mod c;
mod d;
mod p;
mod q;
mod r;

use strafe_type::{LogProbability64, Natural64, Probability64, Real64};

pub use self::{c::*, d::*, p::*, q::*, r::*};
use crate::traits::{Distribution, RNG};

/// # Distribution of the Wilcoxon Rank Sum Statistic
///
/// ## Description
///
/// Density, distribution function, quantile function and random generation for the distribution of
/// the Wilcoxon rank sum statistic obtained from samples with size m and n, respectively.
///
/// ## Arguments
///
/// * m, n: numbers of observations in the first and second sample, respectively. Can be vectors of positive integers.
///
/// ## Details
///
/// This distribution is obtained as follows. Let x and y be two random, independent samples of
/// size m and n. Then the Wilcoxon rank sum statistic is the number of all pairs (x\[i\], y\[j\])
/// for which y\[j\] is not greater than x\[i\]. This statistic takes values between 0 and m * n,
/// and its mean and variance are $ m * n / 2 $ and $ m * n * (m + n + 1) / 12 $, respectively.
///
/// If any of the first three arguments are vectors, the recycling rule is used to do the
/// calculations for all combinations of the three up to the length of the longest vector.
///
/// ## Density Plot
///
/// ```rust
/// # use r2rs_base::traits::StatisticalSlice;
/// # use r2rs_nmath::{distribution::WilcoxBuilder, traits::Distribution};
/// # use strafe_plot::prelude::{IntoDrawingArea, Line, Plot, PlotOptions, SVGBackend, BLACK};
/// # use strafe_type::FloatConstraint;
/// let wilcox = WilcoxBuilder::new().build();
/// let x = <[f64]>::sequence_by(-1.0, 2.0, 0.001);
/// let y = x
///     .iter()
///     .map(|x| wilcox.density(x).unwrap())
///     .collect::<Vec<_>>();
///
/// let root = SVGBackend::new("density.svg", (1024, 768)).into_drawing_area();
/// Plot::new()
///     .with_options(PlotOptions {
///         x_axis_label: "x".to_string(),
///         y_axis_label: "density".to_string(),
///         ..Default::default()
///     })
///     .with_plottable(Line {
///         x,
///         y,
///         color: BLACK,
///         ..Default::default()
///     })
///     .plot(&root)
///     .unwrap();
/// # use std::fs::rename;
/// #     drop(root);
/// #     rename(
/// #             format!("density.svg"),
/// #             format!("src/distribution/wilcox/doctest_out/density.svg"),
/// #     )
/// #     .unwrap();
/// ```
#[cfg_attr(feature = "doc_outputs", cfg_attr(all(), doc = embed_doc_image::embed_image!("density", "src/distribution/wilcox/doctest_out/density.svg")))]
#[cfg_attr(feature = "doc_outputs", cfg_attr(all(), doc = "![Density][density]"))]
///
/// ## Warning
///
/// These functions can use large amounts of memory and stack (and even crash R if the stack limit
/// is exceeded and stack-checking is not in place) if one sample is large (several thousands or more).
///
/// ## Note
///
/// S-PLUS uses a different (but equivalent) definition of the Wilcoxon statistic: see wilcox.test
/// for details.
///
/// ## Author(s)
///
/// Kurt Hornik
///
/// ## Source
///
/// These ("d","p","q") are calculated via recursion, based on cwilcox(k, m, n), the number of
/// choices with statistic k from samples of size m and n, which is itself calculated recursively
/// and the results cached. Then dwilcox and pwilcox sum appropriate values of cwilcox, and qwilcox
/// is based on inversion.
///
/// rwilcox generates a random permutation of ranks and evaluates the statistic. Note that it is
/// based on the same C code as sample(), and hence is determined by .Random.seed, notably from
/// RNGkind(sample.kind = ..) which changed with R version 3.6.0.
///
/// ## See Also
///
/// wilcox.test to calculate the statistic from data, find p values and so on.
///
/// Distributions for standard distributions, including dsignrank for the distribution of the
/// one-sample Wilcoxon signed rank statistic.
///
/// ## Examples
///
/// ```r
/// require(graphics)
///
/// x <- -1:(4*6 + 1)
/// fx <- dwilcox(x, 4, 6)
/// Fx <- pwilcox(x, 4, 6)
///
/// layout(rbind(1,2), widths = 1, heights = c(3,2))
/// plot(x, fx, type = "h", col = "violet",
///      main =  "Probabilities (density) of Wilcoxon-Statist.(n=6, m=4)")
/// plot(x, Fx, type = "s", col = "blue",
///      main =  "Distribution of Wilcoxon-Statist.(n=6, m=4)")
/// abline(h = 0:1, col = "gray20", lty = 2)
/// layout(1) # set back
///
/// N <- 200
/// hist(U <- rwilcox(N, m = 4,n = 6), breaks = 0:25 - 1/2,
///      border = "red", col = "pink", sub = paste("N =",N))
/// mtext("N * f(x),  f() = true \"density\"", side = 3, col = "blue")
///  lines(x, N*fx, type = "h", col = "blue", lwd = 2)
/// points(x, N*fx, cex = 2)
///
/// ## Better is a Quantile-Quantile Plot
/// qqplot(U, qw <- qwilcox((1:N - 1/2)/N, m = 4, n = 6),
///        main = paste("Q-Q-Plot of empirical and theoretical quantiles",
///                      "Wilcoxon Statistic,  (m=4, n=6)", sep = "\n"))
/// n <- as.numeric(names(print(tU <- table(U))))
/// text(n+.2, n+.5, labels = tU, col = "red")
/// ```
pub struct Wilcox {
    m: Natural64,
    n: Natural64,
}

