Rename collapse_edges

src/python/CMakeLists.txt

@@ -55,7 +55,6 @@ if(PYTHONINTERP_FOUND)
     # Cython modules
     set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'off_utils', ")
     set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'simplex_tree', ")
-    set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'edge_collapse', ")
     set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'rips_complex', ")
     set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'cubical_complex', ")
     set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'periodic_cubical_complex', ")
@@ -78,6 +77,7 @@ if(PYTHONINTERP_FOUND)
     set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'weighted_rips_complex', ")
     set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'dtm_rips_complex', ")
     set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'cover_complex', ")
+    set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'flag_filtration', ")
 
     add_gudhi_debug_info("Python version ${PYTHON_VERSION_STRING}")
     add_gudhi_debug_info("Cython version ${CYTHON_VERSION}")
@@ -330,7 +330,7 @@ if(PYTHONINTERP_FOUND)
     file(COPY "gudhi/hera/__init__.py" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi/hera")
     file(COPY "gudhi/datasets" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi" FILES_MATCHING PATTERN "*.py")
     file(COPY "gudhi/sklearn" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi/")
-    file(COPY "gudhi/edge_collapse.py" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi")
+    file(COPY "gudhi/flag_filtration" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi/")
 
 
     # Some files for pip package

src/python/doc/edge_collapse.rst

@@ -5,4 +5,4 @@
 Edge collapse
 =============
 
-.. autofunction:: gudhi.collapse_edges
+.. automodule:: gudhi.flag_filtration.edge_collapse

src/python/doc/rips_complex_user.rst

@@ -54,7 +54,7 @@ regular Rips complex.
 
 Another option which is especially useful if you want to compute persistent homology in "high" dimension (2 or more,
 sometimes even 1), is to build the Rips complex only up to dimension 1 (a graph), then use
-:func:`~gudhi.SimplexTree.collapse_edges` to reduce the size of this graph, and finally call
+:func:`~gudhi.SimplexTree.flag_filtration.edge_collapse.reduce_graph` to reduce the size of this graph, and finally call
 :func:`~gudhi.SimplexTree.expansion` to get a simplicial complex of a suitable dimension to compute its homology. This
 trick gives the same persistence diagram as one would get with a plain use of `RipsComplex`, with a complex that is
 often significantly smaller and thus faster to process.
@@ -157,7 +157,7 @@ or
    cplx.expansion(2)
 
 
-This way, you can easily add a call to :func:`~gudhi.SimplexTree.collapse_edges` before the expansion,
+This way, you can easily add a call to :func:`~gudhi.SimplexTree.flag_filtration.edge_collapse.reduce_graph` before the expansion,
 use a different metric to compute the matrix, or other variations.
 
 Distance matrix

src/python/gudhi/edge_collapse.py → src/python/gudhi/flag_filtration/edge_collapse.py

@@ -7,10 +7,10 @@
 # Modification(s):
 #   - YYYY/MM Author: Description of the modification
 
-from ._edge_collapse import _collapse_edges
+from .._edge_collapse import _collapse_edges
 
 
-def collapse_edges(input_edges, nb_iterations=1):
+def reduce_graph(input_edges, nb_iterations=1):
     """Simplify a clique filtration, preserving its persistent homology.
     The clique filtration is represented as a sparse weighted graph, giving for each edge its 2 vertices
     and its filtration value, and the output is in the same format. An edge may be represented arbitrarily as (i, j)

src/python/test/test_collapse_edges.py

@@ -8,7 +8,7 @@
       - YYYY/MM Author: Description of the modification
 """
 
-from gudhi import collapse_edges
+from gudhi.flag_filtration.edge_collapse import reduce_graph as collapse_edges
 import numpy as np
 from scipy.sparse import coo_matrix
 import pytest