event#

Functions

`from_scalar_industries`(impact, *, ...[, ...])	Creates an Event from a scalar and a list of industries affected.
`from_scalar_regions_sectors`(impact, *, ...)	Creates an Event from a scalar, a list of regions and a list of sectors affected.
`from_series`(impact, *, event_type[, ...])	Create an event for an impact given as a pd.Series.

Classes

`Event`(*, impact[, name, occurrence, duration])	An Event object stores all information about a unique shock during simulation such as time of occurrence, duration, type of shock, amount of damages.
`EventArbitraryProd`(*, impact[, ...])	An EventArbitraryProd object holds an event with arbitrary impact on production capacity.
`EventKapitalDestroyed`(*, impact[, ...])	EventKapitalDestroyed is an abstract class to hold events with where some capital (from industries or households) is destroyed.
`EventKapitalRebuild`(*, impact[, ...])	EventKapitalRebuild holds a `EventKapitalDestroyed` event where the destroyed capital requires to be rebuilt, and creates a reconstruction demand.
`EventKapitalRecover`(*, impact, recovery_tau)	EventKapitalRecover holds a `EventKapitalDestroyed` event where the destroyed capital does not create a reconstruction demand.

Functions documentation

from_scalar_industries(impact, *, event_type, affected_industries, impact_distrib, occurrence=1, duration=1, name=None, event_monetary_factor=None, recovery_tau=None, recovery_function='linear', households_impact=None, rebuild_tau=None, rebuilding_sectors=None, rebuilding_factor=1.0)[source]#

Creates an Event from a scalar and a list of industries affected.

The scalar impact is distributed evenly by default. Otherwise it can be distributed proportionnaly to the GVA of affected industries, or to a custom distribution.

Parameters:

impactScalarImpact: The scalar impact.
event_type: Literal[“recovery”] | Literal[“rebuild”] | Literal[“arbitrary”]: The type of events to generate. See How to define Events.
affected_industriesIndustriesList: The list of industries affected by the impact.
impact_distribpd.Series | Literal[“equal”]: If “equal”, distributes the impact equally between the affected industries. If a pd.Series of industries <> value is given, then the impact is distributed proportionally to that Series.
durationint, default 1: The duration of the event (number of steps before which the recovery starts). Defaults to 1.
occurrenceint, default 1: The ordinal of occurrence of the event (requires to be > 0). Defaults to 1.
nameOptional[str], default None: A possible name for the event, for convenience. Defaults to None.
**kwargs: Keyword arguments to pass to the instantiating method (depending on the type of event).

Returns:

Event: An Event object or one of its subclass.

Raises:

ValueError: Raised if Impact is null, if len(industries) < 1 or if the sum of impact_industries_distrib differs from 1.0.

from_scalar_regions_sectors(impact, *, event_type, affected_regions, affected_sectors, impact_regional_distrib, impact_sectoral_distrib, occurrence=1, duration=1, name=None, event_monetary_factor=None, recovery_tau=None, recovery_function='linear', households_impact=None, rebuild_tau=None, rebuilding_sectors=None, rebuilding_factor=1.0)[source]#

Creates an Event from a scalar, a list of regions and a list of sectors affected.

Parameters:

impactScalarImpact: The scalar impact.
event_type: Literal[“recovery”] | Literal[“rebuild”] | Literal[“arbitrary”]: The type of events to generate. See How to define Events.
affected_regionsRegionsList: The list of regions affected by the impact.
affected_sectorsSectorsList: The list of regions affected by the impact.
impact_regional_distribLiteral[“equal”] | pd.Series,: If “equal”, distributes the impact equally between the affected regions. If a pd.Series of regions : value is given, then the total impact is distributed proportionally to that Series.
impact_sectoral_distribLiteral[“equal”] | pd.Series,: If “equal”, distributes the regional impact equally between the affected sectors. If a pd.Series of sector : value is given, then the regional impact is distributed proportionally to that Series.
durationint, default 1: The duration of the event (number of steps before which the recovery starts). Defaults to 1.
occurrenceint, default 1: The ordinal of occurrence of the event (requires to be > 0). Defaults to 1.
nameOptional[str], default None: A possible name for the event, for convenience. Defaults to None.
**kwargs: Keyword arguments to pass to the instantiating method (depending on the type of event).
occurrenceint, optional: The ordinal of occurrence of the event (requires to be > 0). Defaults to 1.
durationint, optional: The duration of the event (entire impact applied during this number of steps). Defaults to 1.
nameOptional[str], optional: A possible name for the event, for convenience. Defaults to None.
**kwargs: Keyword arguments Other keyword arguments to pass to the instantiate method (depends on the type of event)

Returns:

Event: An Event object or one of its subclass.

Raises:

ValueError: Raise if Impact is null, if len(regions) or len(sectors) < 1,

from_series(impact, *, event_type, occurrence=1, duration=1, name=None, event_monetary_factor=None, recovery_tau=None, recovery_function='linear', households_impact=None, rebuild_tau=None, rebuilding_sectors=None, rebuilding_factor=1.0)[source]#

Create an event for an impact given as a pd.Series.

Parameters:

impactpd.Series: A pd.Series defining the impact per (region, sector)
event_type: Literal[“recovery”] | Literal[“rebuild”] | Literal[“arbitrary”]: The type of events to generate. See How to define Events.
durationint, default 1: The duration of the event (number of steps before which the recovery starts). Defaults to 1.
occurrenceint, default 1: The ordinal of occurrence of the event (requires to be > 0). Defaults to 1.
nameOptional[str], default None: A possible name for the event, for convenience. Defaults to None.
**kwargs: Keyword arguments to pass to the instantiating method (depending on the type of event).

Returns:

Event: An Event object or one of its subclass

Raises:

ValueError: Raised if impact is empty of contains negative values.

Examples

>>> import pandas as pd
>>> import pymrio as pym
>>> from boario.simulation import Simulation
>>> from boario.extended_model import ARIOPsiModel
>>> import boario.event
>>>
>>> mriot = pym.load_test()
>>> mriot.calc_all()
>>>
>>> impact_series = pd.Series({('reg1', 'electricity'): 100000.0, ('reg1', 'mining'): 50000.0})
>>> model = ARIOPsiModel(mriot)
>>> sim = Simulation(model)
>>> event = boario.event.from_series(impact_series, event_type="recovery", occurrence=5, duration=10, recovery_tau=30, name="Event 1")
>>> sim.add_event(event)