impl Distribution for Wilcox {
    fn density<R: Into<Real64>>(&self, x: R) -> Real64 {
        dwilcox(x, self.m, self.n, false)
    }

    fn log_density<R: Into<Real64>>(&self, x: R) -> Real64 {
        dwilcox(x, self.m, self.n, true)
    }

    fn probability<R: Into<Real64>>(&self, q: R, lower_tail: bool) -> Probability64 {
        pwilcox(q, self.m, self.n, lower_tail)
    }

    fn log_probability<R: Into<Real64>>(&self, q: R, lower_tail: bool) -> LogProbability64 {
        log_pwilcox(q, self.m, self.n, lower_tail)
    }

    fn quantile<P: Into<Probability64>>(&self, p: P, lower_tail: bool) -> Real64 {
        qwilcox(p, self.m, self.n, lower_tail)
    }

    fn log_quantile<LP: Into<LogProbability64>>(&self, p: LP, lower_tail: bool) -> Real64 {
        log_qwilcox(p, self.m, self.n, lower_tail)
    }

    fn random_sample<R: RNG>(&self, rng: &mut R) -> Real64 {
        rwilcox(self.m, self.n, rng)
    }
}

pub struct WilcoxBuilder {
    m: Option<Natural64>,
    n: Option<Natural64>,
}

impl WilcoxBuilder {
    pub fn new() -> Self {
        Self { m: None, n: None }
    }

    pub fn with_m<N: Into<Natural64>>(&mut self, m: N) -> &mut Self {
        self.m = Some(m.into());
        self
    }

    pub fn with_n<N: Into<Natural64>>(&mut self, n: N) -> &mut Self {
        self.n = Some(n.into());
        self
    }

    pub fn build(&self) -> Wilcox {
        let m = self.m.unwrap_or(1.0.into());
        let n = self.n.unwrap_or(1.0.into());

        Wilcox { m, n }
    }
}

#[cfg(test)]
mod tests;

#[cfg(all(test, feature = "enable_proptest"))]
mod proptests;

#[cfg(all(test, feature = "enable_covtest"))]
mod covtests;