From d8f04fab98dcb46ba7b300048311bf9e8b0ab3d2 Mon Sep 17 00:00:00 2001
From: vrouvrea <vrouvrea@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Mon, 22 Jan 2018 13:51:28 +0000
Subject: Fix cpplint

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/trunk@3149 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: e1cc797f8c24015168a1f84430666e8a156ababa
---
 src/common/include/gudhi/Unitary_tests_utils.h | 1 +
 1 file changed, 1 insertion(+)

(limited to 'src/common')

diff --git a/src/common/include/gudhi/Unitary_tests_utils.h b/src/common/include/gudhi/Unitary_tests_utils.h
index 7ae5d356..8394a062 100644
--- a/src/common/include/gudhi/Unitary_tests_utils.h
+++ b/src/common/include/gudhi/Unitary_tests_utils.h
@@ -25,6 +25,7 @@
 #include <boost/test/unit_test.hpp>
 
 #include <iostream>
+#include <limits>  // for std::numeric_limits<>
 
 template<typename FloatingType >
 void GUDHI_TEST_FLOAT_EQUALITY_CHECK(FloatingType a, FloatingType b,
-- 
cgit v1.2.3


From 06ff6fac211d2823c7d14a6d2f4a4db03f48d2e3 Mon Sep 17 00:00:00 2001
From: vrouvrea <vrouvrea@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Fri, 26 Jan 2018 14:01:39 +0000
Subject: Seperate installation and examples from main page Move cover complex
 utilities from examples GIC.cpp example was not compiled, nor tested. It is
 removed. Persistence representation : no need to link with Boost_SYSTEM

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/trunk@3164 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: cf2bfa6c6de2ed359aaa165b9f80bca7e06defb1
---
 src/Nerve_GIC/doc/Intro_graph_induced_complex.h    |  29 --
 src/Nerve_GIC/example/CMakeLists.txt               |  12 -
 src/Nerve_GIC/example/GIC.cpp                      |  95 -----
 .../example/KeplerMapperVisuFromTxtFile.py         |  72 ----
 src/Nerve_GIC/example/Nerve.cpp                    |  96 -----
 src/Nerve_GIC/example/Nerve.txt                    |  63 ----
 src/Nerve_GIC/example/VoronoiGIC.cpp               |  90 -----
 src/Nerve_GIC/example/km.py                        | 390 ---------------------
 src/Nerve_GIC/example/km.py.COPYRIGHT              |  26 --
 src/Nerve_GIC/utilities/CMakeLists.txt             |  22 ++
 .../utilities/KeplerMapperVisuFromTxtFile.py       |  72 ++++
 src/Nerve_GIC/utilities/Nerve.cpp                  |  96 +++++
 src/Nerve_GIC/utilities/Nerve.txt                  |  63 ++++
 src/Nerve_GIC/utilities/VoronoiGIC.cpp             |  90 +++++
 src/Nerve_GIC/utilities/km.py                      | 390 +++++++++++++++++++++
 src/Nerve_GIC/utilities/km.py.COPYRIGHT            |  26 ++
 .../example/CMakeLists.txt                         |   5 -
 .../test/CMakeLists.txt                            |  14 +-
 src/common/doc/examples.h                          |  99 ++++++
 src/common/doc/installation.h                      | 263 ++++++++++++++
 src/common/doc/main_page.h                         | 305 +---------------
 21 files changed, 1130 insertions(+), 1188 deletions(-)
 delete mode 100644 src/Nerve_GIC/example/GIC.cpp
 delete mode 100755 src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py
 delete mode 100644 src/Nerve_GIC/example/Nerve.cpp
 delete mode 100644 src/Nerve_GIC/example/Nerve.txt
 delete mode 100644 src/Nerve_GIC/example/VoronoiGIC.cpp
 delete mode 100755 src/Nerve_GIC/example/km.py
 delete mode 100644 src/Nerve_GIC/example/km.py.COPYRIGHT
 create mode 100644 src/Nerve_GIC/utilities/CMakeLists.txt
 create mode 100755 src/Nerve_GIC/utilities/KeplerMapperVisuFromTxtFile.py
 create mode 100644 src/Nerve_GIC/utilities/Nerve.cpp
 create mode 100644 src/Nerve_GIC/utilities/Nerve.txt
 create mode 100644 src/Nerve_GIC/utilities/VoronoiGIC.cpp
 create mode 100755 src/Nerve_GIC/utilities/km.py
 create mode 100644 src/Nerve_GIC/utilities/km.py.COPYRIGHT
 create mode 100644 src/common/doc/examples.h
 create mode 100644 src/common/doc/installation.h

(limited to 'src/common')

diff --git a/src/Nerve_GIC/doc/Intro_graph_induced_complex.h b/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
index 7578cc53..344cb031 100644
--- a/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
+++ b/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
@@ -177,7 +177,6 @@ namespace cover_complex {
  * \image html "funcGICvisu.jpg" "Visualization with neato"
  *
  * \copyright GNU General Public License v3.                         
- * \verbatim  Contact: gudhi-users@lists.gforge.inria.fr \endverbatim
  */
 /** @} */  // end defgroup cover_complex
 
@@ -186,31 +185,3 @@ namespace cover_complex {
 }  // namespace Gudhi
 
 #endif  // DOC_COVER_COMPLEX_INTRO_COVER_COMPLEX_H_
-
-
-/* * \subsection gicexample Example with cover from function
- *
- * This example builds the GIC of a point cloud sampled on a 3D human shape (human.off).
- * The cover C comes from the preimages of intervals (with length 0.075 and gain 0)
- * covering the height function (coordinate 2),
- * and the graph G comes from a Rips complex built with threshold 0.075.
- * Note that if the gain is too big, the number of cliques increases a lot,
- * which make the computation time much larger.
- *
- * \include Nerve_GIC/GIC.cpp
- *
- * When launching:
- *
- * \code $> ./GIC ../../data/points/human.off 0.075 2 0.075 0 --v
- * \endcode
- *
- * the program outputs SC.txt, which can be visualized with python and firefox as before:
- *
- * \image html "gicvisu.jpg" "Visualization with KeplerMapper"
- * */
-
-
-/* * Using e.g.
- *
- * \code $> python KeplerMapperVisuFromTxtFile.py && firefox SC.html
- * \endcode */
diff --git a/src/Nerve_GIC/example/CMakeLists.txt b/src/Nerve_GIC/example/CMakeLists.txt
index 73728dc0..434637fa 100644
--- a/src/Nerve_GIC/example/CMakeLists.txt
+++ b/src/Nerve_GIC/example/CMakeLists.txt
@@ -3,26 +3,14 @@ project(Nerve_GIC_examples)
 
 if (NOT CGAL_VERSION VERSION_LESS 4.8.1)
 
-  add_executable ( Nerve Nerve.cpp )
   add_executable ( CoordGIC CoordGIC.cpp )
   add_executable ( FuncGIC FuncGIC.cpp )
-  add_executable ( VoronoiGIC VoronoiGIC.cpp )
 
   if (TBB_FOUND)
-    target_link_libraries(Nerve ${TBB_LIBRARIES})
     target_link_libraries(CoordGIC ${TBB_LIBRARIES})
     target_link_libraries(FuncGIC ${TBB_LIBRARIES})
-    target_link_libraries(VoronoiGIC ${TBB_LIBRARIES})
   endif()
 
-  file(COPY KeplerMapperVisuFromTxtFile.py km.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/)
-
-  add_test(NAME Nerve_GIC_example_nerve COMMAND $<TARGET_FILE:Nerve>
-      "${CMAKE_SOURCE_DIR}/data/points/human.off" "2" "10" "0.3")
-
-  add_test(NAME Nerve_GIC_example_VoronoiGIC COMMAND $<TARGET_FILE:VoronoiGIC>
-      "${CMAKE_SOURCE_DIR}/data/points/human.off" "100")
-
   add_test(NAME Nerve_GIC_example_CoordGIC COMMAND $<TARGET_FILE:CoordGIC>
       "${CMAKE_SOURCE_DIR}/data/points/tore3D_1307.off" "0")
 
