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5390b089ac |
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||||
]
|
||||
|
||||
[[package]]
|
||||
name = "rand_core"
|
||||
version = "0.4.2"
|
||||
version = "0.5.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "9c33a3c44ca05fa6f1807d8e6743f3824e8509beca625669633be0acbdf509dc"
|
||||
checksum = "90bde5296fc891b0cef12a6d03ddccc162ce7b2aff54160af9338f8d40df6d19"
|
||||
dependencies = [
|
||||
"getrandom",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "rand_hc"
|
||||
version = "0.2.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "ca3129af7b92a17112d59ad498c6f81eaf463253766b90396d39ea7a39d6613c"
|
||||
dependencies = [
|
||||
"rand_core",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "rayon"
|
||||
version = "1.3.0"
|
||||
version = "1.5.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "db6ce3297f9c85e16621bb8cca38a06779ffc31bb8184e1be4bed2be4678a098"
|
||||
checksum = "8b0d8e0819fadc20c74ea8373106ead0600e3a67ef1fe8da56e39b9ae7275674"
|
||||
dependencies = [
|
||||
"autocfg",
|
||||
"crossbeam-deque",
|
||||
"either",
|
||||
"rayon-core",
|
||||
@@ -270,12 +298,12 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "rayon-core"
|
||||
version = "1.7.0"
|
||||
version = "1.9.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "08a89b46efaf957e52b18062fb2f4660f8b8a4dde1807ca002690868ef2c85a9"
|
||||
checksum = "9ab346ac5921dc62ffa9f89b7a773907511cdfa5490c572ae9be1be33e8afa4a"
|
||||
dependencies = [
|
||||
"crossbeam-channel",
|
||||
"crossbeam-deque",
|
||||
"crossbeam-queue",
|
||||
"crossbeam-utils",
|
||||
"lazy_static",
|
||||
"num_cpus",
|
||||
@@ -283,18 +311,9 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "rustc-demangle"
|
||||
version = "0.1.16"
|
||||
version = "0.1.18"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "4c691c0e608126e00913e33f0ccf3727d5fc84573623b8d65b2df340b5201783"
|
||||
|
||||
[[package]]
|
||||
name = "rustc_version"
|
||||
version = "0.2.3"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "138e3e0acb6c9fb258b19b67cb8abd63c00679d2851805ea151465464fe9030a"
|
||||
dependencies = [
|
||||
"semver",
|
||||
]
|
||||
checksum = "6e3bad0ee36814ca07d7968269dd4b7ec89ec2da10c4bb613928d3077083c232"
|
||||
|
||||
[[package]]
|
||||
name = "scopeguard"
|
||||
@@ -302,21 +321,6 @@ version = "1.1.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "d29ab0c6d3fc0ee92fe66e2d99f700eab17a8d57d1c1d3b748380fb20baa78cd"
|
||||
|
||||
[[package]]
|
||||
name = "semver"
|
||||
version = "0.9.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1d7eb9ef2c18661902cc47e535f9bc51b78acd254da71d375c2f6720d9a40403"
|
||||
dependencies = [
|
||||
"semver-parser",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "semver-parser"
|
||||
version = "0.7.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "388a1df253eca08550bef6c72392cfe7c30914bf41df5269b68cbd6ff8f570a3"
|
||||
|
||||
[[package]]
|
||||
name = "strsim"
|
||||
version = "0.8.0"
|
||||
@@ -334,27 +338,33 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "unicode-width"
|
||||
version = "0.1.7"
|
||||
version = "0.1.8"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "caaa9d531767d1ff2150b9332433f32a24622147e5ebb1f26409d5da67afd479"
|
||||
checksum = "9337591893a19b88d8d87f2cec1e73fad5cdfd10e5a6f349f498ad6ea2ffb1e3"
|
||||
|
||||
[[package]]
|
||||
name = "vec_map"
|
||||
version = "0.8.1"
|
||||
version = "0.8.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "05c78687fb1a80548ae3250346c3db86a80a7cdd77bda190189f2d0a0987c81a"
|
||||
checksum = "f1bddf1187be692e79c5ffeab891132dfb0f236ed36a43c7ed39f1165ee20191"
|
||||
|
||||
[[package]]
|
||||
name = "version_check"
|
||||
version = "0.9.1"
|
||||
version = "0.9.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "078775d0255232fb988e6fccf26ddc9d1ac274299aaedcedce21c6f72cc533ce"
|
||||
checksum = "b5a972e5669d67ba988ce3dc826706fb0a8b01471c088cb0b6110b805cc36aed"
|
||||
|
||||
[[package]]
|
||||
name = "wasi"
|
||||
version = "0.9.0+wasi-snapshot-preview1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "cccddf32554fecc6acb585f82a32a72e28b48f8c4c1883ddfeeeaa96f7d8e519"
|
||||
|
||||
[[package]]
|
||||
name = "wham"
|
||||
version = "0.9.6"
|
||||
version = "1.1.0"
|
||||
dependencies = [
|
||||
"GSL",
|
||||
"assert_approx_eq",
|
||||
"clap",
|
||||
"error-chain",
|
||||
"rand",
|
||||
@@ -363,9 +373,9 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "winapi"
|
||||
version = "0.3.8"
|
||||
version = "0.3.9"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "8093091eeb260906a183e6ae1abdba2ef5ef2257a21801128899c3fc699229c6"
|
||||
checksum = "5c839a674fcd7a98952e593242ea400abe93992746761e38641405d28b00f419"
|
||||
dependencies = [
|
||||
"winapi-i686-pc-windows-gnu",
|
||||
"winapi-x86_64-pc-windows-gnu",
|
||||
|
||||
20
Cargo.toml
20
Cargo.toml
@@ -1,20 +1,26 @@
|
||||
[package]
|
||||
name = "wham"
|
||||
version = "0.9.6"
|
||||
version = "1.1.0"
|
||||
authors = ["Daniel Bauer <bauer@cbs.tu-darmstadt.de>"]
|
||||
description = "An implementation of the weighted histogram analysis method"
|
||||
license = "GPL-3.0"
|
||||
repository = "https://github.com/danijoo/WHAM"
|
||||
readme = "README.md"
|
||||
categories = ["science", "command-line-utilities", "algorithms"]
|
||||
keywords = ["math", "statistics", "histogram", "bioinformatics", "molecular-dynamics"]
|
||||
exclude = [
|
||||
"example/*"
|
||||
]
|
||||
|
||||
[dependencies]
|
||||
clap = {version="2.32.0", features=['yaml']}
|
||||
error-chain = "0.12.0"
|
||||
rand = "0.5.5"
|
||||
GSL = "1.0.0"
|
||||
rand = "0.7.*"
|
||||
rayon = "1.0.3"
|
||||
|
||||
[profile.release]
|
||||
opt-level = 2
|
||||
[dev-dependencies]
|
||||
assert_approx_eq = "1.1.0"
|
||||
|
||||
[profile.release]
|
||||
opt-level = 3
|
||||
|
||||
[features]
|
||||
default = ["GSL/v2"]
|
||||
|
||||
103
README.md
103
README.md
@@ -12,19 +12,14 @@ from umbrella sampling simulations. For more details on the method, I suggest *R
|
||||
Features
|
||||
---
|
||||
- Fast, especially for small systems
|
||||
- Multithreaded
|
||||
- Multidimensional
|
||||
- Error analysis
|
||||
- Multithreaded (automatically runs on all available cores)
|
||||
- Multidimensional (any number of collective variables are possible)
|
||||
- Autocorrelation to remove correlated samples
|
||||
- Error analysis via bootstrapping
|
||||
- Unit tested
|
||||
|
||||
Installation
|
||||
---
|
||||
WHAM requires the GSL library to be installed:
|
||||
```bash
|
||||
# on debian/ubuntu:
|
||||
sudo apt-get install libgsl0-dev
|
||||
```
|
||||
|
||||
Installation from source via cargo:
|
||||
```bash
|
||||
# cargo installation
|
||||
@@ -38,7 +33,71 @@ Usage
|
||||
wham has a convenient command line interface. You can see all options with
|
||||
```wham -h```:
|
||||
|
||||
To run the two dimensional example (simulation of dialanine phi and psi angle):
|
||||
```
|
||||
wham 1.1.0
|
||||
D. Bauer <bauer@cbs.tu-darmstadt.de>
|
||||
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.
|
||||
|
||||
Metadata file format:
|
||||
/path/to/timeseries_file1 x_1 x_2 x_N fc_1 fc_2 fc_N
|
||||
/path/to/timeseries_file2 x_1 x_2 x_N fc_1 fc_2 fc_N
|
||||
/path/to/timeseries_file3 x_1 x_2 x_N fc_1 fc_2 fc_N
|
||||
The first column is a path to a timeseries file _relative_ to the metadata file (see below). This is followed by the
|
||||
position of the umbrella potential x in N dimensions and the force constant fc in each dimension. Lines starting with a
|
||||
# are treated as comments and will not be parsed.
|
||||
|
||||
Timeseries file format:
|
||||
time x_1 x_2 x_N
|
||||
time x_1 x_2 x_N
|
||||
time x_1 x_2 x_N
|
||||
The first column will be ignored and is followed by N reaction coordinates x.
|
||||
|
||||
Shipped under the GPLv3 license.
