New format for persistence #925
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mglisse
commented
Jul 28, 2023
| return self.pcohptr.get_persistence() | ||
| if output_type == 'array by dimension': | ||
| v = self.pcohptr.intervals_by_dimension() | ||
| return [ np.asarray(dgm) for dgm in v ] # std::move(dgm)? |
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We should probably do the usual workaround for empty dgm to get a shape (0,2).
|
(this PR requires to merge master for compilation to pass) import gudhi
rips_complex = gudhi.RipsComplex(points=[[1, 1], [7, 0], [4, 6], [9, 6], [0, 14], [2, 19], [9, 17]],
max_edge_length=12.0)
simplex_tree = rips_complex.create_simplex_tree(max_dimension=1)
simplex_tree.persistence(output_type='old')
# [(0, (0.0, inf)), (0, (0.0, 8.94427190999916)), (0, (0.0, 7.280109889280518)), (0, (0.0, 6.082762530298219)),
# (0, (0.0, 5.830951894845301)), (0, (0.0, 5.385164807134504)), (0, (0.0, 5.0))]
simplex_tree.persistence(output_type='array by dimension')
# [array([[0. , 5. ],
# [0. , 5.38516481],
# [0. , 5.83095189],
# [0. , 6.08276253],
# [0. , 7.28010989],
# [0. , 8.94427191],
# [0. , inf]])]This format is quite interesting and was initiated from discussion on #395 |
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(not ready: missing tests at least, and doing the same for cubical)
As a step towards using more numpy arrays, this provides an option to SimplexTree.persistence() to output a list of arrays (1 numpy array per dimension), instead of our current list of tuple (all together, with the dimension being part of the tuple).
I open the PR to start the discussion.