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report standard error (SE) instead of standard deviations

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This commit is contained in:
Daniel Bauer
2020-09-15 14:09:36 +02:00
parent 7116142c2a
commit 1e27cfd457
5 changed files with 198 additions and 197 deletions
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176
Cargo.lock generated
View File

@@ -11,6 +11,21 @@ dependencies = [
"pkg-config", "pkg-config",
] ]
[[package]]
name = "addr2line"
version = "0.13.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
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dependencies = [
"gimli",
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[[package]] [[package]]
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@@ -39,32 +54,24 @@ dependencies = [
[[package]] [[package]]
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[[package]] [[package]]
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@@ -77,12 +84,6 @@ version = "1.0.12"
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[[package]] [[package]]
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@@ -91,9 +92,9 @@ checksum = "4785bdd1c96b2a846b2bd7cc02e86b6b3dbf14e7e53446c4f54c92a361040822"
[[package]] [[package]]
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@@ -114,6 +115,16 @@ dependencies = [
"bitflags", "bitflags",
] ]
[[package]]
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source = "registry+https://github.com/rust-lang/crates.io-index"
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@@ -140,16 +151,6 @@ dependencies = [
"scopeguard", "scopeguard",
] ]
[[package]]
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"crossbeam-utils",
]
[[package]] [[package]]
name = "crossbeam-utils" name = "crossbeam-utils"
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@@ -163,15 +164,15 @@ dependencies = [
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@@ -184,10 +185,16 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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dependencies = [ dependencies = [
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@@ -200,9 +207,9 @@ checksum = "e2abad23fbc42b3700f2f279844dc832adb2b2eb069b2df918f455c4e18cc646"
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[[package]] [[package]]
name = "maybe-uninit" name = "maybe-uninit"
@@ -212,28 +219,44 @@ checksum = "60302e4db3a61da70c0cb7991976248362f30319e88850c487b9b95bbf059e00"
[[package]] [[package]]
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] ]
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@@ -265,10 +288,11 @@ checksum = "9c33a3c44ca05fa6f1807d8e6743f3824e8509beca625669633be0acbdf509dc"
[[package]] [[package]]
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dependencies = [ dependencies = [
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"either", "either",
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@@ -276,12 +300,12 @@ dependencies = [
[[package]] [[package]]
name = "rayon-core" name = "rayon-core"
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source = "registry+https://github.com/rust-lang/crates.io-index" source = "registry+https://github.com/rust-lang/crates.io-index"
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@@ -293,36 +317,12 @@ version = "0.1.16"
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@@ -340,25 +340,25 @@ dependencies = [
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[[package]] [[package]]
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@@ -370,9 +370,9 @@ dependencies = [
[[package]] [[package]]
name = "winapi" name = "winapi"
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dependencies = [ dependencies = [
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"winapi-x86_64-pc-windows-gnu", "winapi-x86_64-pc-windows-gnu",

2
Cargo.toml
View File

@@ -1,6 +1,6 @@
[package] [package]
name = "wham" name = "wham"
version = "0.9.6" version = "0.9.7"
authors = ["Daniel Bauer <bauer@cbs.tu-darmstadt.de>"] authors = ["Daniel Bauer <bauer@cbs.tu-darmstadt.de>"]
description = "An implementation of the weighted histogram analysis method" description = "An implementation of the weighted histogram analysis method"
license = "GPL-3.0" license = "GPL-3.0"

4
README.md
View File

@@ -81,8 +81,8 @@ one can estimate the error as standard deviation between the N bootstrapping run
Autocorrelation Estimates, JCTC, 6(12), 3713-3720*. Autocorrelation Estimates, JCTC, 6(12), 3713-3720*.
To perform bayesian bootstrapping in WHAM, use the ```-bt <RUNS>``` flag to perform <RUNS> individual bootstrapping 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. runs. The error estimates of bin probabilities and free energy will be given as standard error (SE) in a
If no error analysis is performed, these columns are set to 0.0. separate column (+/-) in the output file. If no error analysis is performed, these columns are set to 0.0.
Examples Examples
--- ---

