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-rw-r--r--src/python/CMakeLists.txt90
-rw-r--r--src/python/doc/bottleneck_distance_user.rst4
-rw-r--r--src/python/doc/cubical_complex_user.rst22
-rw-r--r--src/python/doc/representations.rst64
-rw-r--r--src/python/doc/wasserstein_distance_user.rst7
-rwxr-xr-xsrc/python/example/alpha_complex_diagram_persistence_from_off_file_example.py2
-rwxr-xr-xsrc/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py2
-rwxr-xr-xsrc/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py2
-rwxr-xr-xsrc/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py2
-rwxr-xr-xsrc/python/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py2
-rwxr-xr-xsrc/python/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py2
-rwxr-xr-xsrc/python/example/rips_complex_diagram_persistence_from_off_file_example.py2
-rwxr-xr-xsrc/python/example/tangential_complex_plain_homology_from_off_file_example.py2
-rw-r--r--src/python/gudhi/representations/vector_methods.py17
-rw-r--r--src/python/gudhi/simplex_tree.pxd7
-rw-r--r--src/python/gudhi/simplex_tree.pyx57
-rw-r--r--src/python/include/Alpha_complex_factory.h9
-rw-r--r--src/python/include/Simplex_tree_interface.h10
-rwxr-xr-xsrc/python/test/test_bottleneck_distance.py12
-rwxr-xr-xsrc/python/test/test_representations.py20
-rwxr-xr-xsrc/python/test/test_simplex_tree.py19
21 files changed, 224 insertions, 130 deletions
diff --git a/src/python/CMakeLists.txt b/src/python/CMakeLists.txt
index c09996fe..56b6876c 100644
--- a/src/python/CMakeLists.txt
+++ b/src/python/CMakeLists.txt
@@ -345,29 +345,27 @@ if(PYTHONINTERP_FOUND)
COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_rips_persistence_bottleneck_distance.py"
-f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -t 0.15 -d 3)
- if(MATPLOTLIB_FOUND AND NUMPY_FOUND)
- # Tangential
- add_test(NAME tangential_complex_plain_homology_from_off_file_example_py_test
- WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
- COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
- ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/tangential_complex_plain_homology_from_off_file_example.py"
- --no-diagram -i 2 -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off)
-
- add_gudhi_py_test(test_tangential_complex)
-
- # Witness complex AND Subsampling
- add_test(NAME euclidean_strong_witness_complex_diagram_persistence_from_off_file_example_py_test
- WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
- COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
- ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py"
- --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2)
-
- add_test(NAME euclidean_witness_complex_diagram_persistence_from_off_file_example_py_test
- WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
- COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
- ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py"
- --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2)
- endif()
+ # Tangential
+ add_test(NAME tangential_complex_plain_homology_from_off_file_example_py_test
+ WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+ COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
+ ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/tangential_complex_plain_homology_from_off_file_example.py"
+ --no-diagram -i 2 -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off)
+
+ add_gudhi_py_test(test_tangential_complex)
+
+ # Witness complex
+ add_test(NAME euclidean_strong_witness_complex_diagram_persistence_from_off_file_example_py_test
+ WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+ COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
+ ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py"
+ --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2)
+
+ add_test(NAME euclidean_witness_complex_diagram_persistence_from_off_file_example_py_test
+ WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+ COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
+ ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py"
+ --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 1.0 -n 20 -d 2)
# Subsampling
add_gudhi_py_test(test_subsampling)
@@ -422,13 +420,11 @@ if(PYTHONINTERP_FOUND)
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_complex_from_points_example.py")
- if(MATPLOTLIB_FOUND AND NUMPY_FOUND)
- add_test(NAME alpha_complex_diagram_persistence_from_off_file_example_py_test
- WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
- COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
- ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_complex_diagram_persistence_from_off_file_example.py"
- --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 0.6)
- endif()
+ add_test(NAME alpha_complex_diagram_persistence_from_off_file_example_py_test
+ WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+ COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
+ ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/alpha_complex_diagram_persistence_from_off_file_example.py"
+ --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -a 0.6)
add_gudhi_py_test(test_alpha_complex)
endif (NOT CGAL_WITH_EIGEN3_VERSION VERSION_LESS 4.11.0)
@@ -445,30 +441,26 @@ if(PYTHONINTERP_FOUND)
${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py"
--no-barcode -f ${CMAKE_SOURCE_DIR}/data/bitmap/CubicalTwoSphere.txt)
- if(NUMPY_FOUND)
- add_test(NAME random_cubical_complex_persistence_example_py_test
- WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
- COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
- ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/random_cubical_complex_persistence_example.py"
- 10 10 10)
- endif()
+ add_test(NAME random_cubical_complex_persistence_example_py_test
+ WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+ COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
+ ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/random_cubical_complex_persistence_example.py"
+ 10 10 10)
add_gudhi_py_test(test_cubical_complex)
# Rips
- if(MATPLOTLIB_FOUND AND NUMPY_FOUND)
- add_test(NAME rips_complex_diagram_persistence_from_distance_matrix_file_example_py_test
- WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
- COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
- ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py"
- --no-diagram -f ${CMAKE_SOURCE_DIR}/data/distance_matrix/lower_triangular_distance_matrix.csv -e 12.0 -d 3)
+ add_test(NAME rips_complex_diagram_persistence_from_distance_matrix_file_example_py_test
+ WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+ COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
+ ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py"
+ --no-diagram -f ${CMAKE_SOURCE_DIR}/data/distance_matrix/lower_triangular_distance_matrix.csv -e 12.0 -d 3)
- add_test(NAME rips_complex_diagram_persistence_from_off_file_example_py_test
- WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
- COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
- ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_off_file_example.py
- --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -e 0.25 -d 3)
- endif()
+ add_test(NAME rips_complex_diagram_persistence_from_off_file_example_py_test
+ WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+ COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}"
+ ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/example/rips_complex_diagram_persistence_from_off_file_example.py
+ --no-diagram -f ${CMAKE_SOURCE_DIR}/data/points/tore3D_300.off -e 0.25 -d 3)
add_test(NAME rips_complex_from_points_example_py_test
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
diff --git a/src/python/doc/bottleneck_distance_user.rst b/src/python/doc/bottleneck_distance_user.rst
index 6c6e08d9..7baa76cc 100644
--- a/src/python/doc/bottleneck_distance_user.rst
+++ b/src/python/doc/bottleneck_distance_user.rst
@@ -47,7 +47,7 @@ The following example explains how the distance is computed:
:figclass: align-center
The point (0, 13) is at distance 6.5 from the diagonal and more
- specifically from the point (6.5, 6.5)
+ specifically from the point (6.5, 6.5).
Basic example
@@ -72,6 +72,6 @@ The output is:
.. testoutput::
- Bottleneck distance approximation = 0.81
+ Bottleneck distance approximation = 0.72
Bottleneck distance value = 0.75
diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst
index 3fd4e27a..6a211347 100644
--- a/src/python/doc/cubical_complex_user.rst
+++ b/src/python/doc/cubical_complex_user.rst
@@ -47,8 +47,8 @@ be a set of two elements).
For further details and theory of cubical complexes, please consult :cite:`kaczynski2004computational` as well as the
following paper :cite:`peikert2012topological`.
-Data structure.
----------------
+Data structure
+--------------
The implementation of Cubical complex provides a representation of complexes that occupy a rectangular region in
:math:`\mathbb{R}^n`. This extra assumption allows for a memory efficient way of storing cubical complexes in a form
@@ -77,8 +77,8 @@ Knowing the sizes of the bitmap, by a series of modulo operation, we can determi
present in the product that gives the cube :math:`C`. In a similar way, we can compute boundary and the coboundary of
each cube. Further details can be found in the literature.
-Input Format.
--------------
+Input Format
+------------
In the current implantation, filtration is given at the maximal cubes, and it is then extended by the lower star
filtration to all cubes. There are a number of constructors that can be used to construct cubical complex by users
@@ -108,8 +108,8 @@ the program output is:
Cubical complex is of dimension 2 - 49 simplices.
-Periodic boundary conditions.
------------------------------
+Periodic boundary conditions
+----------------------------
Often one would like to impose periodic boundary conditions to the cubical complex (cf.
:doc:`periodic_cubical_complex_ref`).