|
||||
|
||||
USAGE:
|
||||
wham [FLAGS] [OPTIONS] --bins <BINS> --max <HIST_MAX> --file <METADATA> --min <HIST_MIN> --temperature <temperature>
|
||||
|
||||
FLAGS:
|
||||
-c, --cyclic For periodic reaction coordinates. If this is set, the first and last coordinate bin in each
|
||||
dimension are treated as neighbors for the bias calculation.
|
||||
-h, --help Prints help information
|
||||
-g, --uncorr Estimates statistical inefficiency of each timeseries via autocorrelation and removes correlated
|
||||
samples (default is off).
|
||||
-V, --version Prints version information
|
||||
-v, --verbose Enables verbose output.
|
||||
|
||||
OPTIONS:
|
||||
-b, --bins <BINS> Number of histogram bins (comma separated).
|
||||
--bt <bootstrap> Number of bayesian bootstrapping runs for error analysis by assigning random
|
||||
weights (defaults to 0).
|
||||
--seed <bootstrap_seed> Random seed for bootstrapping runs.
|
||||
--convdt <convdt> Performs WHAM for slices with the given delta in time and returns an output file
|
||||
for each slice. THis is useful to check the result for convergence. Example: with
|
||||
--convdt 100 and a timeseries ranging from 0-300, free energy surfaces for slices
|
||||
0-100, 0-200 and 0-300 will be given returned.
|
||||
--end <end> Skip rows in timeseries with an index larger than this value (defaults to 1e+20)
|
||||
-i, --iterations <ITERATIONS> Stop WHAM after this many iterations without convergence (defaults to 100,000).
|
||||
--max <HIST_MAX> Histogram maxima (comma separated). Also accepts "pi".
|
||||
-f, --file <METADATA> Path to the metadata file.
|
||||
--min <HIST_MIN> Histogram minima (comma separated for multiple dimensions). Also accepts "pi".
|
||||
-o, --output <output> Free energy output file (defaults to wham.out).
|
||||
--start <start> Skip rows in timeseries with an index smaller than this value (defaults to 0)
|
||||
-T, --temperature <temperature> WHAM temperature in Kelvin.
|
||||
-t, --tolerance <TOLERANCE> Abortion criteria for WHAM calculation. WHAM stops if abs(F_new - F_old) <
|
||||
tolerance (defaults to 0.000001).
|
||||
```
|
||||
|
||||
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
|
||||
@@ -55,7 +114,6 @@ 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).
|
||||
```
|
||||
@@ -75,27 +133,30 @@ 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*.
|
||||
|
||||
To perform bayesian bootstrapping in WHAM, use the ```-bt <RUNS>``` flag to perform <RUNS> individual bootstrapping
|
||||
runs. The error estimates of bin probabilities and free energy will be given as separate column (+/-) in the output file.
|
||||
If no error analysis is performed, these columns are set to 0.0.
|
||||
runs. The error estimates of bin probabilities and free energy will be given as standard error (SE) in a
|
||||
separate column (+/-) in the output file. If no error analysis is performed, these columns are set to 0.0.
|
||||
|
||||
Examples
|
||||
Autocorrelation analysis
|
||||
---
|
||||
The example folder contains input and output files for two simple test systems:
|
||||
With the ```--uncorr``` flag, WHAM calculates the autocorrelation time ```tau``` for all timeseries and all collective
|
||||
variables. Timeseries are then filtered based on their highest autocorrelation time to remove correlated samples from
|
||||
the dataset. This reduces the number of data points but can improve the accuracy of the result.
|
||||
|
||||
- 1d_cyclic: Phi torsion angle of dialanine in vaccum
|
||||
- 2d_cyclic: Phi and psi torsion angles of the same system
|
||||
For filtering, the statistical inefficiency `g` is calculated: ```g = 1 + 2*tau```, and only every `g`th element of the
|
||||
timeseries is used for unbiasing. A more detailed description of the method can be found in
|
||||
*Chodera, J.D. et al. (2007). Use of the weighted histogram analysis method for the analysis of simulated and parallel
|
||||
tempering simulations, JCTC 3(1):26-41*
|
||||
|
||||
|
||||
TODO
|
||||
---
|
||||
- better error estimation
|
||||
- Autocorrelation
|
||||
- Option to output histograms
|
||||
- Replica exchange
|
||||
|
||||
License & Citing
|
||||
@@ -103,7 +164,7 @@ 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*).
|
||||
|
||||
@@ -1,101 +1,101 @@
|
||||
#coord1 Free Energy +/- Probability +/-
|
||||
-3.110177 7.158102 0.476037 0.003494 0.000667
|
||||
-3.047345 5.365727 0.470738 0.007168 0.001353
|
||||
-2.984513 3.873190 0.433120 0.013039 0.002264
|
||||
-2.921681 2.953162 0.441412 0.018855 0.003337
|
||||
-2.858849 1.949554 0.443549 0.028195 0.005014
|
||||
-2.796017 1.391747 0.407397 0.035261 0.005759
|
||||
-2.733186 1.128270 0.380789 0.039189 0.005983
|
||||
-2.670354 0.839970 0.370008 0.043991 0.006526
|
||||
-2.607522 0.624769 0.381081 0.047955 0.007326
|
||||
-2.544690 0.663757 0.389082 0.047211 0.007364
|
||||
-2.481858 1.051932 0.379999 0.040407 0.006156
|
||||
-2.419026 1.463048 0.394914 0.034267 0.005425
|
||||
-2.356194 1.990616 0.335141 0.027734 0.003726
|
||||
-2.293363 2.190692 0.196497 0.025597 0.002016
|
||||
-2.230531 2.553036 0.177045 0.022136 0.001571
|
||||
-2.167699 2.572522 0.208798 0.021964 0.001839
|
||||
-2.104867 2.472360 0.168929 0.022863 0.001548
|
||||
-2.042035 2.517562 0.166774 0.022453 0.001501
|
||||
-1.979203 2.469778 0.182308 0.022887 0.001673
|
||||
-1.916372 2.223125 0.405294 0.025266 0.004105
|
||||
-1.853540 2.080157 0.340376 0.026756 0.003651
|
||||
-1.790708 1.793841 0.394974 0.030011 0.004752
|
||||
-1.727876 1.458784 0.335956 0.034326 0.004623
|
||||
-1.665044 0.914441 0.336724 0.042697 0.005764
|
||||
-1.602212 0.303813 0.320695 0.054540 0.007012
|
||||
-1.539380 0.268335 0.330495 0.055321 0.007330
|
||||
-1.476549 0.000000 0.338143 0.061604 0.008351
|
||||
-1.413717 0.537061 0.369220 0.049671 0.007352
|
||||
-1.350885 1.439940 0.381006 0.034586 0.005283
|
||||
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||||
|
||||
101
example/1d_cyclic/wham_uncorrelated.out
Normal file
101
example/1d_cyclic/wham_uncorrelated.out
Normal file
@@ -0,0 +1,101 @@
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||||
#coord1 Free Energy +/- Probability +/-
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|
||||
3.110177 9.461494 0.000000 0.001420 0.000000
|
||||
15
src/cli.yml
15
src/cli.yml
@@ -1,6 +1,6 @@
|
||||
name: wham
|
||||
version: "0.9.0"
|
||||
author: D. Bauer <bauer@bio.tu-darmstadt.de>
|
||||
version: "1.1.0"
|
||||
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.
|
||||
|
||||
@@ -101,3 +101,14 @@ args:
|
||||
help: Skip rows in timeseries with an index larger than this value (defaults to 1e+20)
|
||||
takes_value: true
|
||||
required: false
|
||||
- uncorr:
|
||||
short: g
|
||||
long: uncorr
|
||||
help: Estimates statistical inefficiency of each timeseries via autocorrelation and removes correlated samples (default is off).
|
||||
takes_value: false
|
||||
required: false
|
||||
- convdt:
|
||||
long: convdt
|
||||
help: "Performs WHAM for slices with the given delta in time and returns an output file for each slice. THis is useful to check the result for convergence. Example: with --convdt 100 and a timeseries ranging from 0-300, free energy surfaces for slices 0-100, 0-200 and 0-300 will be given returned."
