Fix documentation

README.md

@@ -1,8 +1,7 @@
 [![PyPI version](https://badge.fury.io/py/persim.svg)](https://badge.fury.io/py/persim)
-[![Downloads](https://pypip.in/download/persim/badge.svg)](https://pypi.python.org/pypi/persim/) 
+![PyPI - Downloads](https://img.shields.io/pypi/dm/persim)
 [![Conda Version](https://img.shields.io/conda/vn/conda-forge/persim.svg)](https://anaconda.org/conda-forge/persim)
 [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/persim.svg)](https://anaconda.org/conda-forge/persim)
-[![Build Status](https://travis-ci.org/scikit-tda/persim.svg?branch=master)](https://travis-ci.org/scikit-tda/persim)
 [![codecov](https://codecov.io/gh/scikit-tda/persim/branch/master/graph/badge.svg)](https://codecov.io/gh/scikit-tda/persim)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 

docs/conf.py

@@ -1,29 +1,34 @@
 # -*- coding: utf-8 -*-
 import os
 import sys
-sys.path.insert(0, os.path.abspath('.'))
-from persim import __version__
+
+sys.path.insert(0, os.path.abspath("."))
 from sktda_docs_config import *
 
-project = u'Persim'
-copyright = u'2019, Nathaniel Saul'
-author = u'Nathaniel Saul'
+from persim import __version__
+
+project = "Persim"
+copyright = "2019, Nathaniel Saul"
+author = "Nathaniel Saul"
 
 version = __version__
 release = __version__
 
-html_theme_options.update({
-  # Google Analytics info
-  'ga_ua': 'UA-124965309-3',
-  'ga_domain': '',
-  'gh_url': 'scikit-tda/persim'
-})
+language = "en"
+
+html_theme_options.update(
+    {
+        "collapse_naviation": False,
+        # Google Analytics info
+        "ga_ua": "UA-124965309-3",
+        "ga_domain": "",
+        "gh_url": "scikit-tda/persim",
+    }
+)
 
 html_short_title = project
-htmlhelp_basename = 'Persimdoc'
+htmlhelp_basename = "Persimdoc"
 
-autodoc_default_options = {
-    'members': True
-}
+autodoc_default_options = {"members": False, "maxdepth": 1}
 
-autodoc_member_order = 'groupwise'
+autodoc_member_order = "groupwise"

docs/index.rst

@@ -36,6 +36,7 @@ Documentation
 
     notebooks/Persistence images
     notebooks/distances
+    notebooks/Persistence landscapes
     reference/index
 
 

docs/notebooks/Differentiation with Persistence Landscapes.ipynb

@@ -18,6 +18,7 @@
     "The experiment is to determine if persistent homology can distinguish between spheres of different dimensions. Here we restrict to differentiating $S^2$ from $S^3$. We will further restrict ourselves to persistent homology in degree one (but one can use all relevant homological degrees as well). A priori, we would not expect this to be an effective discriminator, since the ordinary first homology of both spheres is trivial. Note that this is a simplified version of the experiment from Bubenik and Dlotko's *A persistence landscapes toolbox for topological statistics* [1]. \n",
     "\n",
     "In detail: \n",
+    "\n",
     "- Repeat the following `num_runs = 100` times:\n",
     "  - Sample `num_pts = 100` points from the 2-sphere and the 3-sphere. We rescale the spheres so the average distance between the points on each sphere is approximately 1.\n",
     "  - Compute the VR persistent homology (using `ripser`) and compute the associated landscapes. Store each of these landscapes.\n",
@@ -356,7 +357,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -370,7 +371,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,

docs/notebooks/Persistence images.ipynb

@@ -494,9 +494,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "persimenv",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "persimenv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -508,7 +508,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

persim/__init__.py

@@ -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"]

persim/landscapes/approximate.py

@@ -87,8 +87,8 @@ class are much faster compared to `PersLandscapeExact` in general.
 
         ValueError: Start values of grids do not coincide
 
-        >>> from persim import snap_PL
-        >>> [snapped_pla, snapped_wide_pl] = snap_PL([pla,wide_pl])
+        >>> from persim import snap_pl
+        >>> [snapped_pla, snapped_wide_pl] = snap_pl([pla,wide_pl])
         >>> print(snapped_pla, snapped_wide_pl)
 
         Approximate persistence landscape in homological degree 0 on grid from -1 to 4 with 1000 steps Approximate persistence landscape in homological degree 0 on grid from -1 to 4 with 1000 steps
@@ -128,7 +128,6 @@ def __init__(
         values=np.array([]),
         compute: bool = True,
     ) -> None:
-
         super().__init__(dgms=dgms, hom_deg=hom_deg)
         if not dgms and values.size == 0:
             raise ValueError("dgms and values cannot both be emtpy")

persim/landscapes/auxiliary.py

@@ -44,7 +44,10 @@ def union_crit_pairs(A, B):
         else:
             result_pairs.append(
                 slope_to_pos_interp(
-                    sum_slopes(pos_to_slope_interp(a), pos_to_slope_interp(b),)
+                    sum_slopes(
+                        pos_to_slope_interp(a),
+                        pos_to_slope_interp(b),
+                    )
                 )
             )
     return result_pairs
@@ -141,7 +144,7 @@ def ndsnap_regular(points, *grid_axes):
 
 def _p_norm(p: float, critical_pairs: list = []):
     """
-    Compute `p` norm of interpolated piecewise linear function defined from list of 
+    Compute `p` norm of interpolated piecewise linear function defined from list of
     critical pairs.
     """
     result = 0.0

persim/landscapes/exact.py

@@ -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"]
@@ -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 norm::
 
-           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__(
@@ -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.
@@ -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):