Am 10.04.20 um 22:29 schrieb Edwin Lock:
> Hi Tiago,
>
> Thanks again for your helpful reply. I have now changed my approach to
> modifying the beta parameter instead, as you suggested in your previous
> reply. However, I’m still running into unexpected behaviour.
>
> I would expect that I can change the beta parameters after the model is
> instantiated, to modify the progress of infection between iterations of the
> model; at least that is what I thought the constant_beta flag is for.
> However, it seems that the model ignores any changes that I make to the beta
> map after instantiating. What am I doing or understanding wrong?
>
> Here’s my minimum working example (adapted to the new approach), where I set
> beta to 0 for all edges but new infections still happen.
>
> # SETUP
> from graph_tool.generation import price_network
> from graph_tool.dynamics import SIRSState
> g = price_network(20, directed=False)
> # Initialise beta map with transmission probability 1 for all edges
> beta_map = g.new_edge_property('float', 1)
> model = SIRSState(g, beta=beta_map, r=0,
> constant_beta=False) # ensure no spontaneous infections
> state = model.get_state()
>
> # TEST
> print("before quarantining:\n", state.a)
> # Set transmission probability to 0 for all edges
> beta_map.a = 0
> # Run model one time step
> model.iterate_sync()
> # New nodes in the filtered graph are infected
> print("filtered state after quarantining:\n", state.a)
>
> Essentially, I am looking for some way to isolate vertices and ensure they
> cannot infect others.
This is in fact a bug with constant_beta=False: for efficiency, the
state keeps track of the total transmission probability per node,
separately from what beta says. So the change in beta will take effect
only the next time a node is infected.
So for now you will have to re-initialize the state every time you
change beta (or filter the graph), until I fix things for the next release.
> On a related note, I am running into issues implementing the following
> subroutine efficiently: for a list of vertices (as a numpy integer array),
> determine the indices of all edges in the graph incident to at least one of
> the vertices in the list. Here the index is w.r.t. edge property maps. Is
> there an efficient way using a graph-tool function that I’m missing?
You can get something similar to this by marking the nodes with a
boolean property map and then calling edge_endpoint_property() which
will mark the incident edges.
Best,
Tiago
--
Tiago de Paula Peixoto <[email protected]>
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