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Diffstat (limited to 'src/cython/example/persistence_representations_landscapes_on_grid_example.py')
-rwxr-xr-x | src/cython/example/persistence_representations_landscapes_on_grid_example.py | 110 |
1 files changed, 0 insertions, 110 deletions
diff --git a/src/cython/example/persistence_representations_landscapes_on_grid_example.py b/src/cython/example/persistence_representations_landscapes_on_grid_example.py deleted file mode 100755 index 60b0e873..00000000 --- a/src/cython/example/persistence_representations_landscapes_on_grid_example.py +++ /dev/null @@ -1,110 +0,0 @@ -#!/usr/bin/env python - -import gudhi - - -"""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): Pawel Dlotko - - Copyright (C) 2017 Swansea University - - 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__ = "Pawel Dlotko" -__copyright__ = "Copyright (C) 2017 Swansea University" -__license__ = "GPL v3" - -print("#####################################################################") -print("Persistence representations landscapes on a grid example") - -persistence1 = [(1, 2),(6, 8),(0, 4),(3, 8)] -persistence2 = [(2, 9),(1, 6),(3, 5),(6, 10)] - -#create two persistence landscapes based on persistence1 and persistence2: -l1 = PersistenceLandscapeOnGrid(persistence1, 0, 11, 20) -l2 = PersistenceLandscapeOnGrid(persistence2, 0, 11, 20) - -#This is how to compute integral of landscapes: -print "Integral of the first landscape : " , l1.compute_integral_of_landscape() -print "Integral of the second landscape : " , l2.compute_integral_of_landscape() - -#here are the maxima of the functions: -print "Maximum of l1 : " , l1.compute_maximum() -print "Maximum of l2 : " , l2.compute_maximum() - -#here are the norms of landscapes: -print "L^1 Norm of l1 : " , l1.compute_norm_of_landscape(1.) -print "L^1 Norm of l2 : " , l2.compute_norm_of_landscape(1.) - -#here is the average of landscapes: -average = PersistenceLandscapeOnGrid(); -average.compute_average(to_average=[l1, l2]); - - -#here is the distance of landscapes: -print "Distance : " , l1.distance(l2) - -#here is the scalar product of landscapes: -print "Scalar product : " , l1.compute_scalar_product(l2) - - - - - - - - - - - - - - - - - - -persistence1 = [(1,2),(6,8),(0,4),(3,8)] -persistence2 = [(2,9),(1,6),(3,5),(6,10)] - - -#create two persistence landscapes based on persistence1 and persistence2: -l1 = gudhi.PersistenceLandscapes(vector_of_intervals=persistence1, dimension=3) -l2 = gudhi.PersistenceLandscapes(vector_of_intervals=persistence2) - -#This is how to compute integral of landscapes: -print "Integral of the first landscape : ", l1.compute_integral_of_landscape() -print "Integral of the second landscape : ", l2.compute_integral_of_landscape() - -#here are the maxima of the functions: -print "Maximum of l1 : ", l1.compute_maximum() -print "Maximum of l2 : ", l2.compute_maximum() - -#here are the norms of landscapes: -print "L^1 Norm of l1 : ", l1.compute_norm_of_landscape(1.) -print "L^1 Norm of l2 : ", l2.compute_norm_of_landscape(1.) - -#here is the average of landscapes: -average = gudhi.PersistenceLandscapes() -average.compute_average(to_average=[l1, l2]) - -#here is the distance of landscapes: -print "Distance : ", l1.distance(average,1) - -#here is the scalar product of landscapes: -print "Scalar product : ", l1.compute_scalar_product(l2) |