|
||||
takes_value: true
|
||||
required: false
|
||||
|
||||
78
src/correlation_analysis.rs
Normal file
78
src/correlation_analysis.rs
Normal file
@@ -0,0 +1,78 @@
|
||||
use super::statistics;
|
||||
|
||||
// calculates the statistical inefficiency g of the given timeseries
|
||||
// the quantity g can be thought of: N/g is the number of uncorrelated
|
||||
// configurations in the timeseries, where samples are separated by
|
||||
// the a multiple of g
|
||||
// For details, see "Chodera et al. (2007). Use of a Weighted Histogram Analysis
|
||||
// Method for the Analysis of Simulated and Parallel Tempering Simulations, JCTC"
|
||||
pub fn statistical_ineff(timeseries: &[f64]) -> f64 {
|
||||
let n = timeseries.len();
|
||||
let mean = statistics::mean(timeseries);
|
||||
let d_mean = timeseries.iter().map(|x| x-mean).collect::<Vec<f64>>();
|
||||
let cov = statistics::autocov(timeseries);
|
||||
|
||||
let mut g = 1.0;
|
||||
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 = tmp.map(|x| x+x).sum::<f64>() / (2.0 * (n as f64 - t as f64)*cov);
|
||||
if c <= 0.0 {
|
||||
break;
|
||||
}
|
||||
g = g + (2.0*c*(1.0-t as f64/n as f64))
|
||||
}
|
||||
if g < 1.0 {
|
||||
1.0
|
||||
} else {
|
||||
g
|
||||
}
|
||||
}
|
||||
|
||||
// The autocorrelation time of a timeseries can be deduced from the
|
||||
// `statistical_ineff` by (g-1)/2.0
|
||||
pub fn autocorrelation_time(g: f64) -> f64 {
|
||||
(g - 1.0) / 2.0
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use std::io::{BufRead, BufReader};
|
||||
use std::fs::File;
|
||||
|
||||
fn read_timeseries(filename: &str) -> Vec<f64> {
|
||||
let mut timeseries: Vec<f64> = Vec::new();
|
||||
let file = File::open(filename).unwrap();
|
||||
let reader = BufReader::new(&file);
|
||||
for line in reader.lines() {
|
||||
let val = line.unwrap().split_whitespace()
|
||||
.collect::<Vec<&str>>()[1].parse::<f64>().unwrap();
|
||||
timeseries.push(val);
|
||||
}
|
||||
timeseries
|
||||
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn statistical_ineff() {
|
||||
let timeseries = read_timeseries("example/1d_cyclic/COLVAR-2.5.xvg");
|
||||
let g = super::statistical_ineff(×eries);
|
||||
println!("{:?}", g);
|
||||
assert!((g - 3.859).abs() < 0.001);
|
||||
|
||||
// a "random" timeseries with g < 1.0
|
||||
let timeseries = [1_f64, 4_f64, 921_f64, 121213_f64, 23192_f64,
|
||||
8913_f64, 1232_f64, 2_f64, 151_f64, 123091_f64];
|
||||
let g = super::statistical_ineff(×eries);
|
||||
assert_approx_eq!(g, 1.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn autocorrelation_time() {
|
||||
let timeseries = read_timeseries("example/1d_cyclic/COLVAR-2.5.xvg");
|
||||
let g = super::statistical_ineff(×eries);
|
||||
let tau = super::autocorrelation_time(g);
|
||||
println!("{:?}", tau);
|
||||
assert!((tau - 1.430).abs() < 0.001)
|
||||
}
|
||||
|
||||
}
|
||||
@@ -1,8 +1,8 @@
|
||||
use rand::{SeedableRng, StdRng, Rng};
|
||||
use rand::prelude::*;
|
||||
use super::histogram::{Dataset};
|
||||
use super::perform_wham;
|
||||
use super::Config;
|
||||
use rgsl::statistics;
|
||||
use super::{Config,calc_free_energy};
|
||||
use super::statistics;
|
||||
|
||||
// returns a set of num_windows continious weights by
|
||||
// a) generate num_windows-1 random variables and sort them
|
||||
@@ -20,7 +20,7 @@ fn generate_random_weights(num_windows: usize, rng: &mut StdRng) -> Vec<f64> {
|
||||
for i in 0..num_windows {
|
||||
weights[i] = rnds[i+1] - rnds[i]
|
||||
}
|
||||
return weights
|
||||
weights
|
||||
}
|
||||
|
||||
// Generate a random weighted dataset from the given dataset by changing the weights
|
||||
@@ -32,7 +32,7 @@ fn generate_random_weighted_dataset(ds: Dataset, rng: &mut StdRng) -> Dataset {
|
||||
// Perform bootstrap error analysis. This runs the WHAM analysis num_runs times on random weighted
|
||||
// datasets. The standard deviation is calculated on the bootstrapped probabilities of each bin. The
|
||||
// standard deviation of the free eneergy is then deduced by error propagation (A_std = kT*1/P*P_std)
|
||||
pub fn run_bootstrap(cfg: &Config, ds: Dataset, P: &[f64], num_runs: usize) -> (Vec<f64>,Vec<f64>) {
|
||||
pub fn run_bootstrap(cfg: &Config, ds: Dataset, num_runs: usize) -> (Vec<f64>,Vec<f64>) {
|
||||
// seed the rng
|
||||
let mut rng: StdRng = SeedableRng::seed_from_u64(cfg.bootstrap_seed);
|
||||
|
||||
@@ -43,19 +43,30 @@ pub fn run_bootstrap(cfg: &Config, ds: Dataset, P: &[f64], num_runs: usize) -> (
|
||||
perform_wham(cfg, &rnd_weighted_dataset).unwrap().0
|
||||
}).collect();
|
||||
|
||||
// Evaulate standard deviation of P per bin
|
||||
let mut P_std = vec![0.0; ds.num_bins];
|
||||
// Standard error (SE) of P per bin
|
||||
// SE = SD/sqrt(n)
|
||||
let mut P_se = vec![0.0; ds.num_bins];
|
||||
for bin in 0..ds.num_bins {
|
||||
let Ps = bootstrapped_Ps.iter().map(|window| window[bin]).collect::<Vec<f64>>();
|
||||
P_std[bin] = statistics::sd(&Ps, 1, num_runs);
|
||||
P_se[bin] = statistics::sd(&Ps)/(num_runs as f64).sqrt();
|
||||
}
|
||||
|
||||
// A_std by error propagation
|
||||
let A_std = P_std.iter().zip(P.iter()).map(|(std,P)| ds.kT*1.0/P*std).collect();
|
||||
(P_std, A_std)
|
||||
// SE of A
|
||||
let bootstrapped_As: Vec<Vec<f64>> = (0..num_runs).map(|x| {
|
||||
let run_Ps = &bootstrapped_Ps[x];
|
||||
calc_free_energy(&ds, run_Ps)
|
||||
}).collect();
|
||||
|
||||
let mut A_se = vec![0.0; ds.num_bins];
|
||||
for bin in 0..ds.num_bins {
|
||||
let As = bootstrapped_As.iter().map(|window| window[bin]).collect::<Vec<f64>>();
|
||||
A_se[bin] = statistics::sd(&As)/(num_runs as f64).sqrt();
|
||||
}
|
||||
|
||||
(P_se, A_se)
|
||||
}
|
||||
|
||||
#[cfg(tests)]
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use super::super::k_B;
|
||||
@@ -88,8 +99,9 @@ mod tests {
|
||||
|
||||
#[test]
|
||||
fn random_weights() {
|
||||
let mut rng = StdRng::from_entropy();
|
||||
let num_windows = 5;
|
||||
let weights = generate_random_weights(num_windows);
|
||||
let weights = generate_random_weights(num_windows, &mut rng);
|
||||
assert_eq!(num_windows, weights.len());
|
||||
for w in weights {
|
||||
assert!(0.0 < w && w < 1.0);
|
||||
@@ -98,8 +110,9 @@ mod tests {
|
||||
|
||||
#[test]
|
||||
fn random_weighted_dataset() {
|
||||
let mut rng = StdRng::from_entropy();
|
||||
let ds = build_hist_set();
|
||||
let rnd_weights_ds = generate_random_weighted_dataset(ds);
|
||||
let rnd_weights_ds = generate_random_weighted_dataset(ds, &mut rng);
|
||||
println!("{:?}", rnd_weights_ds.weights);
|
||||
for w in rnd_weights_ds.weights {
|
||||
assert!(w > 0.0);
|
||||
|
||||
@@ -61,7 +61,9 @@ pub struct Dataset {
|
||||
|
||||
impl Dataset {
|
||||
|
||||
pub fn new(num_bins: usize, dimens_lengths: Vec<usize>, bin_width: Vec<f64>, hist_min: Vec<f64>, hist_max: Vec<f64>, bias_pos: Vec<f64>, bias_fc: Vec<f64>, kT: f64, histograms: Vec<Histogram>, cyclic: bool) -> Dataset {
|
||||
pub fn new(num_bins: usize, dimens_lengths: Vec<usize>, bin_width: Vec<f64>,
|
||||
hist_min: Vec<f64>, hist_max: Vec<f64>, bias_pos: Vec<f64>,
|
||||
bias_fc: Vec<f64>, kT: f64, histograms: Vec<Histogram>, cyclic: bool) -> Dataset {
|
||||
let num_windows = histograms.len();
|
||||
let bias: Vec<f64> = vec![0.0; num_bins*num_windows];
|
||||
let weights = vec![1.0; num_windows];
|
||||
@@ -92,7 +94,7 @@ impl Dataset {
|
||||
|
||||
pub fn new_weighted(ds: Dataset, weights: Vec<f64>) -> Dataset {
|
||||
Dataset {
|
||||
weights: weights,
|
||||
weights,
|
||||
..ds
|
||||
}
|
||||
}
|
||||
@@ -105,9 +107,9 @@ impl Dataset {
|
||||
let mut tmp = bin;
|
||||
let mut idx = vec![0; lengths.len()];
|
||||
for dimen in (1..lengths.len()).rev() {
|
||||
let denom = lengths.iter().take(dimen).fold(1, |s,&x| s*x);
|
||||
let denom: usize = lengths.iter().take(dimen).product();
|
||||
idx[dimen] = tmp / denom;
|
||||
tmp = tmp % denom;
|
||||
tmp %= denom;
|
||||
}
|
||||
idx[0] = tmp;
|
||||
idx
|
||||
@@ -151,8 +153,7 @@ impl Dataset {
|
||||
// store exp(U/kT) for better performance
|
||||
bias_sum += 0.5 * bias_fc[i] * dist * dist
|
||||
}
|
||||
let bias_sum = (-bias_sum/self.kT).exp();
|
||||
bias_sum
|
||||
(-bias_sum/self.kT).exp()
|
||||
}
|
||||
}
|
||||
|
||||
@@ -205,13 +206,13 @@ mod tests {
|
||||
let ds = build_hist_set(); // k = 10
|
||||
|
||||
// 3th element -> x=3.5, x0=3.5
|
||||
assert_delta!(0.134722337796, ds.calc_bias(3, 0), 0.00000001);
|
||||
assert_delta!(0.134_722_337_796, ds.calc_bias(3, 0), 0.000_000_01);
|
||||
|
||||
// 8th element -> x=8.5, x0=3.5
|
||||
assert_delta!(1.0, ds.calc_bias(4,0), 0.00000001);
|
||||
assert_delta!(1.0, ds.calc_bias(4,0), 0.000_000_01);
|
||||
|
||||
// 1st element -> x=0.5, x0=3.5. non-cyclic!
