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https://github.com/dnlbauer/WHAM.git
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remove unneccessary parameter in bootstrapping function
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@@ -32,7 +32,7 @@ fn generate_random_weighted_dataset(ds: Dataset, rng: &mut StdRng) -> Dataset {
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// Perform bootstrap error analysis. This runs the WHAM analysis num_runs times on random weighted
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// Perform bootstrap error analysis. This runs the WHAM analysis num_runs times on random weighted
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// datasets. The standard deviation is calculated on the bootstrapped probabilities of each bin. The
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// datasets. The standard deviation is calculated on the bootstrapped probabilities of each bin. The
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// standard deviation of the free eneergy is then deduced by error propagation (A_std = kT*1/P*P_std)
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// standard deviation of the free eneergy is then deduced by error propagation (A_std = kT*1/P*P_std)
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pub fn run_bootstrap(cfg: &Config, ds: Dataset, P: &[f64], num_runs: usize) -> (Vec<f64>,Vec<f64>) {
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pub fn run_bootstrap(cfg: &Config, ds: Dataset, num_runs: usize) -> (Vec<f64>,Vec<f64>) {
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// seed the rng
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// seed the rng
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let mut rng: StdRng = SeedableRng::seed_from_u64(cfg.bootstrap_seed);
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let mut rng: StdRng = SeedableRng::seed_from_u64(cfg.bootstrap_seed);
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@@ -189,7 +189,7 @@ pub fn run(cfg: &Config) -> Result<()>{
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let (P_std, free_energy_std) = if cfg.bootstrap > 0 {
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let (P_std, free_energy_std) = if cfg.bootstrap > 0 {
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println!("Bootstrapping..");
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println!("Bootstrapping..");
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error_analysis::run_bootstrap(&cfg, dataset.clone(), &P, cfg.bootstrap)
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error_analysis::run_bootstrap(&cfg, dataset.clone(), cfg.bootstrap)
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} else {
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} else {
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(vec![0.0; P.len()], vec![0.0; P.len()])
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(vec![0.0; P.len()], vec![0.0; P.len()])
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};
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};
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