Simulation

class Simulation(model, register_stocks=False, n_temporal_units_to_sim=365, events_list=None, separate_sims=False, save_events=False, save_params=False, save_index=False, save_records=[], boario_output_dir='/tmp/boarioidvaarbm', results_dir_name=None)

Bases: object

Defines a simulation object with a set of parameters and an IOSystem.

This class wraps a ARIOBaseModel or descendant, and create the context for simulations using this model. It stores execution parameters as well as events perturbing the model.

Methods

__init__(model[, register_stocks, ...])	A Simulation instance can be initialized with the following parameters:
add_event(ev)	Add an event to the simulation.
add_events(events)	Add a list of events to the simulation.
check_equilibrium(n_checks)	Checks the status of production, stocks and rebuilding demand.
loop([progress])	Launch the simulation loop.
next_step([check_period, min_steps_check, ...])	Advance the model to the next temporal step.
reset_sim_full()	Resets the model to its initial status and remove all events.
reset_sim_with_same_events()	Resets the model to its initial status (without removing the events).
write_index(index_file)	Write the index of the dataframes used in the model in a json file.

Attributes

final_demand	Return the evolution of final demand asked of each industry.
final_demand_unmet	Return the evolution of the final demand that could not be answered by industries.
inputs_stocks	Return the evolution of the inventory amount of each input for each industry (region,sector).
intermediate_demand	Return the evolution of intermediate demand asked of each industry (Total orders).
limiting_inputs	Return the evolution of the inputs lacking for each industry (region,sector)
overproduction	Return the evolution of the overproduction factor of each industry (region,sector)
production_capacity	Return the evolution of the production capacity of each industry (region,sector)
production_realised	Return the evolution of the production realised by each industry (region,sector)
productive_capital_to_recover	Return the evolution of remaining capital destroyed/to recover for each industry (region,sector) if it exists.
rebuild_demand	Return the evolution of rebuild demand asked of each industry (Total orders).
rebuild_prod	Return the production allocated for the rebuilding demand by each industry (region,sector).
output_dir	pathlib.Path, optional: Optional path to the directory where output are stored.
results_storage	str: Name of the folder in output_dir where the results will be stored if saved.
model	Union[ARIOBaseModel, ARIOPsiModel] : The model to run the simulation with.
all_events	list[Event]: A list containing all events associated with the simulation.
currently_happening_events	list[Event]: A list containing all events that are happening at the current timestep of the simulation.
n_temporal_units_to_sim	int: The total number of temporal_units to simulate.
current_temporal_unit	int: Tracks the number of temporal_units elapsed since simulation start.
records_storage	pathlib.Path: Place where records are stored if stored
params_dict	dict: A dictionary saving the parameters the simulation was run with.

output_dir

pathlib.Path, optional: Optional path to the directory where output are stored.

results_storage

str: Name of the folder in output_dir where the results will be stored if saved.

model

Union[ARIOBaseModel, ARIOPsiModel] : The model to run the simulation with. See ARIOBaseModel.

all_events

list[Event]: A list containing all events associated with the simulation.

currently_happening_events

list[Event]: A list containing all events that are happening at the current timestep of the simulation.

n_temporal_units_to_sim

int: The total number of temporal_units to simulate.

current_temporal_unit

int: Tracks the number of temporal_units elapsed since simulation start. This may differs from the number of steps if the parameter n_temporal_units_by_step differs from 1 temporal_unit as current_temporal_unit is actually step * n_temporal_units_by_step.

records_storage

pathlib.Path: Place where records are stored if stored

params_dict

dict: A dictionary saving the parameters the simulation was run with.

loop(progress=True)

Launch the simulation loop.

This method launch the simulation for the number of steps to simulate described by the attribute self.n_temporal_units_to_sim, calling the next_step() method. For convenience, it dumps the parameters used in the logs just before running the loop. Once the loop is completed, it flushes the different memmaps generated.

Parameters:

progress: bool, default: True

If True, shows a progress bar of the loop in the console.

next_step(check_period=182, min_steps_check=None, min_failing_regions=None)

Advance the model to the next temporal step.

This method wraps all computation required to advance to the next step of the simulation.

