 0.000000
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-1.853540 2.361827 0.000000 0.024466 0.000000
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-1.790708 1.516746 0.000000 0.034332 0.000000
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-1.727876 1.526829 0.000000 0.034194 0.000000
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-1.665044 0.884114 0.000000 0.044244 0.000000
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-1.602212 0.323912 0.000000 0.055385 0.000000
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-1.539380 0.197985 0.000000 0.058253 0.000000
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-1.476549 0.000000 0.000000 0.063065 0.000000
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-1.413717 0.458247 0.000000 0.052481 0.000000
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-1.350885 1.389410 0.000000 0.036131 0.000000
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-1.288053 2.386522 0.000000 0.024225 0.000000
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-1.225221 3.743253 0.000000 0.014062 0.000000
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-1.162389 5.566654 0.000000 0.006770 0.000000
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-1.099557 7.822800 0.000000 0.002740 0.000000
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-1.036726 10.128719 0.000000 0.001087 0.000000
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-0.973894 12.199246 0.000000 0.000474 0.000000
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-0.911062 14.488129 0.000000 0.000189 0.000000
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-0.848230 16.902310 0.000000 0.000072 0.000000
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-0.785398 18.910200 0.000000 0.000032 0.000000
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-0.722566 21.241681 0.000000 0.000013 0.000000
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-0.659734 22.706373 0.000000 0.000007 0.000000
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-0.596903 24.531129 0.000000 0.000003 0.000000
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-0.534071 25.936227 0.000000 0.000002 0.000000
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-0.471239 27.000262 0.000000 0.000001 0.000000
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-0.408407 28.673293 0.000000 0.000001 0.000000
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-0.345575 29.335203 0.000000 0.000000 0.000000
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-0.282743 30.841118 0.000000 0.000000 0.000000
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-0.219911 31.983859 0.000000 0.000000 0.000000
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-0.157080 32.144015 0.000000 0.000000 0.000000
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-0.094248 33.885395 0.000000 0.000000 0.000000
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-0.031416 33.783105 0.000000 0.000000 0.000000
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0.031416 34.243727 0.000000 0.000000 0.000000
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0.094248 33.975567 0.000000 0.000000 0.000000
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0.157080 32.994787 0.000000 0.000000 0.000000
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0.219911 32.607398 0.000000 0.000000 0.000000
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0.282743 31.401902 0.000000 0.000000 0.000000
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0.345575 29.911670 0.000000 0.000000 0.000000
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0.408407 28.603574 0.000000 0.000001 0.000000
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0.471239 26.925443 0.000000 0.000001 0.000000
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0.534071 25.298070 0.000000 0.000002 0.000000
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0.596903 23.638560 0.000000 0.000005 0.000000
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0.659734 21.156231 0.000000 0.000013 0.000000
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0.722566 19.126480 0.000000 0.000029 0.000000
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0.785398 17.351953 0.000000 0.000060 0.000000
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0.848230 15.135525 0.000000 0.000146 0.000000
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0.911062 13.188112 0.000000 0.000319 0.000000
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0.973894 11.536983 0.000000 0.000618 0.000000
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1.036726 10.158328 0.000000 0.001074 0.000000
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1.099557 9.109036 0.000000 0.001636 0.000000
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1.162389 8.282343 0.000000 0.002279 0.000000
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1.225221 8.022102 0.000000 0.002530 0.000000
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1.288053 8.162415 0.000000 0.002391 0.000000
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1.350885 8.600135 0.000000 0.002006 0.000000
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1.413717 9.837348 0.000000 0.001222 0.000000
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1.476549 11.363156 0.000000 0.000663 0.000000
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1.539380 13.077849 0.000000 0.000333 0.000000
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1.602212 15.353594 0.000000 0.000134 0.000000
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1.665044 17.565051 0.000000 0.000055 0.000000
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1.727876 19.710884 0.000000 0.000023 0.000000
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1.790708 21.721260 0.000000 0.000010 0.000000
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1.853540 23.567649 0.000000 0.000005 0.000000
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1.916372 25.008817 0.000000 0.000003 0.000000
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1.979203 26.405367 0.000000 0.000002 0.000000
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2.042035 28.070821 0.000000 0.000001 0.000000
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2.104867 28.877213 0.000000 0.000001 0.000000
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2.167699 29.378146 0.000000 0.000000 0.000000
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2.230531 31.093267 0.000000 0.000000 0.000000
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2.293363 30.704994 0.000000 0.000000 0.000000
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2.356194 30.563093 0.000000 0.000000 0.000000
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2.419026 31.215952 0.000000 0.000000 0.000000
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2.481858 30.331416 0.000000 0.000000 0.000000
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2.544690 29.005123 0.000000 0.000001 0.000000
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2.607522 27.674618 0.000000 0.000001 0.000000
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2.670354 24.911788 0.000000 0.000003 0.000000
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2.733186 22.746417 0.000000 0.000007 0.000000
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2.796017 20.608288 0.000000 0.000016 0.000000
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2.858849 18.018280 0.000000 0.000046 0.000000
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2.921681 15.949215 0.000000 0.000105 0.000000
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2.984513 13.617809 0.000000 0.000268 0.000000
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3.047345 11.432193 0.000000 0.000645 0.000000
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3.110177 9.461494 0.000000 0.001420 0.000000
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@@ -6,7 +6,7 @@ use rgsl::statistics;
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// the a multiple of g
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// For details, see "Chodera et al. (2007). Use of a Weighted Histogram Analysis
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// Method for the Analysis of Simulated and Parallel Tempering Simulations, JCTC"
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fn statistical_ineff(timeseries: &[f64]) -> f64 {
