Quickstart example

In the following example, we use the “test” MRIOT of the pymrio module, which doesn’t require to load additional data and has a low number of sectors and regions, allowing fast computation.

Instantiating a model from a MRIOT system

First import the required elements. We use the ARIOPsiModel version here (which you probably should).

# import pymrio for the test MRIOT
import pymrio
# import the different classes
from boario.extended_models import ARIOPsiModel  # The core of the model

Some attributes of the test MRIOT are not computed. Calling calc_all() insure all required tables are present in the IOSystem object.

mriot = pymrio.load_test().calc_all()

If you see multiple: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...) without axis instead.` Don’t panic, this comes from pymrio but it is not an error, just a synthax warning.

In case you find it annoying, you can use the following beforehand:

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

Instantiating the model is very straightforward:

model = ARIOPsiModel(mriot)

This sets the model with all the default parameters. We highly recommand you to study the different parameters and their options for actual use. See Changing the model parameters.

Similarly, instantiating a simulation is also quite easy:

from boario.simulation import Simulation  # Simulation wraps the model

sim = Simulation(model,n_temporal_units_to_sim=730)

Once the model and simulation have been instantiated, you can now define an event:

from boario.event import EventKapitalRebuild  # A class defining a shock on capital

# Instantiate an event.
ev = EventKapitalRebuild.from_scalar_regions_sectors(
  impact=500000,
  regions=["reg1"],
  sectors=["manufactoring", "mining"],
  impact_sectoral_distrib = "gdp",
  rebuilding_sectors={"construction": 0.55,"manufactoring": 0.45},
  rebuilding_factor=1.0,
  rebuild_tau=90,
)

Here we define an event happening in region reg1 impacting the productive capital of the manufactoring [1] and mining sectors. The impact is distributed among the two sectors proportional to their participation to reg1’s GDP.

Here the entire impact is translated into a reconstruction demand, handled by the construction (55%) and manufactoring (45%) sectors. This reconstruction has a characteristic time of 90 unit steps.

There are different types of Event subclasses implented in BoARIO, each with there specificities, to allow the modeling of different types of shocks. See Defining events, for more in depth explanation on how to define events.

[1]

This typo in the “manufacturing” sector’s name comes from pymrio.

The event can then be added to the simulation:

# Add the event to the simulation
sim.add_event(ev)

And the loop can be run:

# Launch the simulation
sim.loop()

Processed: Step: 730 ~ 100% Time:  0:00:01
Processed: Step: 730 ~ 100% Time:  0:00:01

Once the simulation ran, you can access and plot the timeseries of the variables (see Monitoring the variables for a list of accessible variables).

Here is an example showing the relative change in production in the different sectors in "reg1".

# You should be able to generate a dataframe of
# the production with the following line
df = sim.production_realised
# This allows to normalize production at its initial level
df = df / df.loc[0]

df.loc[:, ("reg1", slice(None))].plot()

<Axes: xlabel='step'>