diff --git a/src/Nerve_GIC/example/GIC.cpp b/src/Nerve_GIC/example/GIC.cpp
deleted file mode 100644
index 2bc24a4d..00000000
--- a/src/Nerve_GIC/example/GIC.cpp
+++ /dev/null
@@ -1,95 +0,0 @@
-/*    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 Carrière
- *
- *    Copyright (C) 2017  INRIA
- *
- *    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/>.
- */
-
-#include <gudhi/GIC.h>
-
-#include <string>
-#include <vector>
-
-void usage(int nbArgs, char *const progName) {
-  std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n";
-  std::cerr << "Usage: " << progName << " filename.off threshold coordinate resolution gain [--v] \n";
-  std::cerr << "       i.e.: " << progName << " ../../data/points/human.off 0.075 2 0.075 0 --v \n";
-  exit(-1);  // ----- >>
-}
-
-int main(int argc, char **argv) {
-  if ((argc != 6) && (argc != 7)) usage(argc, argv[0]);
-
-  using Point = std::vector<float>;
-
-  std::string off_file_name(argv[1]);
-  double threshold = atof(argv[2]);
-  int coord = atoi(argv[3]);
-  double resolution = atof(argv[4]);
-  double gain = atof(argv[5]);
-  bool verb = 0;
-  if (argc == 7) verb = 1;
-
-  // ----------------------------------------------------------------------------
-  // Init of a graph induced complex from an OFF file
-  // ----------------------------------------------------------------------------
-
-  Gudhi::graph_induced_complex::Graph_induced_complex<Point> GIC;
-  GIC.set_verbose(verb);
-
-  bool check = GIC.read_point_cloud(off_file_name);
-
-  if (!check) {
-    std::cout << "Incorrect OFF file." << std::endl;
-  } else {
-    GIC.set_color_from_coordinate(coord);
-    GIC.set_function_from_coordinate(coord);
-
-    GIC.set_graph_from_rips(threshold, Gudhi::Euclidean_distance());
-
-    GIC.set_resolution_with_interval_length(resolution);
-    GIC.set_gain(gain);
-    GIC.set_cover_from_function();
-
-    GIC.find_GIC_simplices();
-
-    GIC.plot_TXT_for_KeplerMapper();
-
-    Gudhi::Simplex_tree<> stree;
-    GIC.create_complex(stree);
-
-    // ----------------------------------------------------------------------------
-    // Display information about the graph induced complex
-    // ----------------------------------------------------------------------------
-
-    if (verb) {
-      std::cout << "Graph induced complex is of dimension " << stree.dimension() << " - " << stree.num_simplices()
-                << " simplices - " << stree.num_vertices() << " vertices." << std::endl;
-
-      std::cout << "Iterator on graph induced complex simplices" << std::endl;
-      for (auto f_simplex : stree.filtration_simplex_range()) {
-        for (auto vertex : stree.simplex_vertex_range(f_simplex)) {
-          std::cout << vertex << " ";
-        }
-        std::cout << std::endl;
-      }
-    }
-  }
-
-  return 0;
-}
diff --git a/src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py b/src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py
deleted file mode 100755
index d2897774..00000000
--- a/src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py
+++ /dev/null
@@ -1,72 +0,0 @@
-#!/usr/bin/env python
-
-import km
-import numpy as np
-from collections import defaultdict
-
-"""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) 2017 INRIA
-
-   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/>.
-"""
-
-__author__ = "Mathieu Carriere"
-__copyright__ = "Copyright (C) 2017 INRIA"
-__license__ = "GPL v3"
-
-network = {}
-mapper = km.KeplerMapper(verbose=0)
-data = np.zeros((3,3))
-projected_data = mapper.fit_transform( data, projection="sum", scaler=None )
-
-f = open('SC.txt','r')
-nodes = defaultdict(list)
-links = defaultdict(list)
-custom = defaultdict(list)
-
-dat = f.readline()
-lens = f.readline()
-color = f.readline();
-param = [float(i) for i in f.readline().split(" ")]
-
-nums = [int(i) for i in f.readline().split(" ")]
-num_nodes = nums[0]
-num_edges = nums[1]
-
-for i in range(0,num_nodes):
-	point = [float(j) for j in f.readline().split(" ")]
-	nodes[  str(int(point[0]))  ] = [  int(point[0]), point[1], int(point[2])  ]
-	links[  str(int(point[0]))  ] = []
-	custom[  int(point[0])  ] = point[1]
-
-m = min([custom[i] for i in range(0,num_nodes)])
-M = max([custom[i] for i in range(0,num_nodes)])
-
-for i in range(0,num_edges):
-	edge = [int(j) for j in f.readline().split(" ")]	
-	links[  str(edge[0])  ].append(  str(edge[1])  )	
-	links[  str(edge[1])  ].append(  str(edge[0])  )
-
-network["nodes"] = nodes
-network["links"] = links
-network["meta"] = lens
-
-mapper.visualize(network, color_function = color, path_html="SC.html", title=dat,
-graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=custom, width_html=0, 
-height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=param[0],gain=param[1], minimum=m,maximum=M)
diff --git a/src/Nerve_GIC/example/Nerve.cpp b/src/Nerve_GIC/example/Nerve.cpp
deleted file mode 100644
index 6abdedc7..00000000
--- a/src/Nerve_GIC/example/Nerve.cpp
+++ /dev/null
@@ -1,96 +0,0 @@
-/*    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 Carrière
- *
- *    Copyright (C) 2017  INRIA
- *
- *    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/>.
- */
-
-#include <gudhi/GIC.h>
-
-#include <string>
-#include <vector>
-
-void usage(int nbArgs, char *const progName) {
-  std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n";
-  std::cerr << "Usage: " << progName << " filename.off coordinate resolution gain [--v] \n";
-  std::cerr << "       i.e.: " << progName << " ../../data/points/human.off 2 10 0.3 --v \n";
-  exit(-1);  // ----- >>
-}
-
-int main(int argc, char **argv) {
-  if ((argc != 5) && (argc != 6)) usage(argc, argv[0]);
-
-  using Point = std::vector<float>;
-
-  std::string off_file_name(argv[1]);
-  int coord = atoi(argv[2]);
-  int resolution = atoi(argv[3]);
-  double gain = atof(argv[4]);
-  bool verb = 0;
-  if (argc == 6) verb = 1;
-
-  // --------------------------------
-  // Init of a Nerve from an OFF file
-  // --------------------------------
-
-  Gudhi::cover_complex::Cover_complex<Point> SC;
-  SC.set_verbose(verb);
-
-  bool check = SC.read_point_cloud(off_file_name);
-
-  if (!check) {
-    std::cout << "Incorrect OFF file." << std::endl;
-  } else {
-    SC.set_type("Nerve");
-
-    SC.set_color_from_coordinate(coord);
-    SC.set_function_from_coordinate(coord);
-
-    SC.set_graph_from_OFF();
-    SC.set_resolution_with_interval_number(resolution);
-    SC.set_gain(gain);
-    SC.set_cover_from_function();
-
-    SC.find_simplices();
-
-    SC.write_info();
-
-    Gudhi::Simplex_tree<> stree;
-    SC.create_complex(stree);
-    SC.compute_PD();
-
-    // ----------------------------------------------------------------------------
-    // Display information about the graph induced complex
-    // ----------------------------------------------------------------------------
-
-    if (verb) {
-      std::cout << "Nerve is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - "
-                << stree.num_vertices() << " vertices." << std::endl;
-
-      std::cout << "Iterator on Nerve simplices" << std::endl;
-      for (auto f_simplex : stree.filtration_simplex_range()) {
-        for (auto vertex : stree.simplex_vertex_range(f_simplex)) {
-          std::cout << vertex << " ";
-        }
-        std::cout << std::endl;
-      }
-    }
-  }
-
-  return 0;
-}
diff --git a/src/Nerve_GIC/example/Nerve.txt b/src/Nerve_GIC/example/Nerve.txt
deleted file mode 100644
index 839ff45e..00000000
--- a/src/Nerve_GIC/example/Nerve.txt
+++ /dev/null
@@ -1,63 +0,0 @@
-Min function value = -0.979672 and Max function value = 0.816414
-Interval 0 = [-0.979672, -0.761576]
-Interval 1 = [-0.838551, -0.581967]
-Interval 2 = [-0.658942, -0.402359]
-Interval 3 = [-0.479334, -0.22275]
-Interval 4 = [-0.299725, -0.0431415]
-Interval 5 = [-0.120117, 0.136467]
-Interval 6 = [0.059492, 0.316076]
-Interval 7 = [0.239101, 0.495684]
-Interval 8 = [0.418709, 0.675293]
-Interval 9 = [0.598318, 0.816414]
-Computing preimages...
-Computing connected components...
-.txt generated. It can be visualized with e.g. python KeplerMapperVisuFromTxtFile.py and firefox.
-5 interval(s) in dimension 0:
-  [-0.909111, 0.00817529]
-  [-0.171433, 0.367392]
-  [-0.171433, 0.367392]
-  [-0.909111, 0.745853]
-0 interval(s) in dimension 1:
-Nerve is of dimension 1 - 41 simplices - 21 vertices.
-Iterator on Nerve simplices
-1 
-0 
-4 
-4 0 
-2 
-2 1 
-8 
-8 2 
-5 
-5 4 
-9 
-9 8 
-13 
-13 5 
-14 
-14 9 
-19 
-19 13 
-25 
-32 
-20 
-32 20 
-33 
-33 25 
-26 
-26 14 
-26 19 
-42 
-42 26 
-34 
-34 33 
-27 
-27 20 
-35 
-35 27 
-35 34 
-42 35 
-44 
-44 35 
-54 
-54 44 
\ No newline at end of file
diff --git a/src/Nerve_GIC/example/VoronoiGIC.cpp b/src/Nerve_GIC/example/VoronoiGIC.cpp
deleted file mode 100644
index 32431cc2..00000000
--- a/src/Nerve_GIC/example/VoronoiGIC.cpp
+++ /dev/null
@@ -1,90 +0,0 @@
-/*    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 Carrière
- *
- *    Copyright (C) 2017  INRIA
- *
- *    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/>.
- */
-
-#include <gudhi/GIC.h>
-
-#include <string>
-#include <vector>
-
-void usage(int nbArgs, char *const progName) {
-  std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n";
-  std::cerr << "Usage: " << progName << " filename.off N [--v] \n";
-  std::cerr << "       i.e.: " << progName << " ../../data/points/human.off 100 --v \n";
-  exit(-1);  // ----- >>
-}
-
-int main(int argc, char **argv) {
-  if ((argc != 3) && (argc != 4)) usage(argc, argv[0]);
-
-  using Point = std::vector<float>;
-
-  std::string off_file_name(argv[1]);
-  int m = atoi(argv[2]);
-  bool verb = 0;
-  if (argc == 4) verb = 1;
-
-  // ----------------------------------------------------------------------------
-  // Init of a graph induced complex from an OFF file
-  // ----------------------------------------------------------------------------
-
-  Gudhi::cover_complex::Cover_complex<Point> GIC;
-  GIC.set_verbose(verb);
-
-  bool check = GIC.read_point_cloud(off_file_name);
-
-  if (!check) {
-    std::cout << "Incorrect OFF file." << std::endl;
-  } else {
-    GIC.set_type("GIC");
-
-    GIC.set_color_from_coordinate();
-
-    GIC.set_graph_from_OFF();
-    GIC.set_cover_from_Voronoi(Gudhi::Euclidean_distance(), m);
-
-    GIC.find_simplices();
-
-    GIC.plot_OFF();
-
-    Gudhi::Simplex_tree<> stree;
-    GIC.create_complex(stree);
-
-    // ----------------------------------------------------------------------------
-    // Display information about the graph induced complex
-    // ----------------------------------------------------------------------------
-
-    if (verb) {
-      std::cout << "Graph induced complex is of dimension " << stree.dimension() << " - " << stree.num_simplices()
-                << " simplices - " << stree.num_vertices() << " vertices." << std::endl;
-
-      std::cout << "Iterator on graph induced complex simplices" << std::endl;
-      for (auto f_simplex : stree.filtration_simplex_range()) {
-        for (auto vertex : stree.simplex_vertex_range(f_simplex)) {
-          std::cout << vertex << " ";
-        }
-        std::cout << std::endl;
-      }
-    }
-  }
-
-  return 0;
-}
diff --git a/src/Nerve_GIC/example/km.py b/src/Nerve_GIC/example/km.py
deleted file mode 100755
index 53024aab..00000000
--- a/src/Nerve_GIC/example/km.py
+++ /dev/null
@@ -1,390 +0,0 @@
-from __future__ import division
-import numpy as np
-from collections import defaultdict
-import json
-import itertools
-from sklearn import cluster, preprocessing, manifold
-from datetime import datetime
-import sys
-
-class KeplerMapper(object):
-  # With this class you can build topological networks from (high-dimensional) data.
-  #
-  # 1)   	Fit a projection/lens/function to a dataset and transform it. 
-  #     	For instance "mean_of_row(x) for x in X"
-  # 2)   	Map this projection with overlapping intervals/hypercubes. 
-  #    		Cluster the points inside the interval 
-  #    		(Note: we cluster on the inverse image/original data to lessen projection loss).
-  #    		If two clusters/nodes have the same members (due to the overlap), then: 
-  #    		connect these with an edge.
-  # 3)  	Visualize the network using HTML and D3.js.
-  # 
-  # functions
-  # ---------
-  # fit_transform:   Create a projection (lens) from a dataset
-  # map:         	Apply Mapper algorithm on this projection and build a simplicial complex
-  # visualize:    	Turns the complex dictionary into a HTML/D3.js visualization
-  
-  def __init__(self, verbose=2):
-    self.verbose = verbose
-    
-    self.chunk_dist = []
-    self.overlap_dist = []
-    self.d = []
-    self.nr_cubes = 0
-    self.overlap_perc = 0
-    self.clusterer = False
-
-  def fit_transform(self, X, projection="sum", scaler=preprocessing.MinMaxScaler()):
-    # Creates the projection/lens from X. 
-    #
-    # Input:      X. Input features as a numpy array.
-    # Output:     projected_X. original data transformed to a projection (lens).
-    # 
-    # parameters
-    # ----------
-    # projection:   Projection parameter is either a string, 
-    #               a scikit class with fit_transform, like manifold.TSNE(), 
-    #               or a list of dimension indices.
-    # scaler:       if None, do no scaling, else apply scaling to the projection
-    #               Default: Min-Max scaling
-    
-    self.scaler = scaler
-    self.projection = str(projection)
-    
-    # Detect if projection is a class (for scikit-learn)
-    #if str(type(projection))[1:6] == "class": #TODO: de-ugly-fy
-    #  reducer = projection
-    #  if self.verbose > 0:
-    #    try:    
-    #      projection.set_params(**{"verbose":self.verbose})
-    #    except:
-    #      pass
-    #    print("\n..Projecting data using: \n\t%s\n"%str(projection))
-    #  X = reducer.fit_transform(X)
-    
-    # Detect if projection is a string (for standard functions)
-    if isinstance(projection, str):
-      if self.verbose > 0:
-        print("\n..Projecting data using: %s"%(projection))
-      # Stats lenses
-      if projection == "sum": # sum of row
-        X = np.sum(X, axis=1).reshape((X.shape[0],1))
-      if projection == "mean": # mean of row
-        X = np.mean(X, axis=1).reshape((X.shape[0],1))
-      if projection == "median": # mean of row
-        X = np.median(X, axis=1).reshape((X.shape[0],1))
-      if projection == "max": # max of row
-        X = np.max(X, axis=1).reshape((X.shape[0],1))
-      if projection == "min": # min of row
-        X = np.min(X, axis=1).reshape((X.shape[0],1))
-      if projection == "std": # std of row
-        X = np.std(X, axis=1).reshape((X.shape[0],1))
-        
-      if projection == "dist_mean": # Distance of x to mean of X
-        X_mean = np.mean(X, axis=0) 
-        X = np.sum(np.sqrt((X - X_mean)**2), axis=1).reshape((X.shape[0],1))
-
-    # Detect if projection is a list (with dimension indices)
-    if isinstance(projection, list):
-      if self.verbose > 0:
-        print("\n..Projecting data using: %s"%(str(projection)))
-      X = X[:,np.array(projection)]
-      
-    # Scaling
-    if scaler is not None:
-      if self.verbose > 0:
-        print("\n..Scaling with: %s\n"%str(scaler))
-      X = scaler.fit_transform(X)
-    
-    return X
-
-  def map(self, projected_X, inverse_X=None, clusterer=cluster.DBSCAN(eps=0.5,min_samples=3), nr_cubes=10, overlap_perc=0.1):
-    # This maps the data to a simplicial complex. Returns a dictionary with nodes and links.
-    # 
-    # Input:    projected_X. A Numpy array with the projection/lens. 
-    # Output:    complex. A dictionary with "nodes", "links" and "meta information"
-    #
-    # parameters
-    # ----------
-    # projected_X  	projected_X. A Numpy array with the projection/lens. Required.
-    # inverse_X    	Numpy array or None. If None then the projection itself is used for clustering.
-    # clusterer    	Scikit-learn API compatible clustering algorithm. Default: DBSCAN
-    # nr_cubes    	Int. The number of intervals/hypercubes to create.
-    # overlap_perc  Float. The percentage of overlap "between" the intervals/hypercubes.
-    
-    start = datetime.now()
-    
-    # Helper function
-    def cube_coordinates_all(nr_cubes, nr_dimensions):
-      # Helper function to get origin coordinates for our intervals/hypercubes
-      # Useful for looping no matter the number of cubes or dimensions
-      # Example:   	if there are 4 cubes per dimension and 3 dimensions 
-      #       		return the bottom left (origin) coordinates of 64 hypercubes, 
-      #       		as a sorted list of Numpy arrays
-      # TODO: elegance-ify...
-      l = []
-      for x in range(nr_cubes):
-        l += [x] * nr_dimensions
-      return [np.array(list(f)) for f in sorted(set(itertools.permutations(l,nr_dimensions)))]
-    
-    nodes = defaultdict(list)
-    links = defaultdict(list)
-    complex = {}
-    self.nr_cubes = nr_cubes
-    self.clusterer = clusterer
-    self.overlap_perc = overlap_perc
-    
-    if self.verbose > 0:
-      print("Mapping on data shaped %s using dimensions\n"%(str(projected_X.shape)))
-    
-    # If inverse image is not provided, we use the projection as the inverse image (suffer projection loss)
-    if inverse_X is None:
-      inverse_X = projected_X
-      
-    # We chop up the min-max column ranges into 'nr_cubes' parts
-    self.chunk_dist = (np.max(projected_X, axis=0) - np.min(projected_X, axis=0))/nr_cubes
-
-    # We calculate the overlapping windows distance 
-    self.overlap_dist = self.overlap_perc * self.chunk_dist
-
-    # We find our starting point
-    self.d = np.min(projected_X, axis=0)
-    
-    # Use a dimension index array on the projected X
-    # (For now this uses the entire dimensionality, but we keep for experimentation)
-    di = np.array([x for x in range(projected_X.shape[1])])
-    
-    # Prefix'ing the data with ID's
-    ids = np.array([x for x in range(projected_X.shape[0])])
-    projected_X = np.c_[ids,projected_X]
-    inverse_X = np.c_[ids,inverse_X]
-
-    # Subdivide the projected data X in intervals/hypercubes with overlap
-    if self.verbose > 0:
-      total_cubes = len(cube_coordinates_all(nr_cubes,projected_X.shape[1]))
-      print("Creating %s hypercubes."%total_cubes)
-
-    for i, coor in enumerate(cube_coordinates_all(nr_cubes,di.shape[0])):
-      # Slice the hypercube
-      hypercube = projected_X[ np.invert(np.any((projected_X[:,di+1] >= self.d[di] + (coor * self.chunk_dist[di])) & 
-          (projected_X[:,di+1] < self.d[di] + (coor * self.chunk_dist[di]) + self.chunk_dist[di] + self.overlap_dist[di]) == False, axis=1 )) ]
-      
-      if self.verbose > 1:
-        print("There are %s points in cube_%s / %s with starting range %s"%
-              (hypercube.shape[0],i,total_cubes,self.d[di] + (coor * self.chunk_dist[di])))
-      
-      # If at least one sample inside the hypercube
-      if hypercube.shape[0] > 0:
-        # Cluster the data point(s) in the cube, skipping the id-column
-        # Note that we apply clustering on the inverse image (original data samples) that fall inside the cube.
-        inverse_x = inverse_X[[int(nn) for nn in hypercube[:,0]]]
-        
-        clusterer.fit(inverse_x[:,1:])
-        
-        if self.verbose > 1:
-          print("Found %s clusters in cube_%s\n"%(np.unique(clusterer.labels_[clusterer.labels_ > -1]).shape[0],i))
-        
-        #Now for every (sample id in cube, predicted cluster label)
-        for a in np.c_[hypercube[:,0],clusterer.labels_]:
-          if a[1] != -1: #if not predicted as noise
-            cluster_id = str(coor[0])+"_"+str(i)+"_"+str(a[1])+"_"+str(coor)+"_"+str(self.d[di] + (coor * self.chunk_dist[di])) # TODO: de-rudimentary-ify
-            nodes[cluster_id].append( int(a[0]) ) # Append the member id's as integers
-      else:
-        if self.verbose > 1:
-          print("Cube_%s is empty.\n"%(i))
-
-    # Create links when clusters from different hypercubes have members with the same sample id.
-    candidates = itertools.combinations(nodes.keys(),2)
-    for candidate in candidates:
-      # if there are non-unique members in the union
-      if len(nodes[candidate[0]]+nodes[candidate[1]]) != len(set(nodes[candidate[0]]+nodes[candidate[1]])):
-        links[candidate[0]].append( candidate[1] )
-
-    # Reporting
-    if self.verbose > 0:
-      nr_links = 0
-      for k in links:
-        nr_links += len(links[k])
-      print("\ncreated %s edges and %s nodes in %s."%(nr_links,len(nodes),str(datetime.now()-start)))
-    
-    complex["nodes"] = nodes
-    complex["links"] = links
-    complex["meta"] = self.projection
-
-    return complex
-
-  def visualize(self, complex, color_function="", path_html="mapper_visualization_output.html", title="My Data", 
-          graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=None, width_html=0, 
-          height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=0,gain=0,minimum=0,maximum=0):
-    # Turns the dictionary 'complex' in a html file with d3.js
-    #
-    # Input:      complex. Dictionary (output from calling .map())
-    # Output:      a HTML page saved as a file in 'path_html'.
-    # 
-    # parameters
-    # ----------
-    # color_function    	string. Not fully implemented. Default: "" (distance to origin)
-    # path_html        		file path as string. Where to save the HTML page.
-    # title          		string. HTML page document title and first heading.
-    # graph_link_distance  	int. Edge length.
-    # graph_gravity     	float. "Gravity" to center of layout.
-    # graph_charge      	int. charge between nodes.
-    # custom_tooltips   	None or Numpy Array. You could use "y"-label array for this.
-    # width_html        	int. Width of canvas. Default: 0 (full width)
-    # height_html       	int. Height of canvas. Default: 0 (full height)
-    # show_tooltips     	bool. default:True
-    # show_title      		bool. default:True
-    # show_meta        		bool. default:True
-
-    # Format JSON for D3 graph
-    json_s = {}
-    json_s["nodes"] = []
-    json_s["links"] = []
-    k2e = {} # a key to incremental int dict, used for id's when linking
-
-    for e, k in enumerate(complex["nodes"]):
-      # Tooltip and node color formatting, TODO: de-mess-ify
-      if custom_tooltips is not None:
-        tooltip_s = "<h2>Cluster %s</h2>"%k + " ".join(str(custom_tooltips[complex["nodes"][k][0]]).split(" "))
-        if maximum == minimum:
-          tooltip_i = 0
-        else:
-          tooltip_i = int(30*(custom_tooltips[complex["nodes"][k][0]]-minimum)/(maximum-minimum))
-        json_s["nodes"].append({"name": str(k), "tooltip": tooltip_s, "group": 2 * int(np.log(complex["nodes"][k][2])), "color": tooltip_i})
-      else:
-        tooltip_s = "<h2>Cluster %s</h2>Contains %s members."%(k,len(complex["nodes"][k]))
-        json_s["nodes"].append({"name": str(k), "tooltip": tooltip_s, "group": 2 * int(np.log(len(complex["nodes"][k]))), "color": str(k.split("_")[0])})
-      k2e[k] = e
-    for k in complex["links"]:
-      for link in complex["links"][k]:
-        json_s["links"].append({"source": k2e[k], "target":k2e[link],"value":1})
-
-    # Width and height of graph in HTML output
-    if width_html == 0:
-      width_css = "100%"
-      width_js = 'document.getElementById("holder").offsetWidth-20'
-    else:
-      width_css = "%spx" % width_html
-      width_js = "%s" % width_html
-    if height_html == 0:
-      height_css = "100%"
-      height_js = 'document.getElementById("holder").offsetHeight-20'
-    else:
-      height_css = "%spx" % height_html
-      height_js = "%s" % height_html
-    
-    # Whether to show certain UI elements or not
-    if show_tooltips == False:
-      tooltips_display = "display: none;"
-    else:
-      tooltips_display = ""
-      
-    if show_meta == False:
-      meta_display = "display: none;"
-    else:
-      meta_display = ""
-      
-    if show_title == False:
-      title_display = "display: none;"
-    else:
-      title_display = ""  
-    
-    with open(path_html,"wb") as outfile:
-      html = """<!DOCTYPE html>
-    <meta charset="utf-8">
-    <meta name="generator" content="KeplerMapper">
-    <title>%s | KeplerMapper</title>
-    <link href='https://fonts.googleapis.com/css?family=Roboto:700,300' rel='stylesheet' type='text/css'>
-    <style>
-    * {margin: 0; padding: 0;}
-    html { height: 100%%;}
-    body {background: #111; height: 100%%; font: 100 16px Roboto, Sans-serif;}
-    .link { stroke: #999; stroke-opacity: .333;  }
-    .divs div { border-radius: 50%%; background: red; position: absolute; }
-    .divs { position: absolute; top: 0; left: 0; }
-    #holder { position: relative; width: %s; height: %s; background: #111; display: block;}
-    h1 { %s padding: 20px; color: #fafafa; text-shadow: 0px 1px #000,0px -1px #000; position: absolute; font: 300 30px Roboto, Sans-serif;}
-    h2 { text-shadow: 0px 1px #000,0px -1px #000; font: 700 16px Roboto, Sans-serif;}
-    .meta {  position: absolute; opacity: 0.9; width: 220px; top: 80px; left: 20px; display: block; %s background: #000; line-height: 25px; color: #fafafa; border: 20px solid #000; font: 100 16px Roboto, Sans-serif;}
-    div.tooltip { position: absolute; width: 380px; display: block; %s padding: 20px; background: #000; border: 0px; border-radius: 3px; pointer-events: none; z-index: 999; color: #FAFAFA;}
-    }
-    </style>
-    <body>
-    <div id="holder">
-      <h1>%s</h1>
-      <p class="meta">
-      <b>Lens</b><br>%s<br><br>
-      <b>Length of intervals</b><br>%s<br><br>
-      <b>Overlap percentage</b><br>%s%%<br><br>
-      <b>Color Function</b><br>%s
-      </p>
-    </div>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js"></script>
-    <script>
-    var width = %s,
-      height = %s;
-    var color = d3.scale.ordinal()
-      .domain(["0","1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30"])
-      .range(["#FF0000","#FF1400","#FF2800","#FF3c00","#FF5000","#FF6400","#FF7800","#FF8c00","#FFa000","#FFb400","#FFc800","#FFdc00","#FFf000","#fdff00","#b0ff00","#65ff00","#17ff00","#00ff36","#00ff83","#00ffd0","#00e4ff","#00c4ff","#00a4ff","#00a4ff","#0084ff","#0064ff","#0044ff","#0022ff","#0002ff","#0100ff","#0300ff","#0500ff"]);
-    var force = d3.layout.force()
-      .charge(%s)
-      .linkDistance(%s)
-      .gravity(%s)
-      .size([width, height]);
-    var svg = d3.select("#holder").append("svg")
-      .attr("width", width)
-      .attr("height", height);
-    
-    var div = d3.select("#holder").append("div")   
-      .attr("class", "tooltip")               
-      .style("opacity", 0.0);
-    
-    var divs = d3.select('#holder').append('div')
-      .attr('class', 'divs')
-      .attr('style', function(d) { return 'overflow: hidden; width: ' + width + 'px; height: ' + height + 'px;'; });  
-    
-      graph = %s;
-      force
-        .nodes(graph.nodes)
-        .links(graph.links)
-        .start();
-      var link = svg.selectAll(".link")
-        .data(graph.links)
-        .enter().append("line")
-        .attr("class", "link")
-        .style("stroke-width", function(d) { return Math.sqrt(d.value); });
-      var node = divs.selectAll('div')
-      .data(graph.nodes)
-        .enter().append('div')
-        .on("mouseover", function(d) {      
-          div.transition()        
-            .duration(200)      
-            .style("opacity", .9);
-          div .html(d.tooltip + "<br/>")  
-            .style("left", (d3.event.pageX + 100) + "px")     
-            .style("top", (d3.event.pageY - 28) + "px");    
-          })                  
-        .on("mouseout", function(d) {       
-          div.transition()        
-            .duration(500)      
-            .style("opacity", 0);   
-        })
-        .call(force.drag);
-      
-      node.append("title")
-        .text(function(d) { return d.name; });
-      force.on("tick", function() {
-      link.attr("x1", function(d) { return d.source.x; })
-        .attr("y1", function(d) { return d.source.y; })
-        .attr("x2", function(d) { return d.target.x; })
-        .attr("y2", function(d) { return d.target.y; });
-      node.attr("cx", function(d) { return d.x; })
-        .attr("cy", function(d) { return d.y; })
-        .attr('style', function(d) { return 'width: ' + (d.group * 2) + 'px; height: ' + (d.group * 2) + 'px; ' + 'left: '+(d.x-(d.group))+'px; ' + 'top: '+(d.y-(d.group))+'px; background: '+color(d.color)+'; box-shadow: 0px 0px 3px #111; box-shadow: 0px 0px 33px '+color(d.color)+', inset 0px 0px 5px rgba(0, 0, 0, 0.2);'})
-        ;
-      });
-    </script>"""%(title,width_css, height_css, title_display, meta_display, tooltips_display, title,complex["meta"],res,gain*100,color_function,width_js,height_js,graph_charge,graph_link_distance,graph_gravity,json.dumps(json_s))
-      outfile.write(html.encode("utf-8"))
-    if self.verbose > 0:
-      print("\nWrote d3.js graph to '%s'"%path_html)
diff --git a/src/Nerve_GIC/example/km.py.COPYRIGHT b/src/Nerve_GIC/example/km.py.COPYRIGHT
deleted file mode 100644
index bef7b121..00000000
--- a/src/Nerve_GIC/example/km.py.COPYRIGHT
+++ /dev/null
@@ -1,26 +0,0 @@
-km.py is a fork of https://github.com/MLWave/kepler-mapper.
-Only the visualization part has been kept (Mapper part has been removed).
-
-This file has te following Copyright :
-
-The MIT License (MIT)
-
-Copyright (c) 2015 Triskelion - HJ van Veen - info@mlwave.com
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
diff --git a/src/Nerve_GIC/utilities/CMakeLists.txt b/src/Nerve_GIC/utilities/CMakeLists.txt
new file mode 100644
index 00000000..a0508dc2
--- /dev/null
+++ b/src/Nerve_GIC/utilities/CMakeLists.txt
@@ -0,0 +1,22 @@
+cmake_minimum_required(VERSION 2.6)
+project(Nerve_GIC_examples)
+
+if (NOT CGAL_VERSION VERSION_LESS 4.8.1)
+
+  add_executable ( Nerve Nerve.cpp )
+  add_executable ( VoronoiGIC VoronoiGIC.cpp )
+
+  if (TBB_FOUND)
+    target_link_libraries(Nerve ${TBB_LIBRARIES})
+    target_link_libraries(VoronoiGIC ${TBB_LIBRARIES})
+  endif()
+
+  file(COPY KeplerMapperVisuFromTxtFile.py km.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/)
+
+  add_test(NAME Nerve_GIC_utilities_nerve COMMAND $<TARGET_FILE:Nerve>
+      "${CMAKE_SOURCE_DIR}/data/points/human.off" "2" "10" "0.3")
+
+  add_test(NAME Nerve_GIC_utilities_VoronoiGIC COMMAND $<TARGET_FILE:VoronoiGIC>
+      "${CMAKE_SOURCE_DIR}/data/points/human.off" "100")
+
+endif (NOT CGAL_VERSION VERSION_LESS 4.8.1)
diff --git a/src/Nerve_GIC/utilities/KeplerMapperVisuFromTxtFile.py b/src/Nerve_GIC/utilities/KeplerMapperVisuFromTxtFile.py
new file mode 100755
index 00000000..d2897774
--- /dev/null
+++ b/src/Nerve_GIC/utilities/KeplerMapperVisuFromTxtFile.py
@@ -0,0 +1,72 @@
+#!/usr/bin/env python
+
+import km
+import numpy as np
+from collections import defaultdict
+
+"""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) 2017 INRIA
+
+   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/>.
+"""
+
+__author__ = "Mathieu Carriere"
+__copyright__ = "Copyright (C) 2017 INRIA"
+__license__ = "GPL v3"
+
+network = {}
+mapper = km.KeplerMapper(verbose=0)
+data = np.zeros((3,3))
+projected_data = mapper.fit_transform( data, projection="sum", scaler=None )
+
+f = open('SC.txt','r')
+nodes = defaultdict(list)
+links = defaultdict(list)
+custom = defaultdict(list)
+
+dat = f.readline()
+lens = f.readline()
+color = f.readline();
+param = [float(i) for i in f.readline().split(" ")]
+
+nums = [int(i) for i in f.readline().split(" ")]
+num_nodes = nums[0]
+num_edges = nums[1]
+
+for i in range(0,num_nodes):
+	point = [float(j) for j in f.readline().split(" ")]
+	nodes[  str(int(point[0]))  ] = [  int(point[0]), point[1], int(point[2])  ]
+	links[  str(int(point[0]))  ] = []
+	custom[  int(point[0])  ] = point[1]
+
+m = min([custom[i] for i in range(0,num_nodes)])
+M = max([custom[i] for i in range(0,num_nodes)])
+
+for i in range(0,num_edges):
+	edge = [int(j) for j in f.readline().split(" ")]	
+	links[  str(edge[0])  ].append(  str(edge[1])  )	
+	links[  str(edge[1])  ].append(  str(edge[0])  )
+
+network["nodes"] = nodes
+network["links"] = links
+network["meta"] = lens
+
+mapper.visualize(network, color_function = color, path_html="SC.html", title=dat,
+graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=custom, width_html=0, 
+height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=param[0],gain=param[1], minimum=m,maximum=M)
diff --git a/src/Nerve_GIC/utilities/Nerve.cpp b/src/Nerve_GIC/utilities/Nerve.cpp
new file mode 100644
index 00000000..6abdedc7
--- /dev/null
+++ b/src/Nerve_GIC/utilities/Nerve.cpp
@@ -0,0 +1,96 @@
+/*    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 Carrière
+ *
+ *    Copyright (C) 2017  INRIA
+ *
+ *    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/>.
+ */
+
+#include <gudhi/GIC.h>
+
+#include <string>
+#include <vector>
+
+void usage(int nbArgs, char *const progName) {
+  std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n";
+  std::cerr << "Usage: " << progName << " filename.off coordinate resolution gain [--v] \n";
+  std::cerr << "       i.e.: " << progName << " ../../data/points/human.off 2 10 0.3 --v \n";
+  exit(-1);  // ----- >>
+}
+
+int main(int argc, char **argv) {
+  if ((argc != 5) && (argc != 6)) usage(argc, argv[0]);
+
+  using Point = std::vector<float>;
+
+  std::string off_file_name(argv[1]);
+  int coord = atoi(argv[2]);
+  int resolution = atoi(argv[3]);
+  double gain = atof(argv[4]);
+  bool verb = 0;
+  if (argc == 6) verb = 1;
+
+  // --------------------------------
+  // Init of a Nerve from an OFF file
+  // --------------------------------
+
+  Gudhi::cover_complex::Cover_complex<Point> SC;
+  SC.set_verbose(verb);
+
+  bool check = SC.read_point_cloud(off_file_name);
+
+  if (!check) {
+    std::cout << "Incorrect OFF file." << std::endl;
+  } else {
+    SC.set_type("Nerve");
+
+    SC.set_color_from_coordinate(coord);
+    SC.set_function_from_coordinate(coord);
+
+    SC.set_graph_from_OFF();
+    SC.set_resolution_with_interval_number(resolution);
+    SC.set_gain(gain);
+    SC.set_cover_from_function();
+
+    SC.find_simplices();
+
+    SC.write_info();
+
+    Gudhi::Simplex_tree<> stree;
+    SC.create_complex(stree);
+    SC.compute_PD();
+
+    // ----------------------------------------------------------------------------
+    // Display information about the graph induced complex
+    // ----------------------------------------------------------------------------
+
+    if (verb) {
+      std::cout << "Nerve is of dimension " << stree.dimension() << " - " << stree.num_simplices() << " simplices - "
+                << stree.num_vertices() << " vertices." << std::endl;
+
+      std::cout << "Iterator on Nerve simplices" << std::endl;
+      for (auto f_simplex : stree.filtration_simplex_range()) {
+        for (auto vertex : stree.simplex_vertex_range(f_simplex)) {
+          std::cout << vertex << " ";
+        }
+        std::cout << std::endl;
+      }
+    }
+  }
+
+  return 0;
+}
diff --git a/src/Nerve_GIC/utilities/Nerve.txt b/src/Nerve_GIC/utilities/Nerve.txt
new file mode 100644
index 00000000..839ff45e
--- /dev/null
+++ b/src/Nerve_GIC/utilities/Nerve.txt
@@ -0,0 +1,63 @@
+Min function value = -0.979672 and Max function value = 0.816414
+Interval 0 = [-0.979672, -0.761576]
+Interval 1 = [-0.838551, -0.581967]
+Interval 2 = [-0.658942, -0.402359]
+Interval 3 = [-0.479334, -0.22275]
+Interval 4 = [-0.299725, -0.0431415]
+Interval 5 = [-0.120117, 0.136467]
+Interval 6 = [0.059492, 0.316076]
+Interval 7 = [0.239101, 0.495684]
+Interval 8 = [0.418709, 0.675293]
+Interval 9 = [0.598318, 0.816414]
+Computing preimages...
+Computing connected components...
+.txt generated. It can be visualized with e.g. python KeplerMapperVisuFromTxtFile.py and firefox.
+5 interval(s) in dimension 0:
+  [-0.909111, 0.00817529]
+  [-0.171433, 0.367392]
+  [-0.171433, 0.367392]
+  [-0.909111, 0.745853]
+0 interval(s) in dimension 1:
+Nerve is of dimension 1 - 41 simplices - 21 vertices.
+Iterator on Nerve simplices
+1 
+0 
+4 
+4 0 
+2 
+2 1 
+8 
+8 2 
+5 
+5 4 
+9 
+9 8 
+13 
+13 5 
+14 
+14 9 
+19 
+19 13 
+25 
+32 
+20 
+32 20 
+33 
+33 25 
+26 
+26 14 
+26 19 
+42 
+42 26 
+34 
+34 33 
+27 
+27 20 
+35 
+35 27 
+35 34 
+42 35 
+44 
+44 35 
+54 
+54 44 
\ No newline at end of file
diff --git a/src/Nerve_GIC/utilities/VoronoiGIC.cpp b/src/Nerve_GIC/utilities/VoronoiGIC.cpp
new file mode 100644
index 00000000..32431cc2
--- /dev/null
+++ b/src/Nerve_GIC/utilities/VoronoiGIC.cpp
@@ -0,0 +1,90 @@
+/*    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 Carrière
+ *
+ *    Copyright (C) 2017  INRIA
+ *
+ *    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/>.
+ */
+
+#include <gudhi/GIC.h>
+
+#include <string>
+#include <vector>
+
+void usage(int nbArgs, char *const progName) {
+  std::cerr << "Error: Number of arguments (" << nbArgs << ") is not correct\n";
+  std::cerr << "Usage: " << progName << " filename.off N [--v] \n";
+  std::cerr << "       i.e.: " << progName << " ../../data/points/human.off 100 --v \n";
+  exit(-1);  // ----- >>
+}
+
+int main(int argc, char **argv) {
+  if ((argc != 3) && (argc != 4)) usage(argc, argv[0]);
+
+  using Point = std::vector<float>;
+
+  std::string off_file_name(argv[1]);
+  int m = atoi(argv[2]);
+  bool verb = 0;
+  if (argc == 4) verb = 1;
+
+  // ----------------------------------------------------------------------------
+  // Init of a graph induced complex from an OFF file
+  // ----------------------------------------------------------------------------
+
+  Gudhi::cover_complex::Cover_complex<Point> GIC;
+  GIC.set_verbose(verb);
+
+  bool check = GIC.read_point_cloud(off_file_name);
+
+  if (!check) {
+    std::cout << "Incorrect OFF file." << std::endl;
+  } else {
+    GIC.set_type("GIC");
+
+    GIC.set_color_from_coordinate();
+
+    GIC.set_graph_from_OFF();
+    GIC.set_cover_from_Voronoi(Gudhi::Euclidean_distance(), m);
+
+    GIC.find_simplices();
+
+    GIC.plot_OFF();
+
+    Gudhi::Simplex_tree<> stree;
+    GIC.create_complex(stree);
+
+    // ----------------------------------------------------------------------------
+    // Display information about the graph induced complex
+    // ----------------------------------------------------------------------------
+
+    if (verb) {
+      std::cout << "Graph induced complex is of dimension " << stree.dimension() << " - " << stree.num_simplices()
+                << " simplices - " << stree.num_vertices() << " vertices." << std::endl;
+
+      std::cout << "Iterator on graph induced complex simplices" << std::endl;
+      for (auto f_simplex : stree.filtration_simplex_range()) {
+        for (auto vertex : stree.simplex_vertex_range(f_simplex)) {
+          std::cout << vertex << " ";
+        }
+        std::cout << std::endl;
+      }
+    }
+  }
+
+  return 0;
+}
diff --git a/src/Nerve_GIC/utilities/km.py b/src/Nerve_GIC/utilities/km.py
new file mode 100755
index 00000000..53024aab
--- /dev/null
+++ b/src/Nerve_GIC/utilities/km.py
@@ -0,0 +1,390 @@
+from __future__ import division
+import numpy as np
+from collections import defaultdict
+import json
+import itertools
+from sklearn import cluster, preprocessing, manifold
+from datetime import datetime
+import sys
+
+class KeplerMapper(object):
+  # With this class you can build topological networks from (high-dimensional) data.
+  #
+  # 1)   	Fit a projection/lens/function to a dataset and transform it. 
+  #     	For instance "mean_of_row(x) for x in X"
+  # 2)   	Map this projection with overlapping intervals/hypercubes. 
+  #    		Cluster the points inside the interval 
+  #    		(Note: we cluster on the inverse image/original data to lessen projection loss).
+  #    		If two clusters/nodes have the same members (due to the overlap), then: 
+  #    		connect these with an edge.
+  # 3)  	Visualize the network using HTML and D3.js.
+  # 
+  # functions
+  # ---------
+  # fit_transform:   Create a projection (lens) from a dataset
+  # map:         	Apply Mapper algorithm on this projection and build a simplicial complex
+  # visualize:    	Turns the complex dictionary into a HTML/D3.js visualization
+  
+  def __init__(self, verbose=2):
+    self.verbose = verbose
+    
+    self.chunk_dist = []
+    self.overlap_dist = []
+    self.d = []
+    self.nr_cubes = 0
+    self.overlap_perc = 0
+    self.clusterer = False
+
+  def fit_transform(self, X, projection="sum", scaler=preprocessing.MinMaxScaler()):
+    # Creates the projection/lens from X. 
+    #
+    # Input:      X. Input features as a numpy array.
+    # Output:     projected_X. original data transformed to a projection (lens).
+    # 
+    # parameters
+    # ----------
+    # projection:   Projection parameter is either a string, 
+    #               a scikit class with fit_transform, like manifold.TSNE(), 
+    #               or a list of dimension indices.
+    # scaler:       if None, do no scaling, else apply scaling to the projection
+    #               Default: Min-Max scaling
+    
+    self.scaler = scaler
+    self.projection = str(projection)
+    
+    # Detect if projection is a class (for scikit-learn)
+    #if str(type(projection))[1:6] == "class": #TODO: de-ugly-fy
+    #  reducer = projection
+    #  if self.verbose > 0:
+    #    try:    
+    #      projection.set_params(**{"verbose":self.verbose})
+    #    except:
+    #      pass
+    #    print("\n..Projecting data using: \n\t%s\n"%str(projection))
+    #  X = reducer.fit_transform(X)
+    
+    # Detect if projection is a string (for standard functions)
+    if isinstance(projection, str):
+      if self.verbose > 0:
+        print("\n..Projecting data using: %s"%(projection))
+      # Stats lenses
+      if projection == "sum": # sum of row
+        X = np.sum(X, axis=1).reshape((X.shape[0],1))
+      if projection == "mean": # mean of row
+        X = np.mean(X, axis=1).reshape((X.shape[0],1))
+      if projection == "median": # mean of row
+        X = np.median(X, axis=1).reshape((X.shape[0],1))
+      if projection == "max": # max of row
+        X = np.max(X, axis=1).reshape((X.shape[0],1))
+      if projection == "min": # min of row
+        X = np.min(X, axis=1).reshape((X.shape[0],1))
+      if projection == "std": # std of row
+        X = np.std(X, axis=1).reshape((X.shape[0],1))
+        
+      if projection == "dist_mean": # Distance of x to mean of X
+        X_mean = np.mean(X, axis=0) 
+        X = np.sum(np.sqrt((X - X_mean)**2), axis=1).reshape((X.shape[0],1))
+
+    # Detect if projection is a list (with dimension indices)
+    if isinstance(projection, list):
+      if self.verbose > 0:
+        print("\n..Projecting data using: %s"%(str(projection)))
+      X = X[:,np.array(projection)]
+      
+    # Scaling
+    if scaler is not None:
+      if self.verbose > 0:
+        print("\n..Scaling with: %s\n"%str(scaler))
+      X = scaler.fit_transform(X)
+    
+    return X
+
+  def map(self, projected_X, inverse_X=None, clusterer=cluster.DBSCAN(eps=0.5,min_samples=3), nr_cubes=10, overlap_perc=0.1):
+    # This maps the data to a simplicial complex. Returns a dictionary with nodes and links.
+    # 
+    # Input:    projected_X. A Numpy array with the projection/lens. 
+    # Output:    complex. A dictionary with "nodes", "links" and "meta information"
+    #
+    # parameters
+    # ----------
+    # projected_X  	projected_X. A Numpy array with the projection/lens. Required.
+    # inverse_X    	Numpy array or None. If None then the projection itself is used for clustering.
+    # clusterer    	Scikit-learn API compatible clustering algorithm. Default: DBSCAN
+    # nr_cubes    	Int. The number of intervals/hypercubes to create.
+    # overlap_perc  Float. The percentage of overlap "between" the intervals/hypercubes.
+    
+    start = datetime.now()
+    
+    # Helper function
+    def cube_coordinates_all(nr_cubes, nr_dimensions):
+      # Helper function to get origin coordinates for our intervals/hypercubes
+      # Useful for looping no matter the number of cubes or dimensions
+      # Example:   	if there are 4 cubes per dimension and 3 dimensions 
+      #       		return the bottom left (origin) coordinates of 64 hypercubes, 
+      #       		as a sorted list of Numpy arrays
+      # TODO: elegance-ify...
+      l = []
+      for x in range(nr_cubes):
+        l += [x] * nr_dimensions
+      return [np.array(list(f)) for f in sorted(set(itertools.permutations(l,nr_dimensions)))]
+    
+    nodes = defaultdict(list)
+    links = defaultdict(list)
+    complex = {}
+    self.nr_cubes = nr_cubes
+    self.clusterer = clusterer
+    self.overlap_perc = overlap_perc
+    
+    if self.verbose > 0:
+      print("Mapping on data shaped %s using dimensions\n"%(str(projected_X.shape)))
+    
+    # If inverse image is not provided, we use the projection as the inverse image (suffer projection loss)
+    if inverse_X is None:
+      inverse_X = projected_X
+      
+    # We chop up the min-max column ranges into 'nr_cubes' parts
+    self.chunk_dist = (np.max(projected_X, axis=0) - np.min(projected_X, axis=0))/nr_cubes
+
+    # We calculate the overlapping windows distance 
+    self.overlap_dist = self.overlap_perc * self.chunk_dist
+
+    # We find our starting point
+    self.d = np.min(projected_X, axis=0)
+    
+    # Use a dimension index array on the projected X
+    # (For now this uses the entire dimensionality, but we keep for experimentation)
+    di = np.array([x for x in range(projected_X.shape[1])])
+    
+    # Prefix'ing the data with ID's
+    ids = np.array([x for x in range(projected_X.shape[0])])
+    projected_X = np.c_[ids,projected_X]
+    inverse_X = np.c_[ids,inverse_X]
+
+    # Subdivide the projected data X in intervals/hypercubes with overlap
+    if self.verbose > 0:
+      total_cubes = len(cube_coordinates_all(nr_cubes,projected_X.shape[1]))
+      print("Creating %s hypercubes."%total_cubes)
+
+    for i, coor in enumerate(cube_coordinates_all(nr_cubes,di.shape[0])):
+      # Slice the hypercube
+      hypercube = projected_X[ np.invert(np.any((projected_X[:,di+1] >= self.d[di] + (coor * self.chunk_dist[di])) & 
+          (projected_X[:,di+1] < self.d[di] + (coor * self.chunk_dist[di]) + self.chunk_dist[di] + self.overlap_dist[di]) == False, axis=1 )) ]
+      
+      if self.verbose > 1:
+        print("There are %s points in cube_%s / %s with starting range %s"%
+              (hypercube.shape[0],i,total_cubes,self.d[di] + (coor * self.chunk_dist[di])))
+      
+      # If at least one sample inside the hypercube
+      if hypercube.shape[0] > 0:
+        # Cluster the data point(s) in the cube, skipping the id-column
+        # Note that we apply clustering on the inverse image (original data samples) that fall inside the cube.
+        inverse_x = inverse_X[[int(nn) for nn in hypercube[:,0]]]
+        
+        clusterer.fit(inverse_x[:,1:])
+        
+        if self.verbose > 1:
+          print("Found %s clusters in cube_%s\n"%(np.unique(clusterer.labels_[clusterer.labels_ > -1]).shape[0],i))
+        
+        #Now for every (sample id in cube, predicted cluster label)
+        for a in np.c_[hypercube[:,0],clusterer.labels_]:
+          if a[1] != -1: #if not predicted as noise
+            cluster_id = str(coor[0])+"_"+str(i)+"_"+str(a[1])+"_"+str(coor)+"_"+str(self.d[di] + (coor * self.chunk_dist[di])) # TODO: de-rudimentary-ify
+            nodes[cluster_id].append( int(a[0]) ) # Append the member id's as integers
+      else:
+        if self.verbose > 1:
+          print("Cube_%s is empty.\n"%(i))
+
+    # Create links when clusters from different hypercubes have members with the same sample id.
+    candidates = itertools.combinations(nodes.keys(),2)
+    for candidate in candidates:
+      # if there are non-unique members in the union
+      if len(nodes[candidate[0]]+nodes[candidate[1]]) != len(set(nodes[candidate[0]]+nodes[candidate[1]])):
+        links[candidate[0]].append( candidate[1] )
+
+    # Reporting
+    if self.verbose > 0:
+      nr_links = 0
+      for k in links:
+        nr_links += len(links[k])
+      print("\ncreated %s edges and %s nodes in %s."%(nr_links,len(nodes),str(datetime.now()-start)))
+    
+    complex["nodes"] = nodes
+    complex["links"] = links
+    complex["meta"] = self.projection
+
+    return complex
+
+  def visualize(self, complex, color_function="", path_html="mapper_visualization_output.html", title="My Data", 
+          graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=None, width_html=0, 
+          height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=0,gain=0,minimum=0,maximum=0):
+    # Turns the dictionary 'complex' in a html file with d3.js
+    #
+    # Input:      complex. Dictionary (output from calling .map())
+    # Output:      a HTML page saved as a file in 'path_html'.
+    # 
+    # parameters
+    # ----------
+    # color_function    	string. Not fully implemented. Default: "" (distance to origin)
+    # path_html        		file path as string. Where to save the HTML page.
+    # title          		string. HTML page document title and first heading.
+    # graph_link_distance  	int. Edge length.
+    # graph_gravity     	float. "Gravity" to center of layout.
+    # graph_charge      	int. charge between nodes.
+    # custom_tooltips   	None or Numpy Array. You could use "y"-label array for this.
+    # width_html        	int. Width of canvas. Default: 0 (full width)
+    # height_html       	int. Height of canvas. Default: 0 (full height)
+    # show_tooltips     	bool. default:True
+    # show_title      		bool. default:True
+    # show_meta        		bool. default:True
+
+    # Format JSON for D3 graph
+    json_s = {}
+    json_s["nodes"] = []
+    json_s["links"] = []
+    k2e = {} # a key to incremental int dict, used for id's when linking
+
+    for e, k in enumerate(complex["nodes"]):
+      # Tooltip and node color formatting, TODO: de-mess-ify
+      if custom_tooltips is not None:
+        tooltip_s = "<h2>Cluster %s</h2>"%k + " ".join(str(custom_tooltips[complex["nodes"][k][0]]).split(" "))
+        if maximum == minimum:
+          tooltip_i = 0
+        else:
+          tooltip_i = int(30*(custom_tooltips[complex["nodes"][k][0]]-minimum)/(maximum-minimum))
+        json_s["nodes"].append({"name": str(k), "tooltip": tooltip_s, "group": 2 * int(np.log(complex["nodes"][k][2])), "color": tooltip_i})
+      else:
+        tooltip_s = "<h2>Cluster %s</h2>Contains %s members."%(k,len(complex["nodes"][k]))
+        json_s["nodes"].append({"name": str(k), "tooltip": tooltip_s, "group": 2 * int(np.log(len(complex["nodes"][k]))), "color": str(k.split("_")[0])})
+      k2e[k] = e
+    for k in complex["links"]:
+      for link in complex["links"][k]:
+        json_s["links"].append({"source": k2e[k], "target":k2e[link],"value":1})
+
+    # Width and height of graph in HTML output
+    if width_html == 0:
+      width_css = "100%"
+      width_js = 'document.getElementById("holder").offsetWidth-20'
+    else:
+      width_css = "%spx" % width_html
+      width_js = "%s" % width_html
+    if height_html == 0:
+      height_css = "100%"
+      height_js = 'document.getElementById("holder").offsetHeight-20'
+    else:
+      height_css = "%spx" % height_html
+      height_js = "%s" % height_html
+    
+    # Whether to show certain UI elements or not
+    if show_tooltips == False:
+      tooltips_display = "display: none;"
+    else:
+      tooltips_display = ""
+      
+    if show_meta == False:
+      meta_display = "display: none;"
+    else:
+      meta_display = ""
+      
+    if show_title == False:
+      title_display = "display: none;"
+    else:
+      title_display = ""  
+    
+    with open(path_html,"wb") as outfile:
+      html = """<!DOCTYPE html>
+    <meta charset="utf-8">
+    <meta name="generator" content="KeplerMapper">
+    <title>%s | KeplerMapper</title>
+    <link href='https://fonts.googleapis.com/css?family=Roboto:700,300' rel='stylesheet' type='text/css'>
+    <style>
+    * {margin: 0; padding: 0;}
+    html { height: 100%%;}
+    body {background: #111; height: 100%%; font: 100 16px Roboto, Sans-serif;}
+    .link { stroke: #999; stroke-opacity: .333;  }
+    .divs div { border-radius: 50%%; background: red; position: absolute; }
+    .divs { position: absolute; top: 0; left: 0; }
+    #holder { position: relative; width: %s; height: %s; background: #111; display: block;}
+    h1 { %s padding: 20px; color: #fafafa; text-shadow: 0px 1px #000,0px -1px #000; position: absolute; font: 300 30px Roboto, Sans-serif;}
+    h2 { text-shadow: 0px 1px #000,0px -1px #000; font: 700 16px Roboto, Sans-serif;}
+    .meta {  position: absolute; opacity: 0.9; width: 220px; top: 80px; left: 20px; display: block; %s background: #000; line-height: 25px; color: #fafafa; border: 20px solid #000; font: 100 16px Roboto, Sans-serif;}
+    div.tooltip { position: absolute; width: 380px; display: block; %s padding: 20px; background: #000; border: 0px; border-radius: 3px; pointer-events: none; z-index: 999; color: #FAFAFA;}
+    }
+    </style>
+    <body>
+    <div id="holder">
+      <h1>%s</h1>
+      <p class="meta">
+      <b>Lens</b><br>%s<br><br>
+      <b>Length of intervals</b><br>%s<br><br>
+      <b>Overlap percentage</b><br>%s%%<br><br>
+      <b>Color Function</b><br>%s
+      </p>
+    </div>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js"></script>
+    <script>
+    var width = %s,
+      height = %s;
+    var color = d3.scale.ordinal()
+      .domain(["0","1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30"])
+      .range(["#FF0000","#FF1400","#FF2800","#FF3c00","#FF5000","#FF6400","#FF7800","#FF8c00","#FFa000","#FFb400","#FFc800","#FFdc00","#FFf000","#fdff00","#b0ff00","#65ff00","#17ff00","#00ff36","#00ff83","#00ffd0","#00e4ff","#00c4ff","#00a4ff","#00a4ff","#0084ff","#0064ff","#0044ff","#0022ff","#0002ff","#0100ff","#0300ff","#0500ff"]);
+    var force = d3.layout.force()
+      .charge(%s)
+      .linkDistance(%s)
+      .gravity(%s)
+      .size([width, height]);
+    var svg = d3.select("#holder").append("svg")
+      .attr("width", width)
+      .attr("height", height);
+    
+    var div = d3.select("#holder").append("div")   
+      .attr("class", "tooltip")               
+      .style("opacity", 0.0);
+    
+    var divs = d3.select('#holder').append('div')
+      .attr('class', 'divs')
+      .attr('style', function(d) { return 'overflow: hidden; width: ' + width + 'px; height: ' + height + 'px;'; });  
+    
+      graph = %s;
+      force
+        .nodes(graph.nodes)
+        .links(graph.links)
+        .start();
+      var link = svg.selectAll(".link")
+        .data(graph.links)
+        .enter().append("line")
+        .attr("class", "link")
+        .style("stroke-width", function(d) { return Math.sqrt(d.value); });
+      var node = divs.selectAll('div')
+      .data(graph.nodes)
+        .enter().append('div')
+        .on("mouseover", function(d) {      
+          div.transition()        
+            .duration(200)      
+            .style("opacity", .9);
+          div .html(d.tooltip + "<br/>")  
+            .style("left", (d3.event.pageX + 100) + "px")     
+            .style("top", (d3.event.pageY - 28) + "px");    
+          })                  
+        .on("mouseout", function(d) {       
+          div.transition()        
+            .duration(500)      
+            .style("opacity", 0);   
+        })
+        .call(force.drag);
+      
+      node.append("title")
+        .text(function(d) { return d.name; });
+      force.on("tick", function() {
+      link.attr("x1", function(d) { return d.source.x; })
+        .attr("y1", function(d) { return d.source.y; })
+        .attr("x2", function(d) { return d.target.x; })
+        .attr("y2", function(d) { return d.target.y; });
+      node.attr("cx", function(d) { return d.x; })
+        .attr("cy", function(d) { return d.y; })
+        .attr('style', function(d) { return 'width: ' + (d.group * 2) + 'px; height: ' + (d.group * 2) + 'px; ' + 'left: '+(d.x-(d.group))+'px; ' + 'top: '+(d.y-(d.group))+'px; background: '+color(d.color)+'; box-shadow: 0px 0px 3px #111; box-shadow: 0px 0px 33px '+color(d.color)+', inset 0px 0px 5px rgba(0, 0, 0, 0.2);'})
+        ;
+      });
+    </script>"""%(title,width_css, height_css, title_display, meta_display, tooltips_display, title,complex["meta"],res,gain*100,color_function,width_js,height_js,graph_charge,graph_link_distance,graph_gravity,json.dumps(json_s))
+      outfile.write(html.encode("utf-8"))
+    if self.verbose > 0:
+      print("\nWrote d3.js graph to '%s'"%path_html)
diff --git a/src/Nerve_GIC/utilities/km.py.COPYRIGHT b/src/Nerve_GIC/utilities/km.py.COPYRIGHT
new file mode 100644
index 00000000..bef7b121
--- /dev/null
+++ b/src/Nerve_GIC/utilities/km.py.COPYRIGHT
@@ -0,0 +1,26 @@
+km.py is a fork of https://github.com/MLWave/kepler-mapper.
+Only the visualization part has been kept (Mapper part has been removed).
+
+This file has te following Copyright :
+
+The MIT License (MIT)
+
+Copyright (c) 2015 Triskelion - HJ van Veen - info@mlwave.com
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/src/Persistence_representations/example/CMakeLists.txt b/src/Persistence_representations/example/CMakeLists.txt
index b8ce8ea6..eb3258f8 100644
--- a/src/Persistence_representations/example/CMakeLists.txt
+++ b/src/Persistence_representations/example/CMakeLists.txt
@@ -2,28 +2,23 @@ cmake_minimum_required(VERSION 2.6)
 project(Persistence_representations_example)
 