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pub fn statistical_ineff(timeseries: &[f64]) -> f64 {
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let n = timeseries.len();
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let autocorr = autocorrelation(timeseries);
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@@ -43,7 +43,7 @@ fn autocorrelation(timeseries: &[f64]) -> Vec<f64> {
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// The autocorrelation time of a timeseries can be deduced from the
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// `statistical_ineff` by (g-1)/2.0
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fn autocorrelation_time(g: f64) -> f64 {
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pub fn autocorrelation_time(g: f64) -> f64 {
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(g - 1.0) / 2.0
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}
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@@ -71,7 +71,6 @@ mod tests {
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use super::*;
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use super::super::k_B;
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use super::super::histogram::Histogram;
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use rand::prelude::*;
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fn build_hist() -> Histogram {
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Histogram::new(
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39
src/io.rs
39
src/io.rs
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use super::histogram::Dataset;
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use super::histogram::Histogram;
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use super::Config;
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use super::correlation_analysis::{statistical_ineff, autocorrelation_time};
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use std::fs::File;
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use std::io::prelude::*;
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use std::io::{BufReader,BufWriter};
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@@ -155,6 +156,38 @@ fn read_timeseries(window_file: &str, cfg: &Config) -> Result<Vec<Vec<f64>>> {
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Ok(timeseries)
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}
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// calculates the inefficiency for every collective variable
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// filters the timeseries based on the highest inefficiency
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fn uncorrelate(timeseries: Vec<Vec<f64>>, cfg: &Config) -> Vec<Vec<f64>> {
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// calculate inefficiencies and find the highest one
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let gs: Vec<f64> = timeseries[1..].iter().map(|ts| statistical_ineff(ts)).collect();
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let mut max_g = 1.0;
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for g in gs {
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if g > max_g {
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max_g = g;
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}
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}
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// round g up
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let mut trunc_g = max_g.trunc() as usize;
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if (trunc_g as f64 - max_g).abs() > 0.000_000_000_1 {
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trunc_g += 1;
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}
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// filter correlated samples from timeseries
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let prev_len = timeseries[0].len();
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let timeseries = timeseries.into_iter().map(|ts| {
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ts.into_iter().step_by(trunc_g).collect::<Vec<f64>>()
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}).collect::<Vec<Vec<f64>>>();
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let new_len = timeseries[0].len();
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if cfg.verbose {
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let tau = autocorrelation_time(max_g)* (timeseries[0][1]-timeseries[0][0]);
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vprintln(format!("{:?}/{:?} samples are uncorrelated. {:?} samples removed from timeseries (tau={:.5})", new_len, prev_len, prev_len-new_len, tau), true);
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}
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timeseries
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}
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// parse a time series file into a histogram
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fn read_window_file(window_file: &str, cfg: &Config) -> Result<Histogram> {
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// total number of bins is the product of all dimensions length
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@@ -166,9 +199,11 @@ fn read_window_file(window_file: &str, cfg: &Config) -> Result<Histogram> {
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(cfg.hist_max[idx] - cfg.hist_min[idx])/(cfg.num_bins[idx] as f64)
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}).collect();
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let timeseries: Vec<Vec<f64>> = read_timeseries(window_file, cfg)?;
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let mut timeseries: Vec<Vec<f64>> = read_timeseries(window_file, cfg)?;
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// TODO decorrelate
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if cfg.uncorr {
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timeseries = uncorrelate(timeseries, cfg);
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}
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for i in 0..timeseries[0].len() {
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let mut values: Vec<f64> = vec![f64::NAN; cfg.dimens+1];
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@@ -27,6 +27,26 @@ mod integration {
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assert_eq!(output_len, 0);
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}
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#[test]
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fn wham_1d_cyclic_uncorrelated() {
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get_command()
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.args(&["--bins", "100", "--max", "pi", "--min", "-pi", "-T", "300", "--cyclic", "--uncorr"])
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.args(&["--seed", "1234"])
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.args(&["-f", "example/1d_cyclic/metadata.dat"])
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.args(&["-o", "/tmp/wham_test_1d_cyclic.out"])
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.output()
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.expect("failed to execute process");
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assert!(fs::metadata("/tmp/wham_test_1d_cyclic.out").is_ok());
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let output = Command::new("diff")
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.arg("/tmp/wham_test_1d_cyclic.out")
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.arg("example/1d_cyclic/wham_uncorrelated.out")
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.output()
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.expect("failed to run diff");
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let output_len = String::from_utf8_lossy(&output.stdout).len();
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assert_eq!(output_len, 0);
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}
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#[test]
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#[ignore] // expensive
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fn wham_2d_cyclic() {
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