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

2
Cargo.toml
View File

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

200
example/1d_cyclic/wham.out
View File

@@ -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
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-1.916372 2.223125 0.405294 0.025266 0.004105
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-1.790708 1.793841 0.394974 0.030011 0.004752
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-1.476549 0.000000 0.338143 0.061604 0.008351
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-1.288053 2.391838 0.407306 0.023614 0.003856
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-1.036726 9.946594 0.489990 0.001142 0.000224
-0.973894 12.359408 0.544847 0.000434 0.000095
-0.911062 14.954655 0.622373 0.000153 0.000038
-0.848230 17.742242 0.747233 0.000050 0.000015
-0.785398 20.555785 0.921773 0.000016 0.000006
-0.722566 22.811160 1.179343 0.000007 0.000003
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0.031416 35.561946 1.799603 0.000000 0.000000
0.094248 35.382089 1.374764 0.000000 0.000000
0.157080 34.934827 1.659669 0.000000 0.000000
0.219911 33.673460 1.317449 0.000000 0.000000
0.282743 32.731563 1.191975 0.000000 0.000000
0.345575 31.261855 1.199834 0.000000 0.000000
0.408407 29.717377 1.339899 0.000000 0.000000
0.471239 28.076620 1.303458 0.000001 0.000000
0.534071 26.481097 1.281459 0.000002 0.000001
0.596903 24.487358 1.251139 0.000003 0.000002
0.659734 22.344251 1.233967 0.000008 0.000004
0.722566 20.241543 1.251834 0.000018 0.000009
0.785398 18.341869 1.241684 0.000039 0.000020
0.848230 16.261582 1.281159 0.000091 0.000047
0.911062 14.301801 1.294427 0.000199 0.000103
0.973894 12.603788 1.270069 0.000394 0.000200
1.036726 11.249601 1.273360 0.000678 0.000346
1.099557 10.087886 1.280795 0.001079 0.000554
1.162389 9.443303 1.266983 0.001398 0.000710
1.225221 9.152799 1.255009 0.001570 0.000790
1.288053 9.331937 1.256822 0.001462 0.000736
1.350885 9.905546 1.251237 0.001161 0.000583
1.413717 11.042050 1.263816 0.000736 0.000373
1.476549 12.598167 1.275325 0.000395 0.000202
1.539380 14.520167 1.280735 0.000183 0.000094
1.602212 16.569783 1.295384 0.000080 0.000042
1.665044 18.687390 1.309518 0.000034 0.000018
1.727876 20.775408 1.356414 0.000015 0.000008
1.790708 22.905200 1.308568 0.000006 0.000003
1.853540 24.643852 1.425870 0.000003 0.000002
1.916372 26.301740 1.362138 0.000002 0.000001
1.979203 27.372071 1.391716 0.000001 0.000001
2.042035 28.697726 1.504676 0.000001 0.000000
2.104867 29.417901 1.369019 0.000000 0.000000
2.167699 30.008351 1.409081 0.000000 0.000000
2.230531 30.406016 1.240128 0.000000 0.000000
2.293363 30.171275 1.179444 0.000000 0.000000
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2.481858 28.546982 1.351057 0.000001 0.000000
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2.607522 26.505787 1.308430 0.000001 0.000001
2.670354 24.491866 1.039660 0.000003 0.000001
2.733186 22.320664 0.927893 0.000008 0.000003
2.796017 20.052723 0.894483 0.000020 0.000007
2.858849 17.655650 0.893580 0.000052 0.000019
2.921681 15.471590 0.930897 0.000125 0.000047
2.984513 13.138167 0.837026 0.000318 0.000107
3.047345 11.092386 0.674785 0.000722 0.000195
3.110177 9.065722 0.521230 0.001626 0.000340
-3.110177 7.158102 0.047604 0.003494 0.000067
-3.047345 5.365727 0.047074 0.007168 0.000135
-2.984513 3.873190 0.043312 0.013039 0.000226
-2.921681 2.953162 0.044141 0.018855 0.000334
-2.858849 1.949554 0.044355 0.028195 0.000501
-2.796017 1.391747 0.040740 0.035261 0.000576
-2.733186 1.128270 0.038079 0.039189 0.000598
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-2.419026 1.463048 0.039491 0.034267 0.000543
-2.356194 1.990616 0.033514 0.027734 0.000373
-2.293363 2.190692 0.019650 0.025597 0.000202
-2.230531 2.553036 0.017705 0.022136 0.000157
-2.167699 2.572522 0.020880 0.021964 0.000184
-2.104867 2.472360 0.016893 0.022863 0.000155
-2.042035 2.517562 0.016677 0.022453 0.000150
-1.979203 2.469778 0.018231 0.022887 0.000167