 add_executable ( Persistence_representations_example_landscape_on_grid persistence_landscape_on_grid.cpp )
-target_link_libraries(Persistence_representations_example_landscape_on_grid ${Boost_SYSTEM_LIBRARY})
 add_test(NAME Persistence_representations_example_landscape_on_grid
     COMMAND $<TARGET_FILE:Persistence_representations_example_landscape_on_grid>)
 
 add_executable ( Persistence_representations_example_landscape persistence_landscape.cpp )
-target_link_libraries(Persistence_representations_example_landscape ${Boost_SYSTEM_LIBRARY})
 add_test(NAME Persistence_representations_example_landscape
     COMMAND $<TARGET_FILE:Persistence_representations_example_landscape>)
 
 add_executable ( Persistence_representations_example_intervals persistence_intervals.cpp )
-target_link_libraries(Persistence_representations_example_intervals ${Boost_SYSTEM_LIBRARY})
 add_test(NAME Persistence_representations_example_intervals
     COMMAND $<TARGET_FILE:Persistence_representations_example_intervals>
     "${CMAKE_SOURCE_DIR}/data/persistence_diagram/first.pers")
 
 add_executable ( Persistence_representations_example_vectors persistence_vectors.cpp )
-target_link_libraries(Persistence_representations_example_vectors ${Boost_SYSTEM_LIBRARY})
 add_test(NAME Persistence_representations_example_vectors
     COMMAND $<TARGET_FILE:Persistence_representations_example_vectors>)
 
 add_executable ( Persistence_representations_example_heat_maps persistence_heat_maps.cpp )
-target_link_libraries(Persistence_representations_example_heat_maps ${Boost_SYSTEM_LIBRARY})
 add_test(NAME Persistence_representations_example_heat_maps
     COMMAND $<TARGET_FILE:Persistence_representations_example_heat_maps>)
 
diff --git a/src/Persistence_representations/test/CMakeLists.txt b/src/Persistence_representations/test/CMakeLists.txt
index 4483de07..335a71ef 100644
--- a/src/Persistence_representations/test/CMakeLists.txt
+++ b/src/Persistence_representations/test/CMakeLists.txt
@@ -6,38 +6,38 @@ include(GUDHI_test_coverage)
 # copy data directory for tests purpose.
 file(COPY data DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/)
 add_executable ( Persistence_intervals_test_unit persistence_intervals_test.cpp )
-target_link_libraries(Persistence_intervals_test_unit ${Boost_SYSTEM_LIBRARY} ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
+target_link_libraries(Persistence_intervals_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
 
 gudhi_add_coverage_test(Persistence_intervals_test_unit)
 
 add_executable (Vector_representation_test_unit vector_representation_test.cpp )
-target_link_libraries(Vector_representation_test_unit ${Boost_SYSTEM_LIBRARY} ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
+target_link_libraries(Vector_representation_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
 
 gudhi_add_coverage_test(Vector_representation_test_unit)
 
 add_executable (Persistence_lanscapes_test_unit persistence_lanscapes_test.cpp )
-target_link_libraries(Persistence_lanscapes_test_unit ${Boost_SYSTEM_LIBRARY} ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
+target_link_libraries(Persistence_lanscapes_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
 
 gudhi_add_coverage_test(Persistence_lanscapes_test_unit)
 
 add_executable ( Persistence_lanscapes_on_grid_test_unit persistence_lanscapes_on_grid_test.cpp )
-target_link_libraries(Persistence_lanscapes_on_grid_test_unit ${Boost_SYSTEM_LIBRARY} ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
+target_link_libraries(Persistence_lanscapes_on_grid_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
 
 gudhi_add_coverage_test(Persistence_lanscapes_on_grid_test_unit)
 
 add_executable (Persistence_heat_maps_test_unit persistence_heat_maps_test.cpp )
-target_link_libraries(Persistence_heat_maps_test_unit ${Boost_SYSTEM_LIBRARY} ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
+target_link_libraries(Persistence_heat_maps_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
 
 gudhi_add_coverage_test(Persistence_heat_maps_test_unit)
 
 add_executable ( Read_persistence_from_file_test_unit read_persistence_from_file_test.cpp )
-target_link_libraries(Read_persistence_from_file_test_unit ${Boost_SYSTEM_LIBRARY} ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
+target_link_libraries(Read_persistence_from_file_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
 
 gudhi_add_coverage_test(Read_persistence_from_file_test_unit)
 
