/* ---------------------------------------------------------------------
 *
 * Copyright (C) 2018 - 2022 by the deal.II authors
 *
 * This file is part of the deal.II library.
 *
 * The deal.II library is free software; you can use it, 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 2.1 of the License, or (at your option) any later version.
 * The full text of the license can be found in the file LICENSE.md at
 * the top level directory of deal.II.
 *
 * ---------------------------------------------------------------------
 *
 * Authors: Thomas C. Clevenger, Clemson University
 *          Timo Heister, Clemson University and University of Utah
 */

// @sect3{Include files}

// Typical files needed for standard deal.II:
#include <deal.II/base/tensor_function.h>
#include <deal.II/base/quadrature_lib.h>
#include <deal.II/base/function.h>
#include <deal.II/base/timer.h>
#include <deal.II/base/parameter_handler.h>

#include <deal.II/lac/affine_constraints.h>
#include <deal.II/lac/vector.h>
#include <deal.II/lac/full_matrix.h>
#include <deal.II/lac/sparse_matrix.h>
#include <deal.II/lac/dynamic_sparsity_pattern.h>
#include <deal.II/lac/solver_gmres.h>
#include <deal.II/lac/precondition.h>
#include <deal.II/lac/relaxation_block.h>

#include <deal.II/grid/tria.h>
#include <deal.II/grid/grid_generator.h>
#include <deal.II/grid/grid_refinement.h>
#include <deal.II/grid/manifold_lib.h>
#include <deal.II/grid/grid_out.h>

#include <deal.II/dofs/dof_handler.h>
#include <deal.II/dofs/dof_renumbering.h>
#include <deal.II/dofs/dof_tools.h>

#include <deal.II/fe/fe_q.h>
#include <deal.II/fe/mapping_q.h>
#include <deal.II/fe/fe_values.h>

#include <deal.II/numerics/vector_tools.h>
#include <deal.II/numerics/matrix_tools.h>
#include <deal.II/numerics/data_out.h>

// Include all relevant multilevel files:
#include <deal.II/multigrid/mg_constrained_dofs.h>
#include <deal.II/multigrid/multigrid.h>
#include <deal.II/multigrid/mg_transfer.h>
#include <deal.II/multigrid/mg_tools.h>
#include <deal.II/multigrid/mg_coarse.h>
#include <deal.II/multigrid/mg_smoother.h>
#include <deal.II/multigrid/mg_matrix.h>

// C++:
#include <algorithm>
#include <fstream>
#include <iostream>
#include <random>

// We will be using MeshWorker::mesh_loop functionality for assembling matrices:
#include <deal.II/meshworker/mesh_loop.h>


// @sect3{MeshWorker data}

// As always, we will be putting everything related to this program
// into a namespace of its own.
//
// Since we will be using the MeshWorker framework, the first step is
// to define the following structures needed by the assemble_cell()
// function used by MeshWorker::mesh_loop(): `ScratchData`
// contains an FEValues object which is needed for assembling
// a cell's local contribution, while `CopyData` contains the
// output from a cell's local contribution and necessary information
// to copy that to the global system. (Their purpose is also explained
// in the documentation of the WorkStream class.)
namespace Step63
{
  using namespace dealii;

  template <int dim>
  struct ScratchData
  {
    ScratchData(const FiniteElement<dim> &fe,
                const unsigned int        quadrature_degree)
      : fe_values(fe,
                  QGauss<dim>(quadrature_degree),
                  update_values | update_gradients | update_hessians |
                    update_quadrature_points | update_JxW_values)
    {}

    ScratchData(const ScratchData<dim> &scratch_data)
      : fe_values(scratch_data.fe_values.get_fe(),
                  scratch_data.fe_values.get_quadrature(),
                  update_values | update_gradients | update_hessians |
                    update_quadrature_points | update_JxW_values)
    {}

    FEValues<dim> fe_values;
  };



  struct CopyData
  {
    CopyData() = default;

    unsigned int level;
    unsigned int dofs_per_cell;

    FullMatrix<double>                   cell_matrix;
    Vector<double>                       cell_rhs;
    std::vector<types::global_dof_index> local_dof_indices;
  };



  // @sect3{Problem parameters}

  // The second step is to define the classes that deal with run-time
  // parameters to be read from an input file.
  //
  // We will use ParameterHandler to pass in parameters at runtime. The
  // structure `Settings` parses and stores the parameters to be queried
  // throughout the program.
  struct Settings
  {
    enum DoFRenumberingStrategy
    {
      none,
      downstream,
      upstream,
      random
    };

    void get_parameters(const std::string &prm_filename);

    double                 epsilon;
    unsigned int           fe_degree;
    std::string            smoother_type;
    unsigned int           smoothing_steps;
    DoFRenumberingStrategy dof_renumbering;
    bool                   with_streamline_diffusion;
    bool                   output;
  };



  void Settings::get_parameters(const std::string &prm_filename)
  {
    /* First declare the parameters... */
    ParameterHandler prm;

    prm.declare_entry("Epsilon",
                      "0.005",
                      Patterns::Double(0),
                      "Diffusion parameter");

    prm.declare_entry("Fe degree",
                      "1",
                      Patterns::Integer(1),
                      "Finite Element degree");
    prm.declare_entry("Smoother type",
                      "block SOR",
                      Patterns::Selection("SOR|Jacobi|block SOR|block Jacobi"),
                      "Select smoother: SOR|Jacobi|block SOR|block Jacobi");
    prm.declare_entry("Smoothing steps",
                      "2",
                      Patterns::Integer(1),
                      "Number of smoothing steps");
    prm.declare_entry(
      "DoF renumbering",
      "downstream",
      Patterns::Selection("none|downstream|upstream|random"),
      "Select DoF renumbering: none|downstream|upstream|random");
    prm.declare_entry("With streamline diffusion",
                      "true",
                      Patterns::Bool(),
                      "Enable streamline diffusion stabilization: true|false");
    prm.declare_entry("Output",
                      "true",
                      Patterns::Bool(),
                      "Generate graphical output: true|false");

