December 2016 - graph-tool - archives.skewed.de

Some clarifications regarding directness
by bloodymeli 23 Feb '17

23 Feb '17

Hi, Consider the following GraphML file (attached). According to the GraphML file, it represents an undirected graph. Therefore, I would expect that as I set it to directed graph: gtGraph=gt.load_graph("yeast.graphml") gtGraph.set_directed(1) I would obtain two directed edges for every undirected edge. In particular, the in degree of every node should be equal to the out degree. However, this is not the case, for example: v=gtGraph.vertex(1834) v.in_degree() Out[3]: 5 v.out_degree() Out[4]: 2 I'm a bit puzzled. A) Is the source file corrupted? B) When I set the directedness flag to 1, am I guaranteed that every undirected edge would be converted to two directed edges? If not, how can I perform this operation (A = A + A', assuming the adjacency matrix is triangular) or validate it? yeast.graphml <http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com…> -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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Use images as vertex symbols in graph_draw
by P-M 02 Feb '17

02 Feb '17

This is a somewhat general question but perhaps somebody can point me in a useful direction: I am trying to draw a small network of molecules. Ideally I would like to use the molecular structures (which I can save as image file) in the network representation as opposed to the normal little red circles. Is it possible to specify a separate file for each vertex that gets used as position marker? I have found "surface" as a vertex property in "graph_draw" but am not sure if that is what I am after. Best wishes, Philipp -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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Scalar Assortativity - Definition
by P-M 19 Jan '17

19 Jan '17

Hello Tiago, I have a quick question about how the scalar assortativity is calculated. You reference "M. E. J. Newman, “Mixing patterns in networks”, Phys. Rev. E 67, 026126 (2003)" as source for the equation. In the paper the equation seems to consider edges connecting nodes of indegree x to those of outdegree y (or rather "j" and "k"). In your implementation I am able to choose in-degree, out-degree or total as degree type. What does this do to the equation? If I choose, say, "in", will that mean the algorithm will look at edges connecting nodes of indegree x to those of indegree y? If so, is there a way for me to find the assortativity as per the definition in the paper? Best, Philipp -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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Changing default mouse hovering behavior in GTK interactive graphs
by mohit jangid 18 Jan '17

18 Jan '17

Hello, I've created a graph on graph-tool interactive window. I want to visually show the traversal of BFS or DFS. In this process, I want to update the node's and vertex's properties myself. By default, whenever the mouse is hovered over graphs nodes (drawn on GTK window) the node appears with blue halo color and all the connected nodes are highlighted with red edges and borders. I don't want any default highlighting. How can I turn off this default behavior? -- Mohit Kumar Jangid Mob# : +919530153503 “*The most powerful weapon on earth is the human soul on fire.” ~Field Marshal Ferdinand Foch*

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Annotating graph-tool graphs with graph_draw as a matplotlib subplot?
by Steve 18 Jan '17

18 Jan '17

I'm trying to annotate a graph plot with associated data and analysis. To do this, I'd like to use graph_plot as a subplot within a matplotlib graphic, ideally inline within a Jupyter notebook and "%matplotlib inline". Inline graph_plot works great by itself within Jupyter, but the graphs lack annotation or related content within the same output cell. Is there a way to get a graph_plot as a subplot within a matplotlib object? Or some equivalent approach that would allow for general graphical annotations of graph-tool graph plots? -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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boost lgamma numeric overflow in inference.minimize_nested_blockmodel_dl?
by Luke Bergmann 22 Dec '16