|
||||
assert_delta!(0.0, ds.calc_bias(0,0), 0.0000001);
|
||||
assert_delta!(0.0, ds.calc_bias(0,0), 0.000_000_1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -220,28 +221,28 @@ mod tests {
|
||||
ds.cyclic = true;
|
||||
|
||||
// 7th element -> x=3.5, x0=3.5
|
||||
assert_delta!(0.134722337796, ds.calc_bias(3, 0), 0.00000001);
|
||||
assert_delta!(0.134_722_337_796, ds.calc_bias(3, 0), 0.000_000_01);
|
||||
|
||||
// 8th element -> x=4.5, x0=3.5
|
||||
assert_delta!(1.0, ds.calc_bias(4, 0), 0.00000001);
|
||||
assert_delta!(1.0, ds.calc_bias(4, 0), 0.000_000_01);
|
||||
|
||||
|
||||
// 1th element -> x=0.5, x0=3.5
|
||||
// cyclic flag makes bin 0 neighboring bin 9, so the distance is actually 2
|
||||
assert_delta!(0.0000000000000117769, ds.calc_bias(0, 0), 0.00000001);
|
||||
assert_delta!(0.000_000_000_000_011_776_9, ds.calc_bias(0, 0), 0.000_000_01);
|
||||
|
||||
// 2nd element -> x=1.5, x0=3.5
|
||||
assert_delta!(0.00000001, ds.calc_bias(1, 0), 0.00000001);
|
||||
assert_delta!(0.000_000_01, ds.calc_bias(1, 0), 0.000_000_01);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn get_x_for_bin() {
|
||||
let ds = build_hist_set();
|
||||
let expected: Vec<f64> = vec![0,1,2,3,4,5,6,7,8].iter()
|
||||
.map(|x| *x as f64 + 0.5).collect();
|
||||
for i in 0..9 {
|
||||
assert_eq!(expected[i], ds.get_coords_for_bin(i)[0]);
|
||||
}
|
||||
.map(|x| *x as f64 + 0.5).collect();
|
||||
expected.iter().enumerate().for_each(|(i, exp)| {
|
||||
assert_approx_eq!(exp, &ds.get_coords_for_bin(i)[0]);
|
||||
})
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -258,10 +259,10 @@ mod tests {
|
||||
vec![build_hist(), build_hist()], // hists
|
||||
false // cyclic
|
||||
);
|
||||
assert_delta!(2.0, ds.get_weighted_bin_count(0), 0.0000000001);
|
||||
assert_delta!(2.0, ds.get_weighted_bin_count(1), 0.0000000001);
|
||||
assert_delta!(6.0, ds.get_weighted_bin_count(2), 0.0000000001);
|
||||
assert_delta!(10.0, ds.get_weighted_bin_count(3), 0.0000000001);
|
||||
assert_delta!(24.0, ds.get_weighted_bin_count(4), 0.0000000001);
|
||||
assert_delta!(2.0, ds.get_weighted_bin_count(0), 0.000_000_000_1);
|
||||
assert_delta!(2.0, ds.get_weighted_bin_count(1), 0.000_000_000_1);
|
||||
assert_delta!(6.0, ds.get_weighted_bin_count(2), 0.000_000_000_1);
|
||||
assert_delta!(10.0, ds.get_weighted_bin_count(3), 0.000_000_000_1);
|
||||
assert_delta!(24.0, ds.get_weighted_bin_count(4), 0.000_000_000_1);
|
||||
}
|
||||
}
|
||||
433
src/io.rs
433
src/io.rs
@@ -1,6 +1,8 @@
|
||||
use super::histogram::Dataset;
|
||||
use super::histogram::Histogram;
|
||||
use super::Config;
|
||||
use super::correlation_analysis::{statistical_ineff, autocorrelation_time};
|
||||
use std::fs::OpenOptions;
|
||||
use std::fs::File;
|
||||
use std::io::prelude::*;
|
||||
use std::io::{BufReader,BufWriter};
|
||||
@@ -25,28 +27,32 @@ pub fn vprintln(s: String, verbose: bool) {
|
||||
}
|
||||
|
||||
// Read input data into a histogram set by iterating over input files
|
||||
// given in the metadata file
|
||||
pub fn read_data(cfg: &Config) -> Result<Dataset> {
|
||||
// given in the metadata file. This generates at least one Dataset,
|
||||
// or multiple Datasets if convdt is set in the config
|
||||
pub fn read_data(cfg: &Config) -> Result<Vec<Dataset>> {
|
||||
let mut bias_pos: Vec<f64> = Vec::new();
|
||||
let mut bias_fc: Vec<f64> = Vec::new();
|
||||
let mut histograms: Vec<Histogram> = Vec::new();
|
||||
let mut histograms: Vec<Vec<Histogram>> = Vec::new();
|
||||
let mut timeseries_lengths: Vec<usize> = Vec::new();
|
||||
let mut paths = Vec::new();
|
||||
|
||||
let kT = cfg.temperature * k_B;
|
||||
let bin_width: Vec<f64> = (0..cfg.dimens).map(|idx| {
|
||||
(cfg.hist_max[idx] - cfg.hist_min[idx])/(cfg.num_bins[idx] as f64)
|
||||
}).collect();
|
||||
let num_bins = cfg.num_bins.iter().fold(1, |state, &bins| state*bins);
|
||||
let num_bins: usize = cfg.num_bins.iter().product();
|
||||
let dimens_length = cfg.num_bins.clone();
|
||||
|
||||
let f = File::open(&cfg.metadata_file).chain_err(|| "Failed to open metadata file")?;
|
||||
let buf = BufReader::new(&f);
|
||||
|
||||
|
||||
// read each metadata file line and parse it
|
||||
for (line_num,l) in buf.lines().enumerate() {
|
||||
let line = l.chain_err(|| "Failed to read line")?;
|
||||
|
||||
// skip comments and empty lines
|
||||
if line.starts_with("#") || line.len() == 0 {
|
||||
if line.starts_with('#') || line.is_empty() {
|
||||
continue;
|
||||
}
|
||||
|
||||
@@ -55,49 +61,173 @@ pub fn read_data(cfg: &Config) -> Result<Dataset> {
|
||||
bail!(format!("Wrong number of columns in line {} of metadata file. Empty Line?", line_num+1));
|
||||
}
|
||||
|
||||
// parse histogram data
|
||||
let path = get_relative_path(&cfg.metadata_file, split[0]);
|
||||
let h = read_window_file(&path, cfg)
|
||||
.chain_err(|| format!("Failed to parse process data file {}", &path))?;
|
||||
if h.num_points == 0 {
|
||||
bail!(format!("No data points in histogram boundaries: {}", &path))
|
||||
}
|
||||
histograms.push(h);
|
||||
vprintln(format!("{}, {} data points added.", &path,
|
||||
histograms.last().unwrap().num_points), cfg.verbose);
|
||||
|
||||
// parse bias force constants and positions
|
||||
for i in 1..cfg.dimens+1 {
|
||||
let pos = split[i].parse()
|
||||
for val in split.iter().skip(1).take(cfg.dimens) {
|
||||
let pos = val.parse()
|
||||
.chain_err(|| format!("Failed to read bias position in line {} of metadata file", line_num+1))?;
|
||||
bias_pos.push(pos);
|
||||
}
|
||||
for i in (1+cfg.dimens)..(1+2*cfg.dimens) {
|
||||
let fc = split[i].parse()
|
||||
for val in split.iter().skip(1+cfg.dimens).take(cfg.dimens) {
|
||||
let fc = val.parse()
|
||||
.chain_err(|| format!("Failed to read bias fc in line {} of metadata file", line_num+1))?;
|
||||
bias_fc.push(fc);
|
||||
}
|
||||
|
||||
// parse histogram data
|
||||
let path = get_relative_path(&cfg.metadata_file, split[0]);
|
||||
paths.push(path.clone());
|
||||
let (timeseries, timeseries_initial_lengths) = read_window_file(&path, cfg)
|
||||
.chain_err(|| format!("Failed to read time series from {}", &path))?;
|
||||
timeseries_lengths.push(timeseries_initial_lengths);
|
||||
|
||||
|
||||
// for each timeseries, histograms are build for slices according to
|
||||
// start..convdt, start..2*convdt, ...
