From efc05efebd028201d4bd8c1e0bf237dd087de925 Mon Sep 17 00:00:00 2001 From: Douglas Gregor Date: Wed, 20 Apr 2005 20:08:35 +0000 Subject: [PATCH] Document all of the BGL algorithms that are available in the Python bindings. In some cases, tweak the implementations where they didn't make sense. [SVN r28344] --- doc/bc_clustering.html | 144 +++++++++--- doc/bellman_ford_shortest.html | 38 +++- doc/betweenness_centrality.html | 34 ++- doc/biconnected_components.html | 33 ++- doc/breadth_first_search.html | 8 +- doc/circle_layout.html | 40 +++- doc/connected_components.html | 17 +- doc/cuthill_mckee_ordering.html | 51 +++-- doc/dag_shortest_paths.html | 54 ++++- doc/depth_first_search.html | 23 +- doc/depth_first_visit.html | 40 ++-- doc/dijkstra_shortest_paths.html | 55 +++-- doc/fruchterman_reingold.html | 37 +++- doc/isomorphism.html | 15 +- doc/kamada_kawai_spring_layout.html | 332 ++++++++++++++++++++++++---- doc/king_ordering.html | 54 +++-- doc/kruskal_min_spanning_tree.html | 30 ++- doc/prim_minimum_spanning_tree.html | 34 ++- doc/sequential_vertex_coloring.html | 9 +- doc/strong_components.html | 23 +- doc/table_of_contents.html | 47 ++-- doc/topological_sort.html | 19 +- doc/transitive_closure.html | 18 +- doc/undirected_dfs.html | 25 ++- src/python/isomorphism.cpp | 4 +- src/python/transitive_closure.cpp | 2 +- 26 files changed, 920 insertions(+), 266 deletions(-) diff --git a/doc/bc_clustering.html b/doc/bc_clustering.html index 293ce4dc..5f136942 100644 --- a/doc/bc_clustering.html +++ b/doc/bc_clustering.html @@ -1,27 +1,121 @@ -Function betweenness_centrality_clustering

Function betweenness_centrality_clustering

boost::betweenness_centrality_clustering — Graph clustering based on edge betweenness centrality.

Synopsis

-template<typename MutableGraph, typename Done, typename EdgeCentralityMap, 
-         typename VertexIndexMap> 
-  void betweenness_centrality_clustering(MutableGraph & g, Done done, 
-                                         EdgeCentralityMap edge_centrality, 
-                                         VertexIndexMap vertex_index);
-template<typename MutableGraph, typename Done, typename EdgeCentralityMap> 
-  void betweenness_centrality_clustering(MutableGraph & g, Done done, 
-                                         EdgeCentralityMap edge_centrality);
-template<typename MutableGraph, typename Done> 
-  void betweenness_centrality_clustering(MutableGraph & g, Done done);

Description

Parameters

done

The function object that indicates termination of the algorithm. It must be a ternary function object thats accepts the maximum centrality, the descriptor of the edge that will be removed, and the graph g .

edge_centrality

(UTIL/OUT) The property map that will store the betweenness centrality for each edge. When the algorithm terminates, it will contain the edge centralities for the graph. The type of this property map must model the ReadWritePropertyMap concept. Defaults to an iterator_property_map whose value type is Done::centrality_type and using get(edge_index, g) for the index map.

g

The graph on which clustering will be performed. The type of this parameter (MutableGraph ) must be a model of the VertexListGraph, IncidenceGraph, EdgeListGraph, and Mutable Graph concepts.

vertex_index

(IN) The property map that maps vertices to indices in the range [0, num_vertices(g)). This type of this property map must model the ReadablePropertyMap concept and its value type must be an integral type. Defaults to get(vertex_index, g) .

This algorithm implements graph clustering based on edge betweenness centrality. It is an iterative algorithm, where in each step it compute the edge betweenness centrality (via brandes_betweenness_centrality) and removes the edge with the maximum betweenness centrality. The done function object determines when the algorithm terminates (the edge found when the algorithm terminates will not be removed).

- + + + + + +Function betweenness_centrality_clustering + + +
+
+

(Python)Function +betweenness_centrality_clustering

+

boost::betweenness_centrality_clustering — Graph +clustering based on edge betweenness centrality.

+
+

Synopsis

+
+
+template<typename MutableGraph, typename Done, typename EdgeCentralityMap, 
+         typename VertexIndexMap> 
+  void betweenness_centrality_clustering(MutableGraph & g, Done done, 
+                                         EdgeCentralityMap edge_centrality, 
+                                         VertexIndexMap vertex_index);
+template<typename MutableGraph, typename Done, typename EdgeCentralityMap> 
+  void betweenness_centrality_clustering(MutableGraph & g, Done done, 
+                                         EdgeCentralityMap edge_centrality);
+template<typename MutableGraph, typename Done> 
+  void betweenness_centrality_clustering(MutableGraph & g, Done done);
+
+
+

Description

+
+

Parameters

+
+
done
+
+

The function object that indicates termination of the algorithm. +It must be a ternary function object thats accepts the maximum +centrality, the descriptor of the edge that will be removed, and +the graph g .

+
+
edge_centrality
+
+

(UTIL/OUT) The property map that will store the betweenness +centrality for each edge. When the algorithm terminates, it will +contain the edge centralities for the graph. The type of this +property map must model the ReadWritePropertyMap concept. Defaults +to an iterator_property_map whose +value type is Done::centrality_type +and using get(edge_index, g) for +the index map.

+
+
g
+
+

The graph on which clustering will be performed. The type of +this parameter (MutableGraph ) must +be a model of the VertexListGraph, IncidenceGraph, EdgeListGraph, +and Mutable Graph concepts.

+
+
vertex_index
+
+

(IN) The property map that maps vertices to indices in the range +[0, num_vertices(g)). This type of this property map must model the +ReadablePropertyMap concept and its value type must be an integral +type. Defaults to get(vertex_index, +g) .

+
+
+
+

This algorithm implements graph clustering based on edge +betweenness centrality. It is an iterative algorithm, where in each +step it compute the edge betweenness centrality (via brandes_betweenness_centrality) and +removes the edge with the maximum betweenness centrality. The +done function object determines +when the algorithm terminates (the edge found when the algorithm +terminates will not be removed).

+
+ + + + + +

Where Defined

-<boost/graph/bc_clustering.hpp> - +<boost/graph/bc_clustering.hpp>
- - -
Copyright © 2004 -Douglas Gregor, Indiana University (dgregor@cs.indiana.edu)
-Andrew Lumsdaine, -Indiana University (lums@osl.iu.edu) -
- - + + + + + +
Copyright © 2004Douglas Gregor, +Indiana University (dgregor@cs.indiana.edu)
+Andrew Lumsdaine, Indiana +University (lums@osl.iu.edu)
+ + diff --git a/doc/bellman_ford_shortest.html b/doc/bellman_ford_shortest.html index 6de79d8e..ad7130f4 100644 --- a/doc/bellman_ford_shortest.html +++ b/doc/bellman_ford_shortest.html @@ -20,7 +20,7 @@
-

+

(Python) bellman_ford_shortest_paths

@@ -184,14 +184,18 @@ IN: EdgeListGraph& g A directed or undirected graph whose type must be a model of Edge List Graph. If a root vertex is provided, then the graph must also model - Vertex List Graph. + Vertex List Graph.
+ + Python: The parameter is named graph. IN: Size N
The number of vertices in the graph. The type Size must be an integer type.
- Default: num_vertices(g). + Default: num_vertices(g).
+ + Python: Unsupported parameter.
@@ -205,7 +209,10 @@ IN: weight_map(WeightMap w) Map. The key type for this property map must be the edge descriptor of the graph. The value type for the weight map must be Addable with the distance map's value type.
- Default: get(edge_weight, g) + Default: get(edge_weight, g)
+ + Python: Must be an edge_double_map for the graph.
+ Python default: graph.get_edge_double_map("weight") OUT: predecessor_map(PredecessorMap p_map) @@ -219,7 +226,9 @@ OUT: predecessor_map(PredecessorMap p_map) href="../../property_map/ReadWritePropertyMap.html">Read/Write Property Map which key and vertex types the same as the vertex descriptor type of the graph.
- Default: dummy_property_map + Default: dummy_property_map
+ + Python: Must be a vertex_vertex_map for the graph.
IN/OUT: distance_map(DistanceMap d) @@ -233,7 +242,8 @@ IN/OUT: distance_map(DistanceMap d) distance map must be Less Than Comparable.
Default: get(vertex_distance, - g) + g)
+ Python: Must be a vertex_double_map for the graph.
IN: root_vertex(Vertex s) @@ -254,7 +264,13 @@ IN: visitor(BellmanFordVisitor v) The visitor object is passed by value [1].
- Default: bellman_visitor<null_visitor> + Default: bellman_visitor<null_visitor>
+ + Python: The parameter should be an object that derives from + the BellmanFordVisitor type + of the graph. + IN: distance_combine(BinaryFunction combine) @@ -265,7 +281,9 @@ IN: distance_combine(BinaryFunction combine) type. The result type must be the same as the distance map's value type.
Default:std::plus<D> - with D=typename property_traits<DistanceMap>::value_type. + with D=typename property_traits<DistanceMap>::value_type.
+ + Python: Unsupported parameter. IN: distance_compare(BinaryPredicate compare) @@ -274,7 +292,9 @@ IN: distance_compare(BinaryPredicate compare) that compares distances in the relaxation step. The argument types must match the distance map's value type.
Default: std::less<D> - with D=typename property_traits<DistanceMap>::value_type. + with D=typename property_traits<DistanceMap>::value_type.
+ + Python: Unsupported parameter.

diff --git a/doc/betweenness_centrality.html b/doc/betweenness_centrality.html index 453fc54f..5c7c7a57 100644 --- a/doc/betweenness_centrality.html +++ b/doc/betweenness_centrality.html @@ -7,7 +7,7 @@ C++ Boost -

brandes_betweenness_centrality

+

(Python)brandes_betweenness_centrality

@@ -126,7 +126,9 @@ IN: const Graph& g
   href="VertexListGraph.html">Vertex List Graph and Incidence Graph. When an edge
   centrality map is supplied, it must also model Edge List Graph.
+  href="EdgeListGraph.html">Edge List Graph.

