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diff --git a/src/Persistence_representations/include/gudhi/Persistence_landscape_on_grid_exact.h b/src/Persistence_representations/include/gudhi/Persistence_landscape_on_grid_exact.h
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--- a/src/Persistence_representations/include/gudhi/Persistence_landscape_on_grid_exact.h
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-/*    This file is part of the Gudhi Library. The Gudhi library
- *    (Geometric Understanding in Higher Dimensions) is a generic C++
- *    library for computational topology.
- *
- *    Author(s):       Mathieu Carriere
- *
- *    Copyright (C) 2018  INRIA (France)
- *
- *    This program is free software: you can redistribute it and/or modify
- *    it under the terms of the GNU 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 General Public License for more details.
- *
- *    You should have received a copy of the GNU General Public License
- *    along with this program.  If not, see <http://www.gnu.org/licenses/>.
- */
-
-#ifndef LANDSCAPE_H_
-#define LANDSCAPE_H_
-
-// gudhi include
-#include <gudhi/read_persistence_from_file.h>
-#include <gudhi/common_persistence_representations.h>
-#include <gudhi/Debug_utils.h>
-
-// standard include
-#include <cmath>
-#include <iostream>
-#include <vector>
-#include <limits>
-#include <fstream>
-#include <sstream>
-#include <algorithm>
-#include <string>
-#include <utility>
-#include <functional>
-
-namespace Gudhi {
-namespace Persistence_representations {
-
-/**
- * \class Persistence_landscape_on_grid_exact gudhi/Persistence_landscape_on_grid_exact.h
- * \brief A class implementing exact persistence landscapes by approximating them on a collection of grid points
- *
- * \ingroup Persistence_representations
- *
- * \details
- * In this class, we propose a way to approximate landscapes by sampling the x-axis of the persistence diagram and evaluating the (exact) landscape functions on the sample projections onto the diagonal. Note that this is a different approximation scheme 
- * from the one proposed in Persistence_landscape_on_grid, which puts a grid on the diagonal, maps the persistence intervals on this grid and computes the (approximate) landscape functions on the samples. 
- * Hence, the difference is that we do not modify the diagram in this implementation, but the code can be slower to run.
-**/
-
-class Persistence_landscape_on_grid_exact {
-
-  protected:
-   Persistence_diagram diagram;
-   int res_x, nb_ls;
-   double min_x, max_x;
-
-  public:
-
-   /** \brief Persistence_landscape_on_grid_exact constructor.
-    * \ingroup Persistence_landscape_on_grid_exact
-    *
-    * @param[in] _diagram    persistence diagram.
-    * @param[in] _nb_ls      number of landscape functions.
-    * @param[in] _min_x      minimum value of samples.
-    * @param[in] _max_x      maximum value of samples.
-    * @param[in] _res_x      number of samples.
-    *
-    */
-   Persistence_landscape_on_grid_exact(const Persistence_diagram & _diagram, int _nb_ls = 5, double _min_x = 0.0, double _max_x = 1.0, int _res_x = 10){diagram = _diagram; nb_ls = _nb_ls; min_x = _min_x; max_x = _max_x; res_x = _res_x;}
-
-   /** \brief Computes the landscape approximation of a diagram.
-    * \ingroup Persistence_landscape_on_grid_exact
-    *
-    */
-   std::vector<std::vector<double> > vectorize() const {
-     std::vector<std::vector<double> >  ls; for(int i = 0; i < nb_ls; i++)  ls.emplace_back();
-     int num_pts = diagram.size(); double step = (max_x - min_x)/(res_x - 1);
-
-     std::vector<std::vector<double> > ls_t; for(int i = 0; i < res_x; i++)  ls_t.emplace_back();
-     for(int j = 0; j < num_pts; j++){
-       double px = diagram[j].first; double py = diagram[j].second; double mid = (px+py)/2;
-       int first = std::ceil((px-min_x)/step); int middle = std::ceil((mid-min_x)/step); int last = std::ceil((py-min_x)/step); double x = min_x + first*step;
-       for(int i = first; i < middle; i++){  double value = std::sqrt(2)*(x-px); ls_t[i].push_back(value); x += step;  }
-       for(int i = middle; i < last; i++){   double value = std::sqrt(2)*(py-x); ls_t[i].push_back(value); x += step;  }
-     }
-
-     for(int i = 0; i < res_x; i++){
-       std::sort(ls_t[i].begin(), ls_t[i].end(), [](const double & a, const double & b){return a > b;});
-       int nb_events_i = ls_t[i].size();
-       for (int j = 0; j < nb_ls; j++){  if(j < nb_events_i)  ls[j].push_back(ls_t[i][j]);  else  ls[j].push_back(0);  }
-     }
-
-     return ls;
-   }
-
-}; // class Persistence_landscape_on_grid_exact
-}  // namespace Persistence_representations
-}  // namespace Gudhi
-
-#endif  // LANDSCAPE_H_