April 2020 - graph-tool - archives.skewed.de

gt.draw_hierarchy not showing edges?
by Davide Cittaro 10 Apr '20

10 Apr '20

Hello, I'm trying to draw hierarchy from a NestedBlockState made like this <NestedBlockState object, with base <BlockState object with 2263 blocks (91 nonempty), degree-corrected, for graph <Graph object, directed, with 8272 vertices and 52548 edges, 1 internal vertex property, at 0x128d14110>, at 0x13ffa8f10>, and 10 levels of sizes [(8272, 91), (91, 34), (34, 10), (10, 3), (3, 2), (2, 1), (1, 1), (1, 1), (1, 1), (1, 1)] at 0x128cca590> when I run state.draw(output='foo.pdf') I obtain a picture with colored nodes on the outer circle, the hierarchy path but no bezier curves for edges. I've tried to draw only levels > 0 (so 91 nodes here) and it works like in graph_tool documentation examples. In order to draw edges I had to use gt.graph_draw passing edge_control_points (from gt.get_hierarchy_control_points). Is there some threshold in gt.draw_hierarchy that sets edge visibility if over/under a certain number of blocks? graph_tool 2.30 here Thanks d

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Re: [graph-tool] Extending SIR epidemic model with a quarantined/immunised state
by Edwin Lock 10 Apr '20

10 Apr '20

Thanks for getting back to me so quickly! I have a ‘quarantined’ vertex property map that maintains who is quarantined and have set a vertex filter on the graph to filter out quarantined people. Quarantined people are not supposed to be able to infect others. Here is a minimum working example where I quarantine all infected people from the graph but new people are infected in the next time step. # SETUP from graph_tool.generation import price_network from graph_tool.dynamics import SIRSState g = price_network(20) model = SIRSState(g, r=0) # make sure there are no spontaneous infections state = model.get_state() # Keep track of who is quarantined quarantined = g.new_vertex_property('bool', False) # Filter out quarantined nodes from graph g.set_vertex_filter(quarantined, inverted=True) # TEST: print("before quarantining:\n", state.a) # Quarantine all infected individuals quarantined.a = (state.a == 1) # Run model one time step model.iterate_sync() # New nodes in the filtered graph are infected (with some probability): print("filtered state after quarantining:\n", state.fa) For completeness, here is the complete code of my extended model: https://gist.github.com/edwinlock/ccece9b82d13383ef50df8b6e5aedf60 Another question: I want to stop quarantined people from infecting others, but I do want them to recover with the same probability as non-quarantined people. In this case it makes sense to use an edge filter to remove all edges adjacent to a quarantined person instead of filtering the person’s vertex itself, right? Best, Edwin > On 9 Apr 2020, at 22:23, graph-tool-request(a)skewed.de wrote: > > Date: Thu, 9 Apr 2020 23:23:07 +0200 > From: Tiago de Paula Peixoto <tiago(a)skewed.de <mailto:tiago@skewed.de>> > To: graph-tool(a)skewed.de <mailto:graph-tool@skewed.de> > Subject: Re: [graph-tool] Extending SIR epidemic model with a > quarantined/immunised state > Message-ID: <877547d7-5216-3a81-3cce-4e31422bed22(a)skewed.de <mailto:877547d7-5216-3a81-3cce-4e31422bed22@skewed.de>> > Content-Type: text/plain; charset="utf-8" > > Am 09.04.20 um 22:24 schrieb Edwin Lock: >> Initially, I thought I would be able maintain who is quarantined (and for how long) with a vertex property map and filter the quarantined individuals out from the network using a vertex filter. However, I noticed that the iterate_sync() function, which iterates the model by one time step, seems to ignore vertex filters. In particular, if I filter out all infected individuals, it will still infect new people. > > Using the epidemic models on filtered graphs should work. Maybe you > should try explaining in more detail how you are doing it so we can see > what the problem may be. > >> My second idea was to directly edit the vertex property maps that store the state of each individual in the graph (susceptible, infected and recovered), and introduce an additional ?immunised? state (with value 4, say). However, I have noticed that changing the values of instance.s and instance.s_copy does not seem to have any effect on iterate_sync(). In particular, if I mark everyone as susceptible and thus nobody as infected, it still infects new people in the next time step when I call iterate_sync(). So I am assuming that the state is also stored elsewhere? > > Indeed this approach will not work so well, as the logic of the dynamics > only differentiates between three states, not four. > >> Any pointers that might help me extend graph-tool?s SIR model to incorporate quarantining/immunising would be greatly appreciated. I have tried implementing my own model from scratch, but it is significantly slower. > > As mentioned above, filtered graphs should work. In addition, the SIR > model allows for different transmission probabilities on each edge, by > passing an edge property map as the 'beta' parameter, which you can set > to zero for the edges incident on quarantined nodes. If these change > over time, don't forget to pass the 'constant_beta=False' option as well. > > Best, > Tiago

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Extending SIR epidemic model with a quarantined/immunised state
by Edwin Lock 09 Apr '20