+
+Python: The parameter is named graph.
 
     
 UTIL: IncomingMap incoming 
@@ -141,7 +143,9 @@ UTIL: IncomingMap incoming
   href="../../property_map/iterator_property_map.html">
   iterator_property_map created from a
   std::vector of std::vector<Edge>, where
-  Edge is the edge descriptor type of the graph.
+  Edge is the edge descriptor type of the graph.

+
+  Python: Unsupported parameter.
 
 
 UTIL: DistanceMap distance_map 
@@ -162,7 +166,9 @@ UTIL: DistanceMap distance_map
   std::vector of the WeightMap's value type (or the
   vertices_size_type of the graph when no weight map exists)
   of size num_vertices(g) and using the vertex_index for
-  the index map.
+  the index map.

+
+  Python: Unsupported parameter.
 
 
 UTIL: DependencyMap dependency 
@@ -180,7 +186,9 @@ UTIL: DependencyMap dependency
   iterator_property_map created from a
   std::vector of the CentralityMap's value type of
   size num_vertices(g) and using the vertex_index
-  for the index map.
+  for the index map.

+
+  Python: Unsupported parameter.
 
 
 UTIL: PathCountMap path_count 
@@ -199,7 +207,9 @@ UTIL: PathCountMap path_count
   iterator_property_map created from a
   std::vector of the degree_size_type of the graph of
   size num_vertices(g) and using the vertex_index
-  for the index map.
+  for the index map.

+
+  Python: Unsupported parameter.
 
 
 
Named parameters

@@ -214,7 +224,10 @@ OUT/UTIL: CentralityMap centrality_map
   or rational type.

 
   Default: a dummy_property_map, which requires no
-  work to compute and returns no answer.
+  work to compute and returns no answer.

+  Python: The color map must be a vertex_double_map for
+  the graph.

+  Python default: graph.get_vertex_double_map("centrality")
 
 
 OUT/UTIL: EdgeCentralityMap edge_centrality_map
@@ -228,7 +241,10 @@ OUT/UTIL: EdgeCentralityMap edge_centrality_map
   value type of the CentralityMap property map.

 
   Default: a dummy_property_map, which requires no
-  work to compute and returns no answer.
+  work to compute and returns no answer.

+  Python: The color map must be a edge_double_map for
+  the graph.

+  Python default: graph.get_edge_double_map("centrality")
 
 
 IN: vertex_index_map(VertexIndexMap vertex_index) 
@@ -241,6 +257,7 @@ IN: vertex_index_map(VertexIndexMap vertex_index)
   integer type. The vertex descriptor type of the graph needs to be
   usable as the key type of the map.

   Default: get(vertex_index, g)
+  Python: Unsupported parameter.
 
 
 IN: weight_map(WeightMap w_map)   
@@ -255,6 +272,7 @@ IN: weight_map(WeightMap w_map)
   map. The value type for this map must be
   the same as the value type of the distance map.

   Default: All edge weights are assumed to be equivalent.
+  Python: If supplied, must be an edge_double_map for the graph.
 
 
 
Complexity
 
diff --git a/doc/biconnected_components.html b/doc/biconnected_components.html
index 7032dfb3..ab23523c 100644
--- a/doc/biconnected_components.html
+++ b/doc/biconnected_components.html
@@ -19,8 +19,15 @@
 
 

 
-
-
+

+
+(Python)
+biconnected_components
+
+
+and 
+articulation_points
+

 
 
 template<typename Graph, typename ComponentMap, typename OutputIterator,
@@ -85,7 +92,8 @@ IN: const Graph& g
 

 An undirected graph. The graph type must be a model of Vertex List Graph and Incidence Graph.
+href="IncidenceGraph.html">Incidence Graph.

+Python: The parameter is named graph.
 

 
 OUT: ComponentMap c
@@ -99,15 +107,20 @@ href="../../property_map/WritablePropertyMap.html">Writable Property
 Map. The value type shouch be an integer type, preferably the same
 as the edges_size_type of the graph. The key type must be
 the graph's edge descriptor type.

-Default: dummy_property_map.
+Default: dummy_property_map.

+  Python: Must be an edge_int_map for the graph.

+  Python default: graph.get_edge_int_map("bicomponent")
 
 
-
 OUT: OutputIterator out
 

 The algorithm writes articulation points via this output
 iterator and returns the resulting iterator.

-Default: a dummy iterator that ignores values written to it.
+Default: a dummy iterator that ignores values written to it.

+
+Python: this parameter is not used in Python. Instead, both
+algorithms return a Python list containing the articulation
+points.
 

 
 UTIL/OUT: DiscoverTimeMap discover_time
@@ -123,7 +136,9 @@ UTIL/OUT: DiscoverTimeMap discover_time
   iterator_property_map created from a
   std::vector of vertices_size_type of size
   num_vertices(g) and using get(vertex_index, g) for
-  the index map.
+  the index map.

+
+  Python: Unsupported parameter.
 
 
 UTIL/OUT: LowPointMap lowpt
@@ -140,7 +155,9 @@ UTIL/OUT: LowPointMap lowpt
   iterator_property_map created from a
   std::vector of vertices_size_type of size
   num_vertices(g) and using get(vertex_index, g) for
-  the index map.
+  the index map.

+
+  Python: Unsupported parameter.
 
 
 
Complexity

diff --git a/doc/breadth_first_search.html b/doc/breadth_first_search.html
index cbef3c6f..0aab22c9 100644
--- a/doc/breadth_first_search.html
+++ b/doc/breadth_first_search.html
@@ -19,7 +19,8 @@
 
 

 
-
(Python)
+

+(Python)
 breadth_first_search
 

 
@@ -311,11 +312,6 @@ class used has VertexList and EdgeList set
 to listS.
 

 
-
Python example

-
-
TBD...
-
-
 
See Also

 
 bfs_visitor and
diff --git a/doc/circle_layout.html b/doc/circle_layout.html
index 5a1165e1..004b3c30 100644
--- a/doc/circle_layout.html
+++ b/doc/circle_layout.html
@@ -1,14 +1,44 @@
-Function template circle_graph_layout
boost.png (6897 bytes)HomeLibrariesPeopleFAQMore
Function template circle_graph_layout
boost::circle_graph_layout — Layout the graph with the vertices at the points of a regular n-polygon. 
Synopsis
+Function template circle_graph_layout
boost.png (6897 bytes)HomeLibrariesPeopleFAQMore
(Python)Function template circle_graph_layout
boost::circle_graph_layout — Layout the graph with the vertices at the points of a regular n-polygon. 
Synopsis
 template<typename VertexListGraph, typename PositionMap, typename Radius> 
   void circle_graph_layout(const VertexListGraph & g, PositionMap position, 
-                           Radius radius);
Where Defined
boost/graph/circle_layout.hpp
Description
The distance from the center of the polygon to each point is determined by the radius  parameter. The position  parameter must be an Lvalue Property Map whose value type is a class type containing x  and y  members that will be set to the x  and y  coordinates. 

+                           Radius radius);
Where Defined
boost/graph/circle_layout.hpp
Description
The distance from the center of the polygon to each point is determined by the radius  parameter. The position  parameter must be an Lvalue Property Map whose value type is a class type containing x  and y  members that will be set to the x  and y  coordinates. 
+
+          
Parameters

+IN: const VertexListGraph& g
+

+  The graph object on which the algorithm will be applied.  The type
+  VertexListGraph must be a model of Vertex List Graph.

+Python: The parameter is named graph.
+

+
+OUT: PositionMap position
+

+  This property map is used to store the position of each vertex.  The
+  type PositionMap must be a model of Writable Property
+  Map, with the graph's edge descriptor type as its key type. The
+  value type of this property map should be assignable from the
+  type Radius. 

+
+  Python: The position map must be a vertex_point2d_map for
+  the graph.

+  Python default: graph.get_vertex_point2d_map("position")
+

+
+IN: Radius radius
+

+  This is the radius of the circle on which points will be layed
+  out. It must be compatible with double.

+

+
+

 
                            

 

 Copyright © 2004
-Douglas Gregor, Indiana University (dgregor@cs.indiana.edu)

+Douglas Gregor, Indiana University (dgregor -at- cs.indiana.edu)

 Andrew Lumsdaine,
-Indiana University (lums@osl.iu.edu)
+Indiana University (lums -at- osl.iu.edu)
 

                            
diff --git a/doc/connected_components.html b/doc/connected_components.html
index 9fea0a2a..3c0016ec 100644
--- a/doc/connected_components.html
+++ b/doc/connected_components.html
@@ -22,6 +22,7 @@
 
 

 
+(Python)
 connected_components
 

 
@@ -65,7 +66,9 @@ IN: const Graph& g
 

 An undirected graph. The graph type must be a model of Vertex List Graph and Incidence Graph.
+href="IncidenceGraph.html">Incidence Graph.

+
+Python: The parameter is named graph.
 

 
 OUT: ComponentMap c
@@ -78,7 +81,10 @@ recording the component number in the component property map. The
 href="../../property_map/WritablePropertyMap.html">Writable Property
 Map. The value type shouch be an integer type, preferably the same
 as the vertices_size_type of the graph. The key type must be
-the graph's vertex descriptor type.
+the graph's vertex descriptor type.

+
+  Python: Must be an vertex_int_map for the graph.

+  Python default: graph.get_vertex_int_map("component")
 
 
 
Named Parameters

@@ -96,7 +102,9 @@ UTIL: color_map(ColorMap color)
   iterator_property_map created from a
   std::vector of default_color_type of size
   num_vertices(g) and using the i_map for the index
-  map.
+  map.

+  Python: The color map must be a vertex_color_map for
+  the graph.
 
 
 IN: vertex_index_map(VertexIndexMap i_map)
@@ -110,7 +118,8 @@ IN: vertex_index_map(VertexIndexMap i_map)
   vertex descriptor type of the graph needs to be usable as the key
   type of the map.

 
-  Default: get(vertex_index, g)
+  Default: get(vertex_index, g)

+  Python: Unsupported parameter.
 