@@ -154,7 +154,13 @@ the program output is:
Periodic cubical complex is of dimension 2 - 42 simplices.
-Examples.
----------
+Examples
+--------
End user programs are available in python/example/ folder.
+
+Tutorial
+--------
+
+This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-cubical-complexes.ipynb>`_
+explains how to represent sublevels sets of functions using cubical complexes. \ No newline at end of file
diff --git a/src/python/doc/representations.rst b/src/python/doc/representations.rst
index 041e3247..b0477197 100644
--- a/src/python/doc/representations.rst
+++ b/src/python/doc/representations.rst
@@ -12,11 +12,45 @@ This module, originally available at https://github.com/MathieuCarriere/sklearn-
A diagram is represented as a numpy array of shape (n,2), as can be obtained from :func:`~gudhi.SimplexTree.persistence_intervals_in_dimension` for instance. Points at infinity are represented as a numpy array of shape (n,1), storing only the birth time. The classes in this module can handle several persistence diagrams at once. In that case, the diagrams are provided as a list of numpy arrays. Note that it is not necessary for the diagrams to have the same number of points, i.e., for the corresponding arrays to have the same number of rows: all classes can handle arrays with different shapes.
-A small example is provided
+Examples
+--------
-.. only:: builder_html
+Landscapes
+^^^^^^^^^^
- * :download:`diagram_vectorizations_distances_kernels.py <../example/diagram_vectorizations_distances_kernels.py>`
+This example computes the first two Landscapes associated to a persistence diagram with four points. The landscapes are evaluated on ten samples, leading to two vectors with ten coordinates each, that are eventually concatenated in order to produce a single vector representation.
+
+.. testcode::
+
+ import numpy as np
+ from gudhi.representations import Landscape
+ # A single diagram with 4 points
+ D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])
+ diags = [D]
+ l=Landscape(num_landscapes=2,resolution=10).fit_transform(diags)
+ print(l)
+
+The output is:
+
+.. testoutput::
+
+ [[1.02851895 2.05703791 2.57129739 1.54277843 0.89995409 1.92847304
+ 2.95699199 3.08555686 2.05703791 1.02851895 0. 0.64282435
+ 0. 0. 0.51425948 0. 0. 0.
+ 0.77138922 1.02851895]]
+
+Various kernels
+^^^^^^^^^^^^^^^
+
+This small example is also provided
+:download:`diagram_vectorizations_distances_kernels.py <../example/diagram_vectorizations_distances_kernels.py>`
+
+Machine Learning and Topological Data Analysis
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-representations.ipynb>`_ explains how to
+efficiently combine machine learning and topological data analysis with the
+:doc:`representations module<representations>`.
Preprocessing
@@ -46,27 +80,3 @@ Metrics
:members:
:special-members:
:show-inheritance:
-
-Basic example
--------------
-
-This example computes the first two Landscapes associated to a persistence diagram with four points. The landscapes are evaluated on ten samples, leading to two vectors with ten coordinates each, that are eventually concatenated in order to produce a single vector representation.
-
-.. testcode::
-
- import numpy as np
- from gudhi.representations import Landscape
- # A single diagram with 4 points
- D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])
- diags = [D]
- l=Landscape(num_landscapes=2,resolution=10).fit_transform(diags)
- print(l)
-
-The output is:
-
-.. testoutput::
-
- [[1.02851895 2.05703791 2.57129739 1.54277843 0.89995409 1.92847304
- 2.95699199 3.08555686 2.05703791 1.02851895 0. 0.64282435
- 0. 0. 0.51425948 0. 0. 0.
