/*
 * This Source Code Form is subject to the terms of the Mozilla Public
 * License, v. 2.0. If a copy of the MPL was not distributed with this
 * file, You can obtain one at http://mozilla.org/MPL/2.0/.
 *
 * Copyright 2015-2018 Danny Robson <danny@nerdcruft.net>
 */

#pragma once

#include "volume.hpp"

#include "../ops.hpp"

#include "../../coord/fwd.hpp"
#include "../../extent.hpp"
#include "../../random.hpp"

#include <cstddef>
#include <random>


///////////////////////////////////////////////////////////////////////////////
namespace cruft::geom::sample {
    /// Approximate a poisson disc sampling through the "Mitchell's Best
    /// Candidate" algorithm.
    ///
    /// Try to keep adding a new point to a set. Each new point is the
    /// best of a set of candidates. The 'best' is the point that is
    /// furthest from all selected points.
    ///
    /// \return A vector of the computed points
    template <typename SamplerT, typename DistanceT, typename GeneratorT>
    auto
    poisson (SamplerT const &target,
             GeneratorT &&gen,
             DistanceT &&minimum_distance,
             size_t candidates_count)

    {
        using point_type = decltype (target.eval (gen));
        using value_type = typename point_type::value_type;

        std::vector<point_type> selected;
        std::vector<point_type> candidates;

        // prime the found elements list with an initial point we can
        // perform distance calculations on
        selected.push_back (target.eval (gen));

        // keep trying to add one more new point
        while (1) {
            // generate a group of candidate points
            candidates.clear ();
            std::generate_n (
                std::back_inserter (candidates),
                candidates_count,
                [&] (void) {
                    return target.eval (gen);
                }
            );

            // find the point whose minimum distance to the existing
            // points is the greatest (while also being greater than the
            // required minimum distance);
            auto best_distance2 = std::numeric_limits<value_type>::lowest ();
            size_t best_index = 0;

            for (size_t i = 0; i < candidates.size (); ++i) {
                auto const p = candidates[i];
                auto d2 = std::numeric_limits<value_type>::max ();

                // find the minimum distance from this candidate to the
                // selected points
                for (auto q: selected)
                    d2 = cruft::min (d2, cruft::distance2 (p, q));

                // record if it's the furthest away
                if (d2 > best_distance2 && d2 > cruft::pow (minimum_distance (p), 2)) {
                    best_distance2 = d2;
                    best_index = i;
                }
            }

            // if we didn't find a suitable point then we give up and
            // return the points we found, otherwise record the best point
            if (best_distance2 <= 0)
                break;

            selected.push_back (candidates[best_index]);
        }

        return selected;
    }


    /// A surface sampler specialisation for 2d extents.
    ///
    /// The actual work is handed off to the volume sampler, as it's
    /// equivalent in 2 dimensions.
    template <typename T>
    class surface<extent<2,T>> :
        public volume<extent<2,T>>
    { using volume<extent<2,T>>::volume; };
}