22 Dec '16

Dear all, Thanks and apologies in advance if this is something silly on my part. I can blockmodel this dataset using a vector of covariates (two int32’s) without hierarchy no problem, but when I try to add hierarchy, I get an error reproducible on two different platforms (OS X and ubuntu, one without OpenMP support, one with; one from within an ipython notebook, one from without; both Python 2.7.12): RuntimeError Traceback (most recent call last) <ipython-input-441-ae6b18f3b139> in <module>() 1 # infer hierarchical blockmodel with edge covariates ----> 2 get_ipython().magic(u'time hbm_edgecovariates = gt.inference.minimize_nested_blockmodel_dl(g, deg_corr=False, layers=True, state_args=dict(ec=g.ep.weight, layers=False))') //anaconda/envs/geopandas_test_env/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in magic(self, arg_s) 2156 magic_name, _, magic_arg_s = arg_s.partition(' ') 2157 magic_name = magic_name.lstrip(prefilter.ESC_MAGIC) -> 2158 return self.run_line_magic(magic_name, magic_arg_s) 2159 2160 #------------------------------------------------------------------------- //anaconda/envs/geopandas_test_env/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_line_magic(self, magic_name, line) 2077 kwargs['local_ns'] = sys._getframe(stack_depth).f_locals 2078 with self.builtin_trap: -> 2079 result = fn(*args,**kwargs) 2080 return result 2081 <decorator-gen-59> in time(self, line, cell, local_ns) //anaconda/envs/geopandas_test_env/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k) 186 # but it's overkill for just that one bit of state. 187 def magic_deco(arg): --> 188 call = lambda f, *a, **k: f(*a, **k) 189 190 if callable(arg): //anaconda/envs/geopandas_test_env/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns) 1178 else: 1179 st = clock2() -> 1180 exec(code, glob, local_ns) 1181 end = clock2() 1182 out = None <timed exec> in <module>() /usr/local/lib/python2.7/site-packages/graph_tool/inference/minimize.pyc in minimize_nested_blockmodel_dl(g, B_min, B_max, b_min, b_max, Bs, bs, deg_corr, overlap, nonoverlap_init, layers, hierarchy_minimize_args, state_args, bisection_args, mcmc_args, anneal_args, mcmc_equilibrate_args, shrink_args, mcmc_multilevel_args, verbose) 513 verbose=verbose, 514 **dmask(hierarchy_minimize_args, --> 515 ["B_max", "B_min", "bisection_args", "verbose"])) 516 517 return state /usr/local/lib/python2.7/site-packages/graph_tool/inference/nested_blockmodel.pyc in hierarchy_minimize(state, B_min, B_max, b_min, b_max, frozen_levels, sparse_thres, bisection_args, verbose) 749 bstate = state.find_new_level(l, bisection_args=bisection_args, 750 B_min=B_min, B_max=B_max, --> 751 b_min=b_min, b_max=b_max) 752 else: 753 bstate = state.find_new_level(l, bisection_args=bisection_args) /usr/local/lib/python2.7/site-packages/graph_tool/inference/nested_blockmodel.pyc in find_new_level(self, l, sparse_thres, bisection_args, B_min, B_max, b_min, b_max) 482 483 # find new state --> 484 state = bisection_minimize([min_state, max_state], **bisection_args) 485 486 if _bm_test(): /usr/local/lib/python2.7/site-packages/graph_tool/inference/bisection.pyc in