Advertise new SimplexTree functions in Rips user manual

1 files changed, 30 insertions, 90 deletions

diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst
index c41a7803..489fbb4a 100644
--- a/src/python/doc/rips_complex_user.rst
+++ b/src/python/doc/rips_complex_user.rst
@@ -34,9 +34,6 @@ A vertex name corresponds to the index of the point in the given range (aka. the
 On this example, as edges (4,5), (4,6) and (5,6) are in the complex, simplex (4,5,6) is added with the filtration value
 set with :math:`max(filtration(4,5), filtration(4,6), filtration(5,6))`. And so on for simplex (0,1,2,3).
 
-If the :doc:`RipsComplex <rips_complex_ref>` interfaces are not detailed enough for your need, please refer to
-rips_persistence_step_by_step.cpp C++ example, where the graph construction over the Simplex_tree is more detailed.
-
 A Rips complex can easily become huge, even if we limit the length of the edges
 and the dimension of the simplices. One easy trick, before building a Rips
 complex on a point cloud, is to call :func:`~gudhi.sparsify_point_set` which removes points
@@ -117,54 +114,44 @@ Notice that if we use
 asking for a very sparse version (theory only gives some guarantee on the meaning of the output if `sparse<1`),
 2 to 5 edges disappear, depending on the random vertex used to start the sparsification.
 
-Example from OFF file
-^^^^^^^^^^^^^^^^^^^^^
+Example step by step
+^^^^^^^^^^^^^^^^^^^^
 
-This example builds the :doc:`RipsComplex <rips_complex_ref>` from the given
-points in an OFF file, and max_edge_length value.
-Then it creates a :doc:`SimplexTree <simplex_tree_ref>` with it.
+While :doc:`RipsComplex <rips_complex_ref>` is convenient, for instance to build a simplicial complex in one line
 
-Finally, it is asked to display information about the Rips complex.
+.. testcode::
 
+   import gudhi
+   points = [[1, 1], [7, 0], [4, 6], [9, 6], [0, 14], [2, 19], [9, 17]]
+   cplx = gudhi.RipsComplex(points=points, max_edge_length=12.0).create_simplex_tree(max_dimension=2)
+
+you can achieve the same result without this class for more flexibility
 
 .. testcode::
 
-    import gudhi
-    off_file = gudhi.__root_source_dir__ + '/data/points/alphacomplexdoc.off'
-    point_cloud = gudhi.read_points_from_off_file(off_file = off_file)
-    rips_complex = gudhi.RipsComplex(points=point_cloud, max_edge_length=12.0)
-    simplex_tree = rips_complex.create_simplex_tree(max_dimension=1)
-    result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \
-        repr(simplex_tree.num_simplices()) + ' simplices - ' + \
-        repr(simplex_tree.num_vertices()) + ' vertices.'
-    print(result_str)
-    fmt = '%s -> %.2f'
-    for filtered_value in simplex_tree.get_filtration():
-        print(fmt % tuple(filtered_value))
+   import gudhi
+   from scipy.spatial.distance import cdist
+   points = [[1, 1], [7, 0], [4, 6], [9, 6], [0, 14], [2, 19], [9, 17]]
+   distance_matrix = cdist(points, points)
+   cplx = gudhi.SimplexTree.create_from_array(distance_matrix, max_filtration=12.0)
+   cplx.expansion(2)
 
-the program output is:
+or
 
-.. testoutput::
+.. testcode::
 
-    Rips complex is of dimension 1 - 18 simplices - 7 vertices.
-    [0] -> 0.00
-    [1] -> 0.00
-    [2] -> 0.00
-    [3] -> 0.00
-    [4] -> 0.00
-    [5] -> 0.00
-    [6] -> 0.00
-    [2, 3] -> 5.00
-    [4, 5] -> 5.39
-    [0, 2] -> 5.83
-    [0, 1] -> 6.08
-    [1, 3] -> 6.32
-    [1, 2] -> 6.71
-    [5, 6] -> 7.28
-    [2, 4] -> 8.94
-    [0, 3] -> 9.43
-    [4, 6] -> 9.49
-    [3, 6] -> 11.00
+   import gudhi
+   from scipy.spatial import cKDTree
+   points = [[1, 1], [7, 0], [4, 6], [9, 6], [0, 14], [2, 19], [9, 17]]
+   tree = cKDTree(points)
+   edges = tree.sparse_distance_matrix(tree, max_distance=12.0, output_type="coo_matrix")
+   cplx = gudhi.SimplexTree()
+   cplx.insert_edges_from_coo_matrix(edges)
+   cplx.expansion(2)
+
+
+This way, you can easily add a call to :func:`~gudhi.SimplexTree.collapse_edges` before the expansion,
+use a different metric to compute the matrix, or other variations.
 
 Distance matrix
 ---------------
@@ -223,54 +210,7 @@ until dimension 1 - one skeleton graph in other words), the output is:
     [4, 6] -> 9.49
     [3, 6] -> 11.00
 
-Example from csv file
-^^^^^^^^^^^^^^^^^^^^^
-
-This example builds the :doc:`RipsComplex <rips_complex_ref>` from the given
-distance matrix in a csv file, and max_edge_length value.
-Then it creates a :doc:`SimplexTree <simplex_tree_ref>` with it.
-
-Finally, it is asked to display information about the Rips complex.
-
-
-.. testcode::
-
-    import gudhi
-    distance_matrix = gudhi.read_lower_triangular_matrix_from_csv_file(csv_file=gudhi.__root_source_dir__ + \
-        '/data/distance_matrix/full_square_distance_matrix.csv')
-    rips_complex = gudhi.RipsComplex(distance_matrix=distance_matrix, max_edge_length=12.0)
-    simplex_tree = rips_complex.create_simplex_tree(max_dimension=1)
-    result_str = 'Rips complex is of dimension ' + repr(simplex_tree.dimension()) + ' - ' + \
-        repr(simplex_tree.num_simplices()) + ' simplices - ' + \
-        repr(simplex_tree.num_vertices()) + ' vertices.'
-    print(result_str)
-    fmt = '%s -> %.2f'
-    for filtered_value in simplex_tree.get_filtration():
-        print(fmt % tuple(filtered_value))
-
-the program output is:
-
-.. testoutput::
-
-    Rips complex is of dimension 1 - 18 simplices - 7 vertices.
-    [0] -> 0.00
-    [1] -> 0.00
-    [2] -> 0.00
-    [3] -> 0.00
-    [4] -> 0.00
-    [5] -> 0.00
-    [6] -> 0.00
-    [2, 3] -> 5.00
-    [4, 5] -> 5.39
-    [0, 2] -> 5.83
-    [0, 1] -> 6.08
-    [1, 3] -> 6.32
-    [1, 2] -> 6.71
-    [5, 6] -> 7.28
-    [2, 4] -> 8.94
-    [0, 3] -> 9.43
-    [4, 6] -> 9.49
-    [3, 6] -> 11.00
+In case this lower triangular matrix is stored in a CSV file, like data/distance_matrix/full_square_distance_matrix.csv in the Gudhi distribution, you can read it with :func:`~gudhi.read_lower_triangular_matrix_from_csv_file`.
 
 Correlation matrix
 ------------------