The whole histogram code has been redone, and the code has been
simplified. The three-point vertex-edge-vertex correlation has been
scrapped, since it's not frequently used, and would make compilation
even more expensive.

This also adds some missing files to the generation routine.

It is now much simpler, and works better.

This adds the graph_tool.test module, which can be run with
graph_tool.test.run()

Put the run_action function in a separate submodule, and make it work
properly with the new code.

Now graphml files properly contain all the supported value types, which
are all perfectly preserved when read (floating point data is now saved
in hexadecimal format). Several other improvements were made, such as
the ability to read and write to python file-like objects.

It is also now possible to have arbitrary python object properties, and
store them persistently (which is done internally with the pickling
interface).

vector<bool> was totally abolished, since its implementation is quite
broken. See: http://www.gotw.ca/publications/N1211.pdf and
http://www.gotw.ca/publications/N1185.pdf Now a uint8_t (aka. char) is
used in graph properties instead of a bool.

Graph types can now be fully pickled (this may not be feasible
memory-wise if the graph is too large, since the whole XML
representation is dumped to a string before it is saved on disc).

This commit splits libraph_tool into different libraries:
 
   - libgraph_tool_core
   - libgraph_tool_clustering (*)
   - libgraph_tool_community (*)
   - libgraph_tool_correlations (*)
   - libgraph_tool_distance (*)
   - libgraph_tool_generation (*)
   - libgraph_tool_layout (*)
   - libgraph_tool_misc (*)
   - libgraph_tool_stats (*)

It also adds the python sub-module 'test', which provides extensive unit
testing of the core functionality. The core library is fully functional
and all test pass successfully.

(*) -> module needs to be ported to new refactoring, and does not yet build

This is a huge commit which completely refactors the metaprogramming
engine which generates and selects (at run time) the graph view type and
the desired algorithm implementation (template instantiation) that runs
on it.

Things are laid out now as following. There exists a main underlying
graph type (GraphInterface::multigraph_t) and several other template
classes that mask it some way or another, in a hierarchic fashion:

     multigraph_t -> filtered_graph (edges only, vertices only, both)
         |                               |           |           |
         |                               |           |           |
         |-------(reversed_graph)--------|-----------|-----------|
         |                               |           |           |
         \------(UndirectedAdaptor)------------------------------/

The filtered_graph filters out edges and/or vertices from the graph
based on some scalar boolean property. The reversed_graph reversed the
direction of the edges and, finally, the UndirectedAdaptor treats the
original directed graphs as undirected, transversing the in- and
out-edges of each vertex indifferently. Thus, the total number of graph
view types is 12. (The option --disable-graph-filtering can be passed to
the configure script, which will disable graph filtering altogether and
bring the total number down to 3, to reduce compile time and memory
usage)

In general, some specific algorithm, implemented as a template function
object, needs to be instantiated for each of those types. Furthermore,
the algorithm may also depend on other types, such as specific
property_maps. Thus, the following scheme is used:

    struct my_algorithm // algorithm to be implemented
    {
        template <class Graph, class PropertyMap>
        void operator()(Graph *g, PropertyMap p, double& result) const
        {
            // ...
        }
    };

    // in order for the above code to be instantiated at compile time
    // and selected at run time, the run_action template function object
    // is used from a member function of the GraphInterface class:

    double GraphInterface::MyAlgorithm(string prop_name)
    {
        double result;
        boost::any vprop = prop(property, _vertex_index, _properties);
        run_action<>()(*this, bind<void>(my_algorithm(), _1, _2,
                                         var(result)),
                       vertex_scalar_properties())(vprop);
        return result;
    }

The whole code was changed to reflect this scheme, but now things are
more centralized and less ad-hoc code needed to be
written. Unfortunately, due to GCC's high memory usage during template
instantiations, some of the code (namely all the degree correlation
things) had to be split in multiple compilation units... Maybe this will
change in the future if GCC gets optimized.

This commit also touches other parts of code. More specifically, the way
filtering gets done is very different. Now we only filter on boolean
properties, and with the above scheme, the desired implementation runs
with the correct chosen type, and no implicit type conversions should
ever happen, which would have a bad impact on performance.

Everything was moved into the graph_tool directory, which should have,
after installation, the following structure:

graph_tool/                          Top-level package
      __init__.py                    Initialize the graph_tool package
      libgraph_tool.so               libgraph_tool submodule
      libgraph_tool.la               libtool file for libgraph_tool
      include/                       C++ include files for dynamically
                                     compiled code

Some visibility voodoo is necessary to ensure that RTTI work properly
across DSO boundaries, which is necessary to properly handle dynamic
property maps and such. Additionally, the action template must have a
different name each time the action code changes, to avoid additional
weirdness.