 if (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.8.1)
  add_executable (Persistence_intervals_with_distances_test_unit persistence_intervals_with_distances_test.cpp )
- target_link_libraries(Persistence_intervals_with_distances_test_unit ${Boost_SYSTEM_LIBRARY} ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
+ target_link_libraries(Persistence_intervals_with_distances_test_unit ${Boost_UNIT_TEST_FRAMEWORK_LIBRARY})
  if (TBB_FOUND)
  target_link_libraries(Persistence_intervals_with_distances_test_unit ${TBB_LIBRARIES})
  endif(TBB_FOUND)
diff --git a/src/common/doc/examples.h b/src/common/doc/examples.h
new file mode 100644
index 00000000..40f202c7
--- /dev/null
+++ b/src/common/doc/examples.h
@@ -0,0 +1,99 @@
+// List of GUDHI examples - Doxygen needs at least a file tag to analyse comments
+// In user_version, `find . -name "*.cpp"` in example and utilities folders
+/*! @file Examples
+ * @example Alpha_complex/Alpha_complex_from_off.cpp
+ * @example Alpha_complex/Alpha_complex_from_points.cpp
+ * @example Bottleneck_distance/bottleneck_basic_example.cpp
+ * @example Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp
+ * @example Witness_complex/example_nearest_landmark_table.cpp
+ * @example Witness_complex/example_witness_complex_off.cpp
+ * @example Witness_complex/example_witness_complex_sphere.cpp
+ * @example Witness_complex/example_strong_witness_complex_off.cpp
+ * @example Simplex_tree/mini_simplex_tree.cpp
+ * @example Simplex_tree/graph_expansion_with_blocker.cpp
+ * @example Simplex_tree/simple_simplex_tree.cpp
+ * @example Simplex_tree/simplex_tree_from_cliques_of_graph.cpp
+ * @example Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp
+ * @example Simplex_tree/cech_complex_cgal_mini_sphere_3d.cpp
+ * @example Persistent_cohomology/plain_homology.cpp
+ * @example Persistent_cohomology/persistence_from_file.cpp
+ * @example Persistent_cohomology/rips_persistence_step_by_step.cpp
+ * @example Persistent_cohomology/rips_persistence_via_boundary_matrix.cpp
+ * @example Persistent_cohomology/custom_persistence_sort.cpp
+ * @example Persistent_cohomology/persistence_from_simple_simplex_tree.cpp
+ * @example Persistent_cohomology/rips_multifield_persistence.cpp
+ * @example Skeleton_blocker/Skeleton_blocker_from_simplices.cpp
+ * @example Skeleton_blocker/Skeleton_blocker_iteration.cpp
+ * @example Skeleton_blocker/Skeleton_blocker_link.cpp
+ * @example Contraction/Garland_heckbert.cpp
+ * @example Contraction/Rips_contraction.cpp
+ * @example Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp
+ * @example common/example_CGAL_3D_points_off_reader.cpp
+ * @example common/example_vector_double_points_off_reader.cpp
+ * @example common/example_CGAL_points_off_reader.cpp
+ * @example Rips_complex/example_one_skeleton_rips_from_distance_matrix.cpp
+ * @example Rips_complex/example_one_skeleton_rips_from_points.cpp
+ * @example Rips_complex/example_rips_complex_from_csv_distance_matrix_file.cpp
+ * @example Rips_complex/example_rips_complex_from_off_file.cpp
+ * @example Persistence_representations/persistence_intervals.cpp
+ * @example Persistence_representations/persistence_vectors.cpp
+ * @example Persistence_representations/persistence_heat_maps.cpp
+ * @example Persistence_representations/persistence_landscape_on_grid.cpp
+ * @example Persistence_representations/persistence_landscape.cpp
+ * @example Tangential_complex/example_basic.cpp
+ * @example Tangential_complex/example_with_perturb.cpp
+ * @example Subsampling/example_custom_kernel.cpp
+ * @example Subsampling/example_choose_n_farthest_points.cpp
+ * @example Subsampling/example_sparsify_point_set.cpp
+ * @example Subsampling/example_pick_n_random_points.cpp
+ * @example Nerve_GIC/CoordGIC.cpp
+ * @example Nerve_GIC/Nerve.cpp
+ * @example Nerve_GIC/FuncGIC.cpp
+ * @example Nerve_GIC/VoronoiGIC.cpp
+ * @example Spatial_searching/example_spatial_searching.cpp
+ * @example Alpha_complex/alpha_complex_3d_persistence.cpp
+ * @example Alpha_complex/alpha_complex_persistence.cpp
+ * @example Alpha_complex/weighted_periodic_alpha_complex_3d_persistence.cpp
+ * @example Alpha_complex/weighted_alpha_complex_3d_persistence.cpp
+ * @example Alpha_complex/periodic_alpha_complex_3d_persistence.cpp
+ * @example Alpha_complex/exact_alpha_complex_3d_persistence.cpp
+ * @example Bottleneck_distance/bottleneck_distance.cpp
+ * @example Witness_complex/weak_witness_persistence.cpp
+ * @example Witness_complex/strong_witness_persistence.cpp
+ * @example Bitmap_cubical_complex/cubical_complex_persistence.cpp
+ * @example Bitmap_cubical_complex/periodic_cubical_complex_persistence.cpp
+ * @example common/off_file_from_shape_generator.cpp
+ * @example Rips_complex/rips_distance_matrix_persistence.cpp
+ * @example Rips_complex/rips_persistence.cpp
+ * @example Persistence_representations/persistence_landscapes_on_grid/create_landscapes_on_grid.cpp
+ * @example Persistence_representations/persistence_landscapes_on_grid/plot_landscapes_on_grid.cpp
+ * @example Persistence_representations/persistence_landscapes_on_grid/compute_scalar_product_of_landscapes_on_grid.cpp
+ * @example Persistence_representations/persistence_landscapes_on_grid/compute_distance_of_landscapes_on_grid.cpp
+ * @example Persistence_representations/persistence_landscapes_on_grid/average_landscapes_on_grid.cpp
+ * @example Persistence_representations/persistence_intervals/compute_birth_death_range_in_persistence_diagram.cpp
+ * @example Persistence_representations/persistence_intervals/compute_number_of_dominant_intervals.cpp
+ * @example Persistence_representations/persistence_intervals/plot_persistence_Betti_numbers.cpp
+ * @example Persistence_representations/persistence_intervals/plot_persistence_intervals.cpp
+ * @example Persistence_representations/persistence_intervals/plot_histogram_of_intervals_lengths.cpp
+ * @example Persistence_representations/persistence_intervals/compute_bottleneck_distance.cpp
+ * @example Persistence_representations/persistence_heat_maps/create_pssk.cpp
+ * @example Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_arctan_of_their_persistence.cpp
+ * @example Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_squared_diag_distance.cpp
+ * @example Persistence_representations/persistence_heat_maps/compute_distance_of_persistence_heat_maps.cpp
+ * @example Persistence_representations/persistence_heat_maps/compute_scalar_product_of_persistence_heat_maps.cpp
+ * @example Persistence_representations/persistence_heat_maps/create_p_h_m_weighted_by_distance_from_diagonal.cpp
+ * @example Persistence_representations/persistence_heat_maps/average_persistence_heat_maps.cpp
+ * @example Persistence_representations/persistence_heat_maps/plot_persistence_heat_map.cpp
+ * @example Persistence_representations/persistence_heat_maps/create_persistence_heat_maps.cpp
+ * @example Persistence_representations/persistence_vectors/plot_persistence_vectors.cpp
+ * @example Persistence_representations/persistence_vectors/compute_distance_of_persistence_vectors.cpp
+ * @example Persistence_representations/persistence_vectors/average_persistence_vectors.cpp
+ * @example Persistence_representations/persistence_vectors/create_persistence_vectors.cpp
+ * @example Persistence_representations/persistence_vectors/compute_scalar_product_of_persistence_vectors.cpp
+ * @example Persistence_representations/persistence_landscapes/average_landscapes.cpp
+ * @example Persistence_representations/persistence_landscapes/compute_scalar_product_of_landscapes.cpp
+ * @example Persistence_representations/persistence_landscapes/create_landscapes.cpp
+ * @example Persistence_representations/persistence_landscapes/compute_distance_of_landscapes.cpp
+ * @example Persistence_representations/persistence_landscapes/plot_landscapes.cpp
+ */
+ 
diff --git a/src/common/doc/installation.h b/src/common/doc/installation.h
new file mode 100644
index 00000000..25675cc5
--- /dev/null
+++ b/src/common/doc/installation.h
@@ -0,0 +1,263 @@
+/*! \page installation GUDHI installation
+ *  \tableofcontents
+ * As GUDHI is a header only library, there is no need to install the library.
+ * 
+ * Examples of GUDHI headers inclusion can be found in \ref demos.
+ * 
+ * \section compiling Compiling
+ * The library uses c++11 and requires <a target="_blank" href="http://www.boost.org/">Boost</a> with version 1.48.0 or
+ * more recent. It is a multi-platform library and compiles on Linux, Mac OSX and Visual Studio 2015.
+ * 
+ * \subsection demos Demos and examples
+ * To build the demos and examples, run the following commands in a terminal:
+\verbatim  cd /path-to-gudhi/
+mkdir build
+cd build/
+cmake ..
+make \endverbatim
+ * A list of examples is available <a href="examples.html">here</a>.
+ * 
+ * \subsection testsuites Test suites
+ * To test your build, run the following command in a terminal:
+ * \verbatim  make test \endverbatim
+ * 
+ * \subsection documentationgeneration Documentation
+ * To generate the documentation, <a target="_blank" href="http://www.doxygen.org/">Doxygen</a> is required.
+ * Run the following command in a terminal:
+\verbatim
+make doxygen
+# Documentation will be generated in the folder YYYY-MM-DD-hh-mm-ss_GUDHI_X.Y.Z/doc/html/
+# You can customize the directory name by calling `cmake -DUSER_VERSION_DIR=/my/custom/folder`
+\endverbatim
+ *
+ * \section optionallibrary Optional third-party library
+ * \subsection gmp GMP
+ * The multi-field persistent homology algorithm requires GMP which is a free library for arbitrary-precision
+ * arithmetic, operating on signed integers, rational numbers, and floating point numbers.
+ * 
+ * The following example requires the <a target="_blank" href="http://gmplib.org/">GNU Multiple Precision Arithmetic
+ * Library</a> (GMP) and will not be built if GMP is not installed:
+ * \li <a href="_persistent_cohomology_2rips_multifield_persistence_8cpp-example.html">
+ * Persistent_cohomology/rips_multifield_persistence.cpp</a>
+ *
+ * Having GMP version 4.2 or higher installed is recommended.
+ * 
+ * \subsection cgal CGAL
+ * The \ref alpha_complex data structure, \ref bottleneck_distance, and few examples requires CGAL, which is a C++
+ * library which provides easy access to efficient and reliable geometric algorithms.
+ *
+ * \note There is no need to install CGAL, you can just <CODE>cmake . && make</CODE> CGAL (or even
+ * <CODE>cmake -DCGAL_HEADER_ONLY=ON .</CODE> for CGAL version &ge; 4.8.0), thereafter you will be able to compile
+ * GUDHI by calling <CODE>cmake -DCGAL_DIR=/your/path/to/CGAL-X.Y .. && make</CODE>
+ * 
+ * Having CGAL version 4.4.0 or higher installed is recommended. The procedure to install this library according to
+ * your operating system is detailed here http://doc.cgal.org/latest/Manual/installation.html
+ * 
+ * The following examples/utilities require the <a target="_blank" href="http://www.cgal.org/">Computational Geometry Algorithms
+ * Library</a> (CGAL \cite cgal:eb-15b) and will not be built if CGAL is not installed:
+ * \li <a href="_alpha_complex_2alpha_complex_3d_persistence_8cpp-example.html">
+ * Alpha_complex/alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_alpha_complex_2exact_alpha_complex_3d_persistence_8cpp-example.html">
+ * Alpha_complex/exact_alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_alpha_complex_2weighted_alpha_complex_3d_persistence_8cpp-example.html">
+ * Alpha_complex/weighted_alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html">
+ * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a>
+ * 
+ * The following examples/utilities require CGAL version &ge; 4.6.0:
+ * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html">
+ * Witness_complex/strong_witness_persistence.cpp</a>
+ * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html">
+ * Witness_complex/weak_witness_persistence.cpp</a>
+ * \li <a href="_witness_complex_2example_strong_witness_complex_off_8cpp-example.html">
+ * Witness_complex/example_strong_witness_complex_off.cpp</a>
+ * \li <a href="_witness_complex_2example_witness_complex_off_8cpp-example.html">
+ * Witness_complex/example_witness_complex_off.cpp</a>
+ * \li <a href="_witness_complex_2example_witness_complex_sphere_8cpp-example.html">
+ * Witness_complex/example_witness_complex_sphere.cpp</a>
+ *
+ * The following example requires CGAL version &ge; 4.7.0:
+ * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html">
+ * Alpha_complex/Alpha_complex_from_off.cpp</a>
+ * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html">
+ * Alpha_complex/Alpha_complex_from_points.cpp</a>
+ * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html">
+ * Alpha_complex/alpha_complex_persistence.cpp</a>
+ * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html">
+ * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html">
+ * Persistent_cohomology/custom_persistence_sort.cpp</a>
+ * 
+ * The following example requires CGAL version &ge; 4.8.1:
+ * \li <a href="_bottleneck_distance_2alpha_rips_persistence_bottleneck_distance_8cpp-example.html">
+ * Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp.cpp</a>
+ * \li <a href="_bottleneck_distance_2bottleneck_basic_example_8cpp-example.html">
+ * Bottleneck_distance/bottleneck_basic_example.cpp</a>
+ * \li <a href="_bottleneck_distance_2bottleneck_read_file_8cpp-example.html">
+ * Bottleneck_distance/bottleneck_distance.cpp</a>
+ * \li <a href="_nerve__g_i_c_2_coord_g_i_c_8cpp-example.html">
+ * Nerve_GIC/CoordGIC.cpp</a>
+ * \li <a href="_nerve__g_i_c_2_func_g_i_c_8cpp-example.html">
+ * Nerve_GIC/FuncGIC.cpp</a>
+ * \li <a href="_nerve__g_i_c_2_nerve_8cpp-example.html">
+ * Nerve_GIC/Nerve.cpp</a>
+ * \li <a href="_nerve__g_i_c_2_voronoi_g_i_c_8cpp-example.html">
+ * Nerve_GIC/VoronoiGIC.cpp</a>
+ * \li <a href="_spatial_searching_2example_spatial_searching_8cpp-example.html">
+ * Spatial_searching/example_spatial_searching.cpp</a>
+ * \li <a href="_subsampling_2example_choose_n_farthest_points_8cpp-example.html">
+ * Subsampling/example_choose_n_farthest_points.cpp</a>
+ * \li <a href="_subsampling_2example_custom_kernel_8cpp-example.html">
+ * Subsampling/example_custom_kernel.cpp</a>
+ * \li <a href="_subsampling_2example_pick_n_random_points_8cpp-example.html">
+ * Subsampling/example_pick_n_random_points.cpp</a>
+ * \li <a href="_subsampling_2example_sparsify_point_set_8cpp-example.html">
+ * Subsampling/example_sparsify_point_set.cpp</a>
+ * \li <a href="_tangential_complex_2example_basic_8cpp-example.html">
+ * Tangential_complex/example_basic.cpp</a>
+ * \li <a href="_tangential_complex_2example_with_perturb_8cpp-example.html">
+ * Tangential_complex/example_with_perturb.cpp</a>
+ *
+ * \subsection eigen3 Eigen3
+ * The \ref alpha_complex data structure and few examples requires
+ * <a target="_blank" href="http://eigen.tuxfamily.org/">Eigen3</a> is a C++ template library for linear algebra:
+ * matrices, vectors, numerical solvers, and related algorithms.
+ * 
+ * The following examples/utilities require the <a target="_blank" href="http://eigen.tuxfamily.org/">Eigen3</a> and will not be
+ * built if Eigen3 is not installed:
+ * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html">
+ * Alpha_complex/Alpha_complex_from_off.cpp</a>
+ * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html">
+ * Alpha_complex/Alpha_complex_from_points.cpp</a>
+ * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html">
+ * Alpha_complex/alpha_complex_persistence.cpp</a>
+ * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html">
+ * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_bottleneck_distance_2alpha_rips_persistence_bottleneck_distance_8cpp-example.html">
+ * Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp.cpp</a>
+ * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html">
+ * Persistent_cohomology/custom_persistence_sort.cpp</a>
+ * \li <a href="_spatial_searching_2example_spatial_searching_8cpp-example.html">
+ * Spatial_searching/example_spatial_searching.cpp</a>
+ * \li <a href="_subsampling_2example_choose_n_farthest_points_8cpp-example.html">
+ * Subsampling/example_choose_n_farthest_points.cpp</a>
+ * \li <a href="_subsampling_2example_custom_kernel_8cpp-example.html">
+ * Subsampling/example_custom_kernel.cpp</a>
+ * \li <a href="_subsampling_2example_pick_n_random_points_8cpp-example.html">
+ * Subsampling/example_pick_n_random_points.cpp</a>
+ * \li <a href="_subsampling_2example_sparsify_point_set_8cpp-example.html">
+ * Subsampling/example_sparsify_point_set.cpp</a>
+ * \li <a href="_tangential_complex_2example_basic_8cpp-example.html">
+ * Tangential_complex/example_basic.cpp</a>
+ * \li <a href="_tangential_complex_2example_with_perturb_8cpp-example.html">
+ * Tangential_complex/example_with_perturb.cpp</a>
+ * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html">
+ * Witness_complex/strong_witness_persistence.cpp</a>
+ * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html">
+ * Witness_complex/weak_witness_persistence.cpp</a>
+ * \li <a href="_witness_complex_2example_strong_witness_complex_off_8cpp-example.html">
+ * Witness_complex/example_strong_witness_complex_off.cpp</a>
+ * \li <a href="_witness_complex_2example_witness_complex_off_8cpp-example.html">
+ * Witness_complex/example_witness_complex_off.cpp</a>
+ * \li <a href="_witness_complex_2example_witness_complex_sphere_8cpp-example.html">
+ * Witness_complex/example_witness_complex_sphere.cpp</a>
+ *
+ * \subsection tbb Threading Building Blocks
+ * <a target="_blank" href="https://www.threadingbuildingblocks.org/">Intel&reg; TBB</a> lets you easily write parallel
+ * C++ programs that take full advantage of multicore performance, that are portable and composable, and that have
+ * future-proof scalability.
+ * 
+ * Having Intel&reg; TBB installed is recommended to parallelize and accelerate some GUDHI computations.
+ * 
+ * The following examples/utilities are using Intel&reg; TBB if installed:
+ * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html">
+ * Alpha_complex/Alpha_complex_from_off.cpp</a>
+ * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html">
+ * Alpha_complex/Alpha_complex_from_points.cpp</a>
+ * \li <a href="_alpha_complex_2alpha_complex_3d_persistence_8cpp-example.html">
+ * Alpha_complex/alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html">
+ * Alpha_complex/alpha_complex_persistence.cpp</a>
+ * \li <a href="_alpha_complex_2exact_alpha_complex_3d_persistence_8cpp-example.html">
+ * Alpha_complex/exact_alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html">
+ * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_alpha_complex_2weighted_alpha_complex_3d_persistence_8cpp-example.html">
+ * Alpha_complex/weighted_alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_bitmap_cubical_complex_2_bitmap_cubical_complex_8cpp-example.html">
+ * Bitmap_cubical_complex/cubical_complex_persistence.cpp</a>
+ * \li <a href="_bitmap_cubical_complex_2_bitmap_cubical_complex_periodic_boundary_conditions_8cpp-example.html">
+ * Bitmap_cubical_complex/periodic_cubical_complex_persistence.cpp</a>
+ * \li <a href="_bitmap_cubical_complex_2_random_bitmap_cubical_complex_8cpp-example.html">
+ * Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp</a>
+ * \li <a href="_nerve__g_i_c_2_coord_g_i_c_8cpp-example.html">
+ * Nerve_GIC/CoordGIC.cpp</a>
+ * \li <a href="_nerve__g_i_c_2_func_g_i_c_8cpp-example.html">
+ * Nerve_GIC/FuncGIC.cpp</a>
+ * \li <a href="_nerve__g_i_c_2_nerve_8cpp-example.html">
+ * Nerve_GIC/Nerve.cpp</a>
+ * \li <a href="_nerve__g_i_c_2_voronoi_g_i_c_8cpp-example.html">
+ * Nerve_GIC/VoronoiGIC.cpp</a>
+ * \li <a href="_simplex_tree_2simple_simplex_tree_8cpp-example.html">
+ * Simplex_tree/simple_simplex_tree.cpp</a>
+ * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html">
+ * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a>
+ * \li <a href="_simplex_tree_2simplex_tree_from_cliques_of_graph_8cpp-example.html">
+ * Simplex_tree/simplex_tree_from_cliques_of_graph.cpp</a>
+ * \li <a href="_simplex_tree_2graph_expansion_with_blocker_8cpp-example.html">
+ * Simplex_tree/graph_expansion_with_blocker.cpp</a>
+ * \li <a href="_persistent_cohomology_2alpha_complex_3d_persistence_8cpp-example.html">
+ * Persistent_cohomology/alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html">
+ * Persistent_cohomology/alpha_complex_persistence.cpp</a>
+ * \li <a href="_persistent_cohomology_2rips_persistence_via_boundary_matrix_8cpp-example.html">
+ * Persistent_cohomology/rips_persistence_via_boundary_matrix.cpp</a>
+ * \li <a href="_persistent_cohomology_2persistence_from_file_8cpp-example.html">
+ * Persistent_cohomology/persistence_from_file.cpp</a>
+ * \li <a href="_persistent_cohomology_2persistence_from_simple_simplex_tree_8cpp-example.html">
+ * Persistent_cohomology/persistence_from_simple_simplex_tree.cpp</a>
+ * \li <a href="_persistent_cohomology_2plain_homology_8cpp-example.html">
+ * Persistent_cohomology/plain_homology.cpp</a>
+ * \li <a href="_persistent_cohomology_2rips_multifield_persistence_8cpp-example.html">
+ * Persistent_cohomology/rips_multifield_persistence.cpp</a>
+ * \li <a href="_persistent_cohomology_2rips_persistence_step_by_step_8cpp-example.html">
+ * Persistent_cohomology/rips_persistence_step_by_step.cpp</a>
+ * \li <a href="_persistent_cohomology_2exact_alpha_complex_3d_persistence_8cpp-example.html">
+ * Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_persistent_cohomology_2weighted_alpha_complex_3d_persistence_8cpp-example.html">
+ * Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp</a>
+ * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html">
+ * Persistent_cohomology/custom_persistence_sort.cpp</a>
+ * \li <a href="_rips_complex_2example_one_skeleton_rips_from_points_8cpp-example.html">
+ * Rips_complex/example_one_skeleton_rips_from_points.cpp</a>
+ * \li <a href="_rips_complex_2example_rips_complex_from_off_file_8cpp-example.html">
+ * Rips_complex/example_rips_complex_from_off_file.cpp</a>
+ * \li <a href="_rips_complex_2rips_distance_matrix_persistence_8cpp-example.html">
+ * Rips_complex/rips_distance_matrix_persistence.cpp</a>
+ * \li <a href="_rips_complex_2rips_persistence_8cpp-example.html">
+ * Rips_complex/rips_persistence.cpp</a>
+ * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html">
+ * Witness_complex/strong_witness_persistence.cpp</a>
+ * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html">
+ * Witness_complex/weak_witness_persistence.cpp</a>
+ * \li <a href="_witness_complex_2example_nearest_landmark_table_8cpp-example.html">
+ * Witness_complex/example_nearest_landmark_table.cpp</a>
+ *
+ * \section Contributions Bug reports and contributions
+ * Please help us improving the quality of the GUDHI library. You may report bugs or suggestions to:
+ * \verbatim  Contact: gudhi-users@lists.gforge.inria.fr \endverbatim
+ * 
+ * GUDHI is open to external contributions. If you want to join our development team, please contact us.
+ * 
+*/
+
+/*! \page Citation Acknowledging the GUDHI library
+ * We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use
+ * of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.
+ * Feel free to contact us in case you have any question or remark on this topic.
+ * 
+ * We provide \ref GudhiBibtex entries for the modules of the User and Reference Manual, as well as for publications
+ * directly related to the GUDHI library.
+ * \section GudhiBibtex GUDHI bibtex
+ * \verbinclude  biblio/how_to_cite_gudhi.bib
+*/
diff --git a/src/common/doc/main_page.h b/src/common/doc/main_page.h
index 148ee670..b3e9ea03 100644
--- a/src/common/doc/main_page.h
+++ b/src/common/doc/main_page.h
@@ -93,7 +93,7 @@
     </td>
  </tr>
 </table>
- \subsection CoverComplexDataStructure Cover Complexes: Nerves and Graph Induced Complexes
+ \subsection CoverComplexDataStructure Cover Complexes
  \image html "gicvisu.jpg" "Graph Induced Complex of a point cloud."
 <table border="0">
   <tr>
@@ -101,6 +101,7 @@
       <b>Author:</b> Mathieu Carri&egrave;re<br>
       <b>Introduced in:</b> GUDHI 2.1.0<br>
       <b>Copyright:</b> GPL v3<br>
+      <b>Requires:</b> \ref cgal &ge; 4.8.1
     </td>
     <td width="75%">
     Nerves and Graph Induced Complexes are cover complexes, i.e. simplicial complexes that provably contain
@@ -250,305 +251,3 @@
 </table>
  