    /* ...and then try to read their values from the input file: */
    if (prm_filename.empty())
      {
        prm.print_parameters(std::cout, ParameterHandler::Text);
        AssertThrow(
          false, ExcMessage("Please pass a .prm file as the first argument!"));
      }

    prm.parse_input(prm_filename);

    epsilon         = prm.get_double("Epsilon");
    fe_degree       = prm.get_integer("Fe degree");
    smoother_type   = prm.get("Smoother type");
    smoothing_steps = prm.get_integer("Smoothing steps");

    const std::string renumbering = prm.get("DoF renumbering");
    if (renumbering == "none")
      dof_renumbering = DoFRenumberingStrategy::none;
    else if (renumbering == "downstream")
      dof_renumbering = DoFRenumberingStrategy::downstream;
    else if (renumbering == "upstream")
      dof_renumbering = DoFRenumberingStrategy::upstream;
    else if (renumbering == "random")
      dof_renumbering = DoFRenumberingStrategy::random;
    else
      AssertThrow(false,
                  ExcMessage("The <DoF renumbering> parameter has "
                             "an invalid value."));

    with_streamline_diffusion = prm.get_bool("With streamline diffusion");
    output                    = prm.get_bool("Output");
  }


  // @sect3{Cell permutations}
  //
  // The ordering in which cells and degrees of freedom are traversed
  // will play a role in the speed of convergence for multiplicative
  // methods. Here we define functions which return a specific ordering
  // of cells to be used by the block smoothers.
  //
  // For each type of cell ordering, we define a function for the
  // active mesh and one for a level mesh (i.e., for the cells at one
  // level of a multigrid hierarchy). While the only reordering
  // necessary for solving the system will be on the level meshes, we
  // include the active reordering for visualization purposes in
  // output_results().
  //
  // For the two downstream ordering functions, we first create an
  // array with all of the relevant cells that we then sort in
  // downstream direction using a "comparator" object. The output of
  // the functions is then simply an array of the indices of the cells
  // in the just computed order.
  template <int dim>
  std::vector<unsigned int>
  create_downstream_cell_ordering(const DoFHandler<dim> &dof_handler,
                                  const Tensor<1, dim>   direction,
                                  const unsigned int     level)
  {
    std::vector<typename DoFHandler<dim>::level_cell_iterator> ordered_cells;
    ordered_cells.reserve(dof_handler.get_triangulation().n_cells(level));
    for (const auto &cell : dof_handler.cell_iterators_on_level(level))
      ordered_cells.push_back(cell);

    const DoFRenumbering::
      CompareDownstream<typename DoFHandler<dim>::level_cell_iterator, dim>
        comparator(direction);
    std::sort(ordered_cells.begin(), ordered_cells.end(), comparator);

    std::vector<unsigned> ordered_indices;
    ordered_indices.reserve(dof_handler.get_triangulation().n_cells(level));

    for (const auto &cell : ordered_cells)
      ordered_indices.push_back(cell->index());

    return ordered_indices;
  }



  template <int dim>
  std::vector<unsigned int>
  create_downstream_cell_ordering(const DoFHandler<dim> &dof_handler,
                                  const Tensor<1, dim>   direction)
  {
    std::vector<typename DoFHandler<dim>::active_cell_iterator> ordered_cells;
    ordered_cells.reserve(dof_handler.get_triangulation().n_active_cells());
    for (const auto &cell : dof_handler.active_cell_iterators())
      ordered_cells.push_back(cell);

    const DoFRenumbering::
      CompareDownstream<typename DoFHandler<dim>::active_cell_iterator, dim>
        comparator(direction);
    std::sort(ordered_cells.begin(), ordered_cells.end(), comparator);

    std::vector<unsigned int> ordered_indices;
    ordered_indices.reserve(dof_handler.get_triangulation().n_active_cells());

    for (const auto &cell : ordered_cells)
      ordered_indices.push_back(cell->index());

    return ordered_indices;
  }


  // The functions that produce a random ordering are similar in
  // spirit in that they first put information about all cells into an
  // array. But then, instead of sorting them, they shuffle the
  // elements randomly using the facilities C++ offers to generate
  // random numbers. The way this is done is by iterating over all
  // elements of the array, drawing a random number for another
  // element before that, and then exchanging these elements. The
  // result is a random shuffle of the elements of the array.
  template <int dim>
  std::vector<unsigned int>
  create_random_cell_ordering(const DoFHandler<dim> &dof_handler,
                              const unsigned int     level)
  {
    std::vector<unsigned int> ordered_cells;
    ordered_cells.reserve(dof_handler.get_triangulation().n_cells(level));
    for (const auto &cell : dof_handler.cell_iterators_on_level(level))
      ordered_cells.push_back(cell->index());

    std::mt19937 random_number_generator;
    std::shuffle(ordered_cells.begin(),
                 ordered_cells.end(),
                 random_number_generator);

    return ordered_cells;
  }



  template <int dim>
  std::vector<unsigned int>
  create_random_cell_ordering(const DoFHandler<dim> &dof_handler)
  {
    std::vector<unsigned int> ordered_cells;
    ordered_cells.reserve(dof_handler.get_triangulation().n_active_cells());
    for (const auto &cell : dof_handler.active_cell_iterators())
      ordered_cells.push_back(cell->index());