 
 
diff --git a/doc/cuthill_mckee_ordering.html b/doc/cuthill_mckee_ordering.html
index d2e34e76..90582818 100644
--- a/doc/cuthill_mckee_ordering.html
+++ b/doc/cuthill_mckee_ordering.html
@@ -20,7 +20,8 @@
 
 

 
-

+

+(Python)
 cuthill_mckee_ordering
 

 
@@ -144,23 +145,28 @@ For version 1:
 
 
  •  IncidenceGraph& g  (IN) 
     
   An undirected graph. The graph's type must be a model of     IncidenceGraph.
+  href="./IncidenceGraph.html">IncidenceGraph.
    
+  Python: The parameter is named graph.
 
 
  •  vertex_descriptor s  (IN) 
    
-  The starting vertex.
+  The starting vertex.
    
+  Python: Unsupported parameter.
 
 
  •  OutputIterator inverse_permutation  (OUT) 
     
   The new vertex ordering. The vertices are written to the output
-  iterator in their new order. 
+  iterator in their new order.
    
+  Python: This parameter is unused in Python. The new vertex
+  ordering is returned as a Python list.
   
 
  •  ColorMap color_map  (WORK) 
    
   Used internally to keep track of the progress of the algorithm
-  (to avoid visiting the same vertex twice).
+  (to avoid visiting the same vertex twice).
    
+  Python: Unsupported parameter.
 
 
  •  DegreeMap degree_map  (IN) 
    
-  This must map vertices to their degree.
-
+  This must map vertices to their degree.
    
+  Python: Unsupported parameter.
 
 
 
@@ -170,20 +176,24 @@ For version 2:
 
 
  •  VertexListGraph& g  (IN) 
     
   An undirected graph. The graph's type must be a model of VertexListGraph.
+  href="./VertexListGraph.html">VertexListGraph.
    
+  Python: The parameter is named graph.
 
 
  •  
   OutputIterator inverse_permutation  (OUT) 
     
   The new vertex ordering. The vertices are written to the
-  output iterator in their new order.
+  output iterator in their new order.
    
+  Python: This parameter is unused in Python. The new vertex
+  ordering is returned as a Python list.
 
 
  •  ColorMap color_map  (WORK) 
    
   Used internally to keep track of the progress of the algorithm
-  (to avoid visiting the same vertex twice).
+  (to avoid visiting the same vertex twice).
    
+  Python: Unsupported parameter.
 
 
  •  DegreeMap degree_map  (IN) 
    
-  This must map vertices to their degree.
-
+  This must map vertices to their degree.
    
+  Python: Unsupported parameter.
 
 
 
@@ -193,23 +203,28 @@ For version 3:
 
 
  •  IncidenceGraph& g  (IN) 
     
   An undirected graph. The graph's type must be a model of IncidenceGraph.
+  href="./IncidenceGraph.html">IncidenceGraph.
    
+  Python: The parameter is named graph.
 
 
  •   std::deque< typename graph_traits<Graph>::vertex_descriptor > vertex_queue   (IN) 
    
-  The deque containing the starting vertices for each component.
+  The deque containing the starting vertices for each component.
    
+  Python: Unsupported parameter.
 
 
  •  OutputIterator inverse_permutation  (OUT) 
     
   The new vertex ordering. The vertices are written to the output
-  iterator in their new order. 
+  iterator in their new order.
    
+  Python: This parameter is unused in Python. The new vertex
+  ordering is returned as a Python list.
   
 
  •  ColorMap color_map  (WORK) 
    
   Used internally to keep track of the progress of the algorithm
-  (to avoid visiting the same vertex twice).
+  (to avoid visiting the same vertex twice).
    
+  Python: Unsupported parameter.
 
 
  •  DegreeMap degree_map  (IN) 
    
-  This must map vertices to their degree.
-
+  This must map vertices to their degree.
    
+  Python: Unsupported parameter.
 
 
 
diff --git a/doc/dag_shortest_paths.html b/doc/dag_shortest_paths.html
index 7e061072..4e969b58 100644
--- a/doc/dag_shortest_paths.html
+++ b/doc/dag_shortest_paths.html
@@ -20,6 +20,7 @@
 
    
 
 
    
+(Python)
 dag_shortest_paths
 
    
 
@@ -80,13 +81,17 @@ algorithm's event points.
    
 IN: const VertexListGraph& g 
 
    
   The graph object on which the algorithm will be applied.
-  The type VertexListGraph must be a model of \concept{VertexListGraph}.
+  The type VertexListGraph must be a model of \concept{VertexListGraph}.
    
+
+  Python: The parameter is named graph.
 
    
 
 IN: vertex_descriptor s 
 
    
   The source vertex. All distance will be calculated from this vertex,
-  and the shortest paths tree will be rooted at this vertex.
+  and the shortest paths tree will be rooted at this vertex.
    
+
+  Python: The parameter is named root_vertex.
 
    
 
 
    Named Parameters
    
@@ -99,7 +104,10 @@ IN: weight_map(WeightMap w_map)
   the graph needs to be usable as the key type for the weight
   map. The value type for the map must be
   Addable with the value type of the distance map.
    
-  Default:  get(edge_weight, g)
+  Default:  get(edge_weight, g)
    
+  Python: Must be an edge_double_map for the graph.
    
+  Python default: graph.get_edge_double_map("weight")
+
 
 
 IN: vertex_index_map(VertexIndexMap i_map) 
@@ -111,7 +119,9 @@ IN: vertex_index_map(VertexIndexMap i_map)
   Readable Property Map. The value type of the map must be an
   integer type. The vertex descriptor type of the graph needs to be
   usable as the key type of the map.
    
-  Default: get(vertex_index, g)
+  Default: get(vertex_index, g)
    
+
+  Python: Unsupported parameter.
 
 
 OUT: predecessor_map(PredecessorMap p_map) 
@@ -125,7 +135,8 @@ OUT: predecessor_map(PredecessorMap p_map)
   href="../../property_map/ReadWritePropertyMap.html">Read/Write
   Property Map which key and vertex types the same as the vertex
   descriptor type of the graph.
    
-  Default: dummy_property_map
+  Default: dummy_property_map
    
+  Python: Must be a vertex_vertex_map for the graph.
    
 
 
 UTIL/OUT: distance_map(DistanceMap d_map) 
@@ -150,7 +161,9 @@ UTIL/OUT: distance_map(DistanceMap d_map)
   iterator_property_map created from a
   std::vector of the WeightMap's value type of size
   num_vertices(g) and using the i_map for the index
-  map.
+  map.
    
+
+  Python: Must be a vertex_double_map for the graph.
 
 
 IN: distance_compare(CompareFunction cmp) 
@@ -164,7 +177,9 @@ IN: distance_compare(CompareFunction cmp)
 
   Default:
   std::less<D> with D=typename
-  property_traits<DistanceMap>::value_type
+  property_traits<DistanceMap>::value_type
    
+
+  Python: Unsupported parameter.
 
 
 IN: distance_combine(CombineFunction cmb) 
@@ -179,7 +194,9 @@ IN: distance_combine(CombineFunction cmb)
   type as the distance value type.
    
 
   Default: std::plus<D> with
-   D=typename property_traits<DistanceMap>::value_type
+   D=typename property_traits<DistanceMap>::value_type
    
+
+  Python: Unsupported parameter.
 
 
 IN: distance_inf(D inf) 
@@ -187,7 +204,9 @@ IN: distance_inf(D inf)
   The inf object must be the greatest value of any D object.
   That is, compare(d, inf) == true for any d != inf.
   The type D is the value type of the DistanceMap.
    
-  Default: std::numeric_limits<D>::max()
+  Default: std::numeric_limits<D>::max()
    
+
+  Python: Unsupported parameter.
 
 
 IN: distance_zero(D zero) 
@@ -196,7 +215,9 @@ IN: distance_zero(D zero)
   Monoid formed by the distance values
   and the combine function object.
   The type \code{D} is the value type of the \code{DistanceMap}
-  Default: D()
+  Default: D()
    
+
+  Python: Unsupported parameter.
 
 
 UTIL/OUT: color_map(ColorMap c_map) 
@@ -215,7 +236,11 @@ UTIL/OUT: color_map(ColorMap c_map)
   href="../../property_map/iterator_property_map.html">
   iterator_property_map created from a std::vector
   of default_color_type of size num_vertices(g) and
-  using the i_map for the index map.
+  using the i_map for the index map.
    
+
+  Python: The color map must be a vertex_color_map for
+  the graph.
+
 
   
 OUT: visitor(DijkstraVisitor v)  
@@ -226,7 +251,12 @@ OUT: visitor(DijkstraVisitor v)
   Dijkstra Visitor concept. 
  The visitor object is passed by value [1].
    
-  Default: dijkstra_visitor<null_visitor>
+  Default: dijkstra_visitor<null_visitor>
    
+
+  Python: The parameter should be an object that derives from
+  the DijkstraVisitor type
+  of the graph.
 
 
 
diff --git a/doc/depth_first_search.html b/doc/depth_first_search.html
index 6dc1f05e..17b7f01e 100644
--- a/doc/depth_first_search.html
+++ b/doc/depth_first_search.html
@@ -19,7 +19,7 @@
 
 
    
 
-
    
+
    (Python)
 depth_first_search
 
    
 
@@ -164,7 +164,9 @@ IN: Graph& g
 
    
   A directed graph. The graph type must
   be a model of Incidence Graph
-  and Vertex List Graph.
+  and Vertex List Graph.
    
+
+  Python: The parameter is named graph.
 
    
 
 
@@ -176,7 +178,11 @@ IN: visitor(DFSVisitor vis)
   event-points specified by the DFS
   Visitor concept. The visitor object is passed by value [1]. 
     Default:
-  dfs_visitor<null_visitor>
+  dfs_visitor<null_visitor>
    
+
+  Python: The parameter should be an object that derives from
+  the DFSVisitor type of
+  the graph.
 
 
 UTIL/OUT: color_map(ColorMap color)
@@ -192,7 +198,10 @@ UTIL/OUT: color_map(ColorMap color)
   iterator_property_map created from a
   std::vector of default_color_type of size
   num_vertices(g) and using the i_map for the index
-  map.
+  map.
    
+
+   Python: The color map must be a vertex_color_map for
+  the graph.
 
 
 IN: root_vertex(typename
@@ -201,7 +210,7 @@ graph_traits<VertexListGraph>::vertex_descriptor start)
   This specifies the vertex that the depth-first search should
   originate from. The type is the type of a vertex descriptor for the
   given graph.
    