 */
-
-/*! \page installation GUDHI installation
- *  \tableofcontents
- * As GUDHI is a header only library, there is no need to install the library.
- * 
- * Examples of GUDHI headers inclusion can be found in \ref demos.
- * 
- * \section compiling Compiling
- * The library uses c++11 and requires <a target="_blank" href="http://www.boost.org/">Boost</a> with version 1.48.0 or
- * more recent. It is a multi-platform library and compiles on Linux, Mac OSX and Visual Studio 2015.
- * 
- * \subsection demos Demos and examples
- * To build the demos and examples, run the following commands in a terminal:
-\verbatim  cd /path-to-gudhi/
-mkdir build
-cd build/
-cmake ..
-make \endverbatim
- * A list of examples is available <a href="examples.html">here</a>.
- * 
- * \subsection testsuites Test suites
- * To test your build, run the following command in a terminal:
- * \verbatim  make test \endverbatim
- * 
- * \subsection documentationgeneration Documentation
- * To generate the documentation, <a target="_blank" href="http://www.doxygen.org/">Doxygen</a> is required.
- * Run the following command in a terminal:
-\verbatim
-make doxygen
-# Documentation will be generated in the folder YYYY-MM-DD-hh-mm-ss_GUDHI_X.Y.Z/doc/html/
-# You can customize the directory name by calling `cmake -DUSER_VERSION_DIR=/my/custom/folder`
-\endverbatim
- *
- * \section optionallibrary Optional third-party library
- * \subsection gmp GMP
- * The multi-field persistent homology algorithm requires GMP which is a free library for arbitrary-precision
- * arithmetic, operating on signed integers, rational numbers, and floating point numbers.
- * 
- * The following example requires the <a target="_blank" href="http://gmplib.org/">GNU Multiple Precision Arithmetic
- * Library</a> (GMP) and will not be built if GMP is not installed:
- * \li <a href="_persistent_cohomology_2rips_multifield_persistence_8cpp-example.html">
- * Persistent_cohomology/rips_multifield_persistence.cpp</a>
- *
- * Having GMP version 4.2 or higher installed is recommended.
- * 
- * \subsection cgal CGAL
- * The \ref alpha_complex data structure, \ref bottleneck_distance, and few examples requires CGAL, which is a C++
- * library which provides easy access to efficient and reliable geometric algorithms.
- *
- * \note There is no need to install CGAL, you can just <CODE>cmake . && make</CODE> CGAL (or even
- * <CODE>cmake -DCGAL_HEADER_ONLY=ON .</CODE> for CGAL version &ge; 4.8.0), thereafter you will be able to compile
- * GUDHI by calling <CODE>cmake -DCGAL_DIR=/your/path/to/CGAL-X.Y .. && make</CODE>
- * 
- * Having CGAL version 4.4.0 or higher installed is recommended. The procedure to install this library according to
- * your operating system is detailed here http://doc.cgal.org/latest/Manual/installation.html
- * 
- * The following examples/utilities require the <a target="_blank" href="http://www.cgal.org/">Computational Geometry Algorithms
- * Library</a> (CGAL \cite cgal:eb-15b) and will not be built if CGAL is not installed:
- * \li <a href="_alpha_complex_2alpha_complex_3d_persistence_8cpp-example.html">
- * Alpha_complex/alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_alpha_complex_2exact_alpha_complex_3d_persistence_8cpp-example.html">
- * Alpha_complex/exact_alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_alpha_complex_2weighted_alpha_complex_3d_persistence_8cpp-example.html">
- * Alpha_complex/weighted_alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html">
- * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a>
- * 
- * The following examples/utilities require CGAL version &ge; 4.6.0:
- * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html">
- * Witness_complex/strong_witness_persistence.cpp</a>
- * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html">
- * Witness_complex/weak_witness_persistence.cpp</a>
- * \li <a href="_witness_complex_2example_strong_witness_complex_off_8cpp-example.html">
- * Witness_complex/example_strong_witness_complex_off.cpp</a>
- * \li <a href="_witness_complex_2example_witness_complex_off_8cpp-example.html">
- * Witness_complex/example_witness_complex_off.cpp</a>
- * \li <a href="_witness_complex_2example_witness_complex_sphere_8cpp-example.html">
- * Witness_complex/example_witness_complex_sphere.cpp</a>
- *
- * The following example requires CGAL version &ge; 4.7.0:
- * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html">
- * Alpha_complex/Alpha_complex_from_off.cpp</a>
- * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html">
- * Alpha_complex/Alpha_complex_from_points.cpp</a>
- * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html">
- * Alpha_complex/alpha_complex_persistence.cpp</a>
- * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html">
- * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html">
- * Persistent_cohomology/custom_persistence_sort.cpp</a>
- * 
- * The following example requires CGAL version &ge; 4.8.1:
- * \li <a href="_bottleneck_distance_2alpha_rips_persistence_bottleneck_distance_8cpp-example.html">
- * Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp.cpp</a>
- * \li <a href="_bottleneck_distance_2bottleneck_basic_example_8cpp-example.html">
- * Bottleneck_distance/bottleneck_basic_example.cpp</a>
- * \li <a href="_bottleneck_distance_2bottleneck_read_file_8cpp-example.html">
- * Bottleneck_distance/bottleneck_distance.cpp</a>
- * \li <a href="_spatial_searching_2example_spatial_searching_8cpp-example.html">
- * Spatial_searching/example_spatial_searching.cpp</a>
- * \li <a href="_subsampling_2example_choose_n_farthest_points_8cpp-example.html">
- * Subsampling/example_choose_n_farthest_points.cpp</a>
- * \li <a href="_subsampling_2example_custom_kernel_8cpp-example.html">
- * Subsampling/example_custom_kernel.cpp</a>
- * \li <a href="_subsampling_2example_pick_n_random_points_8cpp-example.html">
- * Subsampling/example_pick_n_random_points.cpp</a>
- * \li <a href="_subsampling_2example_sparsify_point_set_8cpp-example.html">
- * Subsampling/example_sparsify_point_set.cpp</a>
- * \li <a href="_tangential_complex_2example_basic_8cpp-example.html">
- * Tangential_complex/example_basic.cpp</a>
- * \li <a href="_tangential_complex_2example_with_perturb_8cpp-example.html">
- * Tangential_complex/example_with_perturb.cpp</a>
- *
- * \subsection eigen3 Eigen3
- * The \ref alpha_complex data structure and few examples requires
- * <a target="_blank" href="http://eigen.tuxfamily.org/">Eigen3</a> is a C++ template library for linear algebra:
- * matrices, vectors, numerical solvers, and related algorithms.
- * 
- * The following examples/utilities require the <a target="_blank" href="http://eigen.tuxfamily.org/">Eigen3</a> and will not be
- * built if Eigen3 is not installed:
- * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html">
- * Alpha_complex/Alpha_complex_from_off.cpp</a>
- * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html">
- * Alpha_complex/Alpha_complex_from_points.cpp</a>
- * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html">
- * Alpha_complex/alpha_complex_persistence.cpp</a>
- * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html">
- * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_bottleneck_distance_2alpha_rips_persistence_bottleneck_distance_8cpp-example.html">
- * Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp.cpp</a>
- * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html">
- * Persistent_cohomology/custom_persistence_sort.cpp</a>
- * \li <a href="_spatial_searching_2example_spatial_searching_8cpp-example.html">
- * Spatial_searching/example_spatial_searching.cpp</a>
- * \li <a href="_subsampling_2example_choose_n_farthest_points_8cpp-example.html">
- * Subsampling/example_choose_n_farthest_points.cpp</a>
- * \li <a href="_subsampling_2example_custom_kernel_8cpp-example.html">
- * Subsampling/example_custom_kernel.cpp</a>
- * \li <a href="_subsampling_2example_pick_n_random_points_8cpp-example.html">
- * Subsampling/example_pick_n_random_points.cpp</a>
- * \li <a href="_subsampling_2example_sparsify_point_set_8cpp-example.html">
- * Subsampling/example_sparsify_point_set.cpp</a>
- * \li <a href="_tangential_complex_2example_basic_8cpp-example.html">
- * Tangential_complex/example_basic.cpp</a>
- * \li <a href="_tangential_complex_2example_with_perturb_8cpp-example.html">
- * Tangential_complex/example_with_perturb.cpp</a>
- * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html">
- * Witness_complex/strong_witness_persistence.cpp</a>
- * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html">
- * Witness_complex/weak_witness_persistence.cpp</a>
- * \li <a href="_witness_complex_2example_strong_witness_complex_off_8cpp-example.html">
- * Witness_complex/example_strong_witness_complex_off.cpp</a>
- * \li <a href="_witness_complex_2example_witness_complex_off_8cpp-example.html">
- * Witness_complex/example_witness_complex_off.cpp</a>
- * \li <a href="_witness_complex_2example_witness_complex_sphere_8cpp-example.html">
- * Witness_complex/example_witness_complex_sphere.cpp</a>
- *
- * \subsection tbb Threading Building Blocks
- * <a target="_blank" href="https://www.threadingbuildingblocks.org/">Intel&reg; TBB</a> lets you easily write parallel
- * C++ programs that take full advantage of multicore performance, that are portable and composable, and that have
- * future-proof scalability.
- * 
- * Having Intel&reg; TBB installed is recommended to parallelize and accelerate some GUDHI computations.
- * 
- * The following examples/utilities are using Intel&reg; TBB if installed:
- * \li <a href="_alpha_complex_2_alpha_complex_from_off_8cpp-example.html">
- * Alpha_complex/Alpha_complex_from_off.cpp</a>
- * \li <a href="_alpha_complex_2_alpha_complex_from_points_8cpp-example.html">
- * Alpha_complex/Alpha_complex_from_points.cpp</a>
- * \li <a href="_alpha_complex_2alpha_complex_3d_persistence_8cpp-example.html">
- * Alpha_complex/alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_alpha_complex_2alpha_complex_persistence_8cpp-example.html">
- * Alpha_complex/alpha_complex_persistence.cpp</a>
- * \li <a href="_alpha_complex_2exact_alpha_complex_3d_persistence_8cpp-example.html">
- * Alpha_complex/exact_alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_alpha_complex_2periodic_alpha_complex_3d_persistence_8cpp-example.html">
- * Alpha_complex/periodic_alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_alpha_complex_2weighted_alpha_complex_3d_persistence_8cpp-example.html">
- * Alpha_complex/weighted_alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_bitmap_cubical_complex_2_bitmap_cubical_complex_8cpp-example.html">
- * Bitmap_cubical_complex/cubical_complex_persistence.cpp</a>
- * \li <a href="_bitmap_cubical_complex_2_bitmap_cubical_complex_periodic_boundary_conditions_8cpp-example.html">
- * Bitmap_cubical_complex/periodic_cubical_complex_persistence.cpp</a>
- * \li <a href="_bitmap_cubical_complex_2_random_bitmap_cubical_complex_8cpp-example.html">
- * Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp</a>
- * \li <a href="_simplex_tree_2simple_simplex_tree_8cpp-example.html">
- * Simplex_tree/simple_simplex_tree.cpp</a>
- * \li <a href="_simplex_tree_2example_alpha_shapes_3_simplex_tree_from_off_file_8cpp-example.html">
- * Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp</a>
- * \li <a href="_simplex_tree_2simplex_tree_from_cliques_of_graph_8cpp-example.html">
- * Simplex_tree/simplex_tree_from_cliques_of_graph.cpp</a>
- * \li <a href="_simplex_tree_2graph_expansion_with_blocker_8cpp-example.html">
- * Simplex_tree/graph_expansion_with_blocker.cpp</a>
- * \li <a href="_persistent_cohomology_2alpha_complex_3d_persistence_8cpp-example.html">
- * Persistent_cohomology/alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_persistent_cohomology_2alpha_complex_persistence_8cpp-example.html">
- * Persistent_cohomology/alpha_complex_persistence.cpp</a>
- * \li <a href="_persistent_cohomology_2rips_persistence_via_boundary_matrix_8cpp-example.html">
- * Persistent_cohomology/rips_persistence_via_boundary_matrix.cpp</a>
- * \li <a href="_persistent_cohomology_2persistence_from_file_8cpp-example.html">
- * Persistent_cohomology/persistence_from_file.cpp</a>
- * \li <a href="_persistent_cohomology_2persistence_from_simple_simplex_tree_8cpp-example.html">
- * Persistent_cohomology/persistence_from_simple_simplex_tree.cpp</a>
- * \li <a href="_persistent_cohomology_2plain_homology_8cpp-example.html">
- * Persistent_cohomology/plain_homology.cpp</a>
- * \li <a href="_persistent_cohomology_2rips_multifield_persistence_8cpp-example.html">
- * Persistent_cohomology/rips_multifield_persistence.cpp</a>
- * \li <a href="_persistent_cohomology_2rips_persistence_step_by_step_8cpp-example.html">
- * Persistent_cohomology/rips_persistence_step_by_step.cpp</a>
- * \li <a href="_persistent_cohomology_2exact_alpha_complex_3d_persistence_8cpp-example.html">
- * Persistent_cohomology/exact_alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_persistent_cohomology_2weighted_alpha_complex_3d_persistence_8cpp-example.html">
- * Persistent_cohomology/weighted_alpha_complex_3d_persistence.cpp</a>
- * \li <a href="_persistent_cohomology_2custom_persistence_sort_8cpp-example.html">
- * Persistent_cohomology/custom_persistence_sort.cpp</a>
- * \li <a href="_rips_complex_2example_one_skeleton_rips_from_points_8cpp-example.html">
- * Rips_complex/example_one_skeleton_rips_from_points.cpp</a>
- * \li <a href="_rips_complex_2example_rips_complex_from_off_file_8cpp-example.html">
- * Rips_complex/example_rips_complex_from_off_file.cpp</a>
- * \li <a href="_rips_complex_2rips_distance_matrix_persistence_8cpp-example.html">
- * Rips_complex/rips_distance_matrix_persistence.cpp</a>
- * \li <a href="_rips_complex_2rips_persistence_8cpp-example.html">
- * Rips_complex/rips_persistence.cpp</a>
- * \li <a href="_witness_complex_2strong_witness_persistence_8cpp-example.html">
- * Witness_complex/strong_witness_persistence.cpp</a>
- * \li <a href="_witness_complex_2weak_witness_persistence_8cpp-example.html">
- * Witness_complex/weak_witness_persistence.cpp</a>
- * \li <a href="_witness_complex_2example_nearest_landmark_table_8cpp-example.html">
- * Witness_complex/example_nearest_landmark_table.cpp</a>
- *
- * \section Contributions Bug reports and contributions
- * Please help us improving the quality of the GUDHI library. You may report bugs or suggestions to:
- * \verbatim  Contact: gudhi-users@lists.gforge.inria.fr \endverbatim
- * 
- * GUDHI is open to external contributions. If you want to join our development team, please contact us.
- * 
-*/
-
-/*! \page Citation Acknowledging the GUDHI library
- * We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use
- * of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.
- * Feel free to contact us in case you have any question or remark on this topic.
- * 
- * We provide \ref GudhiBibtex entries for the modules of the User and Reference Manual, as well as for publications
- * directly related to the GUDHI library.
- * \section GudhiBibtex GUDHI bibtex
- * \verbinclude  biblio/how_to_cite_gudhi.bib
-*/
-
-// List of GUDHI examples - Doxygen needs at least a file tag to analyse comments
-/*! @file Examples
- * @example Alpha_complex/Alpha_complex_from_off.cpp
- * @example Alpha_complex/Alpha_complex_from_points.cpp
- * @example Alpha_complex/alpha_complex_3d_persistence.cpp
- * @example Alpha_complex/alpha_complex_persistence.cpp
- * @example Alpha_complex/exact_alpha_complex_3d_persistence.cpp
- * @example Alpha_complex/periodic_alpha_complex_3d_persistence.cpp
- * @example Alpha_complex/weighted_alpha_complex_3d_persistence.cpp
- * @example Bottleneck_distance/alpha_rips_persistence_bottleneck_distance.cpp
- * @example Bottleneck_distance/bottleneck_basic_example.cpp
- * @example Bottleneck_distance/bottleneck_distance.cpp
- * @example Bitmap_cubical_complex/cubical_complex_persistence.cpp
- * @example Bitmap_cubical_complex/periodic_cubical_complex_persistence.cpp
- * @example Bitmap_cubical_complex/Random_bitmap_cubical_complex.cpp
- * @example common/example_CGAL_3D_points_off_reader.cpp
- * @example common/example_CGAL_points_off_reader.cpp
- * @example Contraction/Garland_heckbert.cpp
- * @example Contraction/Rips_contraction.cpp
- * @example Persistent_cohomology/rips_persistence_via_boundary_matrix.cpp
- * @example Persistent_cohomology/persistence_from_file.cpp
- * @example Persistent_cohomology/persistence_from_simple_simplex_tree.cpp
- * @example Persistent_cohomology/plain_homology.cpp
- * @example Persistent_cohomology/rips_multifield_persistence.cpp
- * @example Persistent_cohomology/custom_persistence_sort.cpp
- * @example Persistent_cohomology/rips_persistence_step_by_step.cpp
- * @example Rips_complex/example_one_skeleton_rips_from_points.cpp
- * @example Rips_complex/example_rips_complex_from_off_file.cpp
- * @example Rips_complex/rips_persistence.cpp
- * @example Rips_complex/rips_distance_matrix_persistence.cpp
- * @example Simplex_tree/mini_simplex_tree.cpp
- * @example Simplex_tree/simple_simplex_tree.cpp
- * @example Simplex_tree/example_alpha_shapes_3_simplex_tree_from_off_file.cpp
- * @example Simplex_tree/simplex_tree_from_cliques_of_graph.cpp
- * @example Simplex_tree/graph_expansion_with_blocker.cpp
- * @example Skeleton_blocker/Skeleton_blocker_from_simplices.cpp
- * @example Skeleton_blocker/Skeleton_blocker_iteration.cpp
- * @example Skeleton_blocker/Skeleton_blocker_link.cpp
- * @example Spatial_searching/example_spatial_searching.cpp
- * @example Subsampling/example_choose_n_farthest_points.cpp
- * @example Subsampling/example_custom_kernel.cpp
- * @example Subsampling/example_pick_n_random_points.cpp
- * @example Subsampling/example_sparsify_point_set.cpp
- * @example Tangential_complex/example_basic.cpp
- * @example Tangential_complex/example_with_perturb.cpp
- * @example Witness_complex/example_nearest_landmark_table.cpp
- * @example Witness_complex/example_strong_witness_complex_off.cpp
- * @example Witness_complex/example_witness_complex_off.cpp
- * @example Witness_complex/example_witness_complex_sphere.cpp
- * @example Witness_complex/weak_witness_persistence.cpp
- * @example Witness_complex/strong_witness_persistence.cpp
- */
- 
-- 
cgit v1.2.3


From b674e9a5fae8bdbb22eadb9a7c0013ce84451743 Mon Sep 17 00:00:00 2001
From: vrouvrea <vrouvrea@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Fri, 26 Jan 2018 14:55:28 +0000
Subject: Move documentation Copyright in footer Removed from each module

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/trunk@3167 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 7595f2d18cdc3773bbd96fa9fed414876ff9fdc7
---
 src/Alpha_complex/doc/Intro_alpha_complex.h                    |  4 +---
 src/Bitmap_cubical_complex/doc/Gudhi_Cubical_Complex_doc.h     |  1 -
 src/Contraction/include/gudhi/Edge_contraction.h               |  4 ----
 src/Nerve_GIC/doc/Intro_graph_induced_complex.h                |  1 -
 .../doc/Persistence_representations_doc.h                      |  1 -
 src/Persistent_cohomology/doc/Intro_persistent_cohomology.h    |  1 -
 src/Rips_complex/doc/Intro_rips_complex.h                      |  2 --
 src/Simplex_tree/doc/Intro_simplex_tree.h                      |  1 -
 src/Skeleton_blocker/include/gudhi/Skeleton_blocker.h          |  3 ---
 src/Spatial_searching/doc/Intro_spatial_searching.h            |  2 --
 src/Subsampling/doc/Intro_subsampling.h                        |  2 --
 src/Tangential_complex/doc/Intro_tangential_complex.h          |  2 --
 src/Witness_complex/doc/Witness_complex_doc.h                  |  3 ---
 src/common/doc/footer.html                                     | 10 ++--------
 14 files changed, 3 insertions(+), 34 deletions(-)

(limited to 'src/common')

diff --git a/src/Alpha_complex/doc/Intro_alpha_complex.h b/src/Alpha_complex/doc/Intro_alpha_complex.h
index cf1a946a..a08663ca 100644
--- a/src/Alpha_complex/doc/Intro_alpha_complex.h
+++ b/src/Alpha_complex/doc/Intro_alpha_complex.h
@@ -31,7 +31,7 @@ namespace alpha_complex {
 /**  \defgroup alpha_complex Alpha complex
  * 
  * \author    Vincent Rouvreau
- * 
+ *
  * @{
  * 
  * \section definition Definition
@@ -195,8 +195,6 @@ namespace alpha_complex {
  * 
  * \include Alpha_complex/alphaoffreader_for_doc_32.txt
  * 
- * \copyright GNU General Public License v3.                         
- * \verbatim  Contact: gudhi-users@lists.gforge.inria.fr \endverbatim
  */
 /** @} */  // end defgroup alpha_complex
 
diff --git a/src/Bitmap_cubical_complex/doc/Gudhi_Cubical_Complex_doc.h b/src/Bitmap_cubical_complex/doc/Gudhi_Cubical_Complex_doc.h
index ee84e201..a5d7b60f 100644
--- a/src/Bitmap_cubical_complex/doc/Gudhi_Cubical_Complex_doc.h
+++ b/src/Bitmap_cubical_complex/doc/Gudhi_Cubical_Complex_doc.h
@@ -105,7 +105,6 @@ namespace cubical_complex {
  * \section BitmapExamples Examples
  * End user programs are available in example/Bitmap_cubical_complex and utilities/Bitmap_cubical_complex folders.
  * 
- * \copyright GNU General Public License v3.
  */
 /** @} */  // end defgroup cubical_complex
 
diff --git a/src/Contraction/include/gudhi/Edge_contraction.h b/src/Contraction/include/gudhi/Edge_contraction.h
index 61f2d945..cf9a2c27 100644
--- a/src/Contraction/include/gudhi/Edge_contraction.h
+++ b/src/Contraction/include/gudhi/Edge_contraction.h
@@ -210,7 +210,6 @@ int main (int argc, char *argv[])
 }
   \endcode
 
-
 \verbatim
 ./example/Contraction/RipsContraction ../../data/SO3_10000.off 0.3
 [ 50%] [100%] Built target SkeletonBlockerIteration
@@ -223,9 +222,6 @@ Time to simplify and enumerate simplices:
  3.166621s wall, 3.150000s user + 0.010000s system = 3.160000s CPU (99.8%)
 \endverbatim
 
-
-
-\copyright GNU General Public License v3.                         
 */
 /** @} */  // end defgroup
 }  // namespace contraction
diff --git a/src/Nerve_GIC/doc/Intro_graph_induced_complex.h b/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
index 344cb031..f2409087 100644
--- a/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
+++ b/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
@@ -176,7 +176,6 @@ namespace cover_complex {
  *
  * \image html "funcGICvisu.jpg" "Visualization with neato"
  *
- * \copyright GNU General Public License v3.                         
  */
 /** @} */  // end defgroup cover_complex
 
diff --git a/src/Persistence_representations/doc/Persistence_representations_doc.h b/src/Persistence_representations/doc/Persistence_representations_doc.h
index 978fb5bd..d781211a 100644
--- a/src/Persistence_representations/doc/Persistence_representations_doc.h
+++ b/src/Persistence_representations/doc/Persistence_representations_doc.h
@@ -250,7 +250,6 @@ namespace Persistence_representations {
  absolute value of differences between coordinates. A scalar product is a sum of products of
  values at the corresponding positions of two vectors.
 
- \copyright GNU General Public License v3.
  */
 /** @} */  // end defgroup Persistence_representations
 
diff --git a/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h b/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h
index ceaea505..4dbe82c7 100644
--- a/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h
+++ b/src/Persistent_cohomology/doc/Intro_persistent_cohomology.h
@@ -248,7 +248,6 @@ Simplex_tree dim: 3
 Persistent_cohomology/plain_homology.cpp</a> computes the plain homology of a simple simplicial complex without
 filtration values.
 
- \copyright GNU General Public License v3.
  */
 
 }  // namespace persistent_cohomology
diff --git a/src/Rips_complex/doc/Intro_rips_complex.h b/src/Rips_complex/doc/Intro_rips_complex.h
index 124dfec9..8c517516 100644
--- a/src/Rips_complex/doc/Intro_rips_complex.h
+++ b/src/Rips_complex/doc/Intro_rips_complex.h
@@ -146,8 +146,6 @@ namespace rips_complex {
  * 
  * \include Rips_complex/full_skeleton_rips_for_doc.txt
  * 
- * \copyright GNU General Public License v3.                         
- * \verbatim  Contact: gudhi-users@lists.gforge.inria.fr \endverbatim
  */
 /** @} */  // end defgroup rips_complex
 
diff --git a/src/Simplex_tree/doc/Intro_simplex_tree.h b/src/Simplex_tree/doc/Intro_simplex_tree.h
index 769491d9..6b80d1c9 100644
--- a/src/Simplex_tree/doc/Intro_simplex_tree.h
+++ b/src/Simplex_tree/doc/Intro_simplex_tree.h
@@ -79,7 +79,6 @@ Number of vertices = 10   Number of simplices = 98 \endcode
  * 1 incidence relations in a complex. It is consequently faster when accessing the boundary of a simplex, but is less
  * compact and harder to construct from scratch.
  * 
- * \copyright GNU General Public License v3.
  * @}
  */
 
diff --git a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker.h b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker.h
index 32fe411c..aca2aa57 100644
--- a/src/Skeleton_blocker/include/gudhi/Skeleton_blocker.h
+++ b/src/Skeleton_blocker/include/gudhi/Skeleton_blocker.h
@@ -239,9 +239,6 @@ their collaboration to write the two initial papers
  about this data-structure
  and also Dominique for leaving him use a prototype.
 