    std::mt19937 random_number_generator;
    std::shuffle(ordered_cells.begin(),
                 ordered_cells.end(),
                 random_number_generator);

    return ordered_cells;
  }


  // @sect3{Right-hand side and boundary values}

  // The problem solved in this tutorial is an adaptation of Ex. 3.1.3 found
  // on pg. 118 of <a
  // href="https://global.oup.com/academic/product/finite-elements-and-fast-iterative-solvers-9780199678808">
  // Finite Elements and Fast Iterative Solvers: with Applications in
  // Incompressible Fluid Dynamics by Elman, Silvester, and Wathen</a>. The
  // main difference being that we add a hole in the center of our domain with
  // zero Dirichlet boundary conditions.
  //
  // For a complete description, we need classes that implement the
  // zero right-hand side first (we could of course have just used
  // Functions::ZeroFunction):
  template <int dim>
  class RightHandSide : public Function<dim>
  {
  public:
    virtual double value(const Point<dim> & p,
                         const unsigned int component = 0) const override;

    virtual void value_list(const std::vector<Point<dim>> &points,
                            std::vector<double> &          values,
                            const unsigned int component = 0) const override;
  };



  template <int dim>
  double RightHandSide<dim>::value(const Point<dim> &,
                                   const unsigned int component) const
  {
    Assert(component == 0, ExcIndexRange(component, 0, 1));
    (void)component;

    return 0.0;
  }



  template <int dim>
  void RightHandSide<dim>::value_list(const std::vector<Point<dim>> &points,
                                      std::vector<double> &          values,
                                      const unsigned int component) const
  {
    AssertDimension(values.size(), points.size());

    for (unsigned int i = 0; i < points.size(); ++i)
      values[i] = RightHandSide<dim>::value(points[i], component);
  }


  // We also have Dirichlet boundary conditions. On a connected portion of the
  // outer, square boundary we set the value to 1, and we set the value to 0
  // everywhere else (including the inner, circular boundary):
  template <int dim>
  class BoundaryValues : public Function<dim>
  {
  public:
    virtual double value(const Point<dim> & p,
                         const unsigned int component = 0) const override;

    virtual void value_list(const std::vector<Point<dim>> &points,
                            std::vector<double> &          values,
                            const unsigned int component = 0) const override;
  };



  template <int dim>
  double BoundaryValues<dim>::value(const Point<dim> & p,
                                    const unsigned int component) const
  {
    Assert(component == 0, ExcIndexRange(component, 0, 1));
    (void)component;

    // Set boundary to 1 if $x=1$, or if $x>0.5$ and $y=-1$.
    if (std::fabs(p[0] - 1) < 1e-8 ||
        (std::fabs(p[1] + 1) < 1e-8 && p[0] >= 0.5))
      {
        return 1.0;
      }
    else
      {
        return 0.0;
      }
  }



  template <int dim>
  void BoundaryValues<dim>::value_list(const std::vector<Point<dim>> &points,
                                       std::vector<double> &          values,
                                       const unsigned int component) const
  {
    AssertDimension(values.size(), points.size());

    for (unsigned int i = 0; i < points.size(); ++i)
      values[i] = BoundaryValues<dim>::value(points[i], component);
  }



  // @sect3{Streamline diffusion implementation}

  // The streamline diffusion method has a stabilization constant that
  // we need to be able to compute. The choice of how this parameter
  // is computed is taken from <a
  // href="https://link.springer.com/chapter/10.1007/978-3-540-34288-5_27">On
  // Discontinuity-Capturing Methods for Convection-Diffusion
  // Equations by Volker John and Petr Knobloch</a>.
  template <int dim>
  double compute_stabilization_delta(const double         hk,
                                     const double         eps,
                                     const Tensor<1, dim> dir,
                                     const double         pk)
  {
    const double Peclet = dir.norm() * hk / (2.0 * eps * pk);
    const double coth =
      (1.0 + std::exp(-2.0 * Peclet)) / (1.0 - std::exp(-2.0 * Peclet));

    return hk / (2.0 * dir.norm() * pk) * (coth - 1.0 / Peclet);
  }


  // @sect3{<code>AdvectionProlem</code> class}

  // This is the main class of the program, and should look very similar to
  // step-16. The major difference is that, since we are defining our multigrid
  // smoother at runtime, we choose to define a function `create_smoother()` and
  // a class object `mg_smoother` which is a `std::unique_ptr` to a smoother
  // that is derived from MGSmoother. Note that for smoothers derived from
  // RelaxationBlock, we must include a `smoother_data` object for each level.
  // This will contain information about the cell ordering and the method of
  // inverting cell matrices.

  template <int dim>
  class AdvectionProblem
  {
  public:
    AdvectionProblem(const Settings &settings);
    void run();

  private:
    void setup_system();

    template <class IteratorType>
    void assemble_cell(const IteratorType &cell,
                       ScratchData<dim> &  scratch_data,
                       CopyData &          copy_data);
    void assemble_system_and_multigrid();

    void setup_smoother();

    void solve();
    void refine_grid();
    void output_results(const unsigned int cycle) const;

    Triangulation<dim> triangulation;
    DoFHandler<dim>    dof_handler;

    const FE_Q<dim>     fe;
    const MappingQ<dim> mapping;

    AffineConstraints<double> constraints;

    SparsityPattern      sparsity_pattern;
    SparseMatrix<double> system_matrix;

    Vector<double> solution;
    Vector<double> system_rhs;

    MGLevelObject<SparsityPattern> mg_sparsity_patterns;
    MGLevelObject<SparsityPattern> mg_interface_sparsity_patterns;