-  Default: *vertices(g).first
+  Default: *vertices(g).first
    
 
 
 IN: vertex_index_map(VertexIndexMap i_map)
@@ -215,7 +224,9 @@ IN: vertex_index_map(VertexIndexMap i_map)
   vertex descriptor type of the graph needs to be usable as the key
   type of the map.
    
 
-  Default: get(vertex_index, g)
+  Default: get(vertex_index, g)
    
+
+  Python: Unsupported parameter.
 
 
 
    
diff --git a/doc/depth_first_visit.html b/doc/depth_first_visit.html
index 8459be58..afae5dd1 100644
--- a/doc/depth_first_visit.html
+++ b/doc/depth_first_visit.html
@@ -20,6 +20,7 @@
 
    
 
 
    
+(Python)
 depth_first_visit
 
    
 
@@ -54,16 +55,6 @@ actions are taken at each event-point within the algorithm.
 The ColorMap is used by the algorithm to keep track
 of which vertices have been visited.
 
-
    
-The func parameter for the second variant of the algorithm is used to
-prevent the search to proceed past some vertices. It should be functional
-object callable with two parameters - the descriptor of vertex and the
-graph instance - and returing bool. 
-
-
    The call is made immediately after call to 'discover_vertex' visitor
-event. If true is returned, the out edges of the vertex are not
-examined, as if they don't exist.
-
 
    The second variant can be used, for example, to find all marked vertices
 reachable from a start vertex by a path which does not contain any another
 marked vertices.
@@ -81,12 +72,16 @@ marked vertices.
 IN IncidenceGraph& g
 
    
 A directed or undirected graph. The graph's type must be a model of
-Incidence Graph. 
+Incidence Graph.
    
+
+Python: The parameter is named graph.
 
    
 
 IN: vertex_descriptor s
 
    
-  The source vertex from which to start the search.  
+  The source vertex from which to start the search.
    
+
+  Python: The parameter is named root_vertex.
 
    
 
 IN: DFSVisitor visitor
@@ -94,7 +89,11 @@ IN: DFSVisitor visitor
   A visitor object that is invoked inside the algorithm at the
   event-points specified by the DFS
   Visitor concept. The visitor object is passed by value [1].
+  href="#1">[1].
    
+
+  Python: The parameter should be an object that derives from
+  the DFSVisitor type of
+  the graph.
 
 
 UTIL: ColorMap color
@@ -104,9 +103,22 @@ UTIL: ColorMap color
   href="../../property_map/ReadWritePropertyMap.html">Read/Write
   Property Map and its key type must be the graph's vertex
   descriptor type and the value type of the color map map must model
-  Color Value.
+  Color Value.
    
+
+  Python: The color map must be a vertex_color_map for
+  the graph.
 
 
+IN: TerminatorFunc func
+
    
+A function object callable with two parameters - the descriptor of
+vertex and the graph instance - and returning bool. The call is made
+immediately after call to 'discover_vertex' visitor
+event. If true is returned, the out edges of the vertex are
+not examined, as if they don't exist.
    
+
+  Python: Unsupported parameter.
+
    
 
    
 
 
    Complexity
    
diff --git a/doc/dijkstra_shortest_paths.html b/doc/dijkstra_shortest_paths.html
index 20453f49..ba59d342 100644
--- a/doc/dijkstra_shortest_paths.html
+++ b/doc/dijkstra_shortest_paths.html
@@ -19,7 +19,7 @@
 
 
    
 
-
    
+
    (Python)
 dijkstra_shortest_paths
 
    
 
@@ -184,13 +184,17 @@ IN: const Graph& g
   The graph object on which the algorithm will be applied.
   The type Graph must be a model of 
   Vertex List Graph
-  and Incidence Graph.
+  and Incidence Graph.
    
+
+  Python: The parameter is named graph.
 
 
 IN: vertex_descriptor s 
 
    
   The source vertex. All distance will be calculated from this vertex,
-  and the shortest paths tree will be rooted at this vertex.
+  and the shortest paths tree will be rooted at this vertex.
    
+
+  Python: The parameter is named root_vertex.
 
    
 
 
    Named Parameters
    
@@ -206,7 +210,10 @@ IN: weight_map(WeightMap w_map)
   the graph needs to be usable as the key type for the weight
   map. The value type for this map must be
   the same as the value type of the distance map.
    
-  Default:  get(edge_weight, g)
+  Default:  get(edge_weight, g)
    
+
+  Python: Must be an edge_double_map for the graph.
    
+  Python default: graph.get_edge_double_map("weight")
 
 
 IN: vertex_index_map(VertexIndexMap i_map) 
@@ -220,7 +227,9 @@ IN: vertex_index_map(VertexIndexMap i_map)
   Readable Property Map. The value type of the map must be an
   integer type. The vertex descriptor type of the graph needs to be
   usable as the key type of the map.
    
-  Default: get(vertex_index, g)
+  Default: get(vertex_index, g)
    
+
+  Python: Unsupported parameter.
 
 
 OUT: predecessor_map(PredecessorMap p_map) 
@@ -234,7 +243,9 @@ OUT: predecessor_map(PredecessorMap p_map)
   href="../../property_map/ReadWritePropertyMap.html">Read/Write
   Property Map whose key and value types are the same as the vertex
   descriptor type of the graph.
    
-  Default: dummy_property_map
+  Default: dummy_property_map
    
+
+  Python: Must be a vertex_vertex_map for the graph.
    
 
 
 UTIL/OUT: distance_map(DistanceMap d_map) 
@@ -259,7 +270,9 @@ UTIL/OUT: distance_map(DistanceMap d_map)
   iterator_property_map created from a
   std::vector of the WeightMap's value type of size
   num_vertices(g) and using the i_map for the index
-  map.
+  map.
    
+
+  Python: Must be a vertex_double_map for the graph.
    
 
 
 IN: distance_compare(CompareFunction cmp) 
@@ -273,7 +286,9 @@ IN: distance_compare(CompareFunction cmp)
 
   Default:
   std::less<D> with D=typename
-  property_traits<DistanceMap>::value_type
+  property_traits<DistanceMap>::value_type
    
+
+  Python: Unsupported parameter.
 
 
 IN: distance_combine(CombineFunction cmb) 
@@ -288,7 +303,9 @@ IN: distance_combine(CombineFunction cmb)
   type as the distance value type.
    
 
   Default: std::plus<D> with
-   D=typename property_traits<DistanceMap>::value_type
+   D=typename property_traits<DistanceMap>::value_type
    
+
+  Python: Unsupported parameter.
 
 
 IN: distance_inf(D inf) 
@@ -296,7 +313,9 @@ IN: distance_inf(D inf)
   The inf object must be the greatest value of any D object.
   That is, compare(d, inf) == true for any d != inf.
   The type D is the value type of the DistanceMap.
    
-  Default: std::numeric_limits<D>::max()
+  Default: std::numeric_limits<D>::max()
    
+
+  Python: Unsupported parameter.
 
 
 IN: distance_zero(D zero) 
@@ -306,7 +325,9 @@ IN: distance_zero(D zero)
   and the combine function object.
   The type D is the value type of the DistanceMap.
    
   Default: D()with
-   D=typename property_traits<DistanceMap>::value_type
+   D=typename property_traits<DistanceMap>::value_type
    
+
+  Python: Unsupported parameter.
 
 
 UTIL/OUT: color_map(ColorMap c_map) 
@@ -325,7 +346,10 @@ UTIL/OUT: color_map(ColorMap c_map)
   href="../../property_map/iterator_property_map.html">
   iterator_property_map created from a std::vector
   of default_color_type of size num_vertices(g) and
-  using the i_map for the index map.
+  using the i_map for the index map.
    
+
+  Python: The color map must be a vertex_color_map for
+  the graph.
 
   
 OUT: visitor(DijkstraVisitor v)  
@@ -336,7 +360,12 @@ OUT: visitor(DijkstraVisitor v)
   Dijkstra Visitor concept. 
  The visitor object is passed by value [2].
    
-  Default: dijkstra_visitor<null_visitor>
+  Default: dijkstra_visitor<null_visitor>
    
+
+  Python: The parameter should be an object that derives from
+  the DijkstraVisitor type
+  of the graph.
 
 
 
diff --git a/doc/fruchterman_reingold.html b/doc/fruchterman_reingold.html
index a4f56f1f..007f7657 100644
--- a/doc/fruchterman_reingold.html
+++ b/doc/fruchterman_reingold.html
@@ -7,7 +7,7 @@
      ALT="C++ Boost" width="277" height="86"> 
 
 
    
-
+(Python)
 fruchterman_reingold_force_directed_layout
 
    • 
 
@@ -78,7 +78,8 @@ IN: const Graph& g
 
    
   The graph object on which the algorithm will be applied.
   The type Graph must be a model of 
-  Vertex And Edge List Graph.
+  Vertex And Edge List Graph.
    
+  Python: This parameter is named graph in Python.
 
    
 
 IN/OUT: PositionMap position
@@ -91,7 +92,10 @@ IN/OUT: PositionMap position
   Map such that the vertex descriptor type of Graph is
   convertible to its key type. Its value type must be a structure
   with fields x and y, representing the coordinates
-  of the vertex.
+  of the vertex.
    
+  Python: The position map must be a vertex_point2d_map for
+  the graph.
    
+  Python default: graph.get_vertex_point2d_map("position")
 
 
 IN: Dim width
@@ -118,7 +122,8 @@ parameters: the edge descriptor, k, the distance between the
 vertices, and the graph. k is the square root of the ratio
 of the display area to the number of vertices. 
    
 Default: square_distance_attractive_force(), which
-computes the attractive force as dist2/k.
+computes the attractive force as dist2/k.
    
+Python: Any callable Python object that matches the signature will suffice.
 
 
 IN: repulsive_force(RepulsiveForce fr)
@@ -129,7 +134,8 @@ parameters: the two vertex descriptors, k, the distance between the
 vertices, and the graph. k is the square root of the ratio
 of the display area to the number of vertices. 
    
 Default: square_distance_repsulsive_force(), which
-computes the repulsive force as k2/dist.
+computes the repulsive force as k2/dist.
    
+Python: Any callable Python object that matches the signature will suffice.
 
