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

8 files changed, 0 insertions, 844 deletions

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.