bisection_minimize(init_states, random_bisection, mcmc_multilevel_args, extra_entropy_args, verbose) 139 for state in init_states: 140 b_cache[state.B] = (get_ent(state, mcmc_multilevel_args, --> 141 extra_entropy_args), 142 state.copy()) 143 /usr/local/lib/python2.7/site-packages/graph_tool/inference/bisection.pyc in get_ent(state, mcmc_multilevel_args, extra_entropy_args) 68 mcmc_args = mcmc_equilibrate_args.get("mcmc_args", {}) 69 entropy_args = mcmc_args.get("entropy_args", {}) ---> 70 S = state.entropy(**dict(entropy_args, **extra_entropy_args)) 71 return S 72 /usr/local/lib/python2.7/site-packages/graph_tool/inference/layered_blockmodel.pyc in entropy(self, adjacency, dl, partition_dl, degree_dl, degree_dl_kind, edges_dl, dense, multigraph, deg_entropy, exact, **kwargs) 616 dense=dense, multigraph=multigraph, 617 deg_entropy=deg_entropy, exact=exact, --> 618 **dict(kwargs, test=False)) 619 620 if dl and edges_dl: /usr/local/lib/python2.7/site-packages/graph_tool/inference/blockmodel.pyc in entropy(self, adjacency, dl, partition_dl, degree_dl, degree_dl_kind, edges_dl, dense, multigraph, deg_entropy, exact, **kwargs) 845 callback = kwargs.pop("callback", None) 846 if callback is not None: --> 847 S += callback(self) 848 849 if kwargs.pop("test", True) and _bm_test(): /usr/local/lib/python2.7/site-packages/graph_tool/inference/nested_blockmodel.pyc in <lambda>(s) 431 callback=lambda s: get_edges_dl(s, 432 self.hstate_args, --> 433 self.hentropy_args)) 434 mcmc_args = dict(mcmc_args, entropy_args=entropy_args, 435 disable_callback_test=isinstance(self.levels[0], /usr/local/lib/python2.7/site-packages/graph_tool/inference/nested_blockmodel.pyc in get_edges_dl(state, hstate_args, hentropy_args) 38 bstate = state.get_block_state(b=bclabel, **hstate_args) 39 return bstate.entropy(**dict(hentropy_args, dl=True, edges_dl=False, ---> 40 multigraph=True)) 41 42 /usr/local/lib/python2.7/site-packages/graph_tool/inference/layered_blockmodel.pyc in entropy(self, adjacency, dl, partition_dl, degree_dl, degree_dl_kind, edges_dl, dense, multigraph, deg_entropy, exact, **kwargs) 628 629 if dl: --> 630 S += self.__get_layer_entropy() 631 632 if _bm_test() and kwargs.get("test", True): /usr/local/lib/python2.7/site-packages/graph_tool/inference/layered_blockmodel.pyc in __get_layer_entropy(self) 590 be = group_vector_property([ec, ec]) 591 lstate = OverlapBlockState(g, b=be, deg_corr=False) --> 592 self.__layer_entropy = lstate.entropy(dl=True, edges_dl=False) - \ 593 lstate.entropy(dl=False) 594 else: /usr/local/lib/python2.7/site-packages/graph_tool/inference/overlap_blockmodel.pyc in entropy(self, adjacency, dl, partition_dl, degree_dl, degree_dl_kind, edges_dl, dense, multigraph, deg_entropy, exact, **kwargs) 581 multigraph=multigraph, 582 deg_entropy=deg_entropy, exact=exact, --> 583 **kwargs) 584 585 /usr/local/lib/python2.7/site-packages/graph_tool/inference/blockmodel.pyc in entropy(self, adjacency, dl, partition_dl, degree_dl, degree_dl_kind, edges_dl, dense, multigraph, deg_entropy, exact, **kwargs) 824 if dl: 825 if partition_dl: --> 826 S += self._state.get_partition_dl() 827 828 if edges_dl: RuntimeError: Error in function boost::math::lgamma<double>(double): numeric overflow