- 0.77138922 1.02851895]]
diff --git a/src/python/doc/wasserstein_distance_user.rst b/src/python/doc/wasserstein_distance_user.rst
index 96ec7872..9ffc2759 100644
--- a/src/python/doc/wasserstein_distance_user.rst
+++ b/src/python/doc/wasserstein_distance_user.rst
@@ -175,3 +175,10 @@ The output is:
[[0.27916667 0.55416667]
[0.7375 0.7625 ]
[0.2375 0.2625 ]]
+
+Tutorial
+********
+
+This
+`notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-Barycenters-of-persistence-diagrams.ipynb>`_
+presents the concept of barycenter, or Fréchet mean, of a family of persistence diagrams. \ No newline at end of file
diff --git a/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py b/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py
index 727af4fa..1e0273b3 100755
--- a/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py
+++ b/src/python/example/alpha_complex_diagram_persistence_from_off_file_example.py
@@ -3,7 +3,6 @@
import argparse
import errno
import os
-import matplotlib.pyplot as plot
import gudhi
""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ -
@@ -65,6 +64,7 @@ with open(args.file, "r") as f:
print(simplex_tree.betti_numbers())
if args.no_diagram == False:
+ import matplotlib.pyplot as plot
gudhi.plot_persistence_diagram(diag, band=args.band)
plot.show()
else:
diff --git a/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py b/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py
index e1e572df..4e97cfe3 100755
--- a/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py
+++ b/src/python/example/euclidean_strong_witness_complex_diagram_persistence_from_off_file_example.py
@@ -3,7 +3,6 @@
import argparse
import errno
import os
-import matplotlib.pyplot as plot
import gudhi
""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ -
@@ -82,6 +81,7 @@ with open(args.file, "r") as f:
print(simplex_tree.betti_numbers())
if args.no_diagram == False:
+ import matplotlib.pyplot as plot
gudhi.plot_persistence_diagram(diag, band=args.band)
plot.show()
else:
diff --git a/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py b/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py
index 58cb2bb5..29076c74 100755
--- a/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py
+++ b/src/python/example/euclidean_witness_complex_diagram_persistence_from_off_file_example.py
@@ -3,7 +3,6 @@
import argparse
import errno
import os
-import matplotlib.pyplot as plot
import gudhi
""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ -
@@ -79,6 +78,7 @@ with open(args.file, "r") as f:
print(simplex_tree.betti_numbers())
if args.no_diagram == False:
+ import matplotlib.pyplot as plot
gudhi.plot_persistence_diagram(diag, band=args.band)
plot.show()
else:
diff --git a/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py b/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py
index 499171df..ee3290c6 100755
--- a/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py
+++ b/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py
@@ -1,7 +1,6 @@
#!/usr/bin/env python
import argparse
-import matplotlib.pyplot as plot
import errno
import os
import gudhi
@@ -75,6 +74,7 @@ if is_file_perseus(args.file):
print("betti_numbers()=")
print(periodic_cubical_complex.betti_numbers())
if args.no_barcode == False:
+ import matplotlib.pyplot as plot
gudhi.plot_persistence_barcode(diag)
plot.show()
else:
diff --git a/src/python/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py b/src/python/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py
index 1acb187c..ea2eb7e1 100755
--- a/src/python/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py
+++ b/src/python/example/rips_complex_diagram_persistence_from_correlation_matrix_file_example.py
@@ -2,7 +2,6 @@
import sys
import argparse
-import matplotlib.pyplot as plot
import gudhi
""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
@@ -84,5 +83,6 @@ invert_diag = [
]
if args.no_diagram == False:
+ import matplotlib.pyplot as plot
gudhi.plot_persistence_diagram(invert_diag, band=args.band)
plot.show()
diff --git a/src/python/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py b/src/python/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py
index 79ccca96..236d085d 100755
--- a/src/python/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py
+++ b/src/python/example/rips_complex_diagram_persistence_from_distance_matrix_file_example.py
@@ -1,7 +1,6 @@
#!/usr/bin/env python
import argparse
-import matplotlib.pyplot as plot
import gudhi
""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
@@ -60,5 +59,6 @@ print("betti_numbers()=")
print(simplex_tree.betti_numbers())
if args.no_diagram == False:
+ import matplotlib.pyplot as plot
gudhi.plot_persistence_diagram(diag, band=args.band)
plot.show()
diff --git a/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py b/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py
index 6f992508..e80233a9 100755
--- a/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py
+++ b/src/python/example/rips_complex_diagram_persistence_from_off_file_example.py
@@ -3,7 +3,6 @@
import argparse
import errno
import os
-import matplotlib.pyplot as plot
import gudhi
""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ -
@@ -70,6 +69,7 @@ with open(args.file, "r") as f:
print(simplex_tree.betti_numbers())
if args.no_diagram == False:
+ import matplotlib.pyplot as plot
gudhi.plot_persistence_diagram(diag, band=args.band)
plot.show()
else:
diff --git a/src/python/example/tangential_complex_plain_homology_from_off_file_example.py b/src/python/example/tangential_complex_plain_homology_from_off_file_example.py
index 85bade4a..a4b4e9f5 100755
--- a/src/python/example/tangential_complex_plain_homology_from_off_file_example.py
+++ b/src/python/example/tangential_complex_plain_homology_from_off_file_example.py
@@ -3,7 +3,6 @@
import argparse
import errno
import os
-import matplotlib.pyplot as plot
import gudhi
""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ -
@@ -62,6 +61,7 @@ with open(args.file, "r") as f:
print(st.betti_numbers())
if args.no_diagram == False:
+ import matplotlib.pyplot as plot
gudhi.plot_persistence_diagram(diag, band=args.band)
plot.show()
else:
diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index 5ca127f6..cdcb1fde 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -323,22 +323,15 @@ class BettiCurve(BaseEstimator, TransformerMixin):
Returns:
numpy array with shape (number of diagrams) x (**resolution**): output Betti curves.