This also adds some other utility functions/typedefs to make writing
"inline" c++ code easier.

add_edge_property() should return a edge property not a vertex_property

It is now possible to run arbitrary "inline" C++ code from python, using
scipy.weave, as such:

        g = graph_tool.Graph()
        g.load("foo.xml")
        value = 2.0
        g.run_action('cout << num_vertices(g) << " " << value << endl;',
                     ["value"]);

The code gets compiled the first time, and is reused after that. Python
local and global variables can be passed to C++, as shown above, by
specifying a list of passed variables as the second argument.

The graph object passed to the edit function is now a instance of the
Graph class which wraps GraphInterface, not a GraphInterface instance.

Graphs can now be loaded "lazily" with the lazy_load() function, which
works just like load(), but doesn't actually load the graph, which is
delayed until the graph is accessed for the first time, e.g.

    import graph_tool

    g = graph_tool.Graph()
    g.lazy_load("example.xml")

    g.number_of_vertices()  # <- the graph is only loaded here

Move remaining parallelism check out of the outer/inner loops and into the parallel_check() function.

3) missing parallel edge check in uncorrelated strategy
2) "_in()" for target and source of "target swap edge"
1) order of arguments of swap_edge_triad::parallel_check

melodrama time...
"premature optimization is the root of all evil"
"careless abstraction are the fruits of its tree"

The check for parallel edges was cleaned up and abstracted into a
function, and other minor things were changed. Code comments were added
to places where it might have been hard to understand.

Improve formatting of src/graph/graph_rewiring.cc (line breaks at column
80, typedefs, trailing whitespace removal, etc)

Conflicts:

	src/graph-tool
	src/graph/graph_bind.cc

Property maps can now be obtained as such:

         weight = g.edge_properties['weight']
         print weight[v] # v is a Vertex object
         weight[v] = 2.0
         # and so on...

The list of properties is obtained from g.vertex_properties,
g.edge_properties and g.graph_properties, which can be read as
dictionaries. The last can be set also, as such:

         g.graph_properties = {foo:"bar", baz:42}

Functions to add and remove vertices or adges were also added
(add_{vertex|edge}, remove_{vertex|edgge}).

Vertex and Edge types can also now be printed for convenience, as such:

       for v in g.vertices():
           print v
       for e in g.edges():
           print e

which results, for example, in:
0
1
2
3
4
5
6
(0,1)
(1,2)
(2,6)
(3,4)
(4,5)
(4,2)
(5,6)
(6,1)

(this also adds the forgotten graph_tool.py file, which was previously
on .gitignore)

Major changes to both correlated and uncorrelated rewiring code.
Both cases are now functional and use iterative shuffling.
Uncorrelated picks edges to rewire directly, correlated picks
vertices in order to choose edges.

Vertices and edges can be accessed from the graph class, as such:

    import graph_tool
    g = graph_tool.Graph()
    for v in g.vertices():
        for e in v.out_edges():
           # do something...

Additionally, the --edit-{vertex|edge|graph}-property was ported to the
new interface, and is working again, as it used to.

The Vertex and Edge class no longer have the 'get_property' and
'set_property' method. They'll be replaced by a new method of accessing
property maps.

The program was split in two parts:

    1. A python module, graph_tool.py, which encapsulates the graph
       manipulation under a Graph class. Now the following can be done:

              import graph_tool
              g1 = graph_tool.Graph()
              g2 = graph_tool.Graph()
              g1.load("foo.xml")
              g2.load("bar.xml")
              print g1.number_of_vertices(), g2.number_of_vertices()

    2. A standalone command line tool, graph-tool, which imports
       graph_tool.py, and exposes the Graph methods as command line
       options.

The whole command line engine was thus (once again) entirely
rewritten. It is now Crack-Free™, and simply mirrors the methods of the
Graph class as command line options, using, for this, the beauty of
function decorators. It classifies now, I believe, as Pythonic™.

This fixes a bug where properties with 'long long' types were not handled
properly through the python interface.

Several changes in the random rewiring code, making it fully
functional for the correlated case.
The reshuffling operations lag in efficiency, even though already
the algorithm runs in little time.

Conflicts:

	src/graph/graph_extended_clustering.cc

Merge branch 'master' of git://finn.forked.de/~count0/stuff/workspace/graph-tool into rewiring-merge

Line breaks at column 80 were added, and all trailing whitespace was deleted. Code
comments were modified and some more were added.

It should be 'size_t' instead of 'long'.

bool entries in option random-rewire were stated as string,
causing graph-tool to crash before loading.