persim.PersLandscapeExact¶

class persim.PersLandscapeExact(dgms: list = [], hom_deg: int = 0, critical_pairs: list = [], compute: bool = True)[source]¶

Persistence Landscape Exact class.

This class implements an exact version of Persistence Landscapes. The landscape functions are stored as a list of critical pairs, and the actual function is the linear interpolation of these critical pairs. All computations done with these classes are exact. For much faster but approximate methods that should suffice for most applications, consider PersLandscapeApprox.

Parameters:

dgms (list of (-,2) numpy.ndarrays, optional) – A nested list of numpy arrays, e.g., [array( array([:]), array([:]) ),…, array([:])] Each entry in the list corresponds to a single homological degree. Each array represents the birth-death pairs in that homological degree. This is the output format from ripser.py: ripser(data_user)[‘dgms’]. Only one of diagrams or critical pairs should be specified.
hom_deg (int) – Represents the homology degree of the persistence diagram.
critical_pairs (list, optional) – List of lists of critical pairs (points, values) for specifying a persistence landscape. These do not necessarily have to arise from a persistence diagram. Only one of diagrams or critical pairs should be specified.
compute (bool, optional) – Flag determining whether landscape functions are computed upon instantiation.

Examples

Define a persistence diagram and instantiate the landscape:

>>> from persim import PersLandscapeExact
>>> import numpy as np
>>> pd = [ np.array([[0,3],[1,4]]), np.array([[1,4]]) ]
>>> ple = PersLandscapeExact(dgms=pd, hom_deg=0)
>>> ple

PersLandscapeExact instances store the critical pairs of the landscape as a list of lists in the critical_pairs attribute. The i-th entry corresponds to the critical values of the depth i landscape:

>>> ple.critical_pairs

[[[0, 0], [1.5, 1.5], [2.0, 1.0], [2.5, 1.5], [4, 0]],
[[1, 0], [2.0, 1.0], [3, 0]]]

Addition, subtraction, and scalar multiplication between landscapes is implemented:

>>> pd2 = [ np.array([[0.5,7],[3,5],[4.1,6.5]]), np.array([[1,4]])]
>>> pl2 = PersLandscapeExact(dgms=pd2,hom_deg=0)
>>> pl_sum = ple + pl2
>>> pl_sum.critical_pairs

[[[0, 0], [0.5, 0.5], [1.5, 2.5], [2.0, 2.5],
[2.5, 3.5], [3.75, 3.5],[4, 3.0], [7.0, 0.0]],
[[1, 0], [2.0, 1.0], [3, 0.0], [4.0, 1.0],
[4.55, 0.45], [5.3, 1.2], [6.5, 0.0]],
[[4.1, 0], [4.55, 0.45], [5.0, 0]]]

>>> diff_pl = ple - pl2
>>> diff_pl.critical_pairs

[[[0, 0], [0.5, 0.5], [1.5, 0.5], [2.0, -0.5],
[2.5, -0.5], [3.75, -3.0], [4, -3.0], [7.0, 0.0]],
[[1, 0], [2.0, 1.0], [3, 0.0], [4.0, -1.0],
[4.55, -0.45], [5.3, -1.2], [6.5, 0.0]],
[[4.1, 0], [4.55, -0.45], [5.0, 0]]]

>>> (5*ple).critical_pairs

[[(0, 0), (1.5, 7.5), (2.0, 5.0), (2.5, 7.5), (4, 0)],
[(1, 0), (2.0, 5.0), (3, 0)]]

Landscapes are sliced by depth and slicing returns the critical pairs in the range specified:

>>> ple[0]

[[0, 0], [1.5, 1.5], [2.0, 1.0], [2.5, 1.5], [4, 0]]

>>> pl2[1:]

[[[3.0, 0], [4.0, 1.0], [4.55, 0.4500000000000002],
[5.3, 1.2000000000000002], [6.5, 0]],
[[4.1, 0], [4.55, 0.4500000000000002], [5.0, 0]]]

p norms are implemented for all p as well as the supremum norm:

>>> ple.p_norm(p=3)

1.7170713638299977

>>> pl2.sup_norm()

3.25

__init__(dgms: list = [], hom_deg: int = 0, critical_pairs: list = [], compute: bool = True) → None[source]¶

Methods

`__init__`([dgms, hom_deg, critical_pairs, ...])
`compute_landscape`([verbose])	Stores the persistence landscape in self.critical_pairs as a list
`compute_landscape_by_depth`(depth)	Returns the function of depth from self.critical_pairs as a list
`p_norm`([p])	Returns the L_{p} norm of an exact persistence landscape
`sup_norm`()	Returns the supremum norm of an exact persistence landscape