1 files changed, 0 insertions, 89 deletions
diff --git a/utilities/Nerve_GIC/KeplerMapperVisuFromTxtFile.py b/utilities/Nerve_GIC/KeplerMapperVisuFromTxtFile.py
deleted file mode 100755
index 701e7a52..00000000
--- a/utilities/Nerve_GIC/KeplerMapperVisuFromTxtFile.py
+++ /dev/null
@@ -1,89 +0,0 @@
-#!/usr/bin/env python
-
-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++
-   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"
-
-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()