 
 IN: force_pairs(ForcePairs fp)
@@ -150,7 +156,8 @@ layouts for disconnected graphs, but is not better overall: pass
 make_grid_force_pairs(width, height, position, g) as this
 parameter to use the grid variant. Other enumeration strategies may
 yield better results for particular graphs. 
    
-Default: make_grid_force_pairs(width, height, position, g)
+Default: make_grid_force_pairs(width, height, position, g)
    
+Python: Unsupported parameter.
 
 
 IN: cooling(Cooling cool)
@@ -162,7 +169,8 @@ takes no arguments) and returns the temperature for the current
 iteration. When the returned temperature is zero, the algorithm
 terminates. Cooling schedules should begin with some initial
 temperature and gradually reduce the temperature to zero.
    
-Default: linear_cooling<double>(100)
+Default: linear_cooling<double>(100)
    
+Python: Any callable Python object that matches the signature will suffice.
 
 
 UTIL: displacement_map(DisplacementMap displacement)
@@ -171,7 +179,8 @@ The displacement map is used to compute the amount by which each
 vertex will move in each step. The DisplacementMap type
 carries the same requirements as the PositionMap type.
    
 Default: An iterator_property_map with a value type
-of simple_point<double> and using the given vertex index map.
+of simple_point<double> and using the given vertex index map.
    
+Python: Unsupported parameter.
 
 
 IN: vertex_index_map(VertexIndexMap i_map) 
@@ -184,7 +193,17 @@ IN: vertex_index_map(VertexIndexMap i_map)
   Map. The value type of the map must be an integer type. The
   vertex descriptor type of the graph needs to be usable as the key
   type of the map.
    
-Default: get(vertex_index, g)
+Default: get(vertex_index, g)
    
+Python: Unsupported parameter.
+
+
+Python IN: bool progressive
+
    
+  When false, performs a random layout of the graph before
+  running the Fruchterman-Reingold algorithm. If true, the
+  algorithm is executing starting with the vertex configuration in
+  the position map.
    
+  Default: False.
 
    
 
 
    Complexity
    
diff --git a/doc/isomorphism.html b/doc/isomorphism.html
index a015a391..81852255 100644
--- a/doc/isomorphism.html
+++ b/doc/isomorphism.html
@@ -19,7 +19,8 @@
 
 
    
 
-
    
+
    
+(Python)
 isomorphism
 
    
 
@@ -100,7 +101,8 @@ be a     Read/Write
 Property Map.
     Default: an iterator_property_map
 constructed from a std::vector of graph 2's vertex
-descriptor type and the vertex index map for graph 1.
+descriptor type and the vertex index map for graph 1.
    
+Python: Must be a vertex_vertex_map for the first graph.
 
 
 IN: vertex_invariant(VertexInvariant i)
@@ -114,7 +116,8 @@ graph, and the result type is an integer. The vertex invariant must
 work with the types for both graph 1 and graph 2 and therefore may need
 to have a templated operator().
 
    
-Default: degree_vertex_invariant
+Default: degree_vertex_invariant
    
+Python: Any callable Python object will suffice.
 
 
 IN: vertex_index1_map(VertexIndex1Map i1_map)
@@ -127,7 +130,8 @@ href="../../property_map/ReadablePropertyMap.html">Readable Property
 Map. The value type of the map must be an integer type. The vertex
 descriptor type of graph 1 needs to be usable as the key type of the
 map.
    
-Default: get(vertex_index, g1)
+Default: get(vertex_index, g1)
    
+Python: Unsupported parameter.
 
 
 IN: vertex_index2_map(VertexIndex2Map i2_map)
@@ -140,7 +144,8 @@ href="../../property_map/ReadablePropertyMap.html">Readable Property
 Map. The value type of the map must be an integer type. The vertex
 descriptor type of graph 2 needs to be usable as the key type of the
 map.
    
-Default: get(vertex_index, g2)
+Default: get(vertex_index, g2)
    
+Python: Unsupported parameter.
 
 
 
diff --git a/doc/kamada_kawai_spring_layout.html b/doc/kamada_kawai_spring_layout.html
index 30c77d9a..b8e2fd16 100644
--- a/doc/kamada_kawai_spring_layout.html
+++ b/doc/kamada_kawai_spring_layout.html
@@ -1,44 +1,294 @@
-Function kamada_kawai_spring_layout
    boost.png (6897 bytes)HomeLibrariesPeopleFAQMore
    Function kamada_kawai_spring_layout
    boost::kamada_kawai_spring_layout — Kamada-Kawai spring layout for connected, undirected graphs. 
    Synopsis
    -template<typename Graph, typename PositionMap, typename WeightMap, typename T, 
-         bool EdgeOrSideLength, typename Done, typename VertexIndexMap, 
-         typename DistanceMatrix, typename SpringStrengthMatrix, 
-         typename PartialDerivativeMap> 
-  bool kamada_kawai_spring_layout(const Graph & g, PositionMap position, 
-                                  WeightMap weight, 
-                                  unspecified edge_or_side_length, Done done, 
-                                  typename property_traits< WeightMap >::value_type spring_constant, 
-                                  VertexIndexMap index, 
-                                  DistanceMatrix distance, 
-                                  SpringStrengthMatrix spring_strength, 
-                                  PartialDerivativeMap partial_derivatives);
-template<typename Graph, typename PositionMap, typename WeightMap, typename T, 
-         bool EdgeOrSideLength, typename Done, typename VertexIndexMap> 
-  bool kamada_kawai_spring_layout(const Graph & g, PositionMap position, 
-                                  WeightMap weight, 
-                                  unspecified edge_or_side_length, Done done, 
-                                  typename property_traits< WeightMap >::value_type spring_constant, 
-                                  VertexIndexMap index);
-template<typename Graph, typename PositionMap, typename WeightMap, typename T, 
-         bool EdgeOrSideLength, typename Done> 
-  bool kamada_kawai_spring_layout(const Graph & g, PositionMap position, 
-                                  WeightMap weight, 
-                                  unspecified edge_or_side_length, Done done, 
-                                  typename property_traits< WeightMap >::value_type spring_constant = typename property_traits< WeightMap >::value_type(1));
-template<typename Graph, typename PositionMap, typename WeightMap, typename T, 
-         bool EdgeOrSideLength> 
-  bool kamada_kawai_spring_layout(const Graph & g, PositionMap position, 
-                                  WeightMap weight, 
-                                  unspecified edge_or_side_length);
    Where Defined
    boost/graph/kamada_kawai_spring_layout.hpp
    Description
    Parameters
    distance
    (UTIL/OUT) will be used to store the distance from every vertex to every other vertex, which is computed in the first stages of the algorithm. This value's type must be a model of BasicMatrix with value type equal to the value type of the weight map. The default is a a vector of vectors.
    done
    (IN) is a 4-argument function object that is passed the current value of delta_p (i.e., the energy of vertex p ), the vertex p , the graph g , and a boolean flag indicating whether delta_p  is the maximum energy in the system (when true ) or the energy of the vertex being moved. Defaults to layout_tolerance  instantiated over the value type of the weight map.
    edge_or_side_length
    (IN) provides either the unit length e  of an edge in the layout or the length of a side s  of the display area, and must be either boost::edge_length(e)  or boost::side_length(s) , respectively.
    g
    (IN) must be a model of Vertex List Graph, Edge List Graph, and Incidence Graph and must be connected and undirected.
    index
    (IN) is a mapping from vertices to index values between 0 and num_vertices(g) . The default is get(vertex_index,g) .
    partial_derivatives
    (UTIL) will be used to store the partial derivates of each vertex with respect to the x  and y  coordinates. This must be a Read/Write Property Map whose value type is a pair with both types equivalent to the value type of the weight map. The default is an iterator property map.
    position
    (OUT) must be a model of Lvalue Property Map, where the value type is a class containing fields x  and y  that will be set to the x  and y  coordinates of each vertex.
    spring_constant
    (IN) is the constant multiplied by each spring's strength. Larger values create systems with more energy that can take longer to stabilize; smaller values create systems with less energy that stabilize quickly but do not necessarily result in pleasing layouts. The default value is 1.
    spring_strength
    (UTIL/OUT) will be used to store the strength of the spring between every pair of vertices. This value's type must be a model of BasicMatrix with value type equal to the value type of the weight map. The default is a a vector of vectors.
    weight
    (IN) must be a model of Readable Property Map, which provides the weight of each edge in the graph g .
    This algorithm [57] performs graph layout (in two dimensions) for connected, undirected graphs. It operates by relating the layout of graphs to a dynamic spring system and minimizing the energy within that system. The strength of a spring between two vertices is inversely proportional to the square of the shortest distance (in graph terms) between those two vertices. Essentially, vertices that are closer in the graph-theoretic sense (i.e., by following edges) will have stronger springs and will therefore be placed closer together.
    Prior to invoking this algorithm, it is recommended that the vertices be placed along the vertices of a regular n-sided polygon via circle_layout.
    Returns:
+
+
+
+
+
+Function kamada_kawai_spring_layout
+
+
+
+
+
+
+
+
+
+
+
+
    +"boost.pngHomeLibrariesPeopleFAQMore
    
+
    
+
    
+
    
+
    
+
    (Python)Function kamada_kawai_spring_layout
    
+
    boost::kamada_kawai_spring_layout — Kamada-Kawai spring
+layout for connected, undirected graphs.
    