    MGLevelObject<SparseMatrix<double>> mg_matrices;
    MGLevelObject<SparseMatrix<double>> mg_interface_in;
    MGLevelObject<SparseMatrix<double>> mg_interface_out;

    mg::Matrix<Vector<double>> mg_matrix;
    mg::Matrix<Vector<double>> mg_interface_matrix_in;
    mg::Matrix<Vector<double>> mg_interface_matrix_out;

    std::unique_ptr<MGSmoother<Vector<double>>> mg_smoother;

    using SmootherType =
      RelaxationBlock<SparseMatrix<double>, double, Vector<double>>;
    using SmootherAdditionalDataType = SmootherType::AdditionalData;
    MGLevelObject<SmootherAdditionalDataType> smoother_data;

    MGConstrainedDoFs mg_constrained_dofs;

    Tensor<1, dim> advection_direction;

    const Settings settings;
  };



  template <int dim>
  AdvectionProblem<dim>::AdvectionProblem(const Settings &settings)
    : triangulation(Triangulation<dim>::limit_level_difference_at_vertices)
    , dof_handler(triangulation)
    , fe(settings.fe_degree)
    , mapping(settings.fe_degree)
    , settings(settings)
  {
    advection_direction[0] = -std::sin(numbers::PI / 6.0);
    if (dim >= 2)
      advection_direction[1] = std::cos(numbers::PI / 6.0);
    if (dim >= 3)
      AssertThrow(false, ExcNotImplemented());
  }


  // @sect4{<code>AdvectionProblem::setup_system()</code>}

  // Here we first set up the DoFHandler, AffineConstraints, and
  // SparsityPattern objects for both active and multigrid level meshes.
  //
  // We could renumber the active DoFs with the DoFRenumbering class,
  // but the smoothers only act on multigrid levels and as such, this
  // would not matter for the computations. Instead, we will renumber the
  // DoFs on each multigrid level below.
  template <int dim>
  void AdvectionProblem<dim>::setup_system()
  {
    const unsigned int n_levels = triangulation.n_levels();

    dof_handler.distribute_dofs(fe);

    solution.reinit(dof_handler.n_dofs());
    system_rhs.reinit(dof_handler.n_dofs());

    constraints.clear();
    DoFTools::make_hanging_node_constraints(dof_handler, constraints);

    VectorTools::interpolate_boundary_values(
      mapping, dof_handler, 0, BoundaryValues<dim>(), constraints);
    VectorTools::interpolate_boundary_values(
      mapping, dof_handler, 1, BoundaryValues<dim>(), constraints);
    constraints.close();

    DynamicSparsityPattern dsp(dof_handler.n_dofs());
    DoFTools::make_sparsity_pattern(dof_handler,
                                    dsp,
                                    constraints,
                                    /*keep_constrained_dofs = */ false);

    sparsity_pattern.copy_from(dsp);
    system_matrix.reinit(sparsity_pattern);

    dof_handler.distribute_mg_dofs();

    // Having enumerated the global degrees of freedom as well as (in
    // the last line above) the level degrees of freedom, let us
    // renumber the level degrees of freedom to get a better smoother
    // as explained in the introduction.  The first block below
    // renumbers DoFs on each level in downstream or upstream
    // direction if needed. This is only necessary for point smoothers
    // (SOR and Jacobi) as the block smoothers operate on cells (see
    // `create_smoother()`). The blocks below then also implement
    // random numbering.
    if (settings.smoother_type == "SOR" || settings.smoother_type == "Jacobi")
      {
        if (settings.dof_renumbering ==
              Settings::DoFRenumberingStrategy::downstream ||
            settings.dof_renumbering ==
              Settings::DoFRenumberingStrategy::upstream)
          {
            const Tensor<1, dim> direction =
              (settings.dof_renumbering ==
                   Settings::DoFRenumberingStrategy::upstream ?
                 -1.0 :
                 1.0) *
              advection_direction;

            for (unsigned int level = 0; level < n_levels; ++level)
              DoFRenumbering::downstream(dof_handler,
                                         level,
                                         direction,
                                         /*dof_wise_renumbering = */ true);
          }
        else if (settings.dof_renumbering ==
                 Settings::DoFRenumberingStrategy::random)
          {
            for (unsigned int level = 0; level < n_levels; ++level)
              DoFRenumbering::random(dof_handler, level);
          }
        else
          Assert(false, ExcNotImplemented());
      }

    // The rest of the function just sets up data structures. The last
    // lines of the code below is unlike the other GMG tutorials, as
    // it sets up both the interface in and out matrices. We need this
    // since our problem is non-symmetric.
    mg_constrained_dofs.clear();
    mg_constrained_dofs.initialize(dof_handler);

    mg_constrained_dofs.make_zero_boundary_constraints(dof_handler, {0, 1});

    mg_matrices.resize(0, n_levels - 1);
    mg_matrices.clear_elements();
    mg_interface_in.resize(0, n_levels - 1);
    mg_interface_in.clear_elements();
    mg_interface_out.resize(0, n_levels - 1);
    mg_interface_out.clear_elements();
    mg_sparsity_patterns.resize(0, n_levels - 1);
    mg_interface_sparsity_patterns.resize(0, n_levels - 1);

    for (unsigned int level = 0; level < n_levels; ++level)
      {
        {
          DynamicSparsityPattern dsp(dof_handler.n_dofs(level),
                                     dof_handler.n_dofs(level));
          MGTools::make_sparsity_pattern(dof_handler, dsp, level);
          mg_sparsity_patterns[level].copy_from(dsp);
          mg_matrices[level].reinit(mg_sparsity_patterns[level]);
        }
        {
          DynamicSparsityPattern dsp(dof_handler.n_dofs(level),
                                     dof_handler.n_dofs(level));
          MGTools::make_interface_sparsity_pattern(dof_handler,
                                                   mg_constrained_dofs,
                                                   dsp,
                                                   level);
          mg_interface_sparsity_patterns[level].copy_from(dsp);

          mg_interface_in[level].reinit(mg_interface_sparsity_patterns[level]);
          mg_interface_out[level].reinit(mg_interface_sparsity_patterns[level]);
        }
      }
  }


