//! The [`ndarray-stats`] crate exposes statistical routines for `ArrayRef`,

//! the *n*-dimensional array data structure provided by [`ndarray`].

//!

//! Currently available routines include:

//! - [order statistics] (minimum, maximum, median, quantiles, etc.);

//! - [summary statistics] (mean, skewness, kurtosis, central moments, etc.)

//! - [partitioning];

//! - [correlation analysis] (covariance, pearson correlation);

//! - [measures from information theory] (entropy, KL divergence, etc.);

//! - [measures of deviation] (count equal, L1, L2 distances, mean squared err etc.)

//! - [histogram computation].

//!

//! Please feel free to contribute new functionality! A roadmap can be found [here].

//!

//! Our work is inspired by other existing statistical packages such as

//! [`NumPy`] (Python) and [`StatsBase.jl`] (Julia) - any contribution bringing us closer to

//! feature parity is more than welcome!

//!

//! [`ndarray-stats`]: https://github.com/rust-ndarray/ndarray-stats/

//! [`ndarray`]: https://github.com/rust-ndarray/ndarray

//! [order statistics]: trait.QuantileExt.html

//! [partitioning]: trait.Sort1dExt.html

//! [summary statistics]: trait.SummaryStatisticsExt.html

//! [correlation analysis]: trait.CorrelationExt.html

//! [measures of deviation]: trait.DeviationExt.html

//! [measures from information theory]: trait.EntropyExt.html

//! [histogram computation]: histogram/index.html

//! [here]: https://github.com/rust-ndarray/ndarray-stats/issues/1

//! [`NumPy`]: https://docs.scipy.org/doc/numpy-1.14.1/reference/routines.statistics.html

//! [`StatsBase.jl`]: https://juliastats.github.io/StatsBase.jl/latest/

pub use crate::correlation::CorrelationExt;

pub use crate::deviation::DeviationExt;

pub use crate::entropy::EntropyExt;

pub use crate::histogram::HistogramExt;

pub use crate::maybe_nan::{MaybeNan, MaybeNanExt};

pub use crate::quantile::{interpolate, Quantile1dExt, QuantileExt};

pub use crate::sort::Sort1dExt;

pub use crate::summary_statistics::SummaryStatisticsExt;

#[cfg(test)]

#[macro_use]

extern crate approx;

#[macro_use]

mod multi_input_error_macros {

    macro_rules! return_err_if_empty {

        ($arr:expr) => {

            if $arr.len() == 0 {

                return Err(MultiInputError::EmptyInput);

            }

        };

    }

    macro_rules! return_err_unless_same_shape {

        ($arr_a:expr, $arr_b:expr) => {

            use crate::errors::{MultiInputError, ShapeMismatch};

            if $arr_a.shape() != $arr_b.shape() {

                return Err(MultiInputError::ShapeMismatch(ShapeMismatch {

                    first_shape: $arr_a.shape().to_vec(),

                    second_shape: $arr_b.shape().to_vec(),

                })

                .into());

            }

        };

    }

}

#[macro_use]

mod private {

    /// This is a public type in a private module, so it can be included in

    /// public APIs, but other crates can't access it.

    pub struct PrivateMarker;

    /// Defines an associated function for a trait that is impossible for other

    /// crates to implement. This makes it possible to add new associated

    /// types/functions/consts/etc. to the trait without breaking changes.

    macro_rules! private_decl {

        () => {

            /// This method makes this trait impossible to implement outside of

            /// `ndarray-stats` so that we can freely add new methods, etc., to

            /// this trait without breaking changes.

            ///

            /// We don't anticipate any other crates needing to implement this

            /// trait, but if you do have such a use-case, please let us know.

            ///

            /// **Warning** This method is not considered part of the public

            /// API, and client code should not rely on it being present. It

            /// may be removed in a non-breaking release.

            fn __private__(&self, _: crate::private::PrivateMarker);

        };

    }

    /// Implements the associated function defined by `private_decl!`.

    macro_rules! private_impl {

        () => {

            fn __private__(&self, _: crate::private::PrivateMarker) {}

Unexecuted instantiation: <ndarray::ArrayRef<_, ndarray::dimension::dim::Dim<[usize; 2]>> as ndarray_stats::correlation::CorrelationExt<_>>::__private__

Unexecuted instantiation: <ndarray::ArrayRef<_, ndarray::dimension::dim::Dim<[usize; 1]>> as ndarray_stats::sort::Sort1dExt<_>>::__private__

Unexecuted instantiation: <ndarray::ArrayRef<_, _> as ndarray_stats::entropy::EntropyExt<_, _>>::__private__

Unexecuted instantiation: <ndarray::ArrayRef<_, _> as ndarray_stats::quantile::QuantileExt<_, _>>::__private__

Unexecuted instantiation: <ndarray::ArrayRef<_, ndarray::dimension::dim::Dim<[usize; 1]>> as ndarray_stats::quantile::Quantile1dExt<_>>::__private__

Unexecuted instantiation: <ndarray::ArrayRef<_, _> as ndarray_stats::deviation::DeviationExt<_, _>>::__private__

Unexecuted instantiation: <ndarray::ArrayRef<_, _> as ndarray_stats::maybe_nan::MaybeNanExt<_, _>>::__private__

Unexecuted instantiation: <ndarray::ArrayRef<_, _> as ndarray_stats::summary_statistics::SummaryStatisticsExt<_, _>>::__private__

Unexecuted instantiation: <ndarray_stats::quantile::interpolate::Higher as ndarray_stats::quantile::interpolate::Interpolate<_>>::__private__

Unexecuted instantiation: <ndarray_stats::quantile::interpolate::Nearest as ndarray_stats::quantile::interpolate::Interpolate<_>>::__private__

Unexecuted instantiation: <ndarray_stats::quantile::interpolate::Midpoint as ndarray_stats::quantile::interpolate::Interpolate<_>>::__private__

Unexecuted instantiation: <ndarray_stats::quantile::interpolate::Linear as ndarray_stats::quantile::interpolate::Interpolate<_>>::__private__

Unexecuted instantiation: <ndarray_stats::quantile::interpolate::Lower as ndarray_stats::quantile::interpolate::Interpolate<_>>::__private__

Unexecuted instantiation: <ndarray::ArrayRef<_, ndarray::dimension::dim::Dim<[usize; 2]>> as ndarray_stats::histogram::histograms::HistogramExt<_>>::__private__

        };

    }

}

mod correlation;

mod deviation;

mod entropy;

pub mod errors;

pub mod histogram;

mod maybe_nan;

mod quantile;

mod sort;

mod summary_statistics;