// graph-tool -- a general graph modification and manipulation thingy
//
// Copyright (C) 2006-2022 Tiago de Paula Peixoto <tiago@skewed.de>
//
// This program is free software; you can redistribute it and/or modify it under
// the terms of the GNU Lesser General Public License as published by the Free
// Software Foundation; either version 3 of the License, or (at your option) any
// later version.
//
// This program is distributed in the hope that it will be useful, but WITHOUT
// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
// details.
//
// You should have received a copy of the GNU Lesser General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.

#ifndef GRAPH_DISTANCE_SAMPLED_HH
#define GRAPH_DISTANCE_SAMPLED_HH

#include <boost/graph/breadth_first_search.hpp>
#include <boost/graph/dijkstra_shortest_paths.hpp>

#include <boost/python/object.hpp>
#include <boost/python/list.hpp>

#include "histogram.hh"
#include "numpy_bind.hh"
#include "hash_map_wrap.hh"

namespace graph_tool
{
using namespace std;
using namespace boost;

// retrieves the sampled vertex-vertex distance histogram

struct no_weightS {};

template <class Map>
struct get_val_type
{
    typedef typename property_traits<Map>::value_type type;
};

template <>
struct get_val_type<no_weightS>
{
    typedef size_t type;
};

struct get_sampled_distance_histogram
{

    template <class Graph, class VertexIndex, class WeightMap, class RNG>
    void operator()(const Graph& g, VertexIndex vertex_index, WeightMap weights,
                    size_t n_samples, const vector<long double>& obins,
                    python::object& phist, RNG& rng) const
    {
        typedef typename graph_traits<Graph>::vertex_descriptor vertex_t;

        // select get_vertex_dists based on the existence of weights
        typedef typename mpl::if_<std::is_same<WeightMap, no_weightS>,
                                  get_dists_bfs,
                                  get_dists_djk>::type get_vertex_dists_t;


        // distance type
        typedef typename get_val_type<WeightMap>::type val_type;
        typedef Histogram<val_type, size_t, 1> hist_t;

        std::array<vector<val_type>,1> bins;
        bins[0].resize(obins.size());
        for (size_t i = 0; i < obins.size(); ++i)
            bins[0][i] = obins[i];

        hist_t hist(bins);
        SharedHistogram<hist_t> s_hist(hist);

        vector<vertex_t> sources;
        sources.reserve(num_vertices(g));
        for (auto v : vertices_range(g))
            sources.push_back(v);
        n_samples = min(n_samples, sources.size());

        typename hist_t::point_t point;
        get_vertex_dists_t get_vertex_dists;

        #pragma omp parallel for default(shared) private(point) \
            firstprivate(s_hist) schedule(runtime) \
            if (num_vertices(g) * n_samples > OPENMP_MIN_THRESH)
        for (size_t i = 0; i < n_samples; ++i)
        {
            vertex_t v;
            #pragma omp critical
            {
                uniform_int_distribution<size_t> randint(0, sources.size()-1);
                size_t j = randint(rng);
                v = sources[j];
                swap(sources[j], sources.back());
                sources.pop_back();
            }

            unchecked_vector_property_map<val_type,VertexIndex>
                dist_map(vertex_index, num_vertices(g));

            for (auto u : vertices_range(g))
                dist_map[u] = numeric_limits<val_type>::max();

            dist_map[v] = 0;
            get_vertex_dists(g, v, vertex_index, dist_map, weights);

            for (auto v2 : vertices_range(g))
            {
                if (v2 != v && dist_map[v2] != numeric_limits<val_type>::max())
                {
                    point[0] = dist_map[v2];
                    s_hist.put_value(point);
                }
            }
        }
        s_hist.gather();

        python::list ret;
        ret.append(wrap_multi_array_owned(hist.get_array()));
        ret.append(wrap_vector_owned<val_type>(hist.get_bins()[0]));
        phist = ret;
    }

    // weighted version. Use dijkstra_shortest_paths()
    struct get_dists_djk
    {
        template <class Graph, class Vertex, class VertexIndex,
                  class DistanceMap, class WeightMap>
        void operator()(const Graph& g, Vertex s, VertexIndex& vertex_index,
                        DistanceMap& dist_map, WeightMap& weights) const
        {
            dijkstra_shortest_paths(g, s, vertex_index_map(vertex_index).
                                    weight_map(weights).distance_map(dist_map));
        }
    };

    // unweighted version. Use BFS.
    struct get_dists_bfs
    {
        template <class Graph, class Vertex, class VertexIndex,
                  class DistanceMap>
        void operator()(const Graph& g, Vertex s, VertexIndex& vertex_index,
                        DistanceMap& dist_map, no_weightS) const
        {
            typedef typename vprop_map_t<default_color_type>::type::unchecked_t vmap_t;
            vmap_t color_map(vertex_index, num_vertices(g));
            parallel_vertex_loop
                (g,
                 [&](auto v)
                 {
                     color_map[v] = color_traits<default_color_type>::white();
                 });
            breadth_first_visit(g, s,
                                visitor(make_bfs_visitor
                                        (record_distances(dist_map,
                                                          on_tree_edge()))).
                                color_map(color_map));
        }
    };
};

} // boost namespace

#endif // GRAPH_DISTANCE_SAMPLED_HH