-
-\copyright GNU General Public License v3.
-
 @} */
 
 }  // namespace skeleton_blocker
diff --git a/src/Spatial_searching/doc/Intro_spatial_searching.h b/src/Spatial_searching/doc/Intro_spatial_searching.h
index 1ee5e92e..52ed65e4 100644
--- a/src/Spatial_searching/doc/Intro_spatial_searching.h
+++ b/src/Spatial_searching/doc/Intro_spatial_searching.h
@@ -50,8 +50,6 @@ namespace spatial_searching {
  * 
  * \include Spatial_searching/example_spatial_searching.cpp
  * 
- * \copyright GNU General Public License v3.                         
- * \verbatim  Contact: gudhi-users@lists.gforge.inria.fr \endverbatim
  */
 /** @} */  // end defgroup spatial_searching
 
diff --git a/src/Subsampling/doc/Intro_subsampling.h b/src/Subsampling/doc/Intro_subsampling.h
index c84616dd..ab9cdc37 100644
--- a/src/Subsampling/doc/Intro_subsampling.h
+++ b/src/Subsampling/doc/Intro_subsampling.h
@@ -58,8 +58,6 @@ namespace subsampling {
  * This example outputs a subset of 100 points picked randomly.
  *
  * \include Subsampling/example_pick_n_random_points.cpp
- * \copyright GNU General Public License v3.                         
- * \verbatim  Contact: gudhi-users@lists.gforge.inria.fr \endverbatim
  */
 /** @} */  // end defgroup subsampling
 
diff --git a/src/Tangential_complex/doc/Intro_tangential_complex.h b/src/Tangential_complex/doc/Intro_tangential_complex.h
index 3d687c1d..00e00c52 100644
--- a/src/Tangential_complex/doc/Intro_tangential_complex.h
+++ b/src/Tangential_complex/doc/Intro_tangential_complex.h
@@ -107,8 +107,6 @@ dimensions are known at compile-time.
 
 \include Tangential_complex/example_with_perturb.cpp
 
-\copyright GNU General Public License v3.                         
-\verbatim  Contact: gudhi-users@lists.gforge.inria.fr \endverbatim
  */
 /** @} */  // end defgroup tangential_complex
 
diff --git a/src/Witness_complex/doc/Witness_complex_doc.h b/src/Witness_complex/doc/Witness_complex_doc.h
index 5d5c0735..62203054 100644
--- a/src/Witness_complex/doc/Witness_complex_doc.h
+++ b/src/Witness_complex/doc/Witness_complex_doc.h
@@ -117,9 +117,6 @@ int main(int argc, char * const argv[]) {
 
    \include Witness_complex/example_nearest_landmark_table.cpp
 
-   \copyright GNU General Public License v3.
-
-
  */
 
 #endif  // WITNESS_COMPLEX_DOC_H_
diff --git a/src/common/doc/footer.html b/src/common/doc/footer.html
index 7b4cdc5c..a557922b 100644
--- a/src/common/doc/footer.html
+++ b/src/common/doc/footer.html
@@ -6,24 +6,18 @@
 <!--BEGIN PROJECT_NAME--> $projectname
 <!--BEGIN PROJECT_NUMBER-->&#160;Version $projectnumber<!--END PROJECT_NUMBER-->
 <!--BEGIN PROJECT_BRIEF-->&#160;-&#160;$projectbrief<!--END PROJECT_BRIEF-->
+<!--BEGIN PROJECT_BRIEF-->&#160;-&#160;Copyright : GPL v3<!--END PROJECT_BRIEF-->
 <!--END PROJECT_NAME-->
       </td>
       <td class="network-entypo">
 <!--BEGIN GENERATE_TREEVIEW-->
         $generatedby
         <a href="http://www.doxygen.org/index.html">
-        <img class="footer" src="$relpath^doxygen.png" alt="doxygen"/></a> $doxygenversion
+        Doxygen</a> $doxygenversion
 <!--END GENERATE_TREEVIEW-->
       </td>
   </tr>
 </table>
 
-<!--BEGIN !GENERATE_TREEVIEW-->
-<hr class="footer"/><address class="footer"><small> tralala
-$generatedby &#160;<a href="http://www.doxygen.org/index.html">
-<img class="footer" src="$relpath^doxygen.png" alt="doxygen"/>
-</a> $doxygenversion
-</small></address>
-<!--END !GENERATE_TREEVIEW-->
 </body>
 </html>
-- 
cgit v1.2.3


From c2d46e0e6cfea1927875219bd5e03962cef7b010 Mon Sep 17 00:00:00 2001
From: vrouvrea <vrouvrea@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Mon, 29 Jan 2018 16:31:18 +0000
Subject: Fix doc issue for utilities part on the web site

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/trunk@3176 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 3bafd9e13a5646ca93023144705d3ac7ed7c9b3a
---
 src/Alpha_complex/utilities/README                 | 148 ++++++++++-----------
 src/Bitmap_cubical_complex/utilities/README        |  42 ++++--
 src/Bottleneck_distance/utilities/README           |  32 ++++-
 src/Nerve_GIC/doc/Intro_graph_induced_complex.h    |   6 +-
 src/Nerve_GIC/include/gudhi/GIC.h                  |  11 +-
 .../utilities/KeplerMapperVisuFromTxtFile.py       |  97 ++++++++------
 src/Nerve_GIC/utilities/covercomplex.md            |  73 ++++++++++
 src/Rips_complex/utilities/README                  |  67 ++++------
 src/Witness_complex/utilities/README               |  70 +++++-----
 src/common/utilities/README                        |  46 +++++--
 10 files changed, 365 insertions(+), 227 deletions(-)
 create mode 100644 src/Nerve_GIC/utilities/covercomplex.md

(limited to 'src/common')

diff --git a/src/Alpha_complex/utilities/README b/src/Alpha_complex/utilities/README
index 1cd2ca95..56bce602 100644
--- a/src/Alpha_complex/utilities/README
+++ b/src/Alpha_complex/utilities/README
@@ -1,16 +1,35 @@
-# Alpha_complex #
-
-## `alpha_complex_3d_persistence` ##
+---
+layout: page
+title: "Alpha complex"
+meta_title: "alphacomplex"
+subheadline: ""
+teaser: ""
+permalink: "/alphacomplex/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
+
+
+
+## alpha_complex_3d_persistence ##
 This program computes the persistent homology with coefficient field Z/pZ of the 3D alpha complex built from a 3D point cloud. The output diagram contains one bar per line, written with the convention:
 
-`p dim birth death`
+```
+p dim birth death
+```
 
 where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
 
 **Usage**
-`alpha_complex_3d_persistence [options] <input OFF file>`
-where
-`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+
+```
+   alpha_complex_3d_persistence [options] <input OFF file>
+```
+
+where `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
 
 **Allowed options**
 
@@ -20,43 +39,38 @@ where
 * `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
 
 **Example**
-`alpha_complex_3d_persistence ../../data/points/tore3D_300.off -p 2 -m 0.45`
 
-outputs:
 ```
-Simplex_tree dim: 3
-2  0 0 inf
-2  1 0.0682162 1.0001
-2  1 0.0934117 1.00003
-2  2 0.56444 1.03938
+alpha_complex_3d_persistence ../../data/points/tore3D_300.off -p 2 -m 0.45
 ```
 
-Here we retrieve expected Betti numbers on a tore 3D:
-```
-Betti numbers[0] = 1
-Betti numbers[1] = 2
-Betti numbers[2] = 1
-```
+N.B.:
 
-N.B.: 
 * `alpha_complex_3d_persistence` only accepts OFF files in dimension 3.
 * Filtration values are alpha square values.
 
 
+## exact_alpha_complex_3d_persistence ##
+
+Same as `alpha_complex_3d_persistence`, but using exact computation.
+It is slower, but it is necessary when points are on a grid for instance.
 
-## `exact_alpha_complex_3d_persistence` ##
-Same as `alpha_complex_3d_persistence`, but using exact computation. It is slower, but it is necessary when points are on a grid for instance.
 
 
+## weighted_alpha_complex_3d_persistence ##
 
-## `weighted_alpha_complex_3d_persistence` ##
 Same as `alpha_complex_3d_persistence`, but using weighted points.
 
 **Usage**
-`weighted_alpha_complex_3d_persistence [options] <input OFF file> <weights input file>`
+
+```
+   weighted_alpha_complex_3d_persistence [options] <input OFF file> <weights input file>
+```
+
 where
-`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
-`<input weights file>` is the path to the file containing the weights of the points (one value per line).
+
+* `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+* `<input weights file>` is the path to the file containing the weights of the points (one value per line).
 
 **Allowed options**
 
@@ -66,32 +80,33 @@ where
 * `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
 
 **Example**
-`weighted_alpha_complex_3d_persistence ../../data/points/tore3D_300.off ../../data/points/tore3D_300.weights -p 2 -m 0.45`
 
-outputs:
 ```
-Simplex_tree dim: 3
-2  0 -1 inf
-2  1 -0.931784 0.000103311
-2  1 -0.906588 2.60165e-05
-2  2 -0.43556 0.0393798
+   weighted_alpha_complex_3d_persistence ../../data/points/tore3D_300.off ../../data/points/tore3D_300.weights -p 2 -m 0.45
 ```
 
+
 N.B.:
+
 * Weights values are explained on CGAL [Alpha shape](https://doc.cgal.org/latest/Alpha_shapes_3/index.html#title0)
 and [Regular triangulation](https://doc.cgal.org/latest/Triangulation_3/index.html#Triangulation3secclassRegulartriangulation) documentation.
 * Filtration values are alpha square values.
 
 
-## `periodic_alpha_complex_3d_persistence` ##
+## periodic_alpha_complex_3d_persistence ##
 Same as `alpha_complex_3d_persistence`, but using periodic alpha shape 3d.
 Refer to the [CGAL's 3D Periodic Triangulations User Manual](https://doc.cgal.org/latest/Periodic_3_triangulation_3/index.html) for more details.
 
 **Usage**
-`periodic_alpha_complex_3d_persistence [options] <input OFF file> <cuboid file>`
+
+```
+   periodic_alpha_complex_3d_persistence [options] <input OFF file> <cuboid file>
+```
+
 where
-`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
-`<cuboid file>` is the path to the file describing the periodic domain. It must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsIsoCuboid).
+
+* `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+* `<cuboid file>` is the path to the file describing the periodic domain. It must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsIsoCuboid).
 
 **Allowed options**
 
@@ -102,46 +117,36 @@ where
 
 
 **Example**
-`periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off ../../data/points/iso_cuboid_3_in_0_1.txt -p 3 -m 1.0`
 
-outputs:
 ```
-Periodic Delaunay computed.
-Simplex_tree dim: 3
-3  0 0 inf
-3  1 0.0025 inf
-3  1 0.0025 inf
-3  1 0.0025 inf
-3  2 0.005 inf
-3  2 0.005 inf
-3  2 0.005 inf
-3  3 0.0075 inf
+periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off ../../data/points/iso_cuboid_3_in_0_1.txt -p 3 -m 1.0
 ```
 
-Here we retrieve expected Betti numbers on an 3D iso-oriented cuboids:
-```
-Betti numbers[0] = 1
-Betti numbers[1] = 3
-Betti numbers[2] = 3
-Betti numbers[3] = 1
-```
+N.B.:
 
-N.B.: 
 * Cuboid file must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsIsoCuboid).
 * Filtration values are alpha square values.
 
 
 
+## alpha_complex_persistence ##
 
-## `alpha_complex_persistence` ##
-This program computes the persistent homology with coefficient field Z/pZ of the dD alpha complex built from a dD point cloud. The output diagram contains one bar per line, written with the convention:
+This program computes the persistent homology with coefficient field Z/pZ of the dD alpha complex built from a dD point cloud.
+The output diagram contains one bar per line, written with the convention:
 
-`p dim birth death`
+```
+   p dim birth death
+```
 
-where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
+and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
 
 **Usage**
-`alpha_complex_persistence [options] <input OFF file>`
+
+```
+   alpha_complex_persistence [options] <input OFF file>
+```
+
 where
 `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
 
@@ -154,24 +159,11 @@ where
 * `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
 
 **Example**
-`alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off`
 
-outputs:
 ```
-Alpha complex is of dimension 3 - 9273 simplices - 300 vertices.
-Simplex_tree dim: 3
-2  0 0 inf 
-2  1 0.0682162 1.0001 
-2  1 0.0934117 1.00003 
-2  2 0.56444 1.03938 
-```
-
-Here we retrieve expected Betti numbers on a tore 3D:
-```
-Betti numbers[0] = 1
-Betti numbers[1] = 2
-Betti numbers[2] = 1
+   alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off
 ```
 
 N.B.:
+
 * Filtration values are alpha square values.
diff --git a/src/Bitmap_cubical_complex/utilities/README b/src/Bitmap_cubical_complex/utilities/README
index ddff7034..393b2654 100644
--- a/src/Bitmap_cubical_complex/utilities/README
+++ b/src/Bitmap_cubical_complex/utilities/README
@@ -1,18 +1,40 @@
-# Bitmap_cubical_complex #
+---
+layout: page
+title: "Bitmap cubical complex"
+meta_title: "cubicalcomplex"
+subheadline: ""
+teaser: ""
+permalink: "/cubicalcomplex/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
 
-## `cubical_complex_persistence` ##
-This program computes persistent homology, by using the Bitmap_cubical_complex class, of cubical complexes provided in text files in Perseus style. See [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsPerseus) for a description of the file format.
 
-Example:
+## cubical_complex_persistence ##
+This program computes persistent homology, by using the Bitmap_cubical_complex class, of cubical complexes provided in text files in Perseus style.
+See [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsPerseus) for a description of the file format.
 
-* Create a Cubical Complex from the Perseus style file `CubicalTwoSphere.txt`, computes Persistence cohomology from it and writes the results in a persistence file `CubicalTwoSphere.txt_persistence`:
-`cubical_complex_persistence data/bitmap/CubicalTwoSphere.txt`
+**Example**
 
-## `periodic_cubical_complex_persistence` ##
+```
+   cubical_complex_persistence data/bitmap/CubicalTwoSphere.txt
+```
+
+* Creates a Cubical Complex from the Perseus style file `CubicalTwoSphere.txt`,
+computes Persistence cohomology from it and writes the results in a persistence file `CubicalTwoSphere.txt_persistence`.
+
+## periodic_cubical_complex_persistence ##
 
 Same as above, but with periodic boundary conditions.
 
-Example:
+**Example**
+
+```
+   periodic_cubical_complex_persistence data/bitmap/3d_torus.txt
+```
 
-* Create a Periodical Cubical Complex from the Perseus style file `3d_torus.txt`, computes Persistence cohomology from it and writes the results in a persistence file `3d_torus.txt_persistence`:
-`periodic_cubical_complex_persistence data/bitmap/3d_torus.txt`
+* Creates a Periodical Cubical Complex from the Perseus style file `3d_torus.txt`,
+computes Persistence cohomology from it and writes the results in a persistence file `3d_torus.txt_persistence`.
diff --git a/src/Bottleneck_distance/utilities/README b/src/Bottleneck_distance/utilities/README
index d9fdd252..04e1c4bd 100644
--- a/src/Bottleneck_distance/utilities/README
+++ b/src/Bottleneck_distance/utilities/README
@@ -1,10 +1,30 @@
-# Bottleneck_distance #
+---
+layout: page
+title: "Bottleneck distance"
+meta_title: "bottleneckdistance"
+subheadline: ""
+teaser: ""
+permalink: "/bottleneckdistance/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
+
+
+
+## bottleneck_read_file_example ##
 
-## `bottleneck_read_file_example` ##
 This program computes the Bottleneck distance between two persistence diagram files.
 
-Usage:
-`bottleneck_read_file_example <file_1.pers> <file_2.pers> [<tolerance>]`
+**Usage**
+
+```
+   bottleneck_read_file_example <file_1.pers> <file_2.pers> [<tolerance>]
+```
+
 where
-`<file_1.pers>` and `<file_2.pers>` must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsPers).
-`<tolerance>` is an error bound on the bottleneck distance (set by default to the smallest positive double value).
+
+* `<file_1.pers>` and `<file_2.pers>` must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsPers).
+* `<tolerance>` is an error bound on the bottleneck distance (set by default to the smallest positive double value).
diff --git a/src/Nerve_GIC/doc/Intro_graph_induced_complex.h b/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
index f2409087..474f0f0e 100644
--- a/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
+++ b/src/Nerve_GIC/doc/Intro_graph_induced_complex.h
@@ -77,8 +77,8 @@ namespace cover_complex {
  *
  * \include Nerve_GIC/Nerve.txt
  *
- * The program also writes a file SC.txt. The first three lines in this file are the location of the input point cloud
- * and the function used to compute the cover.
+ * The program also writes a file ../../data/points/human_sc.txt. The first three lines in this file are the location
+ * of the input point cloud and the function used to compute the cover.
  * The fourth line contains the number of vertices nv and edges ne of the Nerve.
  * The next nv lines represent the vertices. Each line contains the vertex ID,
  * the number of data points it contains, and their average color function value.
@@ -118,7 +118,7 @@ namespace cover_complex {
  *
  * the program outputs SC.off. Using e.g.
  *
- * \code $> geomview SC.off
+ * \code $> geomview ../../data/points/human_sc.off
  * \endcode
  *
  * one can obtain the following visualization:
diff --git a/src/Nerve_GIC/include/gudhi/GIC.h b/src/Nerve_GIC/include/gudhi/GIC.h
index 58831bbf..ff95b913 100644
--- a/src/Nerve_GIC/include/gudhi/GIC.h
+++ b/src/Nerve_GIC/include/gudhi/GIC.h
@@ -934,7 +934,7 @@ class Cover_complex {
       }
     graphic << "}";
     graphic.close();
-    std::cout << ".dot file generated. It can be visualized with e.g. neato." << std::endl;
+    std::cout << mapp << " file generated. It can be visualized with e.g. neato." << std::endl;
   }
 
  public:  // Create a .txt file that can be compiled with KeplerMapper.
@@ -944,7 +944,7 @@ class Cover_complex {
   void write_info() {
     int num_simplices = simplices.size();
     int num_edges = 0;
-    std::string mapp = point_cloud_name + "_sc.dot";
+    std::string mapp = point_cloud_name + "_sc.txt";
     std::ofstream graphic(mapp.c_str());
 
     for (int i = 0; i < num_simplices; i++)
@@ -970,7 +970,8 @@ class Cover_complex {
         if (cover_color[simplices[i][0]].first > mask && cover_color[simplices[i][1]].first > mask)
           graphic << name2id[simplices[i][0]] << " " << name2id[simplices[i][1]] << std::endl;
     graphic.close();
-    std::cout << ".txt generated. It can be visualized with e.g. python KeplerMapperVisuFromTxtFile.py and firefox."
+    std::cout << mapp
+              << " generated. It can be visualized with e.g. python KeplerMapperVisuFromTxtFile.py and firefox."
               << std::endl;
   }
 
@@ -988,7 +989,7 @@ class Cover_complex {
     std::vector<std::vector<int> > edges, faces;
     int numsimplices = simplices.size();
 
-    std::string mapp = point_cloud_name + "_sc.dot";
+    std::string mapp = point_cloud_name + "_sc.off";
     std::ofstream graphic(mapp.c_str());
 
     graphic << "OFF" << std::endl;
@@ -1016,7 +1017,7 @@ class Cover_complex {
     for (int i = 0; i < numfaces; i++)
       graphic << 3 << " " << faces[i][0] << " " << faces[i][1] << " " << faces[i][2] << std::endl;
     graphic.close();
-    std::cout << ".off generated. It can be visualized with e.g. geomview." << std::endl;
+    std::cout << mapp << " generated. It can be visualized with e.g. geomview." << std::endl;
   }
 
   // *******************************************************************************************************************
diff --git a/src/Nerve_GIC/utilities/KeplerMapperVisuFromTxtFile.py b/src/Nerve_GIC/utilities/KeplerMapperVisuFromTxtFile.py
index d2897774..c811f610 100755
--- a/src/Nerve_GIC/utilities/KeplerMapperVisuFromTxtFile.py
+++ b/src/Nerve_GIC/utilities/KeplerMapperVisuFromTxtFile.py
@@ -3,6 +3,7 @@
 import km
 import numpy as np
 from collections import defaultdict
+import argparse
 
 """This file is part of the Gudhi Library. The Gudhi library
    (Geometric Understanding in Higher Dimensions) is a generic C++
@@ -30,43 +31,59 @@ __author__ = "Mathieu Carriere"
 __copyright__ = "Copyright (C) 2017 INRIA"
 __license__ = "GPL v3"
 
-network = {}
-mapper = km.KeplerMapper(verbose=0)
-data = np.zeros((3,3))
-projected_data = mapper.fit_transform( data, projection="sum", scaler=None )
-
-f = open('SC.txt','r')
-nodes = defaultdict(list)
-links = defaultdict(list)
-custom = defaultdict(list)
-
-dat = f.readline()
-lens = f.readline()
-color = f.readline();
-param = [float(i) for i in f.readline().split(" ")]
-
-nums = [int(i) for i in f.readline().split(" ")]
-num_nodes = nums[0]
-num_edges = nums[1]
-
-for i in range(0,num_nodes):
-	point = [float(j) for j in f.readline().split(" ")]
-	nodes[  str(int(point[0]))  ] = [  int(point[0]), point[1], int(point[2])  ]
-	links[  str(int(point[0]))  ] = []
-	custom[  int(point[0])  ] = point[1]
-
-m = min([custom[i] for i in range(0,num_nodes)])
-M = max([custom[i] for i in range(0,num_nodes)])
-
-for i in range(0,num_edges):
-	edge = [int(j) for j in f.readline().split(" ")]	
-	links[  str(edge[0])  ].append(  str(edge[1])  )	
-	links[  str(edge[1])  ].append(  str(edge[0])  )
-
-network["nodes"] = nodes
-network["links"] = links
-network["meta"] = lens
-
-mapper.visualize(network, color_function = color, path_html="SC.html", title=dat,
-graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=custom, width_html=0, 
-height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=param[0],gain=param[1], minimum=m,maximum=M)
+parser = argparse.ArgumentParser(description='Creates an html Keppler Mapper '
+                                 'file to visualize a SC.txt file.',
+                                 epilog='Example: '
+                                 './KeplerMapperVisuFromTxtFile.py '
+                                 '-f ../../data/points/human.off_sc.txt'
+                                 '- Constructs an human.off_sc.html file.')
+parser.add_argument("-f", "--file", type=str, required=True)
+
+args = parser.parse_args()
+
+with open(args.file, 'r') as f:
+    network = {}
+    mapper = km.KeplerMapper(verbose=0)
+    data = np.zeros((3,3))
+    projected_data = mapper.fit_transform( data, projection="sum", scaler=None )
+
+    nodes = defaultdict(list)
+    links = defaultdict(list)
+    custom = defaultdict(list)
+
+    dat = f.readline()
+    lens = f.readline()
+    color = f.readline();
+    param = [float(i) for i in f.readline().split(" ")]
+
+    nums = [int(i) for i in f.readline().split(" ")]
+    num_nodes = nums[0]
+    num_edges = nums[1]
+
+    for i in range(0,num_nodes):
+        point = [float(j) for j in f.readline().split(" ")]
+        nodes[  str(int(point[0]))  ] = [  int(point[0]), point[1], int(point[2])  ]
+        links[  str(int(point[0]))  ] = []
+        custom[  int(point[0])  ] = point[1]
+
+    m = min([custom[i] for i in range(0,num_nodes)])
+    M = max([custom[i] for i in range(0,num_nodes)])
+
+    for i in range(0,num_edges):
+        edge = [int(j) for j in f.readline().split(" ")]
+        links[  str(edge[0])  ].append(  str(edge[1])  )
+        links[  str(edge[1])  ].append(  str(edge[0])  )
+
+    network["nodes"] = nodes
+    network["links"] = links
+    network["meta"] = lens
+
+    html_output_filename = args.file.rsplit('.', 1)[0] + '.html'
+    mapper.visualize(network, color_function = color, path_html=html_output_filename, title=dat,
+    graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=custom, width_html=0,
+    height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=param[0],gain=param[1], minimum=m,maximum=M)
+    message = repr(html_output_filename) + " is generated. You can now use your favorite web browser to visualize it."
+    print(message)
+
+
+    f.close()
diff --git a/src/Nerve_GIC/utilities/covercomplex.md b/src/Nerve_GIC/utilities/covercomplex.md
new file mode 100644
index 00000000..692e44e7
--- /dev/null
+++ b/src/Nerve_GIC/utilities/covercomplex.md
@@ -0,0 +1,73 @@
+---
+layout: page
+title: "Cover complex"
+meta_title: "covercomplex"
+subheadline: ""
+teaser: ""
+permalink: "/covercomplex/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
+
+
+
+## Nerve ##
+This program builds the Nerve of a point cloud sampled on an OFF file.
+The cover C comes from the preimages of intervals covering a coordinate function,
+which are then refined into their connected components using the triangulation of the .OFF file.
+
+The program also writes a file SC.txt.
+The first three lines in this file are the location of the input point cloud and the function used to compute the cover.
+The fourth line contains the number of vertices nv and edges ne of the Nerve. The next nv lines represent the vertices.
+Each line contains the vertex ID, the number of data points it contains, and their average color function value.
+Finally, the next ne lines represent the edges, characterized by the ID of their vertices.
+
+**Usage**
+
+`Nerve <OFF input file> coordinate resolution gain [--v]`
+
+where
+
+* `coordinate` is the coordinate function to cover
+* `resolution` is the number of the intervals
+* `gain` is the gain for each interval
+* `--v` is optional, it activates verbose mode.
+
+**Example**
+
+`Nerve ../../data/points/human.off 2 10 0.3`
+
+* Builds the Nerve of a point cloud sampled on a 3D human shape (human.off).
+The cover C comes from the preimages of intervals (10 intervals with gain 0.3) covering the height function (coordinate 2).
+
+`python KeplerMapperVisuFromTxtFile.py -f ../../data/points/human.off_sc.txt`
+
+* Constructs `human.off_sc.html` file. You can now use your favorite web browser to visualize it.
+
+## VoronoiGIC ##
+
+This util builds the Graph Induced Complex (GIC) of a point cloud.
+It subsamples *N* points in the point cloud, which act as seeds of a geodesic Voronoï diagram.
+Each cell of the diagram is then an element of C.
+
+The program also writes a file `*_sc.off`, that is an OFF file that can be visualized with GeomView.
+
+**Usage**
+
+`VoroniGIC <OFF input file> samples_number [--v]`
+
+where
+
+* `samples_number` is the number of samples to take from the point cloud
+* `--v` is optional, it activates verbose mode.
+
+**Example**
+
+`VoroniGIC ../../data/points/human.off 700`
+
+* Builds the Voronoi Graph Induced Complex with 700 subsamples from `human.off` file.
+`../../data/points/human_sc.off` can be visualized with GeomView.
+
diff --git a/src/Rips_complex/utilities/README b/src/Rips_complex/utilities/README
index 4d20c806..44a37543 100644
--- a/src/Rips_complex/utilities/README
+++ b/src/Rips_complex/utilities/README
@@ -1,6 +1,20 @@
-# Rips_complex #
-
-## `rips_persistence` ##
+---
+layout: page
+title: "Rips complex"
+meta_title: "ripscomplex"
+subheadline: ""
+teaser: ""
+permalink: "/ripscomplex/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
+
+
+
+## rips_persistence ##
 This program computes the persistent homology with coefficient field *Z/pZ* of a Rips complex defined on a set of input points. The output diagram contains one bar per line, written with the convention:
 
 `p dim birth death`
@@ -8,6 +22,7 @@ This program computes the persistent homology with coefficient field *Z/pZ* of a
 where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
 
 **Usage**
+
 `rips_persistence [options] <OFF input file>`
 
 **Allowed options**
@@ -22,53 +37,25 @@ where `dim` is the dimension of the homological feature, `birth` and `death` are
 Beware: this program may use a lot of RAM and take a lot of time if `max-edge-length` is set to a large value.
 
