rssn 0.2.9

A comprehensive scientific computing library for Rust, aiming for feature parity with NumPy and SymPy.
Documentation 
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use crate::ffi_apis::common::BincodeBuffer;
use crate::ffi_apis::common::from_bincode_buffer;
use crate::ffi_apis::common::to_bincode_buffer;
use crate::symbolic::core::Expr;
use crate::symbolic::stats_inference::{
    self,
};

/// Performs a one-sample t-test.
/// Takes bincode-serialized `Vec<Expr>` (data) and `Expr` (target mean).
/// Returns a bincode-serialized `HypothesisTest` representing the test result.
#[unsafe(no_mangle)]
pub extern "C" fn rssn_bincode_one_sample_t_test(
    data_buf: BincodeBuffer,
    target_mean_buf: BincodeBuffer,
) -> BincodeBuffer {
    let data: Option<Vec<Expr>> = from_bincode_buffer(&data_buf);

    let target: Option<Expr> = from_bincode_buffer(&target_mean_buf);

    match (data, target) {
        | (Some(data), Some(target)) => {
            let result = stats_inference::one_sample_t_test_symbolic(&data, &target);

            to_bincode_buffer(&result)
        },
        | _ => BincodeBuffer::empty(),
    }
}

/// Performs a two-sample t-test.
///
/// Takes bincode-serialized `Vec<Expr>` (two data sets) and `Expr` (hypothesized difference in means).
/// Returns a bincode-serialized `HypothesisTest` representing the test result.
#[unsafe(no_mangle)]
pub extern "C" fn rssn_bincode_two_sample_t_test(
    data1_buf: BincodeBuffer,
    data2_buf: BincodeBuffer,
    mu_diff_buf: BincodeBuffer,
) -> BincodeBuffer {
    let data1: Option<Vec<Expr>> = from_bincode_buffer(&data1_buf);

    let data2: Option<Vec<Expr>> = from_bincode_buffer(&data2_buf);

    let diff: Option<Expr> = from_bincode_buffer(&mu_diff_buf);

    match (data1, data2, diff) {
        | (Some(d1), Some(d2), Some(diff)) => {
            let result = stats_inference::two_sample_t_test_symbolic(&d1, &d2, &diff);

            to_bincode_buffer(&result)
        },
        | _ => BincodeBuffer::empty(),
    }
}

/// Performs a z-test.
///
/// Takes bincode-serialized `Vec<Expr>` (data), `Expr` (target mean), and `Expr` (population standard deviation).
/// Returns a bincode-serialized `HypothesisTest` representing the test result.
#[unsafe(no_mangle)]
pub extern "C" fn rssn_bincode_z_test(
    data_buf: BincodeBuffer,
    target_mean_buf: BincodeBuffer,
    pop_std_dev_buf: BincodeBuffer,
) -> BincodeBuffer {
    let data: Option<Vec<Expr>> = from_bincode_buffer(&data_buf);

    let target: Option<Expr> = from_bincode_buffer(&target_mean_buf);

    let sigma: Option<Expr> = from_bincode_buffer(&pop_std_dev_buf);

    match (data, target, sigma) {
        | (Some(data), Some(target), Some(sigma)) => {
            let result = stats_inference::z_test_symbolic(&data, &target, &sigma);

            to_bincode_buffer(&result)
        },
        | _ => BincodeBuffer::empty(),
    }
}