uncorr flag

src/correlation_analysis.rs

@@ -8,18 +8,12 @@ use rgsl::statistics;
 // Method for the Analysis of Simulated and Parallel Tempering Simulations, JCTC"
 fn statistical_ineff(timeseries: &[f64]) -> f64 {
     let n = timeseries.len();
-    let mean = statistics::mean(timeseries, 1, timeseries.len());
-    let d_mean = timeseries.iter().map(|x| x-mean).collect::<Vec<f64>>();
-    let cov = statistics::covariance(timeseries, 1, timeseries, 1, n);
+    let autocorr = autocorrelation(timeseries);
 
     let mut g = 1.0;
     for t in 1..(n-1) {
-
-        // normalized autocorr C(t) = (<x_n*x_(n+t)> - <x_n>^2) / (<x_n^2> - <x_n>^2)
-        let tmp = d_mean[0..n-t].iter().zip(d_mean[t..n].iter()).map(|(x, y)| x*y);
-        let c: f64 = tmp.map(|x| x+x).sum::<f64>() / (2.0 * (n as f64-t as f64)*cov);
-
-        if c <= 0.0 {  // terminate at first 0 (autocorr gets noisy from here)
+        let c = autocorr[t-1];
+        if c <= 0.0 {
             break;
         }
         g = g + (2.0*c*(1.0-t as f64/n as f64))
@@ -31,6 +25,22 @@ fn statistical_ineff(timeseries: &[f64]) -> f64 {
     }
 }
 
+// calculates the autocorrelation of a simeseries
+fn autocorrelation(timeseries: &[f64]) -> Vec<f64> {
+    let n = timeseries.len();
+    let mean = statistics::mean(timeseries, 1, timeseries.len());
+    let d_mean = timeseries.iter().map(|x| x-mean).collect::<Vec<f64>>();
+    let cov = statistics::covariance(timeseries, 1, timeseries, 1, n);
+
+    let mut autocorr = Vec::new();
+    for t in 1..(n-1) {
+        let tmp = d_mean[0..n-t].iter().zip(d_mean[t..n].iter()).map(|(x, y)| x*y);
+        let c: f64 = tmp.map(|x| x+x).sum::<f64>() / (2.0 * (n as f64-t as f64)*cov);
+        autocorr.push(c);
+    }
+    autocorr
+}
+
 // The autocorrelation time of a timeseries can be deduced from the
 // `statistical_ineff` by (g-1)/2.0 
 fn autocorrelation_time(g: f64) -> f64 {
@@ -55,6 +65,23 @@ mod tests {
 
     }
 
+    #[test]
+    fn autocorrelation() {
+        let timeseries = read_timeseries("example/1d_cyclic/COLVAR-2.5.xvg");
+        let autocorr = super::autocorrelation(&timeseries);
+        let expected = [
+            0.6919008655979143, 0.5331719399671355,
+            0.20472620956463589, -0.002850876458920514,
+            -0.13842850077938146, -0.2652923552973232,
+            -0.31427198272235385, -0.2617505151557693,
+            -0.20594864730290338, -0.13310019091811812,
+            -0.1887568901193426, -0.1944936625424933,
+            -0.1963599673189461, -0.11391587833838003];
+        for (actual, expected) in autocorr.iter().zip(expected.iter()) {
+            assert!((actual-expected).abs() < 0.001);
+        }
+    }
+
     #[test]
     fn statistical_ineff() {
         let timeseries = read_timeseries("example/1d_cyclic/COLVAR-2.5.xvg");