"""
- num_diag, Xfit = len(X), []
+ Xfit = []
x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution)
step_x = x_values[1] - x_values[0]
- for i in range(num_diag):
-
- diagram, num_pts_in_diag = X[i], X[i].shape[0]
-
+ for diagram in X:
+ diagram_int = np.clip(np.ceil((diagram[:,:2] - self.sample_range[0]) / step_x), 0, self.resolution).astype(int)
bc = np.zeros(self.resolution)
- for j in range(num_pts_in_diag):
- [px,py] = diagram[j,:2]
- min_idx = np.clip(np.ceil((px - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
- max_idx = np.clip(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
- for k in range(min_idx, max_idx):
- bc[k] += 1
-
+ for interval in diagram_int:
+ bc[interval[0]:interval[1]] += 1
Xfit.append(np.reshape(bc,[1,-1]))
Xfit = np.concatenate(Xfit, 0)
diff --git a/src/python/gudhi/simplex_tree.pxd b/src/python/gudhi/simplex_tree.pxd
index 3b494ba3..3c4cbed3 100644
--- a/src/python/gudhi/simplex_tree.pxd
+++ b/src/python/gudhi/simplex_tree.pxd
@@ -36,6 +36,12 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi":
Simplex_tree_skeleton_iterator operator++() nogil
bint operator!=(Simplex_tree_skeleton_iterator) nogil
+ cdef cppclass Simplex_tree_boundary_iterator "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>::Boundary_simplex_iterator":
+ Simplex_tree_boundary_iterator() nogil
+ Simplex_tree_simplex_handle& operator*() nogil
+ Simplex_tree_boundary_iterator operator++() nogil
+ bint operator!=(Simplex_tree_boundary_iterator) nogil
+
cdef cppclass Simplex_tree_interface_full_featured "Gudhi::Simplex_tree_interface<Gudhi::Simplex_tree_options_full_featured>":
Simplex_tree() nogil
@@ -67,6 +73,7 @@ cdef extern from "Simplex_tree_interface.h" namespace "Gudhi":
vector[Simplex_tree_simplex_handle].const_iterator get_filtration_iterator_end() nogil
Simplex_tree_skeleton_iterator get_skeleton_iterator_begin(int dimension) nogil
Simplex_tree_skeleton_iterator get_skeleton_iterator_end(int dimension) nogil
+ pair[Simplex_tree_boundary_iterator, Simplex_tree_boundary_iterator] get_boundary_iterators(vector[int] simplex) nogil except +
cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi":
cdef cppclass Simplex_tree_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Simplex_tree<Gudhi::Simplex_tree_options_full_featured>>":
diff --git a/src/python/gudhi/simplex_tree.pyx b/src/python/gudhi/simplex_tree.pyx
index 910711a9..cdd2e87b 100644
--- a/src/python/gudhi/simplex_tree.pyx
+++ b/src/python/gudhi/simplex_tree.pyx
@@ -285,6 +285,22 @@ cdef class SimplexTree:
ct.append((v, filtered_simplex.second))
return ct
+ def get_boundaries(self, simplex):
+ """This function returns a generator with the boundaries of a given N-simplex.