+
    
+
    Synopsis
    
+
    
+
    +template<typename Graph, typename PositionMap, typename WeightMap, typename T, 
+         bool EdgeOrSideLength, typename Done, typename VertexIndexMap, 
+         typename DistanceMatrix, typename SpringStrengthMatrix, 
+         typename PartialDerivativeMap> 
+  bool kamada_kawai_spring_layout(const Graph & g, PositionMap position, 
+                                  WeightMap weight, 
+                                  unspecified edge_or_side_length, Done done, 
+                                  typename property_traits< WeightMap >::value_type spring_constant, 
+                                  VertexIndexMap index, 
+                                  DistanceMatrix distance, 
+                                  SpringStrengthMatrix spring_strength, 
+                                  PartialDerivativeMap partial_derivatives);
+template<typename Graph, typename PositionMap, typename WeightMap, typename T, 
+         bool EdgeOrSideLength, typename Done, typename VertexIndexMap> 
+  bool kamada_kawai_spring_layout(const Graph & g, PositionMap position, 
+                                  WeightMap weight, 
+                                  unspecified edge_or_side_length, Done done, 
+                                  typename property_traits< WeightMap >::value_type spring_constant, 
+                                  VertexIndexMap index);
+template<typename Graph, typename PositionMap, typename WeightMap, typename T, 
+         bool EdgeOrSideLength, typename Done> 
+  bool kamada_kawai_spring_layout(const Graph & g, PositionMap position, 
+                                  WeightMap weight, 
+                                  unspecified edge_or_side_length, Done done, 
+                                  typename property_traits< WeightMap >::value_type spring_constant = typename property_traits< WeightMap >::value_type(1));
+template<typename Graph, typename PositionMap, typename WeightMap, typename T, 
+         bool EdgeOrSideLength> 
+  bool kamada_kawai_spring_layout(const Graph & g, PositionMap position, 
+                                  WeightMap weight, 
+                                  unspecified edge_or_side_length);
+
    
+
    
+
    Where Defined
    
+boost/graph/kamada_kawai_spring_layout.hpp
 
-    true  if layout was successful or false  if a negative weight cycle was detected or the graph is disconnected. 
    
+
    Description
    
+
    This algorithm [57] performs graph layout (in two
+dimensions) for connected, undirected graphs. It operates by
+relating the layout of graphs to a dynamic spring system and
+minimizing the energy within that system. The strength of a spring
+between two vertices is inversely proportional to the square of the
+shortest distance (in graph terms) between those two vertices.
+Essentially, vertices that are closer in the graph-theoretic sense
+(i.e., by following edges) will have stronger springs and will
+therefore be placed closer together.
    
+
    Prior to invoking this algorithm, it is recommended that the
+vertices be placed along the vertices of a regular n-sided polygon
+via circle_layout.
    
+
    
+Returns: true if layout
+was successful or false if a
+negative weight cycle was detected or the graph is
+disconnected.
    
 
-    
-
-
    Copyright © 2004
-Douglas Gregor, Indiana University (dgregor -at cs.indiana.edu)
    
-Andrew Lumsdaine,
-Indiana University (lums@osl.iu.edu)
-
    
+
    Parameters
    
+IN: const Graph& g
+
    
+  The graph, whose type Graph must model the 
+  VertexListGraph, 
+  EdgeListGraph, and 
+  IncidenceGraph concepts. The
+  graph must be undirected and connected. 
    
+  Python: This parameter is named graph in Python.
+
    
 
-    
+OUT: PositionMap position
+
    
+  This property map is used to store the position of each vertex.  The
+  type PositionMap must be a model of Writable Property
+  Map, with the graph's edge descriptor type as its key type.
    
+
+  Python: The position map must be a vertex_point2d_map for
+  the graph.
    
+  Python default: graph.get_vertex_point2d_map("position")
+
    
+
+IN: weight_map(WeightMap w_map)   
+
    
+  The weight or ``length'' of each edge in the graph. The weights
+  must all be non-negative, and the algorithm will throw a
+  negative_edge 
+  exception is one of the edges is negative.
+  The type WeightMap must be a model of
+  Readable Property Map. The edge descriptor type of
+  the graph needs to be usable as the key type for the weight
+  map. The value type for this map must be
+  the same as the value type of the distance map.
    
+  Default:  get(edge_weight, g)
    
+
+  Python: Must be an edge_double_map for the graph.
    
+  Python default: graph.get_edge_double_map("weight")
+
    
+
+IN: EdgeOrSideLength edge_or_side_length
+
    
+Provides either the unit length e of
+an edge in the layout or the length of a side s of the display area, and must be
+either boost::edge_length(e) or boost::side_length(s) , respectively.
+Python: In Python, this value always refers to the side length
+and may only be a double.
+
    
+
+IN: Done done
+
    
+A 4-argument function object that is passed the current
+value of delta_p (i.e., the energy of vertex p ), the vertex p , the graph g , and a boolean flag indicating whether
+delta_p is the maximum energy in
+the system (when true ) or the
+energy of the vertex being moved.
+Default: layout_tolerance instantiated over the
+value type of the weight map.
    
+Python: Any callable Python object with an appropriate signature suffices. 
+
    
+
+IN: typename property_traits<WeightMap>::value_type spring_constant
+
    
+The constant multiplied by each spring's strength.
+Larger values create systems with more energy that can take longer
+to stabilize; smaller values create systems with less energy that
+stabilize quickly but do not necessarily result in pleasing
+layouts.
    
+Default: 1.
+
    
+
+IN: VertexIndexMap index
+
    
+As a mapping from vertices to index values between 0 and
+num_vertices(g) .
    
+Default:get(vertex_index,g).
    
+Python: Unsupported parameter.
+
    
+
+UTIL/OUT: DistanceMap distance
+
    
+This parameter will be used to store the distance from every vertex to
+every other vertex, which is computed in the first stages of the
+algorithm. This value's type must be a model of BasicMatrix with value type equal to
+the value type of the weight map.
    
+Default: A vector of vectors.
    
+Python: Unsupported parameter.
+
    
+
+UTIL/OUT: SpringStrengthMatrix spring_strength
+
    
+
+This matrix will be used to store the strength of the spring between
+every pair of vertices. This value's type must be a model of BasicMatrix with value type equal to the
+value type of the weight map.
    
+Default: A vector of vectors of the value type of the weight map.
    
+Python: Unsupported parameter.
+
    
+
+UTIL: PartialDerivativeMap partial_derivatives
+
    
+A property map that will be used to store the partial derivates of
+each vertex with respect to the x and
+y coordinates. This must be a
+Read/Write
+Property Map whose value type is a pair with both types equivalent
+to the value type of the weight map. The default is an iterator
+property map.
    
+Default: An iterator_property_map created from
+an std::vector of std::pair<weight_type,
+  weight_type>, where weight_type is the value type of
+the WeightMap.
    
+Python: Unsupported parameter.
+
    
+
+Python IN: bool progressive
+
    
+  When false, performs layout of the graph on a circle before
+  running the Kamada-Kawai algorithm. If true, the algorithm
+  is executing starting with the vertex configuration in
+  the position map.
    
+  Default: False.
+
    
+
+
    
+
    
+
+
+
+
+
+
+
    
+
    
+
+
+
+
+
+
    Copyright © 2004Douglas Gregor,
+Indiana University (dgregor -at cs.indiana.edu)
    
+Andrew Lumsdaine, Indiana
+University (lums@osl.iu.edu)
    
+
+
diff --git a/doc/king_ordering.html b/doc/king_ordering.html
index 6c96716f..b88234d2 100644
--- a/doc/king_ordering.html
+++ b/doc/king_ordering.html
@@ -21,7 +21,8 @@
 
 
    
 
-
    
+
    
+(Python)
 king_ordering
 
    
 
@@ -129,22 +130,29 @@ For version 1:
 
 
  •  IncidenceGraph& g  (IN) 
     
   An undirected graph. The graph's type must be a model of     IncidenceGraph.
+  href="./IncidenceGraph.html">IncidenceGraph.
    
+  Python: The parameter is named graph.
 
 
  •  vertex_descriptor s  (IN) 
    
-  The starting vertex.
+  The starting vertex.
    
+  Python: Unsupported parameter.
 
 
  •  OutputIterator permutation  (OUT) 
     
   The new vertex ordering. The vertices are written to the output
-  iterator in their new order. 
+  iterator in their new order. 
    
+  Python: This parameter is unused in Python. The new vertex
+  ordering is returned as a Python list.
+
   
 
  •  ColorMap color_map  (WORK) 
    
   Used internally to keep track of the progress of the algorithm
-  (to avoid visiting the same vertex twice).
+  (to avoid visiting the same vertex twice).
    
+  Python: Unsupported parameter.
 
 
  •  DegreeMap degree_map  (IN) 
    
-  This must map vertices to their degree.
+  This must map vertices to their degree.
    
+  Python: Unsupported parameter.
 
 
 
@@ -155,20 +163,24 @@ For version 2:
 
 
  •  VertexListGraph& g  (IN) 
     
   An undirected graph. The graph's type must be a model of VertexListGraph.
+  href="./VertexListGraph.html">VertexListGraph.
    
+  Python: The name of this parameter is graph.
 
 
  •  
   OutputIterator permutation  (OUT) 
     
   The new vertex ordering. The vertices are written to the
-  output iterator in their new order.
+  output iterator in their new order.
    
+  Python: This parameter is unused in Python. The new vertex
+  ordering is returned as a Python list.
 
 
  •  ColorMap color_map  (WORK) 
    
   Used internally to keep track of the progress of the algorithm
-  (to avoid visiting the same vertex twice).
+  (to avoid visiting the same vertex twice).
    
+  Python: Unsupported parameter.
 
 
  •  DegreeMap degree_map  (IN) 
    
-  This must map vertices to their degree.
-
+  This must map vertices to their degree.
    
+  Python: Unsupported parameter.
 
 
 
@@ -178,23 +190,29 @@ For version 3:
 
 
  •  IncidenceGraph& g  (IN) 
     
   An undirected graph. The graph's type must be a model of IncidenceGraph.
+  href="./IncidenceGraph.html">IncidenceGraph.
    
+  Python: The parameter is named graph.
 
 
  •   std::deque< typename graph_traits<Graph>::vertex_descriptor > vertex_queue   (IN) 
    
-  The deque containing the starting vertices for each component.
+  The deque containing the starting vertices for each component.
    
+  Python: This parameter is unused in Python. The new vertex
+  ordering is returned as a Python list.
 
 
  •  OutputIterator permutation  (OUT) 
     
   The new vertex ordering. The vertices are written to the output
-  iterator in their new order. 
-  
+  iterator in their new order.
     
+  Python: This parameter is unused in Python. The new vertex
+  ordering is returned as a Python list.
+
 
  •  ColorMap color_map  (WORK) 
    
   Used internally to keep track of the progress of the algorithm
-  (to avoid visiting the same vertex twice).
+  (to avoid visiting the same vertex twice).
    
+  Python: Unsupported parameter.
 
 
  •  DegreeMap degree_map  (IN) 
    
-  This must map vertices to their degree.
-
+  This must map vertices to their degree.
    
+  Python: Unsupported parameter.
 
