diff options
Diffstat (limited to 'src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py')
-rwxr-xr-x | src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py | 28 |
1 files changed, 27 insertions, 1 deletions
diff --git a/src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py b/src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py index 06d22abd..406264ba 100755 --- a/src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py +++ b/src/Nerve_GIC/example/KeplerMapperVisuFromTxtFile.py @@ -2,6 +2,32 @@ 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)) @@ -41,4 +67,4 @@ 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)
\ No newline at end of file +height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=param[0],gain=param[1], minimum=m,maximum=M) |