+ If you do not need the filtration values, the boundary can also be obtained as
+ :code:`itertools.combinations(simplex,len(simplex)-1)`.
+
+ :param simplex: The N-simplex, represented by a list of vertex.
+ :type simplex: list of int.
+ :returns: The (simplices of the) boundary of a simplex
+ :rtype: generator with tuples(simplex, filtration)
+ """
+ cdef pair[Simplex_tree_boundary_iterator, Simplex_tree_boundary_iterator] it = self.get_ptr().get_boundary_iterators(simplex)
+
+ while it.first != it.second:
+ yield self.get_ptr().get_simplex_and_filtration(dereference(it.first))
+ preincrement(it.first)
+
def remove_maximal_simplex(self, simplex):
"""This function removes a given maximal N-simplex from the simplicial
complex.
@@ -373,37 +389,39 @@ cdef class SimplexTree:
self.get_ptr().reset_filtration(filtration, min_dim)
def extend_filtration(self):
- """ Extend filtration for computing extended persistence. This function only uses the
- filtration values at the 0-dimensional simplices, and computes the extended persistence
- diagram induced by the lower-star filtration computed with these values.
+ """ Extend filtration for computing extended persistence. This function only uses the filtration values at the
+ 0-dimensional simplices, and computes the extended persistence diagram induced by the lower-star filtration
+ computed with these values.
.. note::
- Note that after calling this function, the filtration
- values are actually modified within the simplex tree.
- The function :func:`extended_persistence`
- retrieves the original values.
+ Note that after calling this function, the filtration values are actually modified within the simplex tree.
+ The function :func:`extended_persistence` retrieves the original values.
.. note::
- Note that this code creates an extra vertex internally, so you should make sure that
- the simplex tree does not contain a vertex with the largest possible value (i.e., 4294967295).
+ Note that this code creates an extra vertex internally, so you should make sure that the simplex tree does
+ not contain a vertex with the largest possible value (i.e., 4294967295).
+
+ This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-extended-persistence.ipynb>`_
+ explains how to compute an extension of persistence called extended persistence.
"""
self.get_ptr().compute_extended_filtration()
def extended_persistence(self, homology_coeff_field=11, min_persistence=0):
- """This function retrieves good values for extended persistence, and separate the diagrams
- into the Ordinary, Relative, Extended+ and Extended- subdiagrams.
+ """This function retrieves good values for extended persistence, and separate the diagrams into the Ordinary,
+ Relative, Extended+ and Extended- subdiagrams.
- :param homology_coeff_field: The homology coefficient field. Must be a
- prime number. Default value is 11.
+ :param homology_coeff_field: The homology coefficient field. Must be a prime number. Default value is 11.
:type homology_coeff_field: int
- :param min_persistence: The minimum persistence value (i.e., the absolute value of the difference between the persistence diagram point coordinates) to take into
- account (strictly greater than min_persistence). Default value is
- 0.0.
- Sets min_persistence to -1.0 to see all values.
+ :param min_persistence: The minimum persistence value (i.e., the absolute value of the difference between the
+ persistence diagram point coordinates) to take into account (strictly greater than min_persistence).
+ Default value is 0.0. Sets min_persistence to -1.0 to see all values.
:type min_persistence: float
- :returns: A list of four persistence diagrams in the format described in :func:`persistence`. The first one is Ordinary, the second one is Relative, the third one is Extended+ and the fourth one is Extended-. See https://link.springer.com/article/10.1007/s10208-008-9027-z and/or section 2.2 in https://link.springer.com/article/10.1007/s10208-017-9370-z for a description of these subtypes.
+ :returns: A list of four persistence diagrams in the format described in :func:`persistence`. The first one is
+ Ordinary, the second one is Relative, the third one is Extended+ and the fourth one is Extended-.
+ See https://link.springer.com/article/10.1007/s10208-008-9027-z and/or section 2.2 in
+ https://link.springer.com/article/10.1007/s10208-017-9370-z for a description of these subtypes.
