points

classes for representing points in parameter space, as well as associated hellper functions

class ema_workbench.em_framework.points.Experiment(name, model_name, policy, scenario, experiment_id)

A convenience object that contains a specification of the model, policy, and scenario to run

name

Type

str

model_name

Type

str

policy

Type

Policy instance

scenario

Type

Scenario instance

experiment_id

Type

int

class ema_workbench.em_framework.points.ExperimentReplication(scenario, policy, constants, replication=None)

helper class that combines scenario, policy, any constants, and replication information (seed etc) into a single dictionary.

This class represent the complete specification of parameters to run for a given experiment.

class ema_workbench.em_framework.points.Policy(name=None, **kwargs)

Helper class representing a policy

name

Type

str, int, or float

id

Type

int

all keyword arguments are wrapped into a dict.

class ema_workbench.em_framework.points.Scenario(name=None, **kwargs)

Helper class representing a scenario

name

Type

str, int, or float

id

Type

int

all keyword arguments are wrapped into a dict.

ema_workbench.em_framework.points.combine_cases_factorial(*point_collections)

Combine collections of cases in a full factorial manner

Parameters

point_collections (collection of collections of Point instances) –

Yields

Point

ema_workbench.em_framework.points.combine_cases_sampling(*point_collection)

Combine collections of cases by iterating over the longest collection while sampling with replacement from the others

Parameters

point_collection (collection of collection of Point instances) –

Yields

Point

ema_workbench.em_framework.points.experiment_generator(scenarios, model_structures, policies, combine='factorial')

generator function which yields experiments

Parameters

• scenarios (iterable of dicts) –

• model_structures (list) –

• policies (list) –

• {'factorial (combine =) – controls how to combine scenarios, policies, and model_structures into experiments.

• sample'} – controls how to combine scenarios, policies, and model_structures into experiments.

Notes

if combine is ‘factorial’ then this generator is essentially three nested loops: for each model structure, for each policy, for each scenario, return the experiment. This means that designs should not be a generator because this will be exhausted after the running the first policy on the first model. if combine is ‘zipover’ then this generator cycles over scenarios, policies and model structures until the longest of the three collections is exhausted.