 
 
diff --git a/doc/kruskal_min_spanning_tree.html b/doc/kruskal_min_spanning_tree.html
index 161d6bfa..ae17be96 100644
--- a/doc/kruskal_min_spanning_tree.html
+++ b/doc/kruskal_min_spanning_tree.html
@@ -21,12 +21,14 @@
 
 
 
    
+(Python)
 kruskal_minimum_spanning_tree
 
    
 
 
     template <class Graph, class OutputIterator, class P, class T, class R>
-void kruskal_minimum_spanning_tree(Graph& g, OutputIterator tree_edges, 
+OutputIterator
+kruskal_minimum_spanning_tree(Graph& g, OutputIterator tree_edges, 
     const bgl_named_params<P, T, R>& params = all defaults);
 
    
 
@@ -85,14 +87,20 @@ IN: const Graph& g
 
    
   An undirected graph. The graph type must be a model of
   Vertex List Graph
-  and Edge List Graph.
+  and Edge List Graph.
    
+
+  Python: The parameter is named graph.
 
    
 
 IN: OutputIterator spanning_tree_edges
 
    
    The edges of the minimum spanning tree are output to this Output
-   Iterator.
+   Iterator.
    
+
+   Python: This parameter is not used in Python. Instead, a
+   Python list containing all of the spanning tree edges is
+   returned.
 
    
 
 
@@ -107,7 +115,9 @@ The weight or ``length'' of
   href="http://www.sgi.com/tech/stl/LessThanComparable.html">Less Than
   Comparable. The key type of this map needs to be the graph's
   edge descriptor type.
    
-  Default: get(edge_weight, g)
+  Default: get(edge_weight, g)
    
+  Python: Must be an edge_double_map for the graph.
    
+  Python default: graph.get_edge_double_map("weight")
 
 
 UTIL: rank_map(RankMap r_map) 
@@ -123,7 +133,9 @@ UTIL: rank_map(RankMap r_map)
   iterator_property_map created from a
   std::vector of the integers of size
   num_vertices(g) and using the i_map for the index
-  map.
+  map.
    
+
+  Python: Unsupported parameter.
 
 
 UTIL: predecessor_map(PredecessorMap p_map) 
@@ -140,7 +152,9 @@ UTIL: predecessor_map(PredecessorMap p_map)
   iterator_property_map created from a
   std::vector of vertex descriptors of size
   num_vertices(g) and using the i_map for the index
-  map.
+  map.
    
+
+  Python: Unsupported parameter.
 
 
 IN: vertex_index_map(VertexIndexMap i_map) 
@@ -153,7 +167,9 @@ IN: vertex_index_map(VertexIndexMap i_map)
   Map. The value type of the map must be an integer type. The
   vertex descriptor type of the graph needs to be usable as the key
   type of the map.
    
-  Default: get(vertex_index, g)
+  Default: get(vertex_index, g)
    
+
+  Python: Unsupported parameter.
 
 
 
diff --git a/doc/prim_minimum_spanning_tree.html b/doc/prim_minimum_spanning_tree.html
index 545733d4..3db1fc7d 100644
--- a/doc/prim_minimum_spanning_tree.html
+++ b/doc/prim_minimum_spanning_tree.html
@@ -22,6 +22,7 @@
 
 
 
    
+(Python)
 prim_minimum_spanning_tree
 
    
 
@@ -134,7 +135,9 @@ IN: const Graph& g
 
    
   An undirected graph. The type Graph must be a
   model of Vertex List Graph
-  and Incidence Graph.
+  and Incidence Graph.
    
+
+  Python: The parameter is named graph.
 
    
 
 OUT: PredecessorMap p_map
@@ -148,7 +151,9 @@ OUT: PredecessorMap p_map
   href="../../property_map/ReadWritePropertyMap.html">Read/Write
   Property Map
   with key and vertex types the same as the vertex descriptor type
-  of the graph.
+  of the graph.
    
+
+  Python: Must be a vertex_vertex_map for the graph.
    
 
 
 
    Named Parameters
    
@@ -156,7 +161,7 @@ OUT: PredecessorMap p_map
 IN: root_vertex(vertex_descriptor r)
 
    
   The vertex that will be the root of the minimum spanning tree.
-  The choice of the root vertex is arbitrary.
+  The choice of the root vertex is arbitrary.
    
   Default: *vertices(g).first
 
    
 
@@ -168,7 +173,9 @@ IN: weight_map(WeightMap w_map)
   the graph needs to be usable as the key type for the weight
   map. The value type for the map must be
   Addable with the value type of the distance map.
    
-  Default:  get(edge_weight, g)
+  Default:  get(edge_weight, g)
    
+  Python: Must be an edge_double_map for the graph.
    
+  Python default: graph.get_edge_double_map("weight")
 
 
 IN: vertex_index_map(VertexIndexMap i_map) 
@@ -180,7 +187,8 @@ IN: vertex_index_map(VertexIndexMap i_map)
   Readable Property Map. The value type of the map must be an
   integer type. The vertex descriptor type of the graph needs to be
   usable as the key type of the map.
    
-  Default: get(vertex_index, g)
+  Default: get(vertex_index, g)
    
+  Python: Unsupported parameter.
 
 
 UTIL/OUT: distance_map(DistanceMap d_map) 
@@ -200,7 +208,9 @@ UTIL/OUT: distance_map(DistanceMap d_map)
   iterator_property_map created from a
   std::vector of the WeightMap's value type of size
   num_vertices(g) and using the i_map for the index
-  map.
+  map.
    
+
+  Python: Must be a vertex_double_map for the graph.
    
 
 
 UTIL/OUT: color_map(ColorMap c_map) 
@@ -219,7 +229,10 @@ UTIL/OUT: color_map(ColorMap c_map)
   href="../../property_map/iterator_property_map.html">
   iterator_property_map created from a std::vector
   of default_color_type of size num_vertices(g) and
-  using the i_map for the index map.
+  using the i_map for the index map.
    
+
+  Python: The color map must be a vertex_color_map for
+  the graph.
 
   
 OUT: visitor(DijkstraVisitor v)  
@@ -230,7 +243,12 @@ OUT: visitor(DijkstraVisitor v)
   Dijkstra Visitor concept. 
   The visitor object is passed by value [1].
    
-  Default: dijkstra_visitor<null_visitor>
+  Default: dijkstra_visitor<null_visitor>
    
+
+  Python: The parameter should be an object that derives from
+  the DijkstraVisitor type
+  of the graph.
 
 
 
    Complexity
    
diff --git a/doc/sequential_vertex_coloring.html b/doc/sequential_vertex_coloring.html
index b4b55a78..8f6974e2 100644
--- a/doc/sequential_vertex_coloring.html
+++ b/doc/sequential_vertex_coloring.html
@@ -7,7 +7,7 @@
   
     C++ Boost 
-
    sequential_vertex_coloring
    
+
    (Python)sequential_vertex_coloring
    
 
     
    
     
    @@ -42,7 +42,8 @@ IN: const Graph& g
   The graph object on which the algorithm will be applied.  The type
   Graph must be a model of Vertex List Graph and Adjacency Graph. 
+  href="AdjacencyGraph.html">Adjacency Graph.
    
+Python: The parameter is named graph.
 
     
 OUT: ColorMap color 
@@ -53,6 +54,7 @@ OUT: ColorMap color
   Property Map whose key type is the same as the vertex descriptor
   type of the graph and whose value type is an integral type that can
   store all values of the graph's vertices_size_type.
    
+  Python: Must be an vertex_int_map for the graph.
 
 
 IN: OrderPA order 
@@ -61,7 +63,8 @@ IN: OrderPA order
   to the vertices of the graph.
    
 
   Default: A property map ordering the vertices in the same way
-  they are ordered by vertices(g).
+  they are ordered by vertices(g).
    
+  Python: Unsupported parameter.
 
 
 
    Complexity
    
diff --git a/doc/strong_components.html b/doc/strong_components.html
index 879acab7..cc44b3dd 100644
--- a/doc/strong_components.html
+++ b/doc/strong_components.html
@@ -22,6 +22,7 @@
 
 
    
 
+(Python)
 strong_components
 
    
 
@@ -74,7 +75,9 @@ IN: const Graph& g
 
    
 A directed graph. The graph type must be a model of Vertex List Graph and Incidence Graph.
+href="IncidenceGraph.html">Incidence Graph.
    
+
+Python: The parameter is named graph.
 
    
 
 OUT: ComponentMap c
@@ -87,7 +90,10 @@ recording the component number in the component property map. The
 href="../../property_map/WritablePropertyMap.html">Writable Property
 Map. The value type shouch be an integer type, preferably the same
 as the vertices_size_type of the graph. The key type must be
-the graph's vertex descriptor type.
+the graph's vertex descriptor type.
    
+
+  Python: Must be an vertex_int_map for the graph.
    
+  Python default: graph.get_vertex_int_map("component")
 
 
 
@@ -109,7 +115,8 @@ UTIL: root_map(RootMap r_map)
   iterator_property_map created from a
   std::vector of vertex descriptors of size
   num_vertices(g) and using the i_map for the index
-  map.
+  map.
    
+  Python: Unsupported parameter.
 
 
 UTIL: discover_time_map(TimeMap t_map)
@@ -124,8 +131,8 @@ UTIL: discover_time_map(TimeMap t_map)
   iterator_property_map created from a
   std::vector of integers with size
   num_vertices(g) and using the i_map for the index
-  map.
-
+  map.
    
+  Python: Unsupported parameter.
 
 
 UTIL: color_map(ColorMap c_map)
@@ -141,7 +148,8 @@ UTIL: color_map(ColorMap c_map)
   iterator_property_map created from a
   std::vector of default_color_type of size
   num_vertices(g) and using the i_map for the index
-  map.
+  map.
    
+  Python: Unsupported parameter.
 
 
 IN: vertex_index_map(VertexIndexMap i_map)
@@ -155,7 +163,8 @@ IN: vertex_index_map(VertexIndexMap i_map)
   vertex descriptor type of the graph needs to be usable as the key
   type of the map.
    
 
-  Default: get(vertex_index, g)
+  Default: get(vertex_index, g)
    
+  Python: Unsupported parameter.
 