200
example/1d_cyclic/wham.out
View File

@@ -1,101 +1,101 @@
#coord1 Free Energy +/- Probability +/- #coord1 Free Energy +/- Probability +/-
-3.110177 7.158102 0.476037 0.003494 0.000667 -3.110177 7.158102 0.047604 0.003494 0.000067
-3.047345 5.365727 0.470738 0.007168 0.001353 -3.047345 5.365727 0.047074 0.007168 0.000135
-2.984513 3.873190 0.433120 0.013039 0.002264 -2.984513 3.873190 0.043312 0.013039 0.000226
-2.921681 2.953162 0.441412 0.018855 0.003337 -2.921681 2.953162 0.044141 0.018855 0.000334
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-2.544690 0.663757 0.389082 0.047211 0.007364 -2.544690 0.663757 0.038908 0.047211 0.000736
-2.481858 1.051932 0.379999 0.040407 0.006156 -2.481858 1.051932 0.038000 0.040407 0.000616
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-2.293363 2.190692 0.196497 0.025597 0.002016 -2.293363 2.190692 0.019650 0.025597 0.000202
-2.230531 2.553036 0.177045 0.022136 0.001571 -2.230531 2.553036 0.017705 0.022136 0.000157
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-1.602212 0.303813 0.320695 0.054540 0.007012 -1.602212 0.303813 0.032070 0.054540 0.000701
-1.539380 0.268335 0.330495 0.055321 0.007330 -1.539380 0.268335 0.033050 0.055321 0.000733
-1.476549 0.000000 0.338143 0.061604 0.008351 -1.476549 0.000000 0.033814 0.061604 0.000835
-1.413717 0.537061 0.369220 0.049671 0.007352 -1.413717 0.537061 0.036922 0.049671 0.000735
-1.350885 1.439940 0.381006 0.034586 0.005283 -1.350885 1.439940 0.038101 0.034586 0.000528
-1.288053 2.391838 0.407306 0.023614 0.003856 -1.288053 2.391838 0.040731 0.023614 0.000386
-1.225221 3.779470 0.413629 0.013538 0.002245 -1.225221 3.779470 0.041363 0.013538 0.000224
-1.162389 5.685555 0.432485 0.006305 0.001093 -1.162389 5.685555 0.043248 0.006305 0.000109
-1.099557 7.661896 0.446994 0.002855 0.000512 -1.099557 7.661896 0.044699 0.002855 0.000051
-1.036726 9.946594 0.489990 0.001142 0.000224 -1.036726 9.946594 0.048999 0.001142 0.000022
-0.973894 12.359408 0.544847 0.000434 0.000095 -0.973894 12.359408 0.054485 0.000434 0.000009
-0.911062 14.954655 0.622373 0.000153 0.000038 -0.911062 14.954655 0.062237 0.000153 0.000004
-0.848230 17.742242 0.747233 0.000050 0.000015 -0.848230 17.742242 0.074723 0.000050 0.000002
-0.785398 20.555785 0.921773 0.000016 0.000006 -0.785398 20.555785 0.092177 0.000016 0.000001
-0.722566 22.811160 1.179343 0.000007 0.000003 -0.722566 22.811160 0.117934 0.000007 0.000000
-0.659734 25.178094 1.484376 0.000003 0.000002 -0.659734 25.178094 0.148438 0.000003 0.000000
-0.596903 26.442288 1.565063 0.000002 0.000001 -0.596903 26.442288 0.156506 0.000002 0.000000
-0.534071 27.897565 1.418232 0.000001 0.000000 -0.534071 27.897565 0.141823 0.000001 0.000000
-0.471239 29.062473 1.246738 0.000001 0.000000 -0.471239 29.062473 0.124674 0.000001 0.000000
-0.408407 30.384521 1.231662 0.000000 0.000000 -0.408407 30.384521 0.123166 0.000000 0.000000
-0.345575 31.638454 1.209295 0.000000 0.000000 -0.345575 31.638454 0.120930 0.000000 0.000000