|
||||
histograms.push(Vec::new());
|
||||
let h_idx = histograms.len()-1;
|
||||
let convdt_stops = get_convdt_boundaries(×eries[0], &cfg);
|
||||
for (idx, interval) in convdt_stops.iter().enumerate() {
|
||||
// build histogram for slice start.._stop
|
||||
let (start, stop) = interval;
|
||||
let timeseries_mask: Vec<bool> = (0..timeseries[0].len()).map(|i| {
|
||||
is_in_time_boundaries(timeseries[0][i], *start, *stop)
|
||||
}).collect();
|
||||
let hist = build_histogram_from_timeseries(×eries, ×eries_mask, cfg);
|
||||
histograms[h_idx].push(hist);
|
||||
|
||||
if (cfg.convdt == 0.00) || idx+1 == convdt_stops.len() {
|
||||
vprintln(format!("{}, {} data points added.", &path,
|
||||
histograms[h_idx].last().unwrap().num_points), cfg.verbose);
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if histograms.len() > 0 {
|
||||
Ok(Dataset::new(num_bins, dimens_length, bin_width, cfg.hist_min.clone(), cfg.hist_max.clone(), bias_pos, bias_fc, kT, histograms, cfg.cyclic))
|
||||
|
||||
// Histograms are stored as timeseries x convdt right now,
|
||||
// but we need convdt x timeseries to create Datasets
|
||||
// this transposes the data
|
||||
let num_datasets: usize = histograms.iter().map(|h| h.len()).max().unwrap();
|
||||
let datasets: Vec<Dataset> = (0..num_datasets).map(|idx| {
|
||||
let mut dataset_histograms: Vec<Histogram> = Vec::with_capacity(histograms.len());
|
||||
for (hs, path) in histograms.iter().zip(&paths) {
|
||||
if hs.len() > idx {
|
||||
dataset_histograms.push(hs[idx].clone())
|
||||
} else {
|
||||
let warning = format!("No data points in histogram boundaries: {}", &path);
|
||||
if idx+1 == num_datasets {
|
||||
bail!(warning);
|
||||
} else {
|
||||
eprintln!("{}", warning);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Ok(Dataset::new(num_bins, dimens_length.clone(), bin_width.clone(),
|
||||
cfg.hist_min.clone(), cfg.hist_max.clone(), bias_pos.clone(),
|
||||
bias_fc.clone(), kT, dataset_histograms, cfg.cyclic))
|
||||
}).collect::<Result<Vec<Dataset>>>().chain_err(|| "Failed to create datasets.")?;
|
||||
|
||||
if datasets.is_empty() {
|
||||
bail!("No datasets created.")
|
||||
} else if datasets[0].histograms.is_empty() {
|
||||
bail!("Dataset has no associated data points.")
|
||||
} else {
|
||||
bail!("Histogram has no datapoints.")
|
||||
if datasets.len() > 1 {
|
||||
println!("Datasets:");
|
||||
println!("Dataset\t\tTime interval\t\tWindows\t\tN_total");
|
||||
for (idx, dataset) in datasets.iter().enumerate() {
|
||||
let n: u32 = dataset.histograms.iter().map(|h| h.num_points).sum();
|
||||
let mut stop = cfg.start+cfg.convdt*(idx+1) as f64;
|
||||
if stop > cfg.end {
|
||||
stop = cfg.end;
|
||||
}
|
||||
println!("{:?}\t\t{:?}-{:?}\t\t{:?}\t\t{:?}", idx+1, cfg.start, stop, dataset.histograms.len(), n);
|
||||
}
|
||||
}
|
||||
|
||||
let histograms = &datasets.last().unwrap().histograms;
|
||||
if cfg.uncorr {
|
||||
println!("Timeseries Correlation:");
|
||||
println!("Window\t\tN\t\tN_uncorr\tN/N_uncorr");
|
||||
for (idx, (n, h)) in timeseries_lengths.iter().zip(histograms.iter()).enumerate() {
|
||||
println!("{:?}\t\t{:?}\t\t{:?}\t\t{:.2}",
|
||||
idx+1, n, h.num_points, h.num_points as f64 / *n as f64);
|
||||
}
|
||||
let total_n = timeseries_lengths.iter().sum::<usize>() as f64;
|
||||
let total_h = histograms.iter().map(|h| h.num_points).sum::<u32>() as f64;
|
||||
println!("\t\t\t\t\tTotal:\t{:.2}", total_h/total_n);
|
||||
}
|
||||
|
||||
Ok(datasets)
|
||||
}
|
||||
}
|
||||
|
||||
// builds a time boundaries for datasets from convdt, timeseries start and end
|
||||
fn get_convdt_boundaries(timeseries: &[f64], cfg: &Config) -> Vec<(f64, f64)> {
|
||||
let mut last_timestep = *timeseries.last().unwrap();
|
||||
if last_timestep > cfg.end {
|
||||
last_timestep = cfg.end;
|
||||
}
|
||||
let mut first_timestep = *timeseries.first().unwrap();
|
||||
if first_timestep < cfg.start {
|
||||
first_timestep = cfg.start;
|
||||
}
|
||||
println!("{} to {} with dt={}", first_timestep, last_timestep, cfg.convdt);
|
||||
if cfg.convdt == 0.0 {
|
||||
vec![(0.0, last_timestep)]
|
||||
} else {
|
||||
let intervals: usize = ((last_timestep - first_timestep) / cfg.convdt).ceil() as usize;
|
||||
println!("{:?}", intervals);
|
||||
(1..intervals+1).map(|i| {
|
||||
i as f64 * cfg.convdt + first_timestep
|
||||
}).map(|end| { (first_timestep, end) }).collect()
|
||||
}
|
||||
}
|
||||
|
||||
// build a histogram from a timeseries
|
||||
// mask is used to filter the timeseries for selected frames
|
||||
fn build_histogram_from_timeseries(timeseries: &[Vec<f64>], mask: &[bool],
|
||||
cfg: &Config) -> Histogram {
|
||||
|
||||
// total number of bins is the product of all dimensions length
|
||||
let total_bins = cfg.num_bins.iter().product();
|
||||
|
||||
// bin width for each dimension: (max-min)/bins
|
||||
let bin_width: Vec<f64> = (0..cfg.dimens).map(|idx| {
|
||||
(cfg.hist_max[idx] - cfg.hist_min[idx])/(cfg.num_bins[idx] as f64)
|
||||
}).collect();
|
||||
|
||||
// build histogram for slice start..convdt_stop
|
||||
let mut hist = vec![0.0; total_bins];
|
||||
for i in (0..timeseries[0].len()).filter(|i| mask[*i]) {
|
||||
let mut values: Vec<f64> = vec![f64::NAN; cfg.dimens+1];
|
||||
for j in 0..values.len() {
|
||||
values[j] = timeseries[j][i];
|
||||
}
|
||||
|
||||
if is_in_hist_boundaries(&values[1..], cfg) {
|
||||
let bin_indeces: Vec<usize> = (0..cfg.dimens).map(|dimen: usize| {
|
||||
let val = values[dimen+1];
|
||||
((val - cfg.hist_min[dimen]) / bin_width[dimen]) as usize
|
||||
}).collect();
|
||||
let index = flat_index(&bin_indeces, &cfg.num_bins);
|
||||
hist[index] += 1.0;
|
||||
}
|
||||
}
|
||||
|
||||
let num_points: f64 = hist.iter().sum();
|
||||
Histogram::new(num_points as u32, hist)
|
||||
}
|
||||
|
||||
// transforms a multidimensional index into a one dimensional index
|
||||
// indeces: multidimensional indeces
|
||||
// lengths: length of the matrix in each dimension
|
||||
// returns an index if the matrix is flattened to a one dimensional vector
|
||||
// example for 3 dimensions N,M,O: idx = i_O + l_O*l_M*i_M + l_O*l_M*l_N*i_N
|
||||
fn flat_index(indeces: &Vec<usize>, lengths: &Vec<usize>) -> usize {
|
||||
let mut idx = 0;
|
||||
for i in 0..indeces.len() {
|
||||
idx += indeces[i]*lengths[0..i].iter()
|
||||
.fold(1, |state, &l| { state * l });
|
||||
}
|
||||
idx
|
||||
fn flat_index(indeces: &[usize], lengths: &[usize]) -> usize {
|
||||
indeces.iter().enumerate().map(|(i, idx)| {
|
||||
idx * lengths.iter().take(i).product::<usize>()
|
||||
}).sum()
|
||||
}
|
||||
|
||||
// returns true if the values are inside the histogram boundaries defined by cfg
|
||||
@@ -111,83 +241,142 @@ fn is_in_hist_boundaries(values: &[f64], cfg: &Config) -> bool {
|
||||
}
|
||||
|
||||
// returns true given time in inside the time boundaries defined by cfg
|
||||
fn is_in_time_boundaries(time: f64, cfg: &Config) -> bool {
|
||||
if cfg.start <= time && time <= cfg.end {
|
||||
fn is_in_time_boundaries(time: f64, start: f64, end: f64) -> bool {
|
||||
if start <= time && time <= end {
|
||||
return true
|
||||
}
|
||||
false
|
||||
}
|
||||
|
||||
// parse a timeseries file into a histogram
|
||||