-1.916372 2.223125 0.040529 0.025266 0.000411
-1.853540 2.080157 0.034038 0.026756 0.000365
-1.790708 1.793841 0.039497 0.030011 0.000475
-1.727876 1.458784 0.033596 0.034326 0.000462
-1.665044 0.914441 0.033672 0.042697 0.000576
-1.602212 0.303813 0.032070 0.054540 0.000701
-1.539380 0.268335 0.033050 0.055321 0.000733
-1.476549 0.000000 0.033814 0.061604 0.000835
-1.413717 0.537061 0.036922 0.049671 0.000735
-1.350885 1.439940 0.038101 0.034586 0.000528
-1.288053 2.391838 0.040731 0.023614 0.000386
-1.225221 3.779470 0.041363 0.013538 0.000224
-1.162389 5.685555 0.043248 0.006305 0.000109
-1.099557 7.661896 0.044699 0.002855 0.000051
-1.036726 9.946594 0.048999 0.001142 0.000022
-0.973894 12.359408 0.054485 0.000434 0.000009
-0.911062 14.954655 0.062237 0.000153 0.000004
-0.848230 17.742242 0.074723 0.000050 0.000002
-0.785398 20.555785 0.092177 0.000016 0.000001
-0.722566 22.811160 0.117934 0.000007 0.000000
-0.659734 25.178094 0.148438 0.000003 0.000000
-0.596903 26.442288 0.156506 0.000002 0.000000
-0.534071 27.897565 0.141823 0.000001 0.000000
-0.471239 29.062473 0.124674 0.000001 0.000000
-0.408407 30.384521 0.123166 0.000000 0.000000
-0.345575 31.638454 0.120930 0.000000 0.000000
-0.282743 32.817727 0.129164 0.000000 0.000000
-0.219911 33.770369 0.132336 0.000000 0.000000
-0.157080 34.505503 0.139645 0.000000 0.000000
-0.094248 35.431659 0.166255 0.000000 0.000000
-0.031416 35.615810 0.176652 0.000000 0.000000
0.031416 35.561946 0.179960 0.000000 0.000000
0.094248 35.382089 0.137476 0.000000 0.000000
0.157080 34.934827 0.165967 0.000000 0.000000
0.219911 33.673460 0.131745 0.000000 0.000000
0.282743 32.731563 0.119197 0.000000 0.000000
0.345575 31.261855 0.119983 0.000000 0.000000
0.408407 29.717377 0.133990 0.000000 0.000000
0.471239 28.076620 0.130346 0.000001 0.000000
0.534071 26.481097 0.128146 0.000002 0.000000
0.596903 24.487358 0.125114 0.000003 0.000000
0.659734 22.344251 0.123397 0.000008 0.000000
0.722566 20.241543 0.125183 0.000018 0.000001
0.785398 18.341869 0.124168 0.000039 0.000002
0.848230 16.261582 0.128116 0.000091 0.000005
0.911062 14.301801 0.129443 0.000199 0.000010
0.973894 12.603788 0.127007 0.000394 0.000020
1.036726 11.249601 0.127336 0.000678 0.000035
1.099557 10.087886 0.128080 0.001079 0.000055
1.162389 9.443303 0.126698 0.001398 0.000071
1.225221 9.152799 0.125501 0.001570 0.000079
1.288053 9.331937 0.125682 0.001462 0.000074
1.350885 9.905546 0.125124 0.001161 0.000058
1.413717 11.042050 0.126382 0.000736 0.000037
1.476549 12.598167 0.127533 0.000395 0.000020
1.539380 14.520167 0.128074 0.000183 0.000009
1.602212 16.569783 0.129538 0.000080 0.000004
1.665044 18.687390 0.130952 0.000034 0.000002
1.727876 20.775408 0.135641 0.000015 0.000001
1.790708 22.905200 0.130857 0.000006 0.000000
1.853540 24.643852 0.142587 0.000003 0.000000
1.916372 26.301740 0.136214 0.000002 0.000000
1.979203 27.372071 0.139172 0.000001 0.000000
2.042035 28.697726 0.150468 0.000001 0.000000
2.104867 29.417901 0.136902 0.000000 0.000000
2.167699 30.008351 0.140908 0.000000 0.000000
2.230531 30.406016 0.124013 0.000000 0.000000
2.293363 30.171275 0.117944 0.000000 0.000000
2.356194 29.884646 0.121937 0.000000 0.000000
2.419026 29.428153 0.117728 0.000000 0.000000
2.481858 28.546982 0.135106 0.000001 0.000000
2.544690 27.757520 0.123869 0.000001 0.000000
2.607522 26.505787 0.130843 0.000001 0.000000
2.670354 24.491866 0.103966 0.000003 0.000000
2.733186 22.320664 0.092789 0.000008 0.000000
2.796017 20.052723 0.089448 0.000020 0.000001
2.858849 17.655650 0.089358 0.000052 0.000002
2.921681 15.471590 0.093090 0.000125 0.000005
2.984513 13.138167 0.083703 0.000318 0.000011
3.047345 11.092386 0.067479 0.000722 0.000020
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
}).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, 1, num_runs)/(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 by error propagation
let A_se = P_se.iter().zip(P.iter()).map(|(se,P)| ds.kT*1.0/P*se).collect();
(P_se, A_se)
}
#[cfg(tests)]