  // @sect4{<code>AdvectionProblem::assemble_cell()</code>}

  // Here we define the assembly of the linear system on each cell to
  // be used by the mesh_loop() function below. This one function
  // assembles the cell matrix for either an active or a level cell
  // (whatever it is passed as its first argument), and only assembles
  // a right-hand side if called with an active cell.

  template <int dim>
  template <class IteratorType>
  void AdvectionProblem<dim>::assemble_cell(const IteratorType &cell,
                                            ScratchData<dim> &  scratch_data,
                                            CopyData &          copy_data)
  {
    copy_data.level = cell->level();

    const unsigned int dofs_per_cell =
      scratch_data.fe_values.get_fe().n_dofs_per_cell();
    copy_data.dofs_per_cell = dofs_per_cell;
    copy_data.cell_matrix.reinit(dofs_per_cell, dofs_per_cell);

    const unsigned int n_q_points =
      scratch_data.fe_values.get_quadrature().size();

    if (cell->is_level_cell() == false)
      copy_data.cell_rhs.reinit(dofs_per_cell);

    copy_data.local_dof_indices.resize(dofs_per_cell);
    cell->get_active_or_mg_dof_indices(copy_data.local_dof_indices);

    scratch_data.fe_values.reinit(cell);

    RightHandSide<dim>  right_hand_side;
    std::vector<double> rhs_values(n_q_points);

    right_hand_side.value_list(scratch_data.fe_values.get_quadrature_points(),
                               rhs_values);

    // If we are using streamline diffusion we must add its contribution
    // to both the cell matrix and the cell right-hand side. If we are not
    // using streamline diffusion, setting $\delta=0$ negates this contribution
    // below and we are left with the standard, Galerkin finite element
    // assembly.
    const double delta = (settings.with_streamline_diffusion ?
                            compute_stabilization_delta(cell->diameter(),
                                                        settings.epsilon,
                                                        advection_direction,
                                                        settings.fe_degree) :
                            0.0);

    for (unsigned int q_point = 0; q_point < n_q_points; ++q_point)
      for (unsigned int i = 0; i < dofs_per_cell; ++i)
        {
          for (unsigned int j = 0; j < dofs_per_cell; ++j)
            {
              // The assembly of the local matrix has two parts. First
              // the Galerkin contribution:
              copy_data.cell_matrix(i, j) +=
                (settings.epsilon *
                 scratch_data.fe_values.shape_grad(i, q_point) *
                 scratch_data.fe_values.shape_grad(j, q_point) *
                 scratch_data.fe_values.JxW(q_point)) +
                (scratch_data.fe_values.shape_value(i, q_point) *
                 (advection_direction *
                  scratch_data.fe_values.shape_grad(j, q_point)) *
                 scratch_data.fe_values.JxW(q_point))
                // and then the streamline diffusion contribution:
                + delta *
                    (advection_direction *
                     scratch_data.fe_values.shape_grad(j, q_point)) *
                    (advection_direction *
                     scratch_data.fe_values.shape_grad(i, q_point)) *
                    scratch_data.fe_values.JxW(q_point) -
                delta * settings.epsilon *
                  trace(scratch_data.fe_values.shape_hessian(j, q_point)) *
                  (advection_direction *
                   scratch_data.fe_values.shape_grad(i, q_point)) *
                  scratch_data.fe_values.JxW(q_point);
            }
          if (cell->is_level_cell() == false)
            {
              // The same applies to the right hand side. First the
              // Galerkin contribution:
              copy_data.cell_rhs(i) +=
                scratch_data.fe_values.shape_value(i, q_point) *
                  rhs_values[q_point] * scratch_data.fe_values.JxW(q_point)
                // and then the streamline diffusion contribution:
                + delta * rhs_values[q_point] * advection_direction *
                    scratch_data.fe_values.shape_grad(i, q_point) *
                    scratch_data.fe_values.JxW(q_point);
            }
        }
  }


  // @sect4{<code>AdvectionProblem::assemble_system_and_multigrid()</code>}

  // Here we employ MeshWorker::mesh_loop() to go over cells and assemble the
  // system_matrix, system_rhs, and all mg_matrices for us.

  template <int dim>
  void AdvectionProblem<dim>::assemble_system_and_multigrid()
  {
    const auto cell_worker_active =
      [&](const decltype(dof_handler.begin_active()) &cell,
          ScratchData<dim> &                          scratch_data,
          CopyData &                                  copy_data) {
        this->assemble_cell(cell, scratch_data, copy_data);
      };

    const auto copier_active = [&](const CopyData &copy_data) {
      constraints.distribute_local_to_global(copy_data.cell_matrix,
                                             copy_data.cell_rhs,
                                             copy_data.local_dof_indices,
                                             system_matrix,
                                             system_rhs);
    };


    MeshWorker::mesh_loop(dof_handler.begin_active(),
                          dof_handler.end(),
                          cell_worker_active,
                          copier_active,
                          ScratchData<dim>(fe, fe.degree + 1),
                          CopyData(),
                          MeshWorker::assemble_own_cells);