fn read_window_file(window_file: &str, cfg: &Config) -> Result<Histogram> {
|
||||
let f = File::open(window_file)
|
||||
// parse a time series file
|
||||
fn read_window_file(window_file: &str, cfg: &Config) -> Result<(Vec<Vec<f64>>, usize)> {
|
||||
let mut timeseries: Vec<Vec<f64>> = read_timeseries(window_file, cfg)?;
|
||||
|
||||
// filter the timeseries based on start/end parameters
|
||||
let time_series_mask: Vec<bool> = timeseries[0].iter()
|
||||
.map(|t| is_in_time_boundaries(*t, cfg.start, cfg.end)).collect();
|
||||
timeseries = timeseries.into_iter().map(|ts| {
|
||||
ts.into_iter().zip(time_series_mask.iter()).filter_map(|(val, mask)| {
|
||||
if *mask {
|
||||
Some(val)
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}).collect()
|
||||
}).collect::<Vec<Vec<f64>>>();
|
||||
|
||||
let timeseries_inital_length = timeseries[0].len();
|
||||
if cfg.uncorr {
|
||||
timeseries = uncorrelate(timeseries, cfg);
|
||||
}
|
||||
|
||||
if timeseries[0].is_empty() {
|
||||
bail!("Time series is empty")
|
||||
}
|
||||
|
||||
Ok((timeseries, timeseries_inital_length))
|
||||
}
|
||||
|
||||
// Read a multidimensional timeseries
|
||||
// The resulting vector contains one vector per dimension
|
||||
fn read_timeseries(window_file: &str, cfg: &Config) -> Result<Vec<Vec<f64>>> {
|
||||
let f = File::open(window_file)
|
||||
.chain_err(|| format!("Failed to open sample data file {}.", window_file))?;
|
||||
let mut buf = BufReader::new(&f);
|
||||
|
||||
// total number of bins is the product of all dimensions length
|
||||
let total_bins = cfg.num_bins.iter().fold(1, |s, &x| { s*x });
|
||||
let mut hist = vec![0.0; total_bins];
|
||||
|
||||
// bin width for each dimension: (max-min)/bins
|
||||
let bin_width: Vec<f64> = (0..cfg.dimens).map(|idx| {
|
||||
(cfg.hist_max[idx] - cfg.hist_min[idx])/(cfg.num_bins[idx] as f64)
|
||||
}).collect();
|
||||
let mut timeseries = vec![Vec::new(); cfg.dimens+1];
|
||||
|
||||
// read and parse each timeseries line
|
||||
let mut line = String::new();
|
||||
let mut linecount = 0;
|
||||
while buf.read_line(&mut line).chain_err(|| "Failed to read line")? > 0 {
|
||||
linecount += 1;
|
||||
|
||||
// skip comments and empty lines
|
||||
if line.starts_with("#") || line.starts_with("@") || line.len() == 0 {
|
||||
if line.starts_with('#') || line.starts_with('@') || line.is_empty() {
|
||||
line.clear();
|
||||
continue;
|
||||
}
|
||||
|
||||
{
|
||||
|
||||
let split: Vec<&str> = line.split_whitespace().collect();
|
||||
if split.len() < cfg.dimens+1 {
|
||||
bail!(format!("Wrong number of columns in line {} of window file {}. Empty Line?.", linecount, window_file));
|
||||
}
|
||||
|
||||
let mut values: Vec<f64> = vec![f64::NAN; cfg.dimens+1];
|
||||
for i in 0..values.len() {
|
||||
values[i] = split[i].parse::<f64>()
|
||||
.chain_err(|| format!("Failed to parse line {} of window file {}.", linecount, window_file))?;
|
||||
}
|
||||
for i in 0..cfg.dimens+1 {
|
||||
timeseries[i].push(split[i].parse::<f64>()
|
||||
.chain_err(|| format!("Failed to parse line {} of window file {}.", linecount, window_file))?
|
||||
|
||||
if is_in_hist_boundaries(&values[1..], cfg) && is_in_time_boundaries(values[0], cfg) {
|
||||
let bin_indeces = (0..cfg.dimens).map(|dimen: usize| {
|
||||
let val = values[dimen+1];
|
||||
((val - cfg.hist_min[dimen]) / bin_width[dimen]) as usize
|
||||
}).collect();
|
||||
let index = flat_index(&bin_indeces, &cfg.num_bins);
|
||||
hist[index] += 1.0;
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
line.clear();
|
||||
}
|
||||
Ok(timeseries)
|
||||
}
|
||||
|
||||
let num_points: f64 = hist.iter().sum();
|
||||
Ok(Histogram::new(num_points as u32, hist))
|
||||
|
||||
// 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>> {
|
||||
// calculate inefficiencies and find the highest one
|
||||
let gs: Vec<f64> = timeseries[1..].iter().map(|ts| statistical_ineff(ts)).collect();
|
||||
let mut max_g = 1.0;
|
||||
for g in gs {
|
||||
if g > max_g {
|
||||
max_g = g;
|
||||
}
|
||||
}
|
||||
|
||||
// round g up
|
||||
let mut trunc_g = max_g.trunc() as usize;
|
||||
if (trunc_g as f64 - max_g).abs() > 0.000_000_000_1 {
|
||||
trunc_g += 1;
|
||||
}
|
||||
|
||||
// filter correlated samples from timeseries
|
||||
let prev_len = timeseries[0].len();
|
||||
let timeseries = timeseries.into_iter().map(|ts| {
|
||||
ts.into_iter().step_by(trunc_g).collect::<Vec<f64>>()
|
||||
}).collect::<Vec<Vec<f64>>>();
|
||||
|
||||
let new_len = timeseries[0].len();
|
||||
if cfg.verbose {
|
||||
let tau = autocorrelation_time(max_g)* (timeseries[0][1]-timeseries[0][0]);
|
||||
vprintln(format!("{:?}/{:?} samples are uncorrelated. {:?} samples removed from timeseries (tau={:.5})", new_len, prev_len, prev_len-new_len, tau), true);
|
||||
}
|
||||
timeseries
|
||||
}
|
||||
|
||||
// Write WHAM calculation results to out_file.
|
||||
pub fn write_results(out_file: &str, ds: &Dataset, free: &Vec<f64>, free_std: &Vec<f64>, prob: &Vec<f64>, prob_std: &Vec<f64>) -> Result<()> {
|
||||
let output = File::create(out_file)
|
||||
pub fn write_results(out_file: &str, append: bool, ds: &Dataset, free: &[f64],
|
||||
free_std: &[f64], prob: &[f64], prob_std: &[f64], index: Option<usize>) -> Result<()> {
|
||||
|
||||
if !append && Path::new(out_file).exists() {
|
||||
std::fs::remove_file(out_file).chain_err(|| "Failed to delete file.")?;
|
||||
}
|
||||
let output = OpenOptions::new().write(true)
|
||||
.append(true)
|
||||
.create(true)
|
||||
.open(out_file)
|
||||
.chain_err(|| format!("Failed to create file with path {}", out_file))?;
|
||||
let mut buf = BufWriter::new(output);
|
||||
|
||||
let header: String = (0..ds.dimens_lengths.len()).map(|d| format!("coord{}", d+1))
|
||||
.collect::<Vec<String>>().join(" ");
|
||||
writeln!(buf, "#{} {} {} {} {}", header, "Free Energy", "+/-", "Probability", "+/-");
|
||||
if let Some(index) = index {
|
||||
writeln!(buf, "#Dataset {}", index).unwrap();
|
||||
}
|
||||
writeln!(buf, "#{} Free Energy +/- Probability +/-", header).unwrap();
|
||||
|
||||
for bin in 0..free.len() {
|
||||
let coords = ds.get_coords_for_bin(bin);
|
||||
let coords_str: String = coords.iter().map(|c| {format!("{:8.6} ", c)})
|
||||
.collect::<Vec<String>>().join("\t");
|
||||
writeln!(buf, "{}{:8.6} {:8.6} {:8.6} {:8.6}", coords_str, free[bin], free_std[bin], prob[bin], prob_std[bin])
|
||||
writeln!(buf, "{}{:8.6} {:8.6} {:8.6} {:8.6}", coords_str,
|
||||
free[bin], free_std[bin], prob[bin], prob_std[bin])
|
||||
.chain_err(|| "Failed to write to file.")?;
|
||||
}
|
||||
Ok(())
|
||||
@@ -196,10 +385,12 @@ pub fn write_results(out_file: &str, ds: &Dataset, free: &Vec<f64>, free_std: &V
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use assert_approx_eq::assert_approx_eq;
|
||||
|
||||
fn cfg() -> Config {
|
||||
Config {
|
||||
metadata_file: "example/1d_cyclic/metadata.dat".to_string(),
|
||||
|
||||
hist_min: vec![-3.14],
|
||||
hist_max: vec![3.14],
|
||||
num_bins: vec![10],
|
||||
@@ -213,7 +404,9 @@ mod tests {
|
||||
bootstrap: 0,
|
||||
bootstrap_seed: 1234,
|
||||
start: 0.0,
|
||||
end: 1e+20
|
||||
end: 1e+20,
|
||||
uncorr: false,
|
||||
convdt: 0.0,
|
||||
}
|
||||
}
|
||||
|
||||
@@ -221,27 +414,50 @@ mod tests {
