Part 6: Using the model for a study

As we have seen, model components and models are implemented in an object-oriented way as subpackages and modules of the pycopancore package folder. Studies that use a model for a simulation experiment are however implemented as python scripts and reside in the study folder. So let us now switch into the role of a model end user and perform some such ‘study’. Again, we can use a template to get started:

• Copy templates/studies/SOME_STUDY.py to studies/run_my_exploit.py and edit the imports like this:

  import pycopancore.models.my_exploit as M
  

• In this study, we have only one social system and allow our individuals to have only two possible fishing_efforts, hence we adjust the parameters as follows:

  # model parameters:
  
  ncells = 100
  nindseach = 1  # no. of individuals per cell
  link_density = 0.1  # random network link density
  low_effort = 30 * D.person_hours / D.weeks
  high_effort = 60 * D.person_hours / D.weeks
  
  # simulation parameters:
  
  t_max = 100  # interval for which the model will be simulated
  dt = 1  # desired temporal resolution of the resulting output.
  

• Adjust the entity generation as follows:

  world = M.World(environment = env, metabolism = met, culture = cul)
  soc = M.SocialSystem(world = world)
  cells = [M.Cell(social_system = soc) for j in range(ncells)]
  inds = [M.Individual(cell = c,
              fishing_effort = choice([low_effort, high_effort])
              ) for c in cells for j in range(nindseach)]
  

Note how we have already set all individuals’ initial fishing effort here. Another possibility for setting initial values for a whole list of entities at the same time is by using the set_values method of the corresponding Variable object in the entity-type’s class. Let’s do this for the initial fish stocks:

• Replace the random population block by this similar code:

  S0 = uniform(size=ncells)
  M.Cell.fish_stock.set_values(cells, S0)
  

Note that here we did not specify a unit, so the numbers will be interpreted as multiples of the variable’s default unit (t_fish in this case, as specified in the interface of my_exploit_growth).

A third possibility to manipulate the initial value of a variable for some specific entity or process taxon is by accessing the variable’s value directly, so we could have instead written:

for c in cells:
    c.fish_stock = uniform() * M.t_fish

We use this way of accessing values now for initializing the social network between the individuals, which is stored in the variable Culture.acquaintance_network. Since this is shipped with the base component of pycopancore, it was automatically initialized to contain an empty network when Culture was instantiated above. Likewise, each Individual that was generated above has already registered itself automatically as a node of this network. So the only thing that remains for us to do is add some links. Since this is a common task, the template already contains suitable code for this:

# initialize some network:
for index, i in enumerate(inds):
    for j in inds[:index]:
        if uniform() < link_density:
            cul.acquaintance_network.add_edge(i, j)

The subsequent code block eventually runs the model, and we can also leave it as it is:

runner = Runner(model=model)
traj = runner.run(t_0=0, t_1=t_max, dt=dt)

After this simulation has finished, the traj object returned by Runner.run() contains the time evolution of all variables from t_0 to t_1 in steps which are at most dt apart. The actual time steps might vary since our model has irregularly timed events at completely random time points and the runner returns all event time points plus sufficiently many intermediate time points. Since at event time points some variables will display discontinuous behaviour, the runner actually returns two entries for each such event time point t (but not for the intermediate time points), the first containing the variable values right before t, the second those right after t.

The precise data structure of traj is this:

• traj['t'] is the list of reported time points

• traj[M.Cell.fish_stock][c] is the list of corresponding fish stocks of cell c.

Hence if we want the total fish stock and average fishing effort plotted as the final step of our study, we can do it like this:

• Adjust the final plotting code as follows:

  stock = traj[M.Cell.fish_stock]
  effort = traj[M.Individual.fishing_effort]
  total_stock = np.sum([stock[c] for c in cells], axis=0)
  avg_effort = np.mean([effort[i] for i in inds], axis=0)
  plt.plot(traj["t"], total_stock, 'g', label="fish stock")
  plt.plot(traj["t"], avg_effort, 'b', label="fishing effort")
  plt.legend()
  plt.show()
  

This finishes our coding work, so let’s finally try it out and hope we made no typos: Part 7: Running the study script