Crates.io | sampling |
lib.rs | sampling |
version | 0.1.1 |
source | src |
created_at | 2021-09-29 13:31:15.899427 |
updated_at | 2021-09-30 09:22:13.385537 |
description | Large-deviation Algorithms like Wang-Landau, Entropic sampling, Replica-Exchange Wang-Landau, Heatmaps, Histograms and bootstrap resampling. This is intended for scientific simulations |
homepage | https://www.yfeld.de |
repository | https://github.com/Pardoxa/sampling |
max_upload_size | |
id | 458094 |
size | 556,198 |
Minimal Rust version: 1.55.0
Large-deviation sampling methods (Wang-Landau, Replica-exchange Wang-Landau, entropic sampling, Markov-chains), bootstrap resampling, histograms, heat maps and more. It also allows you to create gnuplot scripts for your heatmaps.
The Documentation of the working branch can be found here.
Add this to your Cargo.toml
:
[dependencies]
sampling = "0.1.1"
# for feature "serde_support" (enabled by default) also use
serde = { version = "1.0", features = ["derive"] }
Other features:
sweep_time_optimization
: Enables minor optimizations, which might
or might not benefit you for your large-deviation simulation.
This is disabled by default, as most users will not benefit from it.
sweep_stats
Also activates feature sweep_time_optimization
. This is intended for
testing purposes. You get additional information on how long
the walkers of Rewl
take.
replica_exchange
: enabled by default. Use this, if you want to
use any of the replica exchange types or methods.
If you want to minimize build time and space requirements upon building, you can disable default features and only enable what you need.
[dependencies]
sampling = { version = "0.1.1", default-features = false }
No warranties whatsoever, but since I am writing this library for my own scientific simulations, I do my best to avoid errors.
You can learn more about me and my research on my homepage.
If you notice any bugs, or want to request new features: do not hesitate to open a new issue on the repository.
Licensed under either of
at your option.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.