[package]
name = "std-dev"
description = "Your Swiss Army knife for swiftly processing any amount of data. Implemented for industrial and educational purposes alike."
# strip=true in profile below,
# 1.56 for edition 2021
rust-version = "1.59"
version = "0.1.0"
edition = "2021"
license = "LGPL-3.0-or-later"
homepage = "https://github.com/Icelk/std-dev"
repository = "https://github.com/Icelk/std-dev"
exclude = ["data-samples"]

# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

[[bin]]
name = "std-dev"
path = "src/bin/main.rs"
required-features = ["bin"]

[dependencies]
rand = { version = "0.8", optional = true }

num-traits = { version = "0.2", default-features = false, features = ["std"], optional = true }

nalgebra = { version = "0.32", optional = true }

simba = { version = "0.8", optional = true, default-features = false }
approx = { version = "0.5", optional = true, default-features = false }
rug = { version = "1.15", optional = true }

colored = { version = "2.0", optional = true }
atty = { version = "0.2.14", optional = true }
clap = { version = "4.0", optional = true, features = ["cargo"] }
clap_autocomplete = { version = "0.4", optional = true }
poloto = { version = "18", optional = true, default-features = false }
hypermelon = "0.5.0"
rand_xorshift = { version = "0.3.0", optional = true }

[features]
default = ["bin", "pretty", "completion", "regression", "ols", "percentile-rand", "generic-impls", "binary_search_rng", "random_subset_regression"]

# Very commonly used features
base = ["percentile-rand", "binary_search_rng", "generic-impls"]

##
# Library features (also applies to binary)
##

regression = []

# Enables the random support of the binary search estimator (recommended)
binary_search_rng = ["rand", "rand_xorshift"]

# Enables speedier regression by only considering random subsets of data
random_subset_regression = ["rand"]

# Enables the Ordinary Least Squares estimator.
#
# This also allows Theil-Sen polynomial estimator with degrees > 2
# and polynomial regression in the `best_fit` functions.
ols = ["nalgebra", "regression"]

# Arbitrary precision for regression.
#
# Increases max degree of polynomial (with good results).
# Without this feature, it's basically limited to 10 degrees.
arbitrary-precision = ["rug", "simba", "regression", "num-traits", "num-traits/std", "approx"]

# Enables the recommended pivot_fn for `percentile::*` functions.
percentile-rand = ["rand"]

# Allows for generic implementation of traits from this crate.
generic-impls = ["num-traits"]


##
# Binary features
##

bin = ["clap", "poloto", "regression", "binary_search_rng", "ols"]

# Prettier bin output
pretty = ["bin", "atty", "colored"]

# Shell completion output
completion = ["clap_autocomplete"]

[target.'cfg(all(target_arch = "wasm32", target_os = "unknown"))'.dependencies]
getrandom = { version = "0.2", features = ["js"] }

# Build with `--profile production`
[profile.production]
inherits = "release"
lto = true
strip = true
opt-level = "s"

[package.metadata.docs.rs]
all-features = true
rustdoc-args = ["--cfg", "docsrs"]