 **Example 1 with Z/2Z coefficients**
-`rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 2`
 
-outputs:
-```
-2  0 0 inf
-2  1 0.0983494 inf 
-2  1 0.104347 inf 
-2  2 0.138335 inf 
-```
+`rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 2`
 
 **Example 2 with Z/3Z coefficients**
 
-rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 3
-
-outputs:
-```
-3  0 0 inf
-3  1 0.0983494 inf
-3  1 0.104347 inf
-3  2 0.138335 inf
-```
+`rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 3`
 
 
+## rips_distance_matrix_persistence ##
 
+Same as `rips_persistence` but taking a distance matrix as input.
 
-## `rips_distance_matrix_persistence` ##
-Same as `rips_persistence` but taking a distance matrix as input. 
-
 **Usage**
-`rips_persistence [options] <CSV input file>`
-where
+
+`rips_persistence [options] <CSV input file>`
+
+where
 `<CSV input file>` is the path to the file containing a distance matrix. Can be square or lower triangular matrix. Separator is ';'.
 
 **Example**
-`rips_distance_matrix_persistence data/distance_matrix/full_square_distance_matrix.csv -r 15 -d 3 -p 3 -m 0`
 
-outputs:
-```
-The complex contains 46 simplices 
-   and has dimension 3 
-3  0 0 inf 
-3  0 0 8.94427 
-3  0 0 7.28011 
-3  0 0 6.08276 
-3  0 0 5.83095 
-3  0 0 5.38516 
-3  0 0 5 
-3  1 11 12.0416 
-3  1 6.32456 6.7082 
-```
+`rips_distance_matrix_persistence data/distance_matrix/full_square_distance_matrix.csv -r 15 -d 3 -p 3 -m 0`
diff --git a/src/Witness_complex/utilities/README b/src/Witness_complex/utilities/README
index 1141033e..5cdb1f88 100644
--- a/src/Witness_complex/utilities/README
+++ b/src/Witness_complex/utilities/README
@@ -1,18 +1,34 @@
-# Witness_complex #
+---
+layout: page
+title: "Witness complex"
+meta_title: "witnesscomplex"
+subheadline: ""
+teaser: ""
+permalink: "/witnesscomplex/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
+
 
 For more details about the witness complex, please read the [user manual of the package](http://gudhi.gforge.inria.fr/doc/latest/group__witness__complex.html).
 
-## `weak_witness_persistence` ##
-This program computes the persistent homology with coefficient field *Z/pZ* of a Weak witness complex defined on a set of input points. The output diagram contains one bar per line, written with the convention:
+## weak_witness_persistence ##
+This program computes the persistent homology with coefficient field *Z/pZ* of a Weak witness complex defined on a set of input points.
+The output diagram contains one bar per line, written with the convention:
 
 `p dim birth death`
 
-where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
+and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+
+**Usage**
 
-*Usage*
 `weak_witness_persistence [options] <OFF input file>`
 
-*Allowed options*
+**Allowed options**
 
 * `-h [ --help ]` Produce help message
 * `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
@@ -22,33 +38,28 @@ where `dim` is the dimension of the homological feature, `birth` and `death` are
 * `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
 * `-d [ --cpx-dimension ]` (default = 2147483647) Maximal dimension of the weak witness complex we want to compute.
 
-*Example*
-`weak_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.006`
+**Example**
 
-outputs:
-```
-Successfully read 1307 points.
-Ambient dimension is 3.
-The complex contains 732 simplices and has dimension 8 
-11  0 0 inf 
-11  1 0 inf 
-11  2 0.0275251 0.0534586 
-11  1 0 0.0239952 
-```
+`weak_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.006`
 
 N.B.: output is random as the 20 landmarks are chosen randomly.
 
-## `strong_witness_persistence` ##
-This program computes the persistent homology with coefficient field *Z/pZ* of a Strong witness complex defined on a set of input points. The output diagram contains one bar per line, written with the convention:
+
+## strong_witness_persistence ##
+
+This program computes the persistent homology with coefficient field *Z/pZ* of a Strong witness complex defined on a set of input points.
+The output diagram contains one bar per line, written with the convention:
 
 `p dim birth death`
 
-where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
+and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+
+**Usage**
 
-*Usage*
 `strong_witness_persistence [options] <OFF input file>`
 
-*Allowed options*
+**Allowed options**
 
 * `-h [ --help ]` Produce help message
 * `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
@@ -58,17 +69,8 @@ where `dim` is the dimension of the homological feature, `birth` and `death` are
 * `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
 * `-d [ --cpx-dimension ]` (default = 2147483647) Maximal dimension of the weak witness complex we want to compute.
 
-*Example*
-`strong_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.06`
+**Example**
 
-outputs:
-```
-Successfully read 1307 points.
-Ambient dimension is 3.
-The complex contains 1836 simplices and has dimension 8 
-11  0 0 inf 
-11  1 0.00674748 inf 
-11  2 0.0937751 0.235354 
-```
+`strong_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.06`
 
 N.B.: output is random as the 20 landmarks are chosen randomly.
diff --git a/src/common/utilities/README b/src/common/utilities/README
index 18fa8cc4..f39c63b8 100644
--- a/src/common/utilities/README
+++ b/src/common/utilities/README
@@ -1,19 +1,43 @@
-# Pointset generator #
+---
+layout: page
+title: "Pointset generator"
+meta_title: "pointsetgenerator"
+subheadline: ""
+teaser: ""
+permalink: "/pointsetgenerator/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
 
-## `off_file_from_shape_generator` ##
+## off_file_from_shape_generator ##
 
 Generates a pointset and save it in an OFF file. Command-line is:
-`off_file_from_shape_generator on|in sphere|cube|curve|torus|klein <filename> <num_points> <dimension> <parameter1> <parameter2>...`
+
+```
+off_file_from_shape_generator on|in sphere|cube|curve|torus|klein <filename> <num_points> <dimension> <parameter1> <parameter2>...
+```
 
 Warning: "on cube" generator is not available!
 
-Examples:
+**Examples**
+
+```
+off_file_from_shape_generator on sphere onSphere.off 1000 3 15.2
+```
+
+* Generates an onSphere.off file with 1000 points randomized on a sphere of dimension 3 and radius 15.2.
+
+```
+off_file_from_shape_generator in sphere inSphere.off 100 2
+```
+
+* Generates an inSphere.off file with 100 points randomized in a sphere of dimension 2 (circle) and radius 1.0 (default).
 
-* Generate an onSphere.off file with 1000 points randomized on a sphere of dimension 3 and radius 15.2:
-`off_file_from_shape_generator on sphere onSphere.off 1000 3 15.2`
- 
-* Generate an inSphere.off file with 100 points randomized in a sphere of dimension 2 (circle) and radius 1.0 (default):
-`off_file_from_shape_generator in sphere inSphere.off 100 2`
+```
+off_file_from_shape_generator in cube inCube.off 10000 3 5.8
+```
 
-* Generates a inCube.off file with 10000 points randomized in a cube of dimension 3 and side 5.8:
-`off_file_from_shape_generator in cube inCube.off 10000 3 5.8`
+* Generates a inCube.off file with 10000 points randomized in a cube of dimension 3 and side 5.8.
-- 
cgit v1.2.3


From c50ac9a8e5d1f506cc79b9c1e623228fffd545bf Mon Sep 17 00:00:00 2001
From: vrouvrea <vrouvrea@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Mon, 29 Jan 2018 16:34:16 +0000
Subject: Rename README files for web site generation conformity

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/trunk@3177 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 632b264790c30accb42e731c4a4f6744a2f7ba11
---
 src/Alpha_complex/utilities/README                 | 169 ---------------------
 src/Alpha_complex/utilities/alphacomplex.md        | 169 +++++++++++++++++++++
 src/Bitmap_cubical_complex/utilities/README        |  40 -----
 .../utilities/cubicalcomplex.md                    |  40 +++++
 src/Bottleneck_distance/utilities/README           |  30 ----
 .../utilities/bottleneckdistance.md                |  30 ++++
 src/Rips_complex/utilities/README                  |  61 --------
 src/Rips_complex/utilities/ripscomplex.md          |  61 ++++++++
 src/Witness_complex/utilities/README               |  76 ---------
 src/Witness_complex/utilities/witnesscomplex.md    |  76 +++++++++
 src/common/utilities/README                        |  43 ------
 src/common/utilities/pointsetgenerator.md          |  43 ++++++
 12 files changed, 419 insertions(+), 419 deletions(-)
 delete mode 100644 src/Alpha_complex/utilities/README
 create mode 100644 src/Alpha_complex/utilities/alphacomplex.md
 delete mode 100644 src/Bitmap_cubical_complex/utilities/README
 create mode 100644 src/Bitmap_cubical_complex/utilities/cubicalcomplex.md
 delete mode 100644 src/Bottleneck_distance/utilities/README
 create mode 100644 src/Bottleneck_distance/utilities/bottleneckdistance.md
 delete mode 100644 src/Rips_complex/utilities/README
 create mode 100644 src/Rips_complex/utilities/ripscomplex.md
 delete mode 100644 src/Witness_complex/utilities/README
 create mode 100644 src/Witness_complex/utilities/witnesscomplex.md
 delete mode 100644 src/common/utilities/README
 create mode 100644 src/common/utilities/pointsetgenerator.md

(limited to 'src/common')

diff --git a/src/Alpha_complex/utilities/README b/src/Alpha_complex/utilities/README
deleted file mode 100644
index 56bce602..00000000
--- a/src/Alpha_complex/utilities/README
+++ /dev/null
@@ -1,169 +0,0 @@
----
-layout: page
-title: "Alpha complex"
-meta_title: "alphacomplex"
-subheadline: ""
-teaser: ""
-permalink: "/alphacomplex/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
-
-
-
-## alpha_complex_3d_persistence ##
-This program computes the persistent homology with coefficient field Z/pZ of the 3D alpha complex built from a 3D point cloud. The output diagram contains one bar per line, written with the convention:
-
-```
-p dim birth death
-```
-
-where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
-
-**Usage**
-
-```
-   alpha_complex_3d_persistence [options] <input OFF file>
-```
-
-where `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
-
-**Allowed options**
-
-* `-h [ --help ]` Produce help message
-* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
-* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
-* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
-
-**Example**
-
-```
-alpha_complex_3d_persistence ../../data/points/tore3D_300.off -p 2 -m 0.45
-```
-
-N.B.:
-
-* `alpha_complex_3d_persistence` only accepts OFF files in dimension 3.
-* Filtration values are alpha square values.
-
-
-## exact_alpha_complex_3d_persistence ##
-
-Same as `alpha_complex_3d_persistence`, but using exact computation.
-It is slower, but it is necessary when points are on a grid for instance.
-
-
-
-## weighted_alpha_complex_3d_persistence ##
-
-Same as `alpha_complex_3d_persistence`, but using weighted points.
-
-**Usage**
-
-```
-   weighted_alpha_complex_3d_persistence [options] <input OFF file> <weights input file>
-```
-
-where
-
-* `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
-* `<input weights file>` is the path to the file containing the weights of the points (one value per line).
-
-**Allowed options**
-
-* `-h [ --help ]` Produce help message
-* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
-* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
-* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
-
-**Example**
-
-```
-   weighted_alpha_complex_3d_persistence ../../data/points/tore3D_300.off ../../data/points/tore3D_300.weights -p 2 -m 0.45
-```
-
-
-N.B.:
-
-* Weights values are explained on CGAL [Alpha shape](https://doc.cgal.org/latest/Alpha_shapes_3/index.html#title0)
-and [Regular triangulation](https://doc.cgal.org/latest/Triangulation_3/index.html#Triangulation3secclassRegulartriangulation) documentation.
-* Filtration values are alpha square values.
-
-
-## periodic_alpha_complex_3d_persistence ##
-Same as `alpha_complex_3d_persistence`, but using periodic alpha shape 3d.
-Refer to the [CGAL's 3D Periodic Triangulations User Manual](https://doc.cgal.org/latest/Periodic_3_triangulation_3/index.html) for more details.
-
-**Usage**
-
-```
-   periodic_alpha_complex_3d_persistence [options] <input OFF file> <cuboid file>
-```
-
-where
-
-* `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
-* `<cuboid file>` is the path to the file describing the periodic domain. It must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsIsoCuboid).
-
-**Allowed options**
-
-* `-h [ --help ]` Produce help message
-* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
-* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
-* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals
-
-
-**Example**
-
-```
-periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off ../../data/points/iso_cuboid_3_in_0_1.txt -p 3 -m 1.0
-```
-
-N.B.:
-
-* Cuboid file must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsIsoCuboid).
-* Filtration values are alpha square values.
-
-
-
-## alpha_complex_persistence ##
-
-This program computes the persistent homology with coefficient field Z/pZ of the dD alpha complex built from a dD point cloud.
-The output diagram contains one bar per line, written with the convention:
-
-```
-   p dim birth death
-```
-
-where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
-and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
-
-**Usage**
-
-```
-   alpha_complex_persistence [options] <input OFF file>
-```
-
-where
-`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
-
-**Allowed options**
-
-* `-h [ --help ]` Produce help message
-* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
-* `-r [ --max-alpha-square-value ]` (default = inf) Maximal alpha square value for the Alpha complex construction.
-* `-p [ --field-charac ]` (default = 11)     Characteristic p of the coefficient field Z/pZ for computing homology.
-* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
-
-**Example**
-
-```
-   alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off
-```
-
-N.B.:
-
-* Filtration values are alpha square values.
diff --git a/src/Alpha_complex/utilities/alphacomplex.md b/src/Alpha_complex/utilities/alphacomplex.md
new file mode 100644
index 00000000..56bce602
--- /dev/null
+++ b/src/Alpha_complex/utilities/alphacomplex.md
@@ -0,0 +1,169 @@
+---
+layout: page
+title: "Alpha complex"
+meta_title: "alphacomplex"
+subheadline: ""
+teaser: ""
+permalink: "/alphacomplex/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
+
+
+
+## alpha_complex_3d_persistence ##
+This program computes the persistent homology with coefficient field Z/pZ of the 3D alpha complex built from a 3D point cloud. The output diagram contains one bar per line, written with the convention:
+
+```
+p dim birth death
+```
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
+
+**Usage**
+
+```
+   alpha_complex_3d_persistence [options] <input OFF file>
+```
+
+where `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+
+**Example**
+
+```
+alpha_complex_3d_persistence ../../data/points/tore3D_300.off -p 2 -m 0.45
+```
+
+N.B.:
+
+* `alpha_complex_3d_persistence` only accepts OFF files in dimension 3.
+* Filtration values are alpha square values.
+
+
+## exact_alpha_complex_3d_persistence ##
+
+Same as `alpha_complex_3d_persistence`, but using exact computation.
+It is slower, but it is necessary when points are on a grid for instance.
+
+
+
+## weighted_alpha_complex_3d_persistence ##
+
+Same as `alpha_complex_3d_persistence`, but using weighted points.
+
+**Usage**
+
+```
+   weighted_alpha_complex_3d_persistence [options] <input OFF file> <weights input file>
+```
+
+where
+
+* `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+* `<input weights file>` is the path to the file containing the weights of the points (one value per line).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+
+**Example**
+
+```
+   weighted_alpha_complex_3d_persistence ../../data/points/tore3D_300.off ../../data/points/tore3D_300.weights -p 2 -m 0.45
+```
+
+
+N.B.:
+
+* Weights values are explained on CGAL [Alpha shape](https://doc.cgal.org/latest/Alpha_shapes_3/index.html#title0)
+and [Regular triangulation](https://doc.cgal.org/latest/Triangulation_3/index.html#Triangulation3secclassRegulartriangulation) documentation.
+* Filtration values are alpha square values.
+
+
+## periodic_alpha_complex_3d_persistence ##
+Same as `alpha_complex_3d_persistence`, but using periodic alpha shape 3d.
+Refer to the [CGAL's 3D Periodic Triangulations User Manual](https://doc.cgal.org/latest/Periodic_3_triangulation_3/index.html) for more details.
+
+**Usage**
+
+```
+   periodic_alpha_complex_3d_persistence [options] <input OFF file> <cuboid file>
+```
+
+where
+
+* `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+* `<cuboid file>` is the path to the file describing the periodic domain. It must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsIsoCuboid).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-p [ --field-charac ]` (default=11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals
+
+
+**Example**
+
+```
+periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off ../../data/points/iso_cuboid_3_in_0_1.txt -p 3 -m 1.0
+```
+
+N.B.:
+
+* Cuboid file must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsIsoCuboid).
+* Filtration values are alpha square values.
+
+
+
+## alpha_complex_persistence ##
+
+This program computes the persistent homology with coefficient field Z/pZ of the dD alpha complex built from a dD point cloud.
+The output diagram contains one bar per line, written with the convention:
+
+```
+   p dim birth death
+```
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
+and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
+
+**Usage**
+
+```
+   alpha_complex_persistence [options] <input OFF file>
+```
+
+where
+`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-r [ --max-alpha-square-value ]` (default = inf) Maximal alpha square value for the Alpha complex construction.
+* `-p [ --field-charac ]` (default = 11)     Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+
+**Example**
+
+```
+   alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off
+```
+
+N.B.:
+
+* Filtration values are alpha square values.
diff --git a/src/Bitmap_cubical_complex/utilities/README b/src/Bitmap_cubical_complex/utilities/README
deleted file mode 100644
index 393b2654..00000000
--- a/src/Bitmap_cubical_complex/utilities/README
+++ /dev/null
@@ -1,40 +0,0 @@
----
-layout: page
-title: "Bitmap cubical complex"
-meta_title: "cubicalcomplex"
-subheadline: ""
-teaser: ""
-permalink: "/cubicalcomplex/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
-
-
-## cubical_complex_persistence ##
-This program computes persistent homology, by using the Bitmap_cubical_complex class, of cubical complexes provided in text files in Perseus style.
-See [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsPerseus) for a description of the file format.
-
-**Example**
-
-```
-   cubical_complex_persistence data/bitmap/CubicalTwoSphere.txt
-```
-
-* Creates a Cubical Complex from the Perseus style file `CubicalTwoSphere.txt`,
-computes Persistence cohomology from it and writes the results in a persistence file `CubicalTwoSphere.txt_persistence`.
-
-## periodic_cubical_complex_persistence ##
-
-Same as above, but with periodic boundary conditions.
-
-**Example**
-
-```
-   periodic_cubical_complex_persistence data/bitmap/3d_torus.txt
-```
-
-* Creates a Periodical Cubical Complex from the Perseus style file `3d_torus.txt`,
-computes Persistence cohomology from it and writes the results in a persistence file `3d_torus.txt_persistence`.
diff --git a/src/Bitmap_cubical_complex/utilities/cubicalcomplex.md b/src/Bitmap_cubical_complex/utilities/cubicalcomplex.md
new file mode 100644
index 00000000..393b2654
--- /dev/null
+++ b/src/Bitmap_cubical_complex/utilities/cubicalcomplex.md
@@ -0,0 +1,40 @@
+---
+layout: page
+title: "Bitmap cubical complex"
+meta_title: "cubicalcomplex"
+subheadline: ""
+teaser: ""
+permalink: "/cubicalcomplex/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
+
+
+## cubical_complex_persistence ##
+This program computes persistent homology, by using the Bitmap_cubical_complex class, of cubical complexes provided in text files in Perseus style.
+See [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsPerseus) for a description of the file format.
+
+**Example**
+
+```
+   cubical_complex_persistence data/bitmap/CubicalTwoSphere.txt
+```
+
+* Creates a Cubical Complex from the Perseus style file `CubicalTwoSphere.txt`,
+computes Persistence cohomology from it and writes the results in a persistence file `CubicalTwoSphere.txt_persistence`.
+
+## periodic_cubical_complex_persistence ##
+
+Same as above, but with periodic boundary conditions.
+
+**Example**
+
+```
+   periodic_cubical_complex_persistence data/bitmap/3d_torus.txt
+```
+
+* Creates a Periodical Cubical Complex from the Perseus style file `3d_torus.txt`,
+computes Persistence cohomology from it and writes the results in a persistence file `3d_torus.txt_persistence`.
diff --git a/src/Bottleneck_distance/utilities/README b/src/Bottleneck_distance/utilities/README
deleted file mode 100644
index 04e1c4bd..00000000
--- a/src/Bottleneck_distance/utilities/README
+++ /dev/null
@@ -1,30 +0,0 @@
----
-layout: page
-title: "Bottleneck distance"
-meta_title: "bottleneckdistance"
-subheadline: ""
-teaser: ""
-permalink: "/bottleneckdistance/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
-
-
-
-## bottleneck_read_file_example ##
-
-This program computes the Bottleneck distance between two persistence diagram files.
-
-**Usage**
-
-```
-   bottleneck_read_file_example <file_1.pers> <file_2.pers> [<tolerance>]
-```
-
-where
-
-* `<file_1.pers>` and `<file_2.pers>` must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsPers).
-* `<tolerance>` is an error bound on the bottleneck distance (set by default to the smallest positive double value).
diff --git a/src/Bottleneck_distance/utilities/bottleneckdistance.md b/src/Bottleneck_distance/utilities/bottleneckdistance.md
new file mode 100644
index 00000000..04e1c4bd
--- /dev/null
+++ b/src/Bottleneck_distance/utilities/bottleneckdistance.md
@@ -0,0 +1,30 @@
+---
+layout: page
+title: "Bottleneck distance"
+meta_title: "bottleneckdistance"
+subheadline: ""
+teaser: ""
+permalink: "/bottleneckdistance/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
+
+
+
+## bottleneck_read_file_example ##
+
+This program computes the Bottleneck distance between two persistence diagram files.
+
+**Usage**
+
+```
+   bottleneck_read_file_example <file_1.pers> <file_2.pers> [<tolerance>]
+```
+
+where
+
+* `<file_1.pers>` and `<file_2.pers>` must be in the format described [here](http://gudhi.gforge.inria.fr/doc/latest/fileformats.html#FileFormatsPers).
+* `<tolerance>` is an error bound on the bottleneck distance (set by default to the smallest positive double value).
diff --git a/src/Rips_complex/utilities/README b/src/Rips_complex/utilities/README
deleted file mode 100644
index 44a37543..00000000
--- a/src/Rips_complex/utilities/README
+++ /dev/null
@@ -1,61 +0,0 @@
----
-layout: page
-title: "Rips complex"
-meta_title: "ripscomplex"
-subheadline: ""
-teaser: ""
-permalink: "/ripscomplex/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
-
-
-
-## rips_persistence ##
-This program computes the persistent homology with coefficient field *Z/pZ* of a Rips complex defined on a set of input points. The output diagram contains one bar per line, written with the convention:
-
-`p dim birth death`
-
-where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
-
-**Usage**
-
-`rips_persistence [options] <OFF input file>`
-
-**Allowed options**
-
-* `-h [ --help ]` Produce help message
-* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
-* `-r [ --max-edge-length ]` (default = inf) Maximal length of an edge for the Rips complex construction.
-* `-d [ --cpx-dimension ]` (default = 1) Maximal dimension of the Rips complex we want to compute.
-* `-p [ --field-charac ]` (default = 11)     Characteristic p of the coefficient field Z/pZ for computing homology.
-* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
-
-Beware: this program may use a lot of RAM and take a lot of time if `max-edge-length` is set to a large value.
-
-**Example 1 with Z/2Z coefficients**
-
-`rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 2`
-
-**Example 2 with Z/3Z coefficients**
-
-`rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 3`
-
-
-## rips_distance_matrix_persistence ##
-
-Same as `rips_persistence` but taking a distance matrix as input.
-
-**Usage**
-
-`rips_persistence [options] <CSV input file>`
-
-where
-`<CSV input file>` is the path to the file containing a distance matrix. Can be square or lower triangular matrix. Separator is ';'.
-
-**Example**
-
-`rips_distance_matrix_persistence data/distance_matrix/full_square_distance_matrix.csv -r 15 -d 3 -p 3 -m 0`
diff --git a/src/Rips_complex/utilities/ripscomplex.md b/src/Rips_complex/utilities/ripscomplex.md
new file mode 100644
index 00000000..44a37543
--- /dev/null
+++ b/src/Rips_complex/utilities/ripscomplex.md
@@ -0,0 +1,61 @@
+---
+layout: page
+title: "Rips complex"
+meta_title: "ripscomplex"
+subheadline: ""
+teaser: ""
+permalink: "/ripscomplex/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
+
+
+
+## rips_persistence ##
+This program computes the persistent homology with coefficient field *Z/pZ* of a Rips complex defined on a set of input points. The output diagram contains one bar per line, written with the convention:
+
+`p dim birth death`
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature, and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
+
+**Usage**
+
+`rips_persistence [options] <OFF input file>`
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-r [ --max-edge-length ]` (default = inf) Maximal length of an edge for the Rips complex construction.
+* `-d [ --cpx-dimension ]` (default = 1) Maximal dimension of the Rips complex we want to compute.
+* `-p [ --field-charac ]` (default = 11)     Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+
+Beware: this program may use a lot of RAM and take a lot of time if `max-edge-length` is set to a large value.
+
+**Example 1 with Z/2Z coefficients**
+
+`rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 2`
+
+**Example 2 with Z/3Z coefficients**
+
+`rips_persistence ../../data/points/tore3D_1307.off -r 0.25 -m 0.5 -d 3 -p 3`
+
+
+## rips_distance_matrix_persistence ##
+
+Same as `rips_persistence` but taking a distance matrix as input.
+
+**Usage**
+
+`rips_persistence [options] <CSV input file>`
+
+where
+`<CSV input file>` is the path to the file containing a distance matrix. Can be square or lower triangular matrix. Separator is ';'.
+
+**Example**
+
+`rips_distance_matrix_persistence data/distance_matrix/full_square_distance_matrix.csv -r 15 -d 3 -p 3 -m 0`
diff --git a/src/Witness_complex/utilities/README b/src/Witness_complex/utilities/README
deleted file mode 100644
index 5cdb1f88..00000000
--- a/src/Witness_complex/utilities/README
+++ /dev/null
@@ -1,76 +0,0 @@
----
-layout: page
-title: "Witness complex"
-meta_title: "witnesscomplex"
-subheadline: ""
-teaser: ""
-permalink: "/witnesscomplex/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
-
-
-For more details about the witness complex, please read the [user manual of the package](http://gudhi.gforge.inria.fr/doc/latest/group__witness__complex.html).
-
-## weak_witness_persistence ##
-This program computes the persistent homology with coefficient field *Z/pZ* of a Weak witness complex defined on a set of input points.
-The output diagram contains one bar per line, written with the convention:
-
-`p dim birth death`
-
-where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
-and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
-
-**Usage**
-
-`weak_witness_persistence [options] <OFF input file>`
-
-**Allowed options**
-
-* `-h [ --help ]` Produce help message
-* `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
-* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::cout.
-* `-a [ --max-sq-alpha ]` (default = inf) Maximal squared relaxation parameter.
-* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
-* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
-* `-d [ --cpx-dimension ]` (default = 2147483647) Maximal dimension of the weak witness complex we want to compute.
-
-**Example**
-
-`weak_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.006`
-
-N.B.: output is random as the 20 landmarks are chosen randomly.
-
-
-## strong_witness_persistence ##
-
-This program computes the persistent homology with coefficient field *Z/pZ* of a Strong witness complex defined on a set of input points.
-The output diagram contains one bar per line, written with the convention:
-
-`p dim birth death`
-
-where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
-and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
-
-**Usage**
-
-`strong_witness_persistence [options] <OFF input file>`
-
-**Allowed options**
-
-* `-h [ --help ]` Produce help message
-* `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
-* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::cout.
-* `-a [ --max-sq-alpha ]` (default = inf) Maximal squared relaxation parameter.
-* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
-* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
-* `-d [ --cpx-dimension ]` (default = 2147483647) Maximal dimension of the weak witness complex we want to compute.
-
-**Example**
-
-`strong_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.06`
-
-N.B.: output is random as the 20 landmarks are chosen randomly.
diff --git a/src/Witness_complex/utilities/witnesscomplex.md b/src/Witness_complex/utilities/witnesscomplex.md
new file mode 100644
index 00000000..5cdb1f88
--- /dev/null
+++ b/src/Witness_complex/utilities/witnesscomplex.md
@@ -0,0 +1,76 @@
+---
+layout: page
+title: "Witness complex"
+meta_title: "witnesscomplex"
+subheadline: ""
+teaser: ""
+permalink: "/witnesscomplex/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
+
+
+For more details about the witness complex, please read the [user manual of the package](http://gudhi.gforge.inria.fr/doc/latest/group__witness__complex.html).
+
+## weak_witness_persistence ##
+This program computes the persistent homology with coefficient field *Z/pZ* of a Weak witness complex defined on a set of input points.
+The output diagram contains one bar per line, written with the convention:
+
+`p dim birth death`
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
+and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+
+**Usage**
+
+`weak_witness_persistence [options] <OFF input file>`
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::cout.
+* `-a [ --max-sq-alpha ]` (default = inf) Maximal squared relaxation parameter.
+* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+* `-d [ --cpx-dimension ]` (default = 2147483647) Maximal dimension of the weak witness complex we want to compute.
+
+**Example**
+
+`weak_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.006`
+
+N.B.: output is random as the 20 landmarks are chosen randomly.
+
+
+## strong_witness_persistence ##
+
+This program computes the persistent homology with coefficient field *Z/pZ* of a Strong witness complex defined on a set of input points.
+The output diagram contains one bar per line, written with the convention:
+
+`p dim birth death`
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
+and `p` is the characteristic of the field *Z/pZ* used for homology coefficients.
+
+**Usage**
+
+`strong_witness_persistence [options] <OFF input file>`
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-l [ --landmarks ]` Number of landmarks to choose from the point cloud.
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. By default, print in std::cout.
+* `-a [ --max-sq-alpha ]` (default = inf) Maximal squared relaxation parameter.
+* `-p [ --field-charac ]` (default = 11) Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+* `-d [ --cpx-dimension ]` (default = 2147483647) Maximal dimension of the weak witness complex we want to compute.
+
+**Example**
+
+`strong_witness_persistence data/points/tore3D_1307.off -l 20 -a 0.5 -m 0.06`
+
+N.B.: output is random as the 20 landmarks are chosen randomly.
diff --git a/src/common/utilities/README b/src/common/utilities/README
deleted file mode 100644
index f39c63b8..00000000
--- a/src/common/utilities/README
+++ /dev/null
@@ -1,43 +0,0 @@
----
-layout: page
-title: "Pointset generator"
-meta_title: "pointsetgenerator"
-subheadline: ""
-teaser: ""
-permalink: "/pointsetgenerator/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
-
-## off_file_from_shape_generator ##
-
-Generates a pointset and save it in an OFF file. Command-line is:
-
-```
-off_file_from_shape_generator on|in sphere|cube|curve|torus|klein <filename> <num_points> <dimension> <parameter1> <parameter2>...
-```
-
-Warning: "on cube" generator is not available!
-
-**Examples**
-
-```
-off_file_from_shape_generator on sphere onSphere.off 1000 3 15.2
-```
-
-* Generates an onSphere.off file with 1000 points randomized on a sphere of dimension 3 and radius 15.2.
-
-```
-off_file_from_shape_generator in sphere inSphere.off 100 2
-```
-
-* Generates an inSphere.off file with 100 points randomized in a sphere of dimension 2 (circle) and radius 1.0 (default).
-
-```
-off_file_from_shape_generator in cube inCube.off 10000 3 5.8
-```
-
-* Generates a inCube.off file with 10000 points randomized in a cube of dimension 3 and side 5.8.
diff --git a/src/common/utilities/pointsetgenerator.md b/src/common/utilities/pointsetgenerator.md
new file mode 100644
index 00000000..f39c63b8
--- /dev/null
+++ b/src/common/utilities/pointsetgenerator.md
@@ -0,0 +1,43 @@
+---
+layout: page
+title: "Pointset generator"
+meta_title: "pointsetgenerator"
+subheadline: ""
+teaser: ""
+permalink: "/pointsetgenerator/"
+---
+{::comment}
+These flags above are here for web site generation, please let them.
+cf. https://gitlab.inria.fr/GUDHI/website
+Must be in conformity with _data/navigation.yml
+{:/comment}
+
+## off_file_from_shape_generator ##
+
+Generates a pointset and save it in an OFF file. Command-line is:
+
+```
+off_file_from_shape_generator on|in sphere|cube|curve|torus|klein <filename> <num_points> <dimension> <parameter1> <parameter2>...
+```
+
+Warning: "on cube" generator is not available!
+
+**Examples**
+
+```
+off_file_from_shape_generator on sphere onSphere.off 1000 3 15.2
+```
+
+* Generates an onSphere.off file with 1000 points randomized on a sphere of dimension 3 and radius 15.2.
+
+```
+off_file_from_shape_generator in sphere inSphere.off 100 2
+```
+
+* Generates an inSphere.off file with 100 points randomized in a sphere of dimension 2 (circle) and radius 1.0 (default).
+
+```
+off_file_from_shape_generator in cube inCube.off 10000 3 5.8
+```
+
+* Generates a inCube.off file with 10000 points randomized in a cube of dimension 3 and side 5.8.
-- 
cgit v1.2.3


