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diff --git a/src/python/doc/rips_complex_ref.rst b/src/python/doc/rips_complex_ref.rst index a5b4ffed..9ae3c49c 100644 --- a/src/python/doc/rips_complex_ref.rst +++ b/src/python/doc/rips_complex_ref.rst @@ -23,45 +23,3 @@ Weighted Rips complex reference manual :show-inheritance: .. automethod:: gudhi.weighted_rips_complex.WeightedRipsComplex.__init__ - -Basic examples --------------- - -The following example computes the weighted Rips filtration associated with a distance matrix and weights on vertices. - -.. testcode:: - - from gudhi.weighted_rips_complex import WeightedRipsComplex - dist = [[], [1]] - weights = [1, 100] - w_rips = WeightedRipsComplex(distance_matrix=dist, weights=weights) - st = w_rips.create_simplex_tree(max_dimension=2) - print(list(st.get_filtration())) - -The output is: - -.. testoutput:: - - [([0], 2.0), ([1], 200.0), ([0, 1], 200.0)] - -Combining with DistanceToMeasure, one can compute the DTM-filtration of a point set, as in `this notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-DTM-filtrations.ipynb>`_. - -.. testcode:: - - import numpy as np - from scipy.spatial.distance import cdist - from gudhi.point_cloud.dtm import DistanceToMeasure - from gudhi.weighted_rips_complex import WeightedRipsComplex - pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]]) - dist = cdist(pts,pts) - dtm = DistanceToMeasure(2, q=2, metric="precomputed") - r = dtm.fit_transform(dist) - w_rips = WeightedRipsComplex(distance_matrix=dist, weights=r) - st = w_rips.create_simplex_tree(max_dimension=2) - print(st.persistence()) - -The output is: - -.. testoutput:: - - [(0, (3.1622776601683795, inf)), (0, (3.1622776601683795, 5.39834563766817)), (0, (3.1622776601683795, 5.39834563766817))] |