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#!/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 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 = 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)
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