09 Apr '20

Dear All, I recently started looking into graph-tool because I was looking for a fast graph library to implement an SIR epidemic model. To my delight, I discovered that graph-tool has extremely fast epidemic models implemented already. My issue is that I wish to study the effects of immunising/quarantining individuals on the overall spread of the disease and am looking for a way to extend graph-tool’s model to achieve this. In particular, I wish to be able to quarantine people for a fixed amount of time (or indefinitely in the case of immunisation). Initially, I thought I would be able maintain who is quarantined (and for how long) with a vertex property map and filter the quarantined individuals out from the network using a vertex filter. However, I noticed that the iterate_sync() function, which iterates the model by one time step, seems to ignore vertex filters. In particular, if I filter out all infected individuals, it will still infect new people. My second idea was to directly edit the vertex property maps that store the state of each individual in the graph (susceptible, infected and recovered), and introduce an additional ‘immunised’ state (with value 4, say). However, I have noticed that changing the values of instance.s and instance.s_copy does not seem to have any effect on iterate_sync(). In particular, if I mark everyone as susceptible and thus nobody as infected, it still infects new people in the next time step when I call iterate_sync(). So I am assuming that the state is also stored elsewhere? Any pointers that might help me extend graph-tool’s SIR model to incorporate quarantining/immunising would be greatly appreciated. I have tried implementing my own model from scratch, but it is significantly slower. Best regards, Edwin

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Installation has changed
by Rolf Sander 08 Apr '20

08 Apr '20

Hello, There has been a recent update of the graph-tool installation instructions for Debian & Ubuntu (i.e. also for Linux Mint): https://git.skewed.de/count0/graph-tool/-/wikis/installation-instructions#g… For my Linux Mint 19.2 (Tina) system, I adjusted /etc/apt/sources.list accordingly: deb http://downloads.skewed.de/apt bionic main Unfortunately, apt-get update produces the following error: N: Skipping acquire of configured file 'main/binary-i386/Packages' as repository 'http://downloads.skewed.de/apt bionic InRelease' doesn't support architecture 'i386' Next, I tried to install graph-tool on another computer which has Linux Mint 18.3 (Sylvia) on it. For this I would need DISTRIBUTION = xenial. Unfortunately, it seems that graph-tool does not support xenial anymore :-( Any help is appreciated... Best regards Rolf -- ----------------------------------------------------------------------- Rolf Sander phone: [+49] 6131/305-4610 Max-Planck Institute of Chemistry email: rolf.sander(a)mpic.de PO Box 3060, 55020 Mainz, Germany homepage: www.rolf-sander.net ----------------------------------------------------------------------- https://www.encyclopedia-of-geosciences.net https://www.geoscientific-model-development.net -----------------------------------------------------------------------

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procedure for submitting merge requests?
by Jeff Trull 08 Apr '20

08 Apr '20

Hi! I have been following the usual Github-inspired flow of using a forked version of graph-tool to stage merge requests for graph-tool. Unfortunately it looks like my forked project has been deleted and attempting to re-fork fails (I have reached my "project limit"). What's the current procedure for submitting proposed fixes and improvements? Thanks and Regards, Jeff

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[PATCH 0/3] Fix deprecation warnings
by Alex Henrie 01 Apr '20

01 Apr '20

These commits resolve various warnings that appear when the environment variable PYTHONWARNINGS is set to "default" or "error". Since the ability to submit pull requests through git.skewed.de has apparently been disabled, I am sending the patches to the mailing list for your consideration. Please let me know if there is a different preferred way to submit patches now (e.g. by attaching them to a bug report). -Alex Alex Henrie (3): Turn off escape sequences in docstrings Use inspect.getfullargspec instead of inspect.getarcspec Use collections.abc.Iterable instead of collections.Iterable src/graph_tool/__init__.py | 8 ++++---- src/graph_tool/decorators.py | 2 +- src/graph_tool/inference/minimize.py | 6 +++--- src/graph_tool/inference/nested_blockmodel.py | 2 +- src/graph_tool/topology/__init__.py | 14 +++++++------- 5 files changed, 16 insertions(+), 16 deletions(-) -- 2.26.0

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get_edges_prob for every possible nonedge?
by Deklan Webster 01 Apr '20

01 Apr '20

I'm attempting to use get_edges_prob to find the most likely missing edges out of every possible non-edge. I know every possible edge is O(n^2). Currently I'm sampling the like this: non_edges_probs = [[] for _ in range(len(non_edges))] def collect_edge_probs(s): s = s.levels[0] for i, non_edge in enumerate(non_edges): p = s.get_edges_prob([non_edge], [], entropy_args=dict(partition_dl=False)) non_edges_probs[i].append(p) gt.mcmc_equilibrate(nested_state, force_niter=100, mcmc_args=dict(niter=10), callback=collect_edge_probs, verbose=True) Is there a way to speed this up at all? If not, is there a heuristic I can use to reduce the number of possibilities? Currently I'm using vertex similarity measures to cut the possibilities, but I'm wondering if there is a heuristic involving just the NestedState. Any help appreciated!

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Proper way to depickle NestedState?
by Deklan Webster 01 Apr '20

01 Apr '20

I'm saving NestedState with pickle and then loading it later to use get_edges_prob. nested_state = pickle.load( open(f"output/graphtool_output/pickle/{name}_nested_blockstate.p", "rb")) I realized that NestedState needs a reference to the underlying graph for get_edges_prob to work. So, I try this. G = gt.load_graph(f'output/graphtool_output/{name}.graphml') nested_state.g = G Now, get_edges_prob is working if I use the base level of the state, like so: def collect_edge_probs(s): s = s.levels[0] for i, non_edge in enumerate(non_edges): p = s.get_edges_prob([non_edge], [], entropy_args=dict(partition_dl=False)) non_edges_probs[i].append(p) If I remove this " s = s.levels[0]" I'm getting an error about "invalid edge descriptor". Is there some other reference I need to recover for this to work correctly? Any help appreciated!

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