First it checks if an event is planned to occur at the current step and if so, shocks the model with the corresponding event. Then:

0. If at least one step elapsed, it computes the new overproduction vector for the next step (using calc_overproduction())

1. Computes production for the current step. (See calc_production())

2. Distribute the realised production towards the different demands (intermediate, final, rebuilding) and compute the changes in the inputs stock matrix (see distribute_production())

Note that it is during this step that the model checks if an event is completely rebuild/recovered and removes it from the list the case being.

3. Computes the orders matrix (i.e. the intermediate demand) for the next step (see calc_orders())

Additionally, once every check_period steps elapsed, it checks for crash or equilibrium of the economy (see check_equilibrium()).

Parameters:

check_periodint

The time period in number of temporal units to wait between each “crash/equilibrium” check.

min_steps_checkOptional[int]

The minimum wait before the first check.

min_failing_regionsOptional[int]

The minimum number of failing regions required to consider economy has crashed.

check_equilibrium(n_checks)

Checks the status of production, stocks and rebuilding demand.

This methods checks and store the status of production, inputs stocks and rebuilding demand and store the information in self.equi.

At the moment, the following status are implemented:

• production and stocks can be greater (ie all industries are producing more), equi (ie all industries produce almost the same as at initial equilibrium (0.01 atol)), not equi (ie neither of the previous case)

• rebuilding demand can be finished or not finished depending if some events still have some rebuilding demand unanswered or if all are completely rebuilt.

Parameters:

n_checksint

The number of checks counter.

add_events(events)

Add a list of events to the simulation.

Parameters:

eventslist[Event]

The events to add.

add_event(ev)

Add an event to the simulation.

Parameters:

evEvent

The event to add.

reset_sim_with_same_events()

Resets the model to its initial status (without removing the events). [WIP]

reset_sim_full()

Resets the model to its initial status and remove all events.

write_index(index_file)

Write the index of the dataframes used in the model in a json file.

See write_index() for a more detailed documentation.

Parameters:

index_fileUnion[str, pathlib.Path]

name of the file to save the indexes to.

property production_realised

Return the evolution of the production realised by each industry (region,sector)

Returns:

pd.DataFrame: A pandas DataFrame where the value is the production realised, the columns are the industries and the index is the step considered.

property production_capacity

Return the evolution of the production capacity of each industry (region,sector)

Returns:

pd.DataFrame: A pandas DataFrame where the value is the production capacity, the columns are the industries and the index is the step considered.

property final_demand

Return the evolution of final demand asked of each industry.

Returns:

pd.DataFrame: A pandas DataFrame where the value is the final demand asked, the columns are the industries and the index is the step considered.

property intermediate_demand

Return the evolution of intermediate demand asked of each industry (Total orders).

Returns:

pd.DataFrame: A pandas DataFrame where the value is the intermediate demand asked, the columns are the industries and the index is the step considered.

property rebuild_demand

Return the evolution of rebuild demand asked of each industry (Total orders).

Returns:

pd.DataFrame: A pandas DataFrame where the value is the rebuild demand asked, the columns are the industries and the index is the step considered.

property overproduction

Return the evolution of the overproduction factor of each industry (region,sector)

Returns:

pd.DataFrame: A pandas DataFrame where the value is the overproduction factor, the columns are the industries and the index is the step considered.

property final_demand_unmet

Return the evolution of the final demand that could not be answered by industries.

Returns:

pd.DataFrame: A pandas DataFrame where the value is the final demand not met, the columns are the industries and the index is the step considered.

property rebuild_prod

Return the production allocated for the rebuilding demand by each industry (region,sector).

Returns:

pd.DataFrame: A pandas DataFrame where the value is the production allocated, the columns are the industries and the index is the step considered.

property inputs_stocks

Return the evolution of the inventory amount of each input for each industry (region,sector). Not this is not available if “record_stocks” is not set to True, as the DataFrame can be quite large for “classic” MRIOTs.

Returns:

pd.DataFrame: A pandas DataFrame where the value is the amount in inventory, the columns are the industries and the index are the step and input considered (MultiIndex).

property limiting_inputs

Return the evolution of the inputs lacking for each industry (region,sector)

Returns:

pd.DataFrame: A pandas DataFrame where the value is a boolean set to 1 if considered input constrains production, the columns are the industries and the index are the step and input considered (MultiIndex).

property productive_capital_to_recover

Return the evolution of remaining capital destroyed/to recover for each industry (region,sector) if it exists.

Returns:

pd.DataFrame: A pandas DataFrame where the value is the amount of capital (still) destroyed, the columns are the industries and the index is the step considered.