 
 
diff --git a/doc/table_of_contents.html b/doc/table_of_contents.html
index ddbd2334..96472e8f 100644
--- a/doc/table_of_contents.html
+++ b/doc/table_of_contents.html
@@ -128,38 +128,37 @@
               
      
                 
    1. (Python)breadth_first_search
                 
    2. (Python)breadth_first_visit
-                
    3. (Python)depth_first_search
-                
    4. depth_first_visit
-                
    5. (Python)depth_first_visit
+                
    6. (Python)undirected_dfs
-                
    7. uniform_cost_search (deprecated, use Dijkstra's instead)
               
    
             
  • Graph Algorithms
               
      
                 
    1. Shortest Paths Algorithms
                   
        
-                    
      1. dijkstra_shortest_paths
-                    
      2. bellman_ford_shortest_paths
-                    
      3. dag_shortest_paths
+                    
      4. (Python)dijkstra_shortest_paths
+                    
      5. (Python)bellman_ford_shortest_paths
+                    
      6. (Python)dag_shortest_paths
                     
      7. johnson_all_pairs_shortest_paths
 		    
      8. floyd_warshall_all_pairs_shortest_paths
        9. 
                   
      
                 
    2. Minimum Spanning Tree Algorithms
                   
        
-                    
      1. (Python)kruskal_minimum_spanning_tree
-                    
      2. (Python)prim_minimum_spanning_tree
                   
      
 		
    3. Connected Components Algorithms
 		   
        
-		     
      1. connected_components
-		     
      2. strong_components
+		     
      3. (Python)connected_components
+		     
      4. (Python)strong_components
 		     
-		     
      5. biconnected_components
-		     
      6. articulation_points		     
+		     
      7. (Python)biconnected_components
+		     
      8. (Python)articulation_points		     
 		     
      9. Incremental Connected Components
 		     
          
 		       
        1. initialize_incremental_components
@@ -178,35 +177,35 @@
 
                 
        2. Sparse Matrix Ordering Algorithms
                   
            
-                    
          1. (Python)cuthill_mckee_ordering
-                    
          2. king_ordering
            3. 
+                    
          3. (Python)king_ordering
            4. 
                     
          4. minimum_degree_ordering
                   
          
                 
          3. 
-                
        3. topological_sort
-                
        4. transitive_closure
+                
        5. (Python)topological_sort
+                
        6. (Python)transitive_closure
                 
        7. copy_graph
                 
        8. transpose_graph
-                
        9. isomorphism
+                
        10. (Python)isomorphism
                        
-                
        11. (Python)sequential_vertex_coloring
                 
        12. sloan_ordering
          13. 
                 
        13. ith_wavefront, max_wavefront, aver_wavefront, and rms_wavefront
          14. 
-                
        14. brandes_betweenness_centrality
          15. 
+                
        15. (Python)brandes_betweenness_centrality
          16. 
                 
        16. Layout algorithms
                   
            
 		    
          1. random_graph_layout
            2. 
-                    
          2. circle_layout
            3. 
-                    
          3. kamada_kawai_spring_layout
            4. 
-		    
          4. fruchterman_reingold_force_directed_layout
            5. 
+                    
          5. (Python)circle_layout
            6. 
+                    
          6. (Python)kamada_kawai_spring_layout
            7. 
+		    
          7. (Python)fruchterman_reingold_force_directed_layout
            8. 
                     
          8. gursoy_atun_layout
            9. 
                   
          
                   
          17. 
                 
        17. Clustering algorithms
                   
            
-                    
          1. betweenness_centrality_clustering
            2. 
+                    
          2. (Python)betweenness_centrality_clustering
            3. 
                   
          
                 
          18. 
 		
        18. astar_search
          19. 
diff --git a/doc/topological_sort.html b/doc/topological_sort.html
index 4ef41062..ab7011ec 100644
--- a/doc/topological_sort.html
+++ b/doc/topological_sort.html
@@ -21,6 +21,7 @@
 
 
 
          
+(Python)
 topological_sort
 
          
 
@@ -51,7 +52,8 @@ IN: VertexListGraph& g
   be a model of Vertex List Graph.
   If the graph is not a DAG then a not_a_dag
   exception will be thrown and the
-  user should discard the contents of result range.
+  user should discard the contents of result range.
          
+  Python: The parameter is named graph.
 
 
 OUT: OutputIterator result
@@ -60,7 +62,11 @@ The vertex descriptors of the graph will be output to the
 result output iterator in their topological order.  The
 iterator type must model Output
-Iterator.
+Iterator.
          
+
+Python: This parameter is not used in Python. Instead, a
+Python list containing the vertices in topological order is
+returned.
 
 
 
          Named Parameters
          
@@ -78,7 +84,10 @@ UTIL/OUT: color_map(ColorMap color)
   iterator_property_map created from a
   std::vector of default_color_type of size
   num_vertices(g) and using the i_map for the index
-  map.
+  map.
          
+
+  Python: The color map must be a vertex_color_map for
+  the graph.
 
 
 IN: vertex_index_map(VertexIndexMap i_map)
@@ -92,7 +101,9 @@ IN: vertex_index_map(VertexIndexMap i_map)
   vertex descriptor type of the graph needs to be usable as the key
   type of the map.
          
 
-  Default: get(vertex_index, g)
+  Default: get(vertex_index, g)
          
+
+  Python: Unsupported parameter.
 
 
 
diff --git a/doc/transitive_closure.html b/doc/transitive_closure.html
index e083809b..bd82f600 100644
--- a/doc/transitive_closure.html
+++ b/doc/transitive_closure.html
@@ -20,6 +20,7 @@
 
          
 
 
          
+(Python)
 transitive_closure
 
          
 
@@ -60,14 +61,19 @@ IN: const Graph& g
 
          
  A directed graph, where the Graph type must model the
  Vertex List Graph
- and Adjacency Graph concepts.
+ and Adjacency Graph concepts.
          
+
+  Python: The parameter is named graph.
 
          
 
 OUT: GraphTC& tc
 
          
  A directed graph, where the GraphTC type must model the
  Vertex Mutable Graph 
- and Edge Mutable Graph concepts.
+ and Edge Mutable Graph concepts.
          
+
+ Python: This parameter is not used in Python. Instead, a new
+ graph of the same type is returned.
 
          
 
 
          Named Parameters
          
@@ -75,7 +81,9 @@ OUT: GraphTC& tc
 UTIL/OUT: orig_to_copy(G_to_TC_VertexMap g_to_tc_map)
 
          
   This maps each vertex in the input graph to the new matching
-  vertices in the output transitive closure graph.
+  vertices in the output transitive closure graph.
          
+
+  Python: This must be a vertex_vertex_map of the graph.
 
          
 
 IN: vertex_index_map(VertexIndexMap& index_map)
@@ -89,7 +97,9 @@ IN: vertex_index_map(VertexIndexMap& index_map)
   vertex descriptor type of the graph needs to be usable as the key
   type of the map.
          
 
-  Default: get(vertex_index, g)
+  Default: get(vertex_index, g)
          
+
+  Python: Unsupported parameter.
 
 
 
diff --git a/doc/undirected_dfs.html b/doc/undirected_dfs.html
index da4fd142..b6f488c6 100644
--- a/doc/undirected_dfs.html
+++ b/doc/undirected_dfs.html
@@ -20,6 +20,7 @@
 
          
 
 
          
+(Python)
 undirected_dfs
 
          
 
@@ -176,7 +177,9 @@ IN: Graph& g
   An undirected graph. The graph type must
   be a model of Incidence Graph,
   Vertex List Graph,
-  and Edge List Graph.
+  and Edge List Graph.
          
+
+  Python: The parameter is named graph.  
 
 
 
@@ -188,7 +191,11 @@ IN: visitor(DFSVisitor vis)
   event-points specified by the DFS
   Visitor concept. The visitor object is passed by value [1]. 
           Default:
-  dfs_visitor<null_visitor>
+  dfs_visitor<null_visitor>
          
+
+  Python: The parameter should be an object that derives from
+  the DFSVisitor type of
+  the graph.
 
 
 UTIL/OUT: vertex_color_map(VertexColorMap vertex_color)
@@ -204,7 +211,10 @@ UTIL/OUT: vertex_color_map(VertexColorMap vertex_color)
   iterator_property_map created from a
   std::vector of default_color_type of size
   num_vertices(g) and using the i_map for the index
-  map.
+  map.
          
+
+  Python: The color map must be a vertex_color_map for
+  the graph.
 
 
 UTIL: edge_color_map(EdgeColorMap edge_color)
@@ -215,7 +225,10 @@ UTIL: edge_color_map(EdgeColorMap edge_color)
   Property Map and its key type must be the graph's edge
   descriptor type and the value type of the color map must model
   ColorValue.
          
-  Default: none.
+  Default: none.
          
+
+  Python: The color map must be an edge_color_map for
+  the graph.
 
 
 IN: root_vertex(typename
@@ -238,7 +251,9 @@ IN: vertex_index_map(VertexIndexMap i_map)
   vertex descriptor type of the graph needs to be usable as the key
   type of the map.
          
 
-  Default: get(vertex_index, g)
+  Default: get(vertex_index, g)
          
+
+  Python: Unsupported parameter.
 
 
 
          
diff --git a/src/python/isomorphism.cpp b/src/python/isomorphism.cpp
index 73a995ee..4a9d21d7 100644
--- a/src/python/isomorphism.cpp
+++ b/src/python/isomorphism.cpp
@@ -65,13 +65,13 @@ void export_isomorphism()
   using boost::python::object;
 
   def("isomorphism", &isomorphism,
-      (arg("graph"), 
+      (arg("g1"), arg("g2"),
        arg("isomorphism_map") = 
          (vector_property_map*)0,
        arg("vertex_invariant") = object()));
 
   def("isomorphism", &isomorphism,
-      (arg("graph"), 
+      (arg("g1"), arg("g2"),
        arg("isomorphism_map") = 
          (vector_property_map*)0,
        arg("vertex_invariant") = object()));
diff --git a/src/python/transitive_closure.cpp b/src/python/transitive_closure.cpp
index 1644e8e5..a7655606 100644
--- a/src/python/transitive_closure.cpp
+++ b/src/python/transitive_closure.cpp
@@ -38,7 +38,7 @@ void export_transitive_closure()
 
   def("transitive_closure", &transitive_closure, 
       (arg("graph"),
-       arg("g_to_tc_map") = 
+       arg("orig_to_copy") = 
          (vector_property_map*)0));
 }