/******************************************************************************
* Copyright (c) 2016, Bradley J Chambers (brad.chambers@gmail.com)
*
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*       with the distribution.
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*       names of its contributors may be used to endorse or promote
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*       written permission.
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#include "EigenvaluesFilter.hpp"

#include <pdal/KDIndex.hpp>
#include <pdal/util/ProgramArgs.hpp>
#include <pdal/private/MathUtils.hpp>

#include <Eigen/Dense>

#include <string>

namespace pdal
{

using namespace Dimension;
using namespace Eigen;

static StaticPluginInfo const s_info
{
    "filters.eigenvalues",
    "Returns the eigenvalues for a given point, based on its k-nearest neighbors.",
    "http://pdal.io/stages/filters.eigenvalues.html"
};

CREATE_STATIC_STAGE(EigenvaluesFilter, s_info)

struct EigenvalueArgs
{
    int m_knn;
    bool m_normalize;
    size_t m_stride;
    double m_radius;
    Arg* m_radiusArg;
    int m_minK;
};

EigenvaluesFilter::EigenvaluesFilter() : m_args(new EigenvalueArgs) {}

std::string EigenvaluesFilter::getName() const
{
    return s_info.name;
}

void EigenvaluesFilter::addArgs(ProgramArgs& args)
{
    args.add("knn", "k-Nearest neighbors", m_args->m_knn, 8);
    args.add("normalize", "Normalize eigenvalues?", m_args->m_normalize, false);
    args.add("stride", "Compute features on strided neighbors",
             m_args->m_stride, size_t(1));
    m_args->m_radiusArg = &args.add(
        "radius", "Radius for nearest neighbor search", m_args->m_radius);
    args.add("min_k", "Minimum number of neighbors in radius", m_args->m_minK,
             3);
}

void EigenvaluesFilter::addDimensions(PointLayoutPtr layout)
{
    layout->registerDim(Id::Eigenvalue0);
    layout->registerDim(Id::Eigenvalue1);
    layout->registerDim(Id::Eigenvalue2);
}

void EigenvaluesFilter::prepared(PointTableRef table)
{
    if (m_args->m_radiusArg->set())
    {
        log()->get(LogLevel::Warning)
            << "Radius has been set. Ignoring knn and stride values."
            << std::endl;
        if (m_args->m_radius <= 0.0)
            log()->get(LogLevel::Error)
                << "Radius must be greater than 0." << std::endl;
    }
    else
    {
        log()->get(LogLevel::Warning) << "No radius specified. Proceeding with "
                                         "knn and stride, but ignoring min_k."
                                      << std::endl;
    }
}

void EigenvaluesFilter::filter(PointView& view)
{
    const KD3Index& kdi = view.build3dIndex();

    for (PointRef p : view)
    {
        // find neighbors, either by radius or k nearest neighbors
        PointIdList ids;
        if (m_args->m_radiusArg->set())
        {
            ids = kdi.radius(p, m_args->m_radius);

            // if insufficient number of neighbors, eigen solver will fail
            // anyway, it may be okay to silently return without setting any of
            // the computed features?
            if (ids.size() < (size_t)m_args->m_minK) {
                log()->get(LogLevel::Info)
                    << "Skipping point " << p.pointId() << ". Found " << ids.size()
                    << " neighbors but required " << m_args->m_minK << ".\n";
                continue;
            }
        }
        else
        {
            ids = kdi.neighbors(p, m_args->m_knn + 1, m_args->m_stride);
        }

        // compute covariance of the neighborhood
        Matrix3d B = math::computeCovariance(view, ids);

        // Check if the covariance matrix is all zeros
        if (B.isZero())
        {
            log()->get(LogLevel::Info)
                << "Skipping point " << p.pointId()
                << ". Covariance matrix is all zeros. This suggests a large "
                   "number of redundant points. Consider using filters.sample "
                   "with a small radius to remove redundant points.\n";
            continue;
        }

        // perform the eigen decomposition
        Eigen::SelfAdjointEigenSolver<Matrix3d> solver(B);
        if (solver.info() != Eigen::Success)
            throwError("Cannot perform eigen decomposition.");
        Vector3d ev = solver.eigenvalues();

        if (m_args->m_normalize)
        {
            double sum = ev[0] + ev[1] + ev[2];
            ev /= sum;
        }

        p.setField(Id::Eigenvalue0, ev[0]);
        p.setField(Id::Eigenvalue1, ev[1]);
        p.setField(Id::Eigenvalue2, ev[2]);
    }
}

} // namespace pdal