|
||||
fn read_window_file() {
|
||||
let f = "example/1d_cyclic/COLVAR+0.0.xvg";
|
||||
let cfg = cfg();
|
||||
let h = super::read_window_file(&f, &cfg).unwrap();
|
||||
let (timeseries, timeseries_inital_length) = super::read_window_file(&f, &cfg).unwrap();
|
||||
let mask = vec![true; timeseries[0].len()];
|
||||
let h = build_histogram_from_timeseries(×eries, &mask, &cfg);
|
||||
println!("{:?}", h);
|
||||
assert_eq!(5000, timeseries_inital_length);
|
||||
assert_eq!(5000, h.num_points);
|
||||
assert_eq!(0.0, h.bins[2]);
|
||||
assert_eq!(11.0, h.bins[3]);
|
||||
assert_eq!(2236.0, h.bins[4]);
|
||||
assert_eq!(2714.0, h.bins[5]);
|
||||
assert_eq!(39.0, h.bins[6]);
|
||||
assert_eq!(0.0, h.bins[7]);
|
||||
assert_approx_eq!(0.0, h.bins[2]);
|
||||
assert_approx_eq!(11.0, h.bins[3]);
|
||||
assert_approx_eq!(2236.0, h.bins[4]);
|
||||
assert_approx_eq!(2714.0, h.bins[5]);
|
||||
assert_approx_eq!(39.0, h.bins[6]);
|
||||
assert_approx_eq!(0.0, h.bins[7]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn read_timeseries() {
|
||||
let f = "example/1d_cyclic/COLVAR+0.0.xvg";
|
||||
let cfg = cfg();
|
||||
let ts = super::read_timeseries(&f, &cfg).unwrap();
|
||||
let expected = [
|
||||
-0.153_145,
|
||||
-0.377_860,
|
||||
0.010_992,
|
||||
0.123_074,
|
||||
0.108_291,
|
||||
0.261_607,
|
||||
];
|
||||
assert!(ts.len() == 2);
|
||||
assert!(ts[0].len() == 5000);
|
||||
println!("{:?}", ts);
|
||||
for (actual, expected) in ts[1].iter().zip(expected.iter()) {
|
||||
assert!((actual-expected).abs() < 0.001, format!("{:?} != {:?}", actual, expected));
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn read_data() {
|
||||
let cfg = cfg();
|
||||
let ds = super::read_data(&cfg).unwrap();
|
||||
let ds = &super::read_data(&cfg).unwrap()[0];
|
||||
println!("{:?}", ds);
|
||||
assert_eq!(25, ds.num_windows);
|
||||
assert_eq!(cfg.num_bins.len(), ds.dimens_lengths.len());
|
||||
assert_eq!(cfg.num_bins[0], ds.dimens_lengths[0]);
|
||||
assert_eq!(cfg.temperature * k_B, ds.kT);
|
||||
assert_approx_eq!(cfg.temperature * k_B, ds.kT);
|
||||
assert_eq!(25, ds.histograms.len())
|
||||
}
|
||||
|
||||
@@ -255,4 +471,73 @@ mod tests {
|
||||
let relative3 = super::get_relative_path(&path1, &path3);
|
||||
assert_eq!("path/to/subfolder/another_file.dat" ,relative3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn is_in_time_boundaries() {
|
||||
let start = 10.0;
|
||||
let end = 20.0;
|
||||
assert!(super::is_in_time_boundaries(15.0, start, end));
|
||||
assert!(super::is_in_time_boundaries(10.0, start, end));
|
||||
assert!(super::is_in_time_boundaries(20.0, start, end));
|
||||
assert!(!super::is_in_time_boundaries(9.9999999, start, end));
|
||||
assert!(!super::is_in_time_boundaries(20.000001, start, end));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn get_convdt_boundaries() {
|
||||
let mut cfg = cfg();
|
||||
|
||||
let timeseries: Vec<f64> = (0..31).map(|i| i as f64).collect();
|
||||
println!("{:?}", timeseries);
|
||||
|
||||
cfg.start = 10.0;
|
||||
cfg.end = 20.0;
|
||||
cfg.convdt = 10.0;
|
||||
let test = super::get_convdt_boundaries(×eries, &cfg);
|
||||
println!("{:?}", test);
|
||||
assert!(test.len() == 1);
|
||||
assert_approx_eq!(test[0].0, 10.0);
|
||||
assert_approx_eq!(test[0].1, 20.0);
|
||||
|
||||
cfg.start = 10.0;
|
||||
cfg.end = 20.0;
|
||||
cfg.convdt = 5.0;
|
||||
let test = super::get_convdt_boundaries(×eries, &cfg);
|
||||
println!("{:?}", test);
|
||||
assert!(test.len() == 2);
|
||||
assert_approx_eq!(test[0].0, 10.0);
|
||||
assert_approx_eq!(test[0].1, 15.0);
|
||||
assert_approx_eq!(test[1].0, 10.0);
|
||||
assert_approx_eq!(test[1].1, 20.0);
|
||||
|
||||
let timeseries: Vec<f64> = (10..21).map(|i| i as f64).collect();
|
||||
println!("{:?}", timeseries);
|
||||
|
||||
cfg.start = 10.0;
|
||||
cfg.end = 20.0;
|
||||
cfg.convdt = 10.0;
|
||||
let test = super::get_convdt_boundaries(×eries, &cfg);
|
||||
println!("{:?}", test);
|
||||
assert!(test.len() == 1);
|
||||
assert_approx_eq!(test[0].0, 10.0);
|
||||
assert_approx_eq!(test[0].1, 20.0);
|
||||
|
||||
cfg.start = 5.0;
|
||||
cfg.end = 20.0;
|
||||
cfg.convdt = 10.0;
|
||||
let test = super::get_convdt_boundaries(×eries, &cfg);
|
||||
println!("{:?}", test);
|
||||
assert!(test.len() == 1);
|
||||
assert_approx_eq!(test[0].0, 10.0);
|
||||
assert_approx_eq!(test[0].1, 20.0);
|
||||
|
||||
cfg.start = 5.0;
|
||||
cfg.end = 30.0;
|
||||
cfg.convdt = 10.0;
|
||||
let test = super::get_convdt_boundaries(×eries, &cfg);
|
||||
println!("{:?}", test);
|
||||
assert!(test.len() == 1);
|
||||
assert_approx_eq!(test[0].0, 10.0);
|
||||
assert_approx_eq!(test[0].1, 20.0);
|
||||
}
|
||||
}
|
||||
193
src/lib.rs
193
src/lib.rs
@@ -3,12 +3,17 @@
|
||||
#[macro_use]
|
||||
extern crate error_chain;
|
||||
extern crate rand;
|
||||
extern crate rgsl;
|
||||
extern crate rayon;
|
||||
#[cfg(test)]
|
||||
#[macro_use]
|
||||
extern crate assert_approx_eq;
|
||||
|
||||
|
||||
pub mod io;
|
||||
pub mod histogram;
|
||||
pub mod error_analysis;
|
||||
pub mod correlation_analysis;
|
||||
pub mod statistics;
|
||||
|
||||
use histogram::Dataset;
|
||||
use std::f64;
|
||||
@@ -21,7 +26,7 @@ pub mod errors { error_chain!{} }
|
||||
use errors::*;
|
||||
|
||||
#[allow(non_upper_case_globals)]
|
||||
static k_B: f64 = 0.0083144621; // kJ/mol*K
|
||||
static k_B: f64 = 0.008_314_462_1; // kJ/mol*K
|
||||
|
||||
// Application config
|
||||
#[derive(Debug)]
|
||||
@@ -41,14 +46,20 @@ pub struct Config {
|
||||
pub bootstrap_seed: u64,
|
||||
pub start: f64,
|
||||
pub end: f64,
|
||||
pub uncorr: bool,
|
||||
pub convdt: f64,
|
||||
}
|
||||
|
||||
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={:?}, uncorr={:?}, bootstrap={:?}, seed={:?},
|
||||
uncorr={:?}, start={:?}, end={:?}, convdt={:?}",
|
||||
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)
|
||||
self.cyclic, self.uncorr, self.bootstrap, self.bootstrap_seed,
|
||||
self.uncorr, self.start, self.end, self.convdt)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -56,25 +67,34 @@ 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 +104,18 @@ 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 +123,20 @@ 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;
|
||||
@@ -118,33 +148,32 @@ 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() }
|
||||
converged = is_converged(&F_prev, &F, cfg.tolerance);
|
||||
|
||||
println!("Iteration {}: dF={}", &iteration, &diff_avg(&F_prev, &F));
|
||||
if cfg.verbose {
|
||||
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)");
|
||||
@@ -157,30 +186,40 @@ pub fn run(cfg: &Config) -> Result<()>{
|
||||
println!("Supplied WHAM options: {}", &cfg);
|
||||
|
||||
println!("Reading input files.");
|
||||
let dataset = io::read_data(&cfg).chain_err(|| "Failed to create histogram.")?;
|
||||
println!("{}", &dataset);
|
||||
let datasets = io::read_data(&cfg).chain_err(|| "Failed to read data.")?;
|
||||
|
||||
let (P, F, F_prev) = perform_wham(&cfg, &dataset)?;
|
||||
for (idx, dataset) in datasets.iter().enumerate() {
|
||||
if datasets.len() > 1 {
|
||||
println!("Dataset {}/{}: {}", idx+1, datasets.len(), &dataset);
|
||||
}
|
||||
else {
|
||||
println!("{}", &dataset);
|
||||
}
|
||||
