diff options
Diffstat (limited to 'src/cython/example')
-rwxr-xr-x | src/cython/example/rips_complex_from_file_example.py | 8 | ||||
-rwxr-xr-x | src/cython/example/witness_complex_from_file_example.py | 17 |
2 files changed, 10 insertions, 15 deletions
diff --git a/src/cython/example/rips_complex_from_file_example.py b/src/cython/example/rips_complex_from_file_example.py index 9385aba0..fae72443 100755 --- a/src/cython/example/rips_complex_from_file_example.py +++ b/src/cython/example/rips_complex_from_file_example.py @@ -37,7 +37,7 @@ parser = argparse.ArgumentParser(description='RipsComplex creation from ' 'points read in a file.', epilog='Example: ' 'example/rips_complex_from_file_example.py ' - 'data/500_random_points_on_3D_Torus.csv ' + 'data/2000_random_points_on_3D_Torus.csv ' '- Constructs a rips complex with the ' 'points from the given file. File format ' 'is X1, X2, ..., Xn') @@ -46,13 +46,13 @@ args = parser.parse_args() points = pandas.read_csv(args.file, header=None) -print("RipsComplex with max_edge_length=1.9") +print("RipsComplex with max_edge_length=0.7") rips_complex = gudhi.RipsComplex(points=points.values, - max_dimension=len(points.values[0]), max_edge_length=1.9) + max_dimension=len(points.values[0]), max_edge_length=0.7) rips_complex.initialize_filtration() -diag = rips_complex.persistence(homology_coeff_field=2, min_persistence=0.1) +diag = rips_complex.persistence(homology_coeff_field=2, min_persistence=0.3) print("betti_numbers()=") print(rips_complex.betti_numbers()) diff --git a/src/cython/example/witness_complex_from_file_example.py b/src/cython/example/witness_complex_from_file_example.py index 9d1a940f..d67d1c34 100755 --- a/src/cython/example/witness_complex_from_file_example.py +++ b/src/cython/example/witness_complex_from_file_example.py @@ -37,7 +37,7 @@ parser = argparse.ArgumentParser(description='WitnessComplex creation from ' 'points read in a file.', epilog='Example: ' 'example/witness_complex_from_file_example.py' - ' data/500_random_points_on_3D_Torus.csv ' + ' data/2000_random_points_on_3D_Torus.csv ' '- Constructs a witness complex with the ' 'points from the given file. File format ' 'is X1, X2, ..., Xn') @@ -46,25 +46,20 @@ args = parser.parse_args() points = pandas.read_csv(args.file, header=None) -print("WitnessComplex with number_of_landmarks=5") +print("WitnessComplex with number_of_landmarks=100 alpha=0.7 epsilon_mu=0.001 max_dim=10") witness_complex = gudhi.WitnessComplex(points=points.values, - number_of_landmarks=200) - -print("filtered_tree=", witness_complex.get_filtered_tree()) + number_of_landmarks=100, + max_alpha_square=0.7, + mu_epsilon=0.001, + dimension_limit=10) witness_complex.initialize_filtration() diag = witness_complex.persistence(homology_coeff_field=2, min_persistence=0.1) -print("diag=", diag) - -gudhi.diagram_persistence(diag) - -""" print("betti_numbers()=") print(witness_complex.betti_numbers()) gudhi.diagram_persistence(diag) gudhi.barcode_persistence(diag) -"""
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