/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
   |                                                                        |
   | Copyright (c) 2005-2024, Individual contributors, see AUTHORS file     |
   | See: https://www.mrpt.org/Authors - All rights reserved.               |
   | Released under BSD License. See: https://www.mrpt.org/License          |
   +------------------------------------------------------------------------+ */
#pragma once

#include <mrpt/math/CMatrixDynamic.h>
#include <mrpt/math/CProbabilityDensityFunction.h>
#include <mrpt/poses/CPose2D.h>
#include <mrpt/serialization/CSerializable.h>

namespace mrpt::poses
{
class CPosePDFGaussian;  // frd decl.

/** Declares a class that represents a probability density function (pdf) of a
 * 2D pose (x,y,phi).
 *   This class is just the base class for unifying many different ways this pdf
 * can be implemented.
 *
 *  For convenience, a pose composition is also defined for any pdf derived
 * class,
 *   changeCoordinatesReference, in the form of a method rather than an
 * operator.
 *
 *  See also:
 *  [probabilistic spatial representations](tutorial-pdf-over-poses.html)
 *
 * \sa CPose2D, CPose3DPDF, CPoseRandomSampler
 * \ingroup poses_pdf_grp
 */
class CPosePDF :
    public mrpt::serialization::CSerializable,
    public mrpt::math::CProbabilityDensityFunction<CPose2D, 3>
{
  DEFINE_VIRTUAL_SERIALIZABLE(CPosePDF, mrpt::poses)

 public:
  /** Copy operator, translating if necessary (for example, between particles
   * and gaussian representations)
   */
  virtual void copyFrom(const CPosePDF& o) = 0;

  /** Bayesian fusion of two pose distributions (product of two
   * distributions->new distribution), then save the result in this object
   * (WARNING: See implementing classes to see classes that can and cannot be
   * mixtured!)
   * \param p1 The first distribution to fuse
   * \param p2 The second distribution to fuse
   * \param minMahalanobisDistToDrop If set to different of 0, the result of
   * very separate Gaussian modes (that will result in negligible components)
   * in SOGs will be dropped to reduce the number of modes in the output.
   */
  virtual void bayesianFusion(
      const CPosePDF& p1, const CPosePDF& p2, const double minMahalanobisDistToDrop = 0) = 0;

  /** Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF
   */
  virtual void inverse(CPosePDF& o) const = 0;

  virtual void changeCoordinatesReference(const CPose3D& newReferenceBase) = 0;

  /** This static method computes the pose composition Jacobians, with these
   formulas:
    \code
      df_dx =
      [ 1, 0, -sin(phi_x)*x_u-cos(phi_x)*y_u ]
      [ 0, 1,  cos(phi_x)*x_u-sin(phi_x)*y_u ]
      [ 0, 0,                              1 ]

      df_du =
      [ cos(phi_x) , -sin(phi_x) ,  0  ]
      [ sin(phi_x) ,  cos(phi_x) ,  0  ]
      [         0  ,          0  ,  1  ]
    \endcode
  */
  static void jacobiansPoseComposition(
      const CPose2D& x,
      const CPose2D& u,
      mrpt::math::CMatrixDouble33& df_dx,
      mrpt::math::CMatrixDouble33& df_du,
      const bool compute_df_dx = true,
      const bool compute_df_du = true);

  /** \overload */
  static void jacobiansPoseComposition(
      const CPosePDFGaussian& x,
      const CPosePDFGaussian& u,
      mrpt::math::CMatrixDouble33& df_dx,
      mrpt::math::CMatrixDouble33& df_du);

  enum
  {
    is_3D_val = 0
  };
  static constexpr bool is_3D() { return is_3D_val != 0; }
  enum
  {
    is_PDF_val = 1
  };
  static constexpr bool is_PDF() { return is_PDF_val != 0; }
  /** Returns a 3D representation of this PDF (it doesn't clear the current
   * contents of out_obj, but append new OpenGL objects to that list)
   * \note Needs the mrpt-opengl library, and using
   * mrpt::opengl::CSetOfObjects::Ptr as template argument.
   * \note By default, ellipsoids for the confidence intervals of  "q=3" are
   * drawn; for more mathematical details, see
   * CGeneralizedEllipsoidTemplate::setQuantiles()
   */
  template <class OPENGL_SETOFOBJECTSPTR>
  inline void getAs3DObject(OPENGL_SETOFOBJECTSPTR& out_obj) const
  {
    using SETOFOBJECTS = typename OPENGL_SETOFOBJECTSPTR::element_type;
    out_obj->insertCollection(*SETOFOBJECTS::posePDF2opengl(*this));
  }

  /** Returns a 3D representation of this PDF.
   * \note Needs the mrpt-opengl library, and using
   * mrpt::opengl::CSetOfObjects::Ptr as template argument.
   */
  template <class OPENGL_SETOFOBJECTSPTR>
  inline OPENGL_SETOFOBJECTSPTR getAs3DObject() const
  {
    using SETOFOBJECTS = typename OPENGL_SETOFOBJECTSPTR::element_type;
    return SETOFOBJECTS::posePDF2opengl(*this);
  }

 public:
};  // End of class def.

}  // namespace mrpt::poses