From 37ded65d83935d417fc3bc484594a83b36f63514 Mon Sep 17 00:00:00 2001
From: vrouvrea <vrouvrea@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Tue, 30 Jan 2018 17:05:03 +0000
Subject: Fix after web review

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/trunk@3189 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: fd42bab9b27a3c086adeaf75068c83a911ede000
---
 src/Alpha_complex/utilities/alphacomplex.md        | 100 +++++++++------------
 .../utilities/cubicalcomplex.md                    |  17 +---
 .../utilities/bottleneckdistance.md                |  16 +---
 src/Nerve_GIC/utilities/covercomplex.md            |  15 +---
 src/Rips_complex/utilities/ripscomplex.md          |  16 +---
 src/Witness_complex/utilities/witnesscomplex.md    |  22 ++---
 src/common/utilities/pointsetgenerator.md          |  16 +---
 7 files changed, 62 insertions(+), 140 deletions(-)

(limited to 'src/common')

diff --git a/src/Alpha_complex/utilities/alphacomplex.md b/src/Alpha_complex/utilities/alphacomplex.md
index 0e500e53..aace85d3 100644
--- a/src/Alpha_complex/utilities/alphacomplex.md
+++ b/src/Alpha_complex/utilities/alphacomplex.md
@@ -1,18 +1,47 @@
----
-layout: page
-title: "Alpha complex"
-meta_title: "alphacomplex"
-subheadline: ""
-teaser: ""
-permalink: "/alphacomplex/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
 
 
+# Alpha complex #
+
+
+## alpha_complex_persistence ##
+
+This program computes the persistent homology with coefficient field Z/pZ of the dD alpha complex built from a dD point cloud.
+The output diagram contains one bar per line, written with the convention:
+
+```
+   p dim birth death
+```
+
+where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
+and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
+
+**Usage**
+
+```
+   alpha_complex_persistence [options] <input OFF file>
+```
+
+where
+`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
+
+**Allowed options**
+
+* `-h [ --help ]` Produce help message
+* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
+* `-r [ --max-alpha-square-value ]` (default = inf) Maximal alpha square value for the Alpha complex construction.
+* `-p [ --field-charac ]` (default = 11)     Characteristic p of the coefficient field Z/pZ for computing homology.
+* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
+
+**Example**
+
+```
+   alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off
+```
+
+N.B.:
+
+* Filtration values are alpha square values.
+
 
 ## alpha_complex_3d_persistence ##
 This program computes the persistent homology with coefficient field Z/pZ of the 3D alpha complex built from a 3D point cloud. The output diagram contains one bar per line, written with the convention:
@@ -107,7 +136,7 @@ where
 
 * `<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
 * `<cuboid file>` is the path to the file describing the periodic domain. It must be in the format described
-[here](../doc/latest/fileformats.html#FileFormatsIsoCuboid).
+[here](/doc/latest/fileformats.html#FileFormatsIsoCuboid).
 
 **Allowed options**
 
@@ -125,46 +154,5 @@ periodic_alpha_complex_3d_persistence ../../data/points/grid_10_10_10_in_0_1.off
 
 N.B.:
 
-* Cuboid file must be in the format described [here](../doc/latest/fileformats.html#FileFormatsIsoCuboid).
-* Filtration values are alpha square values.
-
-
-
-## alpha_complex_persistence ##
-
-This program computes the persistent homology with coefficient field Z/pZ of the dD alpha complex built from a dD point cloud.
-The output diagram contains one bar per line, written with the convention:
-
-```
-   p dim birth death
-```
-
-where `dim` is the dimension of the homological feature, `birth` and `death` are respectively the birth and death of the feature,
-and `p` is the characteristic of the field *Z/pZ* used for homology coefficients (`p` must be a prime number).
-
-**Usage**
-
-```
-   alpha_complex_persistence [options] <input OFF file>
-```
-
-where
-`<input OFF file>` is the path to the input point cloud in [nOFF ASCII format](http://www.geomview.org/docs/html/OFF.html).
-
-**Allowed options**
-
-* `-h [ --help ]` Produce help message
-* `-o [ --output-file ]` Name of file in which the persistence diagram is written. Default print in standard output.
-* `-r [ --max-alpha-square-value ]` (default = inf) Maximal alpha square value for the Alpha complex construction.
-* `-p [ --field-charac ]` (default = 11)     Characteristic p of the coefficient field Z/pZ for computing homology.
-* `-m [ --min-persistence ]` (default = 0) Minimal lifetime of homology feature to be recorded. Enter a negative value to see zero length intervals.
-
-**Example**
-
-```
-   alpha_complex_persistence -r 32 -p 2 -m 0.45 ../../data/points/tore3D_300.off
-```
-
-N.B.:
-
+* Cuboid file must be in the format described [here](/doc/latest/fileformats.html#FileFormatsIsoCuboid).
 * Filtration values are alpha square values.
diff --git a/src/Bitmap_cubical_complex/utilities/cubicalcomplex.md b/src/Bitmap_cubical_complex/utilities/cubicalcomplex.md
index 3a9e6aac..6e1b2578 100644
--- a/src/Bitmap_cubical_complex/utilities/cubicalcomplex.md
+++ b/src/Bitmap_cubical_complex/utilities/cubicalcomplex.md
@@ -1,21 +1,10 @@
----
-layout: page
-title: "Bitmap cubical complex"
-meta_title: "cubicalcomplex"
-subheadline: ""
-teaser: ""
-permalink: "/cubicalcomplex/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
 
 
+# Cubical complex#
+
 ## cubical_complex_persistence ##
 This program computes persistent homology, by using the Bitmap_cubical_complex class, of cubical complexes provided in text files in Perseus style.
-See [here](../doc/latest/fileformats.html#FileFormatsPerseus) for a description of the file format.
+See [here](/doc/latest/fileformats.html#FileFormatsPerseus) for a description of the file format.
 
 **Example**
 
diff --git a/src/Bottleneck_distance/utilities/bottleneckdistance.md b/src/Bottleneck_distance/utilities/bottleneckdistance.md
index e9079058..526f5822 100644
--- a/src/Bottleneck_distance/utilities/bottleneckdistance.md
+++ b/src/Bottleneck_distance/utilities/bottleneckdistance.md
@@ -1,18 +1,6 @@
----
-layout: page
-title: "Bottleneck distance"
-meta_title: "bottleneckdistance"
-subheadline: ""
-teaser: ""
-permalink: "/bottleneckdistance/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
 
 
+# Bottleneck distance #
 
 ## bottleneck_read_file_example ##
 
@@ -26,5 +14,5 @@ This program computes the Bottleneck distance between two persistence diagram fi
 
 where
 
-* `<file_1.pers>` and `<file_2.pers>` must be in the format described [here](../doc/latest/fileformats.html#FileFormatsPers).
+* `<file_1.pers>` and `<file_2.pers>` must be in the format described [here](/doc/latest/fileformats.html#FileFormatsPers).
 * `<tolerance>` is an error bound on the bottleneck distance (set by default to the smallest positive double value).
diff --git a/src/Nerve_GIC/utilities/covercomplex.md b/src/Nerve_GIC/utilities/covercomplex.md
index 41489b66..f33cb2e0 100644
--- a/src/Nerve_GIC/utilities/covercomplex.md
+++ b/src/Nerve_GIC/utilities/covercomplex.md
@@ -1,18 +1,7 @@
----
-layout: page
-title: "Cover complex"
-meta_title: "covercomplex"
-subheadline: ""
-teaser: ""
-permalink: "/covercomplex/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
 
 
+# Cover complex #
+
 
 ## Nerve ##
 This program builds the Nerve of a point cloud sampled on an OFF file.
diff --git a/src/Rips_complex/utilities/ripscomplex.md b/src/Rips_complex/utilities/ripscomplex.md
index 44a37543..4291fae7 100644
--- a/src/Rips_complex/utilities/ripscomplex.md
+++ b/src/Rips_complex/utilities/ripscomplex.md
@@ -1,21 +1,9 @@
----
-layout: page
-title: "Rips complex"
-meta_title: "ripscomplex"
-subheadline: ""
-teaser: ""
-permalink: "/ripscomplex/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
 
 
+# Rips complex #
 
 ## rips_persistence ##
-This program computes the persistent homology with coefficient field *Z/pZ* of a Rips complex defined on a set of input points. The output diagram contains one bar per line, written with the convention:
+This program computes the persistent homology with coefficient field *Z/pZ* of a Rips complex defined on a set of input points, using Euclidean distance. The output diagram contains one bar per line, written with the convention:
 
 `p dim birth death`
 
diff --git a/src/Witness_complex/utilities/witnesscomplex.md b/src/Witness_complex/utilities/witnesscomplex.md
index 40864871..2341759b 100644
--- a/src/Witness_complex/utilities/witnesscomplex.md
+++ b/src/Witness_complex/utilities/witnesscomplex.md
@@ -1,19 +1,9 @@
----
-layout: page
-title: "Witness complex"
-meta_title: "witnesscomplex"
-subheadline: ""
-teaser: ""
-permalink: "/witnesscomplex/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
-
-
-For more details about the witness complex, please read the [user manual of the package](../doc/latest/group__witness__complex.html).
+
+
+# Witness complex #
+
+
+For more details about the witness complex, please read the [user manual of the package](/doc/latest/group__witness__complex.html).
 
 ## weak_witness_persistence ##
 This program computes the persistent homology with coefficient field *Z/pZ* of a Weak witness complex defined on a set of input points.
diff --git a/src/common/utilities/pointsetgenerator.md b/src/common/utilities/pointsetgenerator.md
index f39c63b8..284715d4 100644
--- a/src/common/utilities/pointsetgenerator.md
+++ b/src/common/utilities/pointsetgenerator.md
@@ -1,16 +1,6 @@
----
-layout: page
-title: "Pointset generator"
-meta_title: "pointsetgenerator"
-subheadline: ""
-teaser: ""
-permalink: "/pointsetgenerator/"
----
-{::comment}
-These flags above are here for web site generation, please let them.
-cf. https://gitlab.inria.fr/GUDHI/website
-Must be in conformity with _data/navigation.yml
-{:/comment}
+
+
+# common #
 
 ## off_file_from_shape_generator ##
 
-- 
cgit v1.2.3


From e15408b4af5cba8966aa8773f6ee6884942c1d95 Mon Sep 17 00:00:00 2001
From: vrouvrea <vrouvrea@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Wed, 31 Jan 2018 08:18:58 +0000
Subject: Modify header (cpp and python) to fit with the new website sections

git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/trunk@3192 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: 1dc2f0745020db4a27649c55dc1d01397b0a403c
---
 src/common/doc/header.html            | 4 ++++
 src/cython/doc/_templates/layout.html | 4 ++++
 2 files changed, 8 insertions(+)

(limited to 'src/common')

diff --git a/src/common/doc/header.html b/src/common/doc/header.html
index 53b5c0a2..9c514381 100644
--- a/src/common/doc/header.html
+++ b/src/common/doc/header.html
@@ -56,6 +56,8 @@ $extrastylesheet
                   <ul class="dropdown">
                       <li><a href="http://gudhi.gforge.inria.fr/licensing/">Licensing</a></li>
                       <li><a href="https://gforge.inria.fr/frs/?group_id=3865" target="_blank">Get the sources</a></li>
+                      <li><a href="https://gforge.inria.fr/frs/download.php/file/37113/GUDHI_2.0.0_OSX_UTILS.beta.tar.gz" target="_blank">Utils for Mac OSx</a></li>
+                      <li><a href="https://gforge.inria.fr/frs/download.php/file/37112/GUDHI_2.0.0_WIN64_UTILS.beta.zip" target="_blank">Utils for Win x64</a></li>
                   </ul>
               </li>
               <li class="divider"></li>
@@ -66,6 +68,8 @@ $extrastylesheet
                       <li><a href="http://gudhi.gforge.inria.fr/doc/latest/installation.html">C++ installation manual</a></li>
                       <li><a href="http://gudhi.gforge.inria.fr/python/latest/">Python documentation</a></li>
                       <li><a href="http://gudhi.gforge.inria.fr/python/latest/installation.html">Python installation manual</a></li>
+                      <li><a href="http://gudhi.gforge.inria.fr/utils/">Utilities</a></li>
+                      <li><a href="http://bertrand.michel.perso.math.cnrs.fr/Enseignements/TDA-Gudhi-Python.html" target="_blank">Tutorial</a></li>
                   </ul>
               </li>
               <li class="divider"></li>
diff --git a/src/cython/doc/_templates/layout.html b/src/cython/doc/_templates/layout.html
index fd3e1cfd..8e4eba40 100644
--- a/src/cython/doc/_templates/layout.html
+++ b/src/cython/doc/_templates/layout.html
@@ -198,6 +198,8 @@
                     <ul class="dropdown">
                         <li><a href="http://gudhi.gforge.inria.fr/licensing/">Licensing</a></li>
                         <li><a href="https://gforge.inria.fr/frs/?group_id=3865" target="_blank">Get the sources</a></li>
+                        <li><a href="https://gforge.inria.fr/frs/download.php/file/37113/GUDHI_2.0.0_OSX_UTILS.beta.tar.gz" target="_blank">Utils for Mac OSx</a></li>
+                        <li><a href="https://gforge.inria.fr/frs/download.php/file/37112/GUDHI_2.0.0_WIN64_UTILS.beta.zip" target="_blank">Utils for Win x64</a></li>
                     </ul>
                 </li>
                 <li class="divider"></li>
@@ -208,6 +210,8 @@
                         <li><a href="http://gudhi.gforge.inria.fr/doc/latest/installation.html">C++ installation manual</a></li>
                         <li><a href="http://gudhi.gforge.inria.fr/python/latest/">Python documentation</a></li>
                         <li><a href="http://gudhi.gforge.inria.fr/python/latest/installation.html">Python installation manual</a></li>
+                        <li><a href="http://gudhi.gforge.inria.fr/utils/">Utilities</a></li>
+                        <li><a href="http://bertrand.michel.perso.math.cnrs.fr/Enseignements/TDA-Gudhi-Python.html" target="_blank">Tutorial</a></li>
                     </ul>
                 </li>
                 <li class="divider"></li>
-- 
cgit v1.2.3