    // Unlike the constraints for the active level, we choose to create
    // constraint objects for each multigrid level local to this function
    // since they are never needed elsewhere in the program.
    std::vector<AffineConstraints<double>> boundary_constraints(
      triangulation.n_global_levels());
    for (unsigned int level = 0; level < triangulation.n_global_levels();
         ++level)
      {
        const IndexSet locally_owned_level_dof_indices =
          DoFTools::extract_locally_relevant_level_dofs(dof_handler, level);
        boundary_constraints[level].reinit(locally_owned_level_dof_indices);
        boundary_constraints[level].add_lines(
          mg_constrained_dofs.get_refinement_edge_indices(level));
        boundary_constraints[level].add_lines(
          mg_constrained_dofs.get_boundary_indices(level));
        boundary_constraints[level].close();
      }

    const auto cell_worker_mg =
      [&](const decltype(dof_handler.begin_mg()) &cell,
          ScratchData<dim> &                      scratch_data,
          CopyData &                              copy_data) {
        this->assemble_cell(cell, scratch_data, copy_data);
      };

    const auto copier_mg = [&](const CopyData &copy_data) {
      boundary_constraints[copy_data.level].distribute_local_to_global(
        copy_data.cell_matrix,
        copy_data.local_dof_indices,
        mg_matrices[copy_data.level]);

      // If $(i,j)$ is an `interface_out` dof pair, then $(j,i)$ is an
      // `interface_in` dof pair. Note: For `interface_in`, we load
      // the transpose of the interface entries, i.e., the entry for
      // dof pair $(j,i)$ is stored in `interface_in(i,j)`. This is an
      // optimization for the symmetric case which allows only one
      // matrix to be used when setting the edge_matrices in
      // solve(). Here, however, since our problem is non-symmetric,
      // we must store both `interface_in` and `interface_out`
      // matrices.
      for (unsigned int i = 0; i < copy_data.dofs_per_cell; ++i)
        for (unsigned int j = 0; j < copy_data.dofs_per_cell; ++j)
          if (mg_constrained_dofs.is_interface_matrix_entry(
                copy_data.level,
                copy_data.local_dof_indices[i],
                copy_data.local_dof_indices[j]))
            {
              mg_interface_out[copy_data.level].add(
                copy_data.local_dof_indices[i],
                copy_data.local_dof_indices[j],
                copy_data.cell_matrix(i, j));
              mg_interface_in[copy_data.level].add(
                copy_data.local_dof_indices[i],
                copy_data.local_dof_indices[j],
                copy_data.cell_matrix(j, i));
            }
    };

    MeshWorker::mesh_loop(dof_handler.begin_mg(),
                          dof_handler.end_mg(),
                          cell_worker_mg,
                          copier_mg,
                          ScratchData<dim>(fe, fe.degree + 1),
                          CopyData(),
                          MeshWorker::assemble_own_cells);
  }


  // @sect4{<code>AdvectionProblem::setup_smoother()</code>}

  // Next, we set up the smoother based on the settings in the `.prm` file. The
  // two options that are of significance is the number of pre- and
  // post-smoothing steps on each level of the multigrid v-cycle and the
  // relaxation parameter.

  // Since multiplicative methods tend to be more powerful than additive method,
  // fewer smoothing steps are required to see convergence independent of mesh
  // size. The same holds for block smoothers over point smoothers. This is
  // reflected in the choice for the number of smoothing steps for each type of
  // smoother below.

  // The relaxation parameter for point smoothers is chosen based on trial and
  // error, and reflects values necessary to keep the iteration counts in
  // the GMRES solve constant (or as close as possible) as we refine the mesh.
  // The two values given for both "Jacobi" and "SOR" in the `.prm` files are
  // for degree 1 and degree 3 finite elements. If the user wants to change to
  // another degree, they may need to adjust these numbers. For block smoothers,
  // this parameter has a more straightforward interpretation, namely that for
  // additive methods in 2D, a DoF can have a repeated contribution from up to 4
  // cells, therefore we must relax these methods by 0.25 to compensate. This is
  // not an issue for multiplicative methods as each cell's inverse application
  // carries new information to all its DoFs.

  // Finally, as mentioned above, the point smoothers only operate on DoFs, and
  // the block smoothers on cells, so only the block smoothers need to be given
  // information regarding cell orderings. DoF ordering for point smoothers has
  // already been taken care of in `setup_system()`.

  template <int dim>
  void AdvectionProblem<dim>::setup_smoother()
  {
    if (settings.smoother_type == "SOR")
      {
        using Smoother = PreconditionSOR<SparseMatrix<double>>;

        auto smoother =
          std::make_unique<MGSmootherPrecondition<SparseMatrix<double>,
                                                  Smoother,
                                                  Vector<double>>>();
        smoother->initialize(mg_matrices,
                             Smoother::AdditionalData(fe.degree == 1 ? 1.0 :
                                                                       0.62));
        smoother->set_steps(settings.smoothing_steps);
        mg_smoother = std::move(smoother);
      }
    else if (settings.smoother_type == "Jacobi")
      {
        using Smoother = PreconditionJacobi<SparseMatrix<double>>;
        auto smoother =
          std::make_unique<MGSmootherPrecondition<SparseMatrix<double>,
                                                  Smoother,
                                                  Vector<double>>>();
        smoother->initialize(mg_matrices,
                             Smoother::AdditionalData(fe.degree == 1 ? 0.6667 :
                                                                       0.47));
        smoother->set_steps(settings.smoothing_steps);
        mg_smoother = std::move(smoother);
      }
    else if (settings.smoother_type == "block SOR" ||
             settings.smoother_type == "block Jacobi")
      {
        smoother_data.resize(0, triangulation.n_levels() - 1);