-0.282743 32.817727 1.291637 0.000000 0.000000 -0.282743 32.817727 0.129164 0.000000 0.000000
-0.219911 33.770369 1.323362 0.000000 0.000000 -0.219911 33.770369 0.132336 0.000000 0.000000
-0.157080 34.505503 1.396454 0.000000 0.000000 -0.157080 34.505503 0.139645 0.000000 0.000000
-0.094248 35.431659 1.662548 0.000000 0.000000 -0.094248 35.431659 0.166255 0.000000 0.000000
-0.031416 35.615810 1.766523 0.000000 0.000000 -0.031416 35.615810 0.176652 0.000000 0.000000
0.031416 35.561946 1.799603 0.000000 0.000000 0.031416 35.561946 0.179960 0.000000 0.000000
0.094248 35.382089 1.374764 0.000000 0.000000 0.094248 35.382089 0.137476 0.000000 0.000000
0.157080 34.934827 1.659669 0.000000 0.000000 0.157080 34.934827 0.165967 0.000000 0.000000
0.219911 33.673460 1.317449 0.000000 0.000000 0.219911 33.673460 0.131745 0.000000 0.000000
0.282743 32.731563 1.191975 0.000000 0.000000 0.282743 32.731563 0.119197 0.000000 0.000000
0.345575 31.261855 1.199834 0.000000 0.000000 0.345575 31.261855 0.119983 0.000000 0.000000
0.408407 29.717377 1.339899 0.000000 0.000000 0.408407 29.717377 0.133990 0.000000 0.000000
0.471239 28.076620 1.303458 0.000001 0.000000 0.471239 28.076620 0.130346 0.000001 0.000000
0.534071 26.481097 1.281459 0.000002 0.000001 0.534071 26.481097 0.128146 0.000002 0.000000
0.596903 24.487358 1.251139 0.000003 0.000002 0.596903 24.487358 0.125114 0.000003 0.000000
0.659734 22.344251 1.233967 0.000008 0.000004 0.659734 22.344251 0.123397 0.000008 0.000000
0.722566 20.241543 1.251834 0.000018 0.000009 0.722566 20.241543 0.125183 0.000018 0.000001
0.785398 18.341869 1.241684 0.000039 0.000020 0.785398 18.341869 0.124168 0.000039 0.000002
0.848230 16.261582 1.281159 0.000091 0.000047 0.848230 16.261582 0.128116 0.000091 0.000005
0.911062 14.301801 1.294427 0.000199 0.000103 0.911062 14.301801 0.129443 0.000199 0.000010
0.973894 12.603788 1.270069 0.000394 0.000200 0.973894 12.603788 0.127007 0.000394 0.000020
1.036726 11.249601 1.273360 0.000678 0.000346 1.036726 11.249601 0.127336 0.000678 0.000035
1.099557 10.087886 1.280795 0.001079 0.000554 1.099557 10.087886 0.128080 0.001079 0.000055
1.162389 9.443303 1.266983 0.001398 0.000710 1.162389 9.443303 0.126698 0.001398 0.000071
1.225221 9.152799 1.255009 0.001570 0.000790 1.225221 9.152799 0.125501 0.001570 0.000079
1.288053 9.331937 1.256822 0.001462 0.000736 1.288053 9.331937 0.125682 0.001462 0.000074
1.350885 9.905546 1.251237 0.001161 0.000583 1.350885 9.905546 0.125124 0.001161 0.000058
1.413717 11.042050 1.263816 0.000736 0.000373 1.413717 11.042050 0.126382 0.000736 0.000037
1.476549 12.598167 1.275325 0.000395 0.000202 1.476549 12.598167 0.127533 0.000395 0.000020
1.539380 14.520167 1.280735 0.000183 0.000094 1.539380 14.520167 0.128074 0.000183 0.000009
1.602212 16.569783 1.295384 0.000080 0.000042 1.602212 16.569783 0.129538 0.000080 0.000004
1.665044 18.687390 1.309518 0.000034 0.000018 1.665044 18.687390 0.130952 0.000034 0.000002
1.727876 20.775408 1.356414 0.000015 0.000008 1.727876 20.775408 0.135641 0.000015 0.000001
