Persistence graphical tools user manual¶
Definition¶
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These graphical tools comes on top of persistence results and allows the user to build easily persistence barcode, diagram or density. |
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Show persistence as a barcode¶
Note
this function requires matplotlib and numpy to be available
This function can display the persistence result as a barcode:
import matplotlib.pyplot as plot
import gudhi
off_file = gudhi.__root_source_dir__ + '/data/points/tore3D_300.off'
point_cloud = gudhi.read_points_from_off_file(off_file=off_file)
rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=0.7)
simplex_tree = rips_complex.create_simplex_tree(max_dimension=3)
diag = simplex_tree.persistence(min_persistence=0.4)
gudhi.plot_persistence_barcode(diag)
plot.show()
Show persistence as a diagram¶
Note
this function requires matplotlib and numpy to be available
This function can display the persistence result as a diagram:
import matplotlib.pyplot as plot
import gudhi
# rips_on_tore3D_1307.pers obtained from write_persistence_diagram method
persistence_file=gudhi.__root_source_dir__ + \
'/data/persistence_diagram/rips_on_tore3D_1307.pers'
gudhi.plot_persistence_diagram(persistence_file=persistence_file,
legend=True)
plot.show()
Persistence density¶
Note
this function requires matplotlib, numpy and scipy to be available
If you want more information on a specific dimension, for instance:
import matplotlib.pyplot as plot
import gudhi
# rips_on_tore3D_1307.pers obtained from write_persistence_diagram method
persistence_file=gudhi.__root_source_dir__ + \
'/data/persistence_diagram/rips_on_tore3D_1307.pers'
birth_death = gudhi.read_persistence_intervals_in_dimension(
persistence_file=persistence_file,
only_this_dim=1)
pers_diag = [(1, elt) for elt in birth_death]
# Use subplots to display diagram and density side by side
fig, axes = plot.subplots(nrows=1, ncols=2, figsize=(12, 5))
gudhi.plot_persistence_diagram(persistence=pers_diag,
axes=axes[0])
gudhi.plot_persistence_density(persistence=pers_diag,
dimension=1, legend=True, axes=axes[1])
plot.show()
