- 15 Jul, 2008 5 commits
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Tiago Peixoto authored
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Tiago Peixoto authored
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Tiago Peixoto authored
No longer functional or relevant.
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Tiago Peixoto authored
A new 'clustering' sub-module was created.
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Tiago Peixoto authored
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- 17 Jun, 2008 2 commits
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Tiago Peixoto authored
This commit removes the internal property maps from the GraphInterface class, and makes all property maps external by default. The internal property maps were moved to the python layer.
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Tiago Peixoto authored
This is to avoid a common error of not being able to correctly normalize a histogram because of truncated integer division.
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- 01 Jun, 2008 1 commit
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Tiago Peixoto authored
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- 30 May, 2008 1 commit
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Tiago Peixoto authored
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- 19 May, 2008 1 commit
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Tiago Peixoto authored
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- 12 May, 2008 1 commit
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Tiago Peixoto authored
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- 09 May, 2008 1 commit
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Tiago Peixoto authored
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- 06 May, 2008 4 commits
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Tiago Peixoto authored
Other objects are now automatically converted (tuple, lists, dicts, str).
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Tiago Peixoto authored
This fixes the representation of property maps which don't have a value type included in the value_types list, such as index properties.
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Tiago Peixoto authored
It was needlessly O(E^2), and now is O(E).
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Tiago Peixoto authored
Edge indexes weren't properly copied.
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- 02 May, 2008 2 commits
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Tiago Peixoto authored
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Tiago Peixoto authored
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- 01 May, 2008 1 commit
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Tiago Peixoto authored
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- 23 Apr, 2008 1 commit
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Tiago Peixoto authored
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- 15 Apr, 2008 1 commit
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Tiago Peixoto authored
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- 14 Apr, 2008 5 commits
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Tiago Peixoto authored
Python binding will follow
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Tiago Peixoto authored
GCC 4.3 complains when a typedef overrides the meaning of another type.
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Tiago Peixoto authored
Avoiding use of lambda::bind when not necessary (or very convenient).
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Tiago Peixoto authored
A couple of bugs fixed. Local frame dict is read-only in python, so the updated arguments are returned in a dict instead.
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Tiago Peixoto authored
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- 10 Apr, 2008 1 commit
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Tiago Peixoto authored
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.
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- 06 Apr, 2008 1 commit
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Tiago Peixoto authored
It is now much simpler, and works better.
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- 04 Apr, 2008 1 commit
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Tiago Peixoto authored
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- 27 Mar, 2008 3 commits
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Tiago Peixoto authored
This adds the graph_tool.test module, which can be run with graph_tool.test.run()
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Tiago Peixoto authored
Put the run_action function in a separate submodule, and make it work properly with the new code.
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Tiago Peixoto authored
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).
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- 17 Feb, 2008 1 commit
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Tiago Peixoto authored
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
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- 10 Feb, 2008 1 commit
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Tiago Peixoto authored
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.
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- 21 Dec, 2007 2 commits
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Tiago Peixoto authored
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Tiago Peixoto authored
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
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- 17 Dec, 2007 1 commit
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Tiago Peixoto authored
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.
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- 14 Dec, 2007 2 commits
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Tiago Peixoto authored
add_edge_property() should return a edge property not a vertex_property
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Tiago Peixoto authored
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.
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- 13 Dec, 2007 1 commit
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Tiago Peixoto authored
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