metawards.utils.run_model(network: Union[metawards._network.Network, metawards._networks.Networks], infections: metawards._infections.Infections, rngs, output_dir: metawards._outputfiles.OutputFiles, population: metawards._population.Population = Population(initial=57104043, susceptibles=0, latent=0, total=0, totals=None, other_totals=None, recovereds=0, n_inf_wards=0, scale_uv=1.0, day=0, date=None), nsteps: Optional[int] = None, profiler: Optional[metawards.utils._profiler.Profiler] = None, nthreads: Optional[int] = None, iterator: Optional[Union[str, Callable[[...], None]]] = None, extractor: Optional[Union[str, Callable[[...], None]]] = None, mixer: Optional[Union[str, Callable[[...], None]]] = None, mover: Optional[Union[str, Callable[[...], None]]] = None) metawards._population.Populations[source]

Actually run the model… Real work happens here. The model will run until completion or until ‘nsteps’ have been completed, whichever happens first.

  • network (Network or Networks) – The network(s) on which to run the model

  • infections (Infections) – The space used to record the infections

  • rngs (list) – The list of random number generators to use, one per thread

  • population (Population) – The initial population at the start of the model outbreak. This is also used to set the date and day of the start of the model outbreak

  • seed (int) – The random number seed used for this model run. If this is None then a very random random number seed will be used

  • output_dir (OutputFiles) – The directory to write all of the output into

  • nsteps (int) – The maximum number of steps to run in the outbreak. If None then run until the outbreak has finished

  • profiler (Profiler) – The profiler to use to profile - a new one is created if one isn’t passed

  • nthreads (int) – Number of threads over which to parallelise this model run

  • iterator (MetaFunction or string) – Function that will be used to dynamically get the functions that will be used at each iteration to advance the model. Any additional files or parameters needed by these functions should be included in the network.params object.

  • extractor (MetaFunction or string) – Function that will be used to dynamically get the functions that will be used at each iteration to extract data from the model run

  • mixer (MetaFunction or string) – Function that will mix data from multiple demographics so that this is shared during a model run

  • mover (MetaFunction or string) – Function that can move the population between different demographics


trajectory – The trajectory of the population for every day of the model run

Return type