version up

README.md

@@ -90,7 +90,16 @@ OPTIONS:
                                        tolerance (defaults to 0.000001).
 ```
 
-To run the two dimensional example (simulation of dialanine phi and psi angle):
+Examples
+---
+The example folder contains input and output files for two simple test systems:
+
+- 1d_cyclic: Phi torsion angle of dialanine in vaccum
+- 2d_cyclic: Phi and psi torsion angles of the same system
+
+The command below will run the two dimensional example (simulation of dialanine phi and psi angle) and calculate the free energy based on the two collective variables
+in the range of -3.14 to 3.14, with 100 bins in each dimension and periodic collective variables:
+ 
 ```bash
 wham --max 3.14,3.14 --min -3.14,-3.14 -T 300 --bins 100,100 --cyclic -f example/2d/metadata.dat       
 > Supplied WHAM options: Metadata=example/2d/metadata.dat, hist_min=[-3.14, -3.14], hist_max=[3.14, 3.14], bins=[100, 100] verbose=false, tolerance=0.000001, iterations=100000, temperature=300, cyclic=true
@@ -107,7 +116,6 @@ wham --max 3.14,3.14 --min -3.14,-3.14 -T 300 --bins 100,100 --cyclic -f example
 ```
 After convergence, final bias offsets (F) and the free energy will be dumped to stdout and the output file is written.
 
-
 The output file contains the free energy and probability for each bin. Probabilities are normalized to sum to P=1.0 and
 the smallest free energy is set to 0 (with other free energies based on that).
 ```
@@ -127,7 +135,7 @@ the smallest free energy is set to 0 (with other free energies based on that).
 Error analysis
 ---
 WHAM can perform error analysis using the bayesian bootstrapping method. Every simulation window is assumed to be an
-individual set of data point. By calculating probabilities N times with randomly assigned weights for each window,
+individual set of data points. By calculating probabilities N times with randomly assigned weights for each window,
 one can estimate the error as standard deviation between the N bootstrapping runs. For more details see
 *Van der Spoel, D. et al. (2010). g_wham—A Free Weighted Histogram Analysis Implementation Including Robust Error and
 Autocorrelation Estimates, JCTC, 6(12), 3713-3720*.
@@ -148,14 +156,6 @@ timeseries is used for unbiasing. A more detailed description of the method can
 tempering simulations, JCTC 3(1):26-41*
 
 
-Examples
----
-The example folder contains input and output files for two simple test systems:
-
-- 1d_cyclic: Phi torsion angle of dialanine in vaccum
-- 2d_cyclic: Phi and psi torsion angles of the same system
-
-
 TODO
 ---
 - Replica exchange
@@ -165,7 +165,7 @@ License & Citing
 WHAM is licensed under the GPL-3.0 license. Please read the LICENSE file in this
 repository for more information.
 
-There's no publication for this WHAM implementation. However, there is a citeabe DOI. If you use this software for your work, please consider citing it: *Bauer, D, WHAM - An efficient weighted histogram analysis implementation written in Rust, Zenodo.  https://doi.org/10.5281/zenodo.1488597*
+There's no publication for this WHAM implementation. However, there is a citeabe DOI. If you use this software for your work, please consider citing it: *Bauer, D., WHAM - An efficient weighted histogram analysis implementation written in Rust, Zenodo. https://doi.org/10.5281/zenodo.1488597*
 
 Parts of this work, especially some perfomance optimizations and the I/O format, are inspired by the
 implementation of A. Grossfield (*Grossfield, A, WHAM: the weighted histogram analysis method, http://membrane.urmc.rochester.edu/content/wham*).

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>> {