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Add all variable, remove unnec docstring
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Michael Catanzaro committed Nov 7, 2023
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14 changes: 9 additions & 5 deletions persim/__init__.py
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@@ -1,9 +1,13 @@
from .images import *
from .sliced_wasserstein import *
from ._version import __version__
from .bottleneck import *
from .wasserstein import *
from .heat import *
from .gromov_hausdorff import *
from .heat import *
from .images import *
from .landscapes.approximate import PersLandscapeApprox
from .landscapes.exact import PersLandscapeExact
from .landscapes.transformer import PersistenceLandscaper
from .sliced_wasserstein import *
from .visuals import *
from .wasserstein import *

from ._version import __version__
__all__ = ["PersLandscapeApprox", "PersistenceLandscaper", "PersLandscapeExact"]
155 changes: 70 additions & 85 deletions persim/landscapes/exact.py
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Expand Up @@ -3,11 +3,12 @@
"""

import itertools
import numpy as np
from operator import itemgetter

import numpy as np

from .approximate import PersLandscapeApprox
from .auxiliary import union_crit_pairs, _p_norm
from .auxiliary import _p_norm, union_crit_pairs
from .base import PersLandscape

__all__ = ["PersLandscapeExact"]
Expand All @@ -16,114 +17,99 @@
class PersLandscapeExact(PersLandscape):
"""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.
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`.
compute : bool, optional
Flag determining whether landscape functions are computed upon instantiation.
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.
Examples
--------
Define a persistence diagram and instantiate the landscape::
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.
>>> 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
compute : bool, optional
Flag determining whether landscape functions are computed upon instantiation.
`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
Examples
--------
Define a persistence diagram and instantiate the landscape::
[[[0, 0], [1.5, 1.5], [2.0, 1.0], [2.5, 1.5], [4, 0]],
[[1, 0], [2.0, 1.0], [3, 0]]]
>>> 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
Addition, subtraction, and scalar multiplication between landscapes is implemented::
`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::
>>> 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
>>> ple.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]]]
[[[0, 0], [1.5, 1.5], [2.0, 1.0], [2.5, 1.5], [4, 0]],
[[1, 0], [2.0, 1.0], [3, 0]]]
>>> diff_pl = ple - pl2
>>> diff_pl.critical_pairs
Addition, subtraction, and scalar multiplication between landscapes is implemented::
[[[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]]]
>>> 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
>>> (5*ple).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]]]
[[(0, 0), (1.5, 7.5), (2.0, 5.0), (2.5, 7.5), (4, 0)],
[(1, 0), (2.0, 5.0), (3, 0)]]
>>> diff_pl = ple - pl2
>>> diff_pl.critical_pairs
Landscapes are sliced by depth and slicing returns the critical pairs in the range specified::
[[[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]]]
>>> ple[0]
>>> (5*ple).critical_pairs
[[0, 0], [1.5, 1.5], [2.0, 1.0], [2.5, 1.5], [4, 0]]
[[(0, 0), (1.5, 7.5), (2.0, 5.0), (2.5, 7.5), (4, 0)],
[(1, 0), (2.0, 5.0), (3, 0)]]
>>> pl2[1:]
Landscapes are sliced by depth and slicing returns the critical pairs in the range specified::
[[[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]]]
>>> ple[0]
`p` norms are implemented for all `p` as well as the supremum norms::
[[0, 0], [1.5, 1.5], [2.0, 1.0], [2.5, 1.5], [4, 0]]
>>> ple.p_norm(p=3)
>>> pl2[1:]
1.7170713638299977
[[[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]]]
>>> pl2.sup_norm()
`p` norms are implemented for all `p` as well as the supremum norms::
3.25
>>> ple.p_norm(p=3)
1.7170713638299977
>>> pl2.sup_norm()
Methods
-------
compute_landscape : computes the set of critical pairs and stores them in
the attribute `critical_pairs`
compute_landscape_by_depth : compute the set of critical pairs in a certain
range.
p_norm : returns p-norm of a landscape
sup_norm : returns sup norm of a landscape
3.25
"""

def __init__(
Expand Down Expand Up @@ -240,7 +226,7 @@ def __getitem__(self, key: slice) -> list:
list
The critical pairs of the landscape function corresponding
to depths given by key
Note
----
If the slice is beyond `self.max_depth` an IndexError is raised.
Expand Down Expand Up @@ -307,7 +293,6 @@ def compute_landscape(self, verbose: bool = False) -> list:
break

while L[landscape_idx][-1] != [np.inf, 0]:

# if d is > = all remaining pairs, then end lambda
# includes edge case with (b,d) pairs with the same death time
if all(d >= _[1] for _ in A):
Expand Down

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