let (P, F, F_prev) = perform_wham(&cfg, &dataset)?;
|
||||
println!("WHAM converged.");
|
||||
|
||||
let P_std: Vec<f64>;
|
||||
let free_energy_std: Vec<f64>;
|
||||
if cfg.bootstrap > 0 {
|
||||
let error_est = error_analysis::run_bootstrap(&cfg, dataset.clone(), &P, cfg.bootstrap);
|
||||
P_std = error_est.0;
|
||||
free_energy_std = error_est.1;
|
||||
} else {
|
||||
P_std = vec![0.0; P.len()];
|
||||
free_energy_std = vec![0.0; P.len()];
|
||||
}
|
||||
let (P_std, free_energy_std) = if cfg.bootstrap > 0 {
|
||||
println!("Bootstrapping..");
|
||||
error_analysis::run_bootstrap(&cfg, dataset.clone(), cfg.bootstrap)
|
||||
} else {
|
||||
(vec![0.0; P.len()], vec![0.0; P.len()])
|
||||
};
|
||||
|
||||
// calculate free energy and dump state
|
||||
println!("Finished. Dumping final PMF");
|
||||
let free_energy = calc_free_energy(&dataset, &P);
|
||||
dump_state(&dataset, &F, &F_prev, &P, &P_std, &free_energy, &free_energy_std);
|
||||
|
||||
io::write_results(&cfg.output, &dataset, &free_energy, &free_energy_std, &P, &P_std)
|
||||
.chain_err(|| "Could not write results to output file")?;
|
||||
// calculate free energy and dump state
|
||||
println!("Finished. Dumping PMF");
|
||||
let free_energy = calc_free_energy(&dataset, &P);
|
||||
|
||||
dump_state(&dataset, &F, &F_prev, &P, &P_std, &free_energy, &free_energy_std);
|
||||
let append = idx > 0 && datasets.len() > 1;
|
||||
let index = if datasets.len() > 1 {
|
||||
Some(idx)
|
||||
} else {
|
||||
None
|
||||
};
|
||||
io::write_results(&cfg.output, append, &dataset, &free_energy, &free_energy_std, &P, &P_std, index)
|
||||
.chain_err(|| "Could not write results to output file")?;
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
@@ -214,19 +253,23 @@ 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();
|
||||
}
|
||||
}
|
||||
|
||||
@@ -268,23 +311,23 @@ 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);
|
||||
for b in 0..dataset.num_bins {
|
||||
let p = super::calc_bin_probability(b, &dataset, &F);
|
||||
assert_delta!(expected[b], p, 0.0000001);
|
||||
}
|
||||
}
|
||||
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);
|
||||
expected.iter().enumerate().for_each(|(i, exp)| {
|
||||
let p = super::calc_bin_probability(i, &dataset, &F);
|
||||
assert_delta!(exp, p, 0.000_000_1);
|
||||
})
|
||||
}
|
||||
|
||||
#[test]
|
||||
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);
|
||||
for window in 0..dataset.num_windows {
|
||||
let F = super::calc_window_F(window, &dataset, &probability);
|
||||
assert_delta!(expected[window], F, 0.0000001);
|
||||
}
|
||||
let expected = vec!(15.927_477_169_990_633, 15.927_477_169_990_633);
|
||||
expected.iter().enumerate().for_each(|(i, exp)| {
|
||||
let F = super::calc_window_F(i, &dataset, &probability);
|
||||
assert_delta!(exp, F, 0.000_000_1);
|
||||
})
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -295,8 +338,8 @@ 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)
|
||||
}
|
||||
|
||||
@@ -3,6 +3,7 @@ extern crate wham;
|
||||
extern crate clap;
|
||||
extern crate rand;
|
||||
|
||||
use rand::prelude::*;
|
||||
use clap::App;
|
||||
use wham::Config;
|
||||
use wham::errors::*;
|
||||
@@ -49,7 +50,6 @@ fn cli() -> Result<Config> {
|
||||
.chain_err(|| "Cannot parse bootstrap iteration.")?;
|
||||
let bootstrap_seed: u64 = matches.value_of("bootstrap_seed")
|
||||
.unwrap_or({
|
||||
use rand::Rng;
|
||||
let mut rng = rand::thread_rng();
|
||||
&rng.gen::<u32>().to_string()
|
||||
}).parse()
|
||||
@@ -58,6 +58,8 @@ fn cli() -> Result<Config> {
|
||||
.chain_err(|| "Cannot parse start time.")?;
|
||||
let end: f64 = matches.value_of("end").unwrap_or("1e+20").parse()
|
||||
.chain_err(|| "Cannot parse end time.")?;
|
||||
|
||||
let uncorr: bool = matches.is_present("uncorr");
|
||||
|
||||
if num_bins.len() != hist_max.len() || num_bins.len() != hist_max.len() {
|
||||
eprintln!("Input dimensions do not match (min: {}, max: {}, bins: {})",
|
||||
@@ -66,10 +68,12 @@ fn cli() -> Result<Config> {
|
||||
}
|
||||
|
||||
let dimens = num_bins.len();
|
||||
let convdt: f64 = matches.value_of("convdt").unwrap_or("0").parse()
|
||||
.chain_err(|| "Cannot parse convdt.")?;
|
||||
|
||||
Ok(wham::Config{metadata_file, hist_min, hist_max, num_bins, dimens,
|
||||
verbose, tolerance, max_iterations, temperature, cyclic, output,
|
||||
bootstrap, bootstrap_seed, start, end})
|
||||
bootstrap, bootstrap_seed, start, end, uncorr, convdt})
|
||||
}
|
||||
|
||||
fn main() {
|
||||
|
||||
59
src/statistics.rs
Normal file
59
src/statistics.rs
Normal file
@@ -0,0 +1,59 @@
|
||||
pub fn mean(x: &[f64]) -> f64 {
|
||||
x.iter().sum::<f64>() / x.len() as f64
|
||||
}
|
||||
|
||||
pub fn autocov(x: &[f64]) -> f64 {
|
||||
let x_mean = mean(x);
|
||||
|
||||
x.iter().map(|xi| {
|
||||
(xi-x_mean).powi(2)
|
||||
}).sum::<f64>() / x.len() as f64
|
||||
}
|
||||
|
||||
pub fn sd(x: &[f64]) -> f64 {
|
||||
let x_mean = mean(x);
|
||||
let n = x.len() as f64;
|
||||
|
||||
let sum = x.iter().map(|xi| {
|
||||
(xi-x_mean).powi(2)
|
||||
}).sum::<f64>();
|
||||
(1.0/(n-1.0) * sum).sqrt()
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use assert_approx_eq::assert_approx_eq;
|
||||
|
||||
// a sine wave
|
||||
fn dataset() -> Vec<f64> {
|
||||
(0..100).map(|i| (i as f64 / 100.0 * std::f64::consts::PI).sin())
|
||||
.collect::<Vec<f64>>()
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn mean() {
|
||||
let ds = dataset();
|
||||
let expected =0.6366;
|
||||
|
||||
let m = super::mean(&ds);
|
||||
assert_approx_eq!(m, expected, 0.0001);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn autocorr() {
|
||||
let ds = dataset();
|
||||
let expected = 0.094_782;
|
||||
|
||||
let m = super::autocov(&ds);
|
||||
assert_approx_eq!(m, expected, 0.000_001);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sd() {
|
||||
let ds = dataset();
|
||||
let expected = 0.309_418;
|
||||
|
||||
let m = super::sd(&ds);
|
||||
assert_approx_eq!(m, expected, 0.000_001);
|
||||
}
|
||||
}
|
||||
@@ -2,8 +2,6 @@ mod command;
|
||||
|
||||
#[cfg(test)]
|
||||
mod integration {
|
||||
|
||||
use std::process::Command;
|
||||
use super::command::get_command;
|
||||
|
||||
#[test]
|
||||
|
||||
@@ -27,6 +27,26 @@ mod integration {
|
||||
assert_eq!(output_len, 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn wham_1d_cyclic_uncorrelated() {
|
||||
get_command()
|
||||
.args(&["--bins", "100", "--max", "pi", "--min", "-pi", "-T", "300", "--cyclic", "--uncorr"])
|
||||
.args(&["--seed", "1234"])
|
||||
.args(&["-f", "example/1d_cyclic/metadata.dat"])
|
||||
.args(&["-o", "/tmp/wham_test_1d_cyclic.out"])
|
||||
.output()
|
||||
.expect("failed to execute process");
|
||||
|
||||
assert!(fs::metadata("/tmp/wham_test_1d_cyclic.out").is_ok());
|
||||
let output = Command::new("diff")
|
||||
.arg("/tmp/wham_test_1d_cyclic.out")
|
||||
.arg("example/1d_cyclic/wham_uncorrelated.out")
|
||||
.output()
|
||||
.expect("failed to run diff");
|
||||
let output_len = String::from_utf8_lossy(&output.stdout).len();
|
||||
assert_eq!(output_len, 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[ignore] // expensive
|
||||
fn wham_2d_cyclic() {
|
||||
|
||||
Reference in New Issue
Block a user