version up

src/cli.yml

@@ -1,6 +1,6 @@
 name: wham
 version: "1.0.0"
-author: D. Bauer <bauer@bio.tu-darmstadt.de>
+author: D. Bauer <bauer@cbs.tu-darmstadt.de>
 about: |
   wham is a fast implementation of the weighted histogram analysis method (WHAM) written in Rust. It currently supports potential of mean force (PMF) calculations in multiple dimensions at constant temperature.
 

src/correlation_analysis.rs

@@ -70,13 +70,13 @@ mod tests {
         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];
+            0.691_900_865_597_914_3, 0.533_171_939_967_135_5,
+            0.204_726_209_564_635_89, -0.002_850_876_458_920_514,
+            -0.138_428_500_779_381_46, -0.265_292_355_297_323_2,
+            -0.314_271_982_722_353_85, -0.261_750_515_155_769_3,
+            -0.205_948_647_302_903_38, -0.133_100_190_918_118_12,
+            -0.188_756_890_119_342_6, -0.194_493_662_542_493_3,
+            -0.196_359_967_318_946_1, -0.113_915_878_338_380_03];
         for (actual, expected) in autocorr.iter().zip(expected.iter()) {
             assert!((actual-expected).abs() < 0.001);
         }

src/io.rs

@@ -132,6 +132,7 @@ fn is_in_time_boundaries(time: f64, cfg: &Config) -> bool {
     false
 }
 
+
 // parse a time series file into a histogram
 fn read_window_file(window_file: &str, cfg: &Config) -> Result<(Histogram, usize)> {
     // total number of bins is the product of all dimensions length
@@ -210,6 +211,7 @@ fn read_timeseries(window_file: &str, cfg: &Config) -> Result<Vec<Vec<f64>>> {
     Ok(timeseries)
 }
 
+
 // calculates the inefficiency for every collective variable
 // filters the timeseries based on the highest inefficiency
 fn uncorrelate(timeseries: Vec<Vec<f64>>, cfg: &Config) -> Vec<Vec<f64>> {