1.790708 22.905200 1.308568 0.000006 0.000003 1.790708 22.905200 0.130857 0.000006 0.000000
1.853540 24.643852 1.425870 0.000003 0.000002 1.853540 24.643852 0.142587 0.000003 0.000000
1.916372 26.301740 1.362138 0.000002 0.000001 1.916372 26.301740 0.136214 0.000002 0.000000
1.979203 27.372071 1.391716 0.000001 0.000001 1.979203 27.372071 0.139172 0.000001 0.000000
2.042035 28.697726 1.504676 0.000001 0.000000 2.042035 28.697726 0.150468 0.000001 0.000000
2.104867 29.417901 1.369019 0.000000 0.000000 2.104867 29.417901 0.136902 0.000000 0.000000
2.167699 30.008351 1.409081 0.000000 0.000000 2.167699 30.008351 0.140908 0.000000 0.000000
2.230531 30.406016 1.240128 0.000000 0.000000 2.230531 30.406016 0.124013 0.000000 0.000000
2.293363 30.171275 1.179444 0.000000 0.000000 2.293363 30.171275 0.117944 0.000000 0.000000
2.356194 29.884646 1.219365 0.000000 0.000000 2.356194 29.884646 0.121937 0.000000 0.000000
2.419026 29.428153 1.177281 0.000000 0.000000 2.419026 29.428153 0.117728 0.000000 0.000000
2.481858 28.546982 1.351057 0.000001 0.000000 2.481858 28.546982 0.135106 0.000001 0.000000
2.544690 27.757520 1.238695 0.000001 0.000000 2.544690 27.757520 0.123869 0.000001 0.000000
2.607522 26.505787 1.308430 0.000001 0.000001 2.607522 26.505787 0.130843 0.000001 0.000000
2.670354 24.491866 1.039660 0.000003 0.000001 2.670354 24.491866 0.103966 0.000003 0.000000
2.733186 22.320664 0.927893 0.000008 0.000003 2.733186 22.320664 0.092789 0.000008 0.000000
2.796017 20.052723 0.894483 0.000020 0.000007 2.796017 20.052723 0.089448 0.000020 0.000001
2.858849 17.655650 0.893580 0.000052 0.000019 2.858849 17.655650 0.089358 0.000052 0.000002
2.921681 15.471590 0.930897 0.000125 0.000047 2.921681 15.471590 0.093090 0.000125 0.000005
2.984513 13.138167 0.837026 0.000318 0.000107 2.984513 13.138167 0.083703 0.000318 0.000011
3.047345 11.092386 0.674785 0.000722 0.000195 3.047345 11.092386 0.067479 0.000722 0.000020
3.110177 9.065722 0.521230 0.001626 0.000340 3.110177 9.065722 0.052123 0.001626 0.000034

13
src/error_analysis.rs
View File

@@ -43,16 +43,17 @@ pub fn run_bootstrap(cfg: &Config, ds: Dataset, P: &[f64], num_runs: usize) -> (
perform_wham(cfg, &rnd_weighted_dataset).unwrap().0 perform_wham(cfg, &rnd_weighted_dataset).unwrap().0
}).collect(); }).collect();
// Evaulate standard deviation of P per bin // Standard error (SE) of P per bin
let mut P_std = vec![0.0; ds.num_bins]; // SE = SD/sqrt(n)
let mut P_se = vec![0.0; ds.num_bins];
for bin in 0..ds.num_bins { for bin in 0..ds.num_bins {
let Ps = bootstrapped_Ps.iter().map(|window| window[bin]).collect::<Vec<f64>>(); 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, 1, num_runs)/(num_runs as f64).sqrt();
} }
// A_std by error propagation // SE of A by error propagation
let A_std = P_std.iter().zip(P.iter()).map(|(std,P)| ds.kT*1.0/P*std).collect(); let A_se = P_se.iter().zip(P.iter()).map(|(se,P)| ds.kT*1.0/P*se).collect();
(P_std, A_std) (P_se, A_se)
} }
#[cfg(tests)] #[cfg(tests)]