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gt.graph_draw throwing error
by Snehal Shekatkar 16 Dec '16

16 Dec '16

Hello all, I am encountering a strange error in the usual gt.graph_draw when I add vertex_size = degree. It works well every time but this time it is not working. My code: G = gt.Graph(directed = False) G.add_edge_list(edges, hashed = True, string_vals = True) gt.remove_parallel_edges(G) lc = gt.Graph(gt.GraphView(G, vfilt = gt.label_largest_component(G)), prune = True) deg = lc.new_vertex_property('int') for v in lc.vertices(): deg[v] = v.out_degree() print(lc) gt.graph_draw(lc, vertex_size = deg) And I am getting the following error: Traceback (most recent call last): File "/usr/lib/python3/dist-packages/graph_tool/draw/gtk_draw.py", line 503, in draw self.fit_to_window(ink=False) File "/usr/lib/python3/dist-packages/graph_tool/draw/gtk_draw.py", line 724, in fit_to_window cr) File "/usr/lib/python3/dist-packages/graph_tool/draw/cairo_draw.py", line 1319, in fit_to_view font_size, cr) File "/usr/lib/python3/dist-packages/graph_tool/draw/cairo_draw.py", line 1292, in get_bb x_delta = [x_range[0] - (pos_x.fa - delta).min(), ValueError: operands could not be broadcast together with shapes (4,) (3397,) I have 3397 vertices in my graph. I would greatly appreciate any help. Thanks in advance. Regards Snehal -- Snehal Madhukar Shekatkar Pune India

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how to use add_edge_list() and then add isolated vertices?
by reckoner 14 Dec '16

14 Dec '16

Hi, I am using add_edge_list and it returns a vector map of vertices it has added, but how do I add in isolated vertices that have no edges (otherwise they would have been put in there by add_edge_list). The vector map of vertices is a mapping between integers -> string vertex names. When I add in isolated vertices, they have string names that somehow have to be incorporated into the vmap that is returned by add_edge_list. Any help appreciated. Thanks!

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layers and edge covariates
by Peter Straka 12 Dec '16

12 Dec '16

Dear list, in the example on Edge layers and covariates <https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge…>, blocks are fitted as follows: state = gt.minimize_blockmodel_dl(g, deg_corr=False, layers=True, state_args=dict(ec=g.ep.value, layers=False)) I'm trying to make sure I understand the LayeredBlockState correctly. Are the following statements correct? 1. The independent layers version is used, which means that there is one layer for every possible number of co-appearances. *This means that number of co-appearances is treated as a categorical, rather than an ordinal variable. * 2. If one wanted to encourage the model to assort actors into the same block if they have many co-appearances, the following fit would be more appropriate: state = gt.minimize_blockmodel_dl(g, deg_corr=False, layers=False, state_args=dict(eweight=g.ep.value)) (If I'm right, then I find that the second model is closer to what an applied scientist would be interested in...) Many thanks for clearing this up, Peter -- Dr Peter Straka Research Fellow (DECRA) Dep. of Statistics | School of Mathematics & Statistics | UNSW Australia Google Scholar <https://scholar.google.com.au/citations?user=BV5PkWUAAAAJ&hl=en&authuser=1> E: p.straka(a)unsw.edu.au T: +61 (2) 938*5 7024 *| +1 313 757 0137

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Installation of graph-tool-2.19 fails on RedHat 6.5
by Amihai 07 Dec '16

07 Dec '16

I try to install graph-tool-2.19 on RedHat 6.5. I did : ./configure CPPFLAGS="-I/usr/include/cairomm-1.0/cairomm/" make I got graph_cairo_draw.cc:38:29: fatal error: cairomm/context.h: No such file or directory compilation terminated. make[4]: *** [graph_cairo_draw.lo] Error 1 make[4]: Leaving directory `/usr/local/graph-tool-2.19/src/graph/draw' make[3]: *** [all-recursive] Error 1 make[3]: Leaving directory `/usr/local/graph-tool-2.19/src/graph' make[2]: *** [all-recursive] Error 1 make[2]: Leaving directory `/usr/local/graph-tool-2.19/src' make[1]: *** [all-recursive] Error 1 make[1]: Leaving directory `/usr/local/graph-tool-2.19' make: *** [all] Error 2 I observe that cairomm/context.h exists : /usr/include/cairomm-1.0/cairomm/context.h as a part of the package cairomm-devel-1.8.0-2.1.el6.x86_64 Attached are the output of configure, make and the config.log. I appreciate your help Amihai config2.out <http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com…> make2.out <http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com…> config.log <http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com…> -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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