        for (unsigned int level = 0; level < triangulation.n_levels(); ++level)
          {
            DoFTools::make_cell_patches(smoother_data[level].block_list,
                                        dof_handler,
                                        level);

            smoother_data[level].relaxation =
              (settings.smoother_type == "block SOR" ? 1.0 : 0.25);
            smoother_data[level].inversion = PreconditionBlockBase<double>::svd;

            std::vector<unsigned int> ordered_indices;
            switch (settings.dof_renumbering)
              {
                case Settings::DoFRenumberingStrategy::downstream:
                  ordered_indices =
                    create_downstream_cell_ordering(dof_handler,
                                                    advection_direction,
                                                    level);
                  break;

                case Settings::DoFRenumberingStrategy::upstream:
                  ordered_indices =
                    create_downstream_cell_ordering(dof_handler,
                                                    -1.0 * advection_direction,
                                                    level);
                  break;

                case Settings::DoFRenumberingStrategy::random:
                  ordered_indices =
                    create_random_cell_ordering(dof_handler, level);
                  break;

                case Settings::DoFRenumberingStrategy::none:
                  break;

                default:
                  AssertThrow(false, ExcNotImplemented());
                  break;
              }

            smoother_data[level].order =
              std::vector<std::vector<unsigned int>>(1, ordered_indices);
          }

        if (settings.smoother_type == "block SOR")
          {
            auto smoother = std::make_unique<MGSmootherPrecondition<
              SparseMatrix<double>,
              RelaxationBlockSOR<SparseMatrix<double>, double, Vector<double>>,
              Vector<double>>>();
            smoother->initialize(mg_matrices, smoother_data);
            smoother->set_steps(settings.smoothing_steps);
            mg_smoother = std::move(smoother);
          }
        else if (settings.smoother_type == "block Jacobi")
          {
            auto smoother = std::make_unique<
              MGSmootherPrecondition<SparseMatrix<double>,
                                     RelaxationBlockJacobi<SparseMatrix<double>,
                                                           double,
                                                           Vector<double>>,
                                     Vector<double>>>();
            smoother->initialize(mg_matrices, smoother_data);
            smoother->set_steps(settings.smoothing_steps);
            mg_smoother = std::move(smoother);
          }
      }
    else
      AssertThrow(false, ExcNotImplemented());
  }


  // @sect4{<code>AdvectionProblem::solve()</code>}

  // Before we can solve the system, we must first set up the multigrid
  // preconditioner. This requires the setup of the transfer between levels,
  // the coarse matrix solver, and the smoother. This setup follows almost
  // identically to Step-16, the main difference being the various smoothers
  // defined above and the fact that we need different interface edge matrices
  // for in and out since our problem is non-symmetric. (In reality, for this
  // tutorial these interface matrices are empty since we are only using global
  // refinement, and thus have no refinement edges. However, we have still
  // included both here since if one made the simple switch to an adaptively
  // refined method, the program would still run correctly.)

  // The last thing to note is that since our problem is non-symmetric, we must
  // use an appropriate Krylov subspace method. We choose here to
  // use GMRES since it offers the guarantee of residual reduction in each
  // iteration. The major disavantage of GMRES is that, for each iteration,
  // the number of stored temporary vectors increases by one, and one also needs
  // to compute a scalar product with all previously stored vectors. This is
  // rather expensive. This requirement is relaxed by using the restarted GMRES
  // method which puts a cap on the number of vectors we are required to store
  // at any one time (here we restart after 50 temporary vectors, or 48
  // iterations). This then has the disadvantage that we lose information we
  // have gathered throughout the iteration and therefore we could see slower
  // convergence. As a consequence, where to restart is a question of balancing
  // memory consumption, CPU effort, and convergence speed.
  // However, the goal of this tutorial is to have very low
  // iteration counts by using a powerful GMG preconditioner, so we have picked
  // the restart length such that all of the results shown below converge prior
  // to restart happening, and thus we have a standard GMRES method. If the user
  // is interested, another suitable method offered in deal.II would be
  // BiCGStab.

  template <int dim>
  void AdvectionProblem<dim>::solve()
  {
    const unsigned int max_iters       = 200;
    const double       solve_tolerance = 1e-8 * system_rhs.l2_norm();
    SolverControl      solver_control(max_iters, solve_tolerance, true, true);
    solver_control.enable_history_data();

    using Transfer = MGTransferPrebuilt<Vector<double>>;
    Transfer mg_transfer(mg_constrained_dofs);
    mg_transfer.build(dof_handler);

    FullMatrix<double> coarse_matrix;
    coarse_matrix.copy_from(mg_matrices[0]);
    MGCoarseGridHouseholder<double, Vector<double>> coarse_grid_solver;
    coarse_grid_solver.initialize(coarse_matrix);

    setup_smoother();

    mg_matrix.initialize(mg_matrices);
    mg_interface_matrix_in.initialize(mg_interface_in);
    mg_interface_matrix_out.initialize(mg_interface_out);

    Multigrid<Vector<double>> mg(
      mg_matrix, coarse_grid_solver, mg_transfer, *mg_smoother, *mg_smoother);
    mg.set_edge_matrices(mg_interface_matrix_out, mg_interface_matrix_in);

    PreconditionMG<dim, Vector<double>, Transfer> preconditioner(dof_handler,
                                                                 mg,
                                                                 mg_transfer);

    std::cout << "     Solving with GMRES to tol " << solve_tolerance << "..."
              << std::endl;
    SolverGMRES<Vector<double>> solver(
      solver_control, SolverGMRES<Vector<double>>::AdditionalData(50, true));

    Timer time;
    time.start();
    solver.solve(system_matrix, solution, system_rhs, preconditioner);
    time.stop();

    std::cout << "          converged in " << solver_control.last_step()
              << " iterations"
              << " in " << time.last_wall_time() << " seconds " << std::endl;

    constraints.distribute(solution);

    mg_smoother.release();
  }


  // @sect4{<code>AdvectionProblem::output_results()</code>}

  // The final function of interest generates graphical output.
  // Here we output the solution and cell ordering in a .vtu format.

