/*
 * (C) Copyright 2013 ECMWF.
 *
 * This software is licensed under the terms of the Apache Licence Version 2.0
 * which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
 * In applying this licence, ECMWF does not waive the privileges and immunities
 * granted to it by virtue of its status as an intergovernmental organisation
 * nor does it submit to any jurisdiction.
 */

#include "atlas/interpolation/method/knn/KNearestNeighbours.h"

#include <limits>

#include "eckit/log/Plural.h"
#include "eckit/types/FloatCompare.h"

#include "atlas/array.h"
#include "atlas/functionspace/NodeColumns.h"
#include "atlas/grid.h"
#include "atlas/interpolation/method/MethodFactory.h"
#include "atlas/mesh/Nodes.h"
#include "atlas/mesh/actions/BuildXYZField.h"
#include "atlas/meshgenerator.h"
#include "atlas/parallel/mpi/mpi.h"
#include "atlas/runtime/Exception.h"
#include "atlas/runtime/Log.h"
#include "atlas/runtime/Trace.h"
#include "atlas/util/CoordinateEnums.h"

namespace atlas {
namespace interpolation {
namespace method {

namespace {

MethodBuilder<KNearestNeighbours> __builder("k-nearest-neighbours");

}  // namespace

KNearestNeighbours::KNearestNeighbours(const Method::Config& config): KNearestNeighboursBase(config) {
    k_ = 1;
    config.get("k-nearest-neighbours", k_);
    ATLAS_ASSERT(k_);
}

void KNearestNeighbours::do_setup(const Grid& source, const Grid& target, const Cache&) {
    if (mpi::size() > 1) {
        ATLAS_NOTIMPLEMENTED;
    }
    auto functionspace = [](const Grid& grid) -> FunctionSpace {
        Mesh mesh;
        if (StructuredGrid(grid)) {
            mesh = MeshGenerator("structured", util::Config("3d", true)).generate(grid);
        }
        else {
            mesh = MeshGenerator("delaunay").generate(grid);
        }
        return functionspace::NodeColumns(mesh);
    };

    do_setup(functionspace(source), functionspace(target));
}

void KNearestNeighbours::do_setup(const FunctionSpace& source, const FunctionSpace& target) {
    source_                        = source;
    target_                        = target;

    // build point-search tree
    buildPointSearchTree(source);

    array::ArrayView<double, 2> lonlat = array::make_view<double, 2>(target.lonlat());

    size_t inp_npts = source.size();
    size_t out_npts = target.size();

    // return early if no output points on this partition reserve is called on
    // the triplets but also during the sparseMatrix constructor. This won't
    // work for empty matrices
    if (out_npts == 0) {
        return;
    }

    // fill the sparse matrix
    std::vector<Triplet> weights_triplets;
    weights_triplets.reserve(out_npts * k_);
    {
        Trace timer(Here(), "atlas::interpolation::method::KNearestNeighbour::do_setup()");

        std::vector<double> weights;

        Log::debug() << "Computing interpolation weights for " << out_npts << " points." << std::endl;
        for (size_t ip = 0; ip < out_npts; ++ip) {
            if (ip && (ip % 1000 == 0)) {
                timer.pause();
                auto elapsed = timer.elapsed();
                timer.resume();
                auto rate = eckit::types::is_approximately_equal(elapsed, 0.) ? std::numeric_limits<double>::infinity()
                                                                              : (ip / elapsed);
                Log::debug() << eckit::BigNum(ip) << " (at " << size_t(rate) << " points/s)... after " << elapsed << " s" << std::endl;
            }

            // find the closest input points to the output point
            auto nn = pTree_.closestPoints(PointLonLat{lonlat(ip, size_t(LON)), lonlat(ip, size_t(LAT))}, k_);

            // calculate weights (individual and total, to normalise) using distance
            // squared
            const size_t npts = nn.size();
            ATLAS_ASSERT(npts);
            weights.resize(npts, 0);

            double sum = 0;
            for (size_t j = 0; j < npts; ++j) {
                const double d  = nn[j].distance();
                const double d2 = d * d;

                weights[j] = 1. / (1. + d2);
                sum += weights[j];
            }
            ATLAS_ASSERT(sum > 0);

            // insert weights into the matrix
            for (size_t j = 0; j < npts; ++j) {
                size_t jp = nn[j].payload();
                ATLAS_ASSERT(jp < inp_npts,
                             "point found which is not covered within the halo of the source function space");
                weights_triplets.emplace_back(ip, jp, weights[j] / sum);
            }
        }
    }

    // fill sparse matrix and return
    setMatrix(out_npts, inp_npts, weights_triplets);
}

}  // namespace method
}  // namespace interpolation
}  // namespace atlas