.. note::
@@ -414,6 +432,9 @@ cdef class SimplexTree:
The coordinates of the persistence diagram points might be a little different than the
original filtration values due to the internal transformation (scaling to [-2,-1]) that is
performed on these values during the computation of extended persistence.
+
+ This `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-extended-persistence.ipynb>`_
+ explains how to compute an extension of persistence called extended persistence.
"""
cdef vector[pair[int, pair[double, double]]] persistence_result
if self.pcohptr != NULL:
diff --git a/src/python/include/Alpha_complex_factory.h b/src/python/include/Alpha_complex_factory.h
index d699ad9b..3405fdd6 100644
--- a/src/python/include/Alpha_complex_factory.h
+++ b/src/python/include/Alpha_complex_factory.h
@@ -48,11 +48,14 @@ static CgalPointType pt_cython_to_cgal(std::vector<double> const& vec) {
class Abstract_alpha_complex {
public:
virtual std::vector<double> get_point(int vh) = 0;
+
virtual bool create_simplex_tree(Simplex_tree_interface<>* simplex_tree, double max_alpha_square,
bool default_filtration_value) = 0;
+
+ virtual ~Abstract_alpha_complex() = default;
};
-class Exact_Alphacomplex_dD : public Abstract_alpha_complex {
+class Exact_Alphacomplex_dD final : public Abstract_alpha_complex {
private:
using Kernel = CGAL::Epeck_d<CGAL::Dynamic_dimension_tag>;
using Point = typename Kernel::Point_d;
@@ -78,7 +81,7 @@ class Exact_Alphacomplex_dD : public Abstract_alpha_complex {
Alpha_complex<Kernel> alpha_complex_;
};
-class Inexact_Alphacomplex_dD : public Abstract_alpha_complex {
+class Inexact_Alphacomplex_dD final : public Abstract_alpha_complex {
private:
using Kernel = CGAL::Epick_d<CGAL::Dynamic_dimension_tag>;
using Point = typename Kernel::Point_d;
@@ -104,7 +107,7 @@ class Inexact_Alphacomplex_dD : public Abstract_alpha_complex {
};
template <complexity Complexity>
-class Alphacomplex_3D : public Abstract_alpha_complex {
+class Alphacomplex_3D final : public Abstract_alpha_complex {
private:
using Point = typename Alpha_complex_3d<Complexity, false, false>::Bare_point_3;
diff --git a/src/python/include/Simplex_tree_interface.h b/src/python/include/Simplex_tree_interface.h
index e288a8cf..baff3850 100644
--- a/src/python/include/Simplex_tree_interface.h
+++ b/src/python/include/Simplex_tree_interface.h
@@ -39,6 +39,7 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> {
using Skeleton_simplex_iterator = typename Base::Skeleton_simplex_iterator;
using Complex_simplex_iterator = typename Base::Complex_simplex_iterator;
using Extended_filtration_data = typename Base::Extended_filtration_data;
+ using Boundary_simplex_iterator = typename Base::Boundary_simplex_iterator;
public:
@@ -219,6 +220,15 @@ class Simplex_tree_interface : public Simplex_tree<SimplexTreeOptions> {
// this specific case works because the range is just a pair of iterators - won't work if range was a vector
return Base::skeleton_simplex_range(dimension).end();
}
+
+ std::pair<Boundary_simplex_iterator, Boundary_simplex_iterator> get_boundary_iterators(const Simplex& simplex) {
+ auto bd_sh = Base::find(simplex);
+ if (bd_sh == Base::null_simplex())
+ throw std::runtime_error("simplex not found - cannot find boundaries");
+ // this specific case works because the range is just a pair of iterators - won't work if range was a vector
+ auto boundary_srange = Base::boundary_simplex_range(bd_sh);
+ return std::make_pair(boundary_srange.begin(), boundary_srange.end());
+ }
};
} // namespace Gudhi
diff --git a/src/python/test/test_bottleneck_distance.py b/src/python/test/test_bottleneck_distance.py