  // At the top of the function, we generate an index for each cell to
  // visualize the ordering used by the smoothers. Note that we do
  // this only for the active cells instead of the levels, where the
  // smoothers are actually used. For the point smoothers we renumber
  // DoFs instead of cells, so this is only an approximation of what
  // happens in reality. Finally, the random ordering is not the
  // random ordering we actually use (see `create_smoother()` for that).
  //
  // The (integer) ordering of cells is then copied into a (floating
  // point) vector for graphical output.
  template <int dim>
  void AdvectionProblem<dim>::output_results(const unsigned int cycle) const
  {
    const unsigned int n_active_cells = triangulation.n_active_cells();
    Vector<double>     cell_indices(n_active_cells);
    {
      std::vector<unsigned int> ordered_indices;
      switch (settings.dof_renumbering)
        {
          case Settings::DoFRenumberingStrategy::downstream:
            ordered_indices =
              create_downstream_cell_ordering(dof_handler, advection_direction);
            break;

          case Settings::DoFRenumberingStrategy::upstream:
            ordered_indices =
              create_downstream_cell_ordering(dof_handler,
                                              -1.0 * advection_direction);
            break;

          case Settings::DoFRenumberingStrategy::random:
            ordered_indices = create_random_cell_ordering(dof_handler);
            break;

          case Settings::DoFRenumberingStrategy::none:
            ordered_indices.resize(n_active_cells);
            for (unsigned int i = 0; i < n_active_cells; ++i)
              ordered_indices[i] = i;
            break;

          default:
            AssertThrow(false, ExcNotImplemented());
            break;
        }

      for (unsigned int i = 0; i < n_active_cells; ++i)
        cell_indices(ordered_indices[i]) = static_cast<double>(i);
    }

    // The remainder of the function is then straightforward, given
    // previous tutorial programs:
    DataOut<dim> data_out;
    data_out.attach_dof_handler(dof_handler);
    data_out.add_data_vector(solution, "solution");
    data_out.add_data_vector(cell_indices, "cell_index");
    data_out.build_patches();

    const std::string filename =
      "solution-" + Utilities::int_to_string(cycle) + ".vtu";
    std::ofstream output(filename.c_str());
    data_out.write_vtu(output);
  }


  // @sect4{<code>AdvectionProblem::run()</code>}

  // As in most tutorials, this function creates/refines the mesh and calls
  // the various functions defined above to set up, assemble, solve, and output
  // the results.

  // In cycle zero, we generate the mesh for the on the square
  // <code>[-1,1]^dim</code> with a hole of radius 3/10 units centered
  // at the origin. For objects with `manifold_id` equal to one
  // (namely, the faces adjacent to the hole), we assign a spherical
  // manifold.

  template <int dim>
  void AdvectionProblem<dim>::run()
  {
    for (unsigned int cycle = 0; cycle < (settings.fe_degree == 1 ? 7 : 5);
         ++cycle)
      {
        std::cout << "  Cycle " << cycle << ':' << std::endl;

        if (cycle == 0)
          {
            GridGenerator::hyper_cube_with_cylindrical_hole(triangulation,
                                                            0.3,
                                                            1.0);

            const SphericalManifold<dim> manifold_description(Point<dim>(0, 0));
            triangulation.set_manifold(1, manifold_description);
          }

        triangulation.refine_global();

        setup_system();

        std::cout << "     Number of active cells:       "
                  << triangulation.n_active_cells() << " ("
                  << triangulation.n_levels() << " levels)" << std::endl;
        std::cout << "     Number of degrees of freedom: "
                  << dof_handler.n_dofs() << std::endl;

        assemble_system_and_multigrid();

        solve();

        if (settings.output)
          output_results(cycle);

        std::cout << std::endl;
      }
  }
} // namespace Step63


// @sect3{The <code>main</code> function}

// Finally, the main function is like most tutorials. The only
// interesting bit is that we require the user to pass a `.prm` file
// as a sole command line argument. If no parameter file is given, the
// program will output the contents of a sample parameter file with
// all default values to the screen that the user can then copy and
// paste into their own `.prm` file.

int main(int argc, char *argv[])
{
  try
    {
      Step63::Settings settings;
      settings.get_parameters((argc > 1) ? (argv[1]) : "");

      Step63::AdvectionProblem<2> advection_problem_2d(settings);
      advection_problem_2d.run();
    }
  catch (std::exception &exc)
    {
      std::cerr << std::endl
                << std::endl
                << "----------------------------------------------------"
                << std::endl;
      std::cerr << "Exception on processing: " << std::endl
                << exc.what() << std::endl
                << "Aborting!" << std::endl
                << "----------------------------------------------------"
                << std::endl;
      return 1;
    }
  catch (...)
    {
      std::cerr << std::endl
                << std::endl
                << "----------------------------------------------------"
                << std::endl;
      std::cerr << "Unknown exception!" << std::endl
                << "Aborting!" << std::endl
                << "----------------------------------------------------"
                << std::endl;
      return 1;
    }

  return 0;
}
