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Diffstat (limited to 'src/cython/test/test_persistence_representations_landscapes.py')
| -rwxr-xr-x | src/cython/test/test_persistence_representations_landscapes.py | 139 |
1 files changed, 0 insertions, 139 deletions
diff --git a/src/cython/test/test_persistence_representations_landscapes.py b/src/cython/test/test_persistence_representations_landscapes.py deleted file mode 100755 index f5e6f351..00000000 --- a/src/cython/test/test_persistence_representations_landscapes.py +++ /dev/null @@ -1,139 +0,0 @@ -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" - -epsilon = 0.0000005; - -def test_check_construction_of_landscape(): - p = gudhi.Persistence_landscape("data/file_with_diagram",0) - q = gudhi.Persistence_landscape() - q.load_landscape_from_file("data/file_with_landscape_from_file_with_diagram") - assert p == q - - -def test_check_construction_of_landscape_form_gudhi_style_file(): - p = gudhi.Persistence_landscape("data/persistence_file_with_four_entries_per_line", 1) - q = gudhi.Persistence_landscape() - q.load_landscape_from_file("data/persistence_file_with_four_entries_per_line_landscape"); - assert p == q; - -def test_check_computations_of_integrals(): - p = gudhi.Persistence_landscape("data/file_with_diagram",0) - integral = p.compute_integral_of_landscape() - assert fabs(integral - 2.34992) <= 0.00001 - - -def test_check_computations_of_integrals_for_each_level_separatelly(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram"); - p = gudhi.Persistence_landscape(diag) - integrals_for_different_levels = [0.216432,0.204763,0.188793,0.178856,0.163142,0.155015,0.143046,0.133765,0.123531,0.117393,0.111269,0.104283,0.0941308,0.0811208,0.0679001,0.0580801,0.0489647,0.0407936,0.0342599,0.02896,0.0239881,0.0171792,0.0071511,0.00462067,0.00229033,0.000195296] - for lv in range(0, len(integrals_for_different_levels)): - integral = p.compute_integral_of_a_level_of_a_landscape(lv); - assert fabs(integral - integrals_fir_different_levels[lv]) <= 0.00001 - -def test_check_computations_of_integrals_of_powers_of_landscape(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - integrals_fir_different_powers = [17.1692,2.34992,0.49857,0.126405,0.0355235] - for power in range(0,5): - integral = p.compute_integral_of_landscape(power) - assert fabs(integral - integrals_fir_different_powers[power]) <= 0.00005 - - -def test_check_computations_of_values_on_different_points(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag); - assert fabs(p.compute_value_at_a_given_point(1, 0.0)) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(1, 0.1) - 0.0692324) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(1, 0.2) - 0.163369) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(1, 0.3) - 0.217115) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(2, 0.0)) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(2, 0.1) - 0.0633688) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(2, 0.2) - 0.122361) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(2, 0.3) - 0.195401) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(3, 0.0)) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(3, 0.1) - 0.0455386) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(3, 0.2) - 0.0954012) <= 0.00001 - assert fabs(p.compute_value_at_a_given_point(3, 0.3) - 0.185282) <= 0.00001 - - -def test_check_computations_of_maxima_and_norms(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - second = gudhi.Persistence_landscape() - second.load_landscape_from_file("data/file_with_landscape_from_file_with_diagram_1") - sum_ = gudhi.Persistence_landscape() - sum_ = p + second; - assert fabs(p.compute_maximum() - 0.431313) <= 0.00001 - assert fabs(p.compute_norm_of_landscape(1) - 2.34992) <= 0.00001 - assert fabs(p.compute_norm_of_landscape(2) - 0.706095) <= 0.00001 - assert fabs(p.compute_norm_of_landscape(3) - 0.501867) <= 0.00001 - assert fabs(compute_distance_of_landscapes(p, sum_, 1) - 27.9323) <= 0.00005 - assert fabs(compute_distance_of_landscapes(p, sum_, 2) - 2.35199) <= 0.00001 - - - -def test_check_default_parameters_of_distances(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - diag1 = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram_1") - q = gudhi.Persistence_landscape(diag1) - dist_numeric_limit_max = p.distance(q, sys.float_info.max); - dist_infinity = p.distance(q, sys.float_info.max); - assert dist_numeric_limit_max == dist_infinity - - -def test_check_computations_of_averages(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - diag2 = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram_1") - q = gudhi.Persistence_landscape(diag2) - av = gudhi.Persistence_landscape() - av.compute_average({p, q}) - template_average = Persistence_landscape() - template_averagetemplate_average.load_landscape_from_file("data/average") - assert template_average == av - - -def test_check_computations_of_distances(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - diag2 = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram_1") - q = Persistence_landscape(diag2) - assert fabs(p.distance(q) - 25.5824) <= 0.00005 - assert fabs(p.distance(q, 2) - 2.12636) <= 0.00001 - assert fabs(p.distance(q, sys.float_info.max) - 0.359068) <= 0.00001 - - -def test_check_computations_of_scalar_product(): - diag = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram") - p = gudhi.Persistence_landscape(diag) - diag2 = read_persistence_intervals_in_one_dimension_from_file("data/file_with_diagram_1") - q = Persistence_landscape(diag2) - assert fabs(p.compute_scalar_product(q) - 0.754498) <= 0.00001 - |