index 6915bea8..07fcc9cc 100755
--- a/src/python/test/test_bottleneck_distance.py
+++ b/src/python/test/test_bottleneck_distance.py
@@ -25,3 +25,15 @@ def test_basic_bottleneck():
assert gudhi.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, abs=0.1)
assert gudhi.hera.bottleneck_distance(diag1, diag2, 0) == 0.75
assert gudhi.hera.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, rel=0.1)
+
+ import numpy as np
+
+ # Translating both diagrams along the diagonal should not affect the distance,
+ # checks that negative numbers are not an issue
+ diag1 = np.array(diag1) - 100
+ diag2 = np.array(diag2) - 100
+
+ assert gudhi.bottleneck_distance(diag1, diag2) == 0.75
+ assert gudhi.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, abs=0.1)
+ assert gudhi.hera.bottleneck_distance(diag1, diag2, 0) == 0.75
+ assert gudhi.hera.bottleneck_distance(diag1, diag2, 0.1) == pytest.approx(0.75, rel=0.1)
diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py
index e5c211a0..43c914f3 100755
--- a/src/python/test/test_representations.py
+++ b/src/python/test/test_representations.py
@@ -39,11 +39,11 @@ def test_multiple():
d2 = BottleneckDistance(epsilon=0.00001).fit_transform(l1)
d3 = pairwise_persistence_diagram_distances(l1, l1b, e=0.00001, n_jobs=4)
assert d1 == pytest.approx(d2)
- assert d3 == pytest.approx(d2, abs=1e-5) # Because of 0 entries (on the diagonal)
+ assert d3 == pytest.approx(d2, abs=1e-5) # Because of 0 entries (on the diagonal)
d1 = pairwise_persistence_diagram_distances(l1, l2, metric="wasserstein", order=2, internal_p=2)
d2 = WassersteinDistance(order=2, internal_p=2, n_jobs=4).fit(l2).transform(l1)
print(d1.shape, d2.shape)
- assert d1 == pytest.approx(d2, rel=.02)
+ assert d1 == pytest.approx(d2, rel=0.02)
def test_dummy_atol():
@@ -53,8 +53,22 @@ def test_dummy_atol():
for weighting_method in ["cloud", "iidproba"]:
for contrast in ["gaussian", "laplacian", "indicator"]:
- atol_vectoriser = Atol(quantiser=KMeans(n_clusters=1, random_state=202006), weighting_method=weighting_method, contrast=contrast)
+ atol_vectoriser = Atol(
+ quantiser=KMeans(n_clusters=1, random_state=202006),
+ weighting_method=weighting_method,
+ contrast=contrast,
+ )
atol_vectoriser.fit([a, b, c])
atol_vectoriser(a)
atol_vectoriser.transform(X=[a, b, c])
+
+from gudhi.representations.vector_methods import BettiCurve
+
+
+def test_infinity():
+ a = np.array([[1.0, 8.0], [2.0, np.inf], [3.0, 4.0]])
+ c = BettiCurve(20, [0.0, 10.0])(a)
+ assert c[1] == 0
+ assert c[7] == 3
+ assert c[9] == 2
diff --git a/src/python/test/test_simplex_tree.py b/src/python/test/test_simplex_tree.py
index ac2b59c7..3b23fa0b 100755
--- a/src/python/test/test_simplex_tree.py
+++ b/src/python/test/test_simplex_tree.py
@@ -380,3 +380,22 @@ def test_reset_filtration():
assert st.filtration(simplex[0]) >= 2.
else:
assert st.filtration(simplex[0]) == 0.
+
+def test_boundaries_iterator():
+ st = SimplexTree()
+
+ assert st.insert([0, 1, 2, 3], filtration=1.0) == True
+ assert st.insert([1, 2, 3, 4], filtration=2.0) == True
+
+ assert list(st.get_boundaries([1, 2, 3])) == [([1, 2], 1.0), ([1, 3], 1.0), ([2, 3], 1.0)]
+ assert list(st.get_boundaries([2, 3, 4])) == [([2, 3], 1.0), ([2, 4], 2.0), ([3, 4], 2.0)]
+ assert list(st.get_boundaries([2])) == []
+
+ with pytest.raises(RuntimeError):
+ list(st.get_boundaries([]))
+
+ with pytest.raises(RuntimeError):
+ list(st.get_boundaries([0, 4])) # (0, 4) does not exist
+
+ with pytest.raises(RuntimeError):
+ list(st.get_boundaries([6])) # (6) does not exist