util

utilities used throughout em_framework.

class ema_workbench.em_framework.util.Counter(startfrom=0)

helper function for generating counter based names for NamedDicts.

class ema_workbench.em_framework.util.NamedDict(name=<function representation>, **kwargs)

Named dictionary class.

copy()

Return a shallow copy of this object.

class ema_workbench.em_framework.util.NamedObject(name: str, **kwargs)

Base object with a name attribute.

class ema_workbench.em_framework.util.NamedObjectMap(kind: type[T])

A named object mapping class.

clear() → None.  Remove all items from D.

keys() → a set-like object providing a view on D's keys

class ema_workbench.em_framework.util.NamedObjectMapDescriptor(kind)

Descriptor class for named objects.

class ema_workbench.em_framework.util.ProgressTrackingMixIn(N, reporting_interval, logger, log_progress=False, log_func=<function ProgressTrackingMixIn.<lambda>>)

Mixin for monitoring progress.

Parameters:

• N (int) – total number of experiments

• reporting_interval (int) – nfe between logging progress

• logger (logger instance)

• log_progress (bool, optional)

• log_func (callable, optional) – function called with self as only argument, should invoke self._logger with custom log message

i

Type:

int

reporting_interval

Type:

int

log_progress

Type:

bool

log_func

Type:

callable

pbar

if log_progress is true, None, if false tqdm.tqdm instance

Type:

{None, tqdm.tqdm instance}

class ema_workbench.em_framework.util.Variable(name: str, variable_name: str | list[str] | None = None)

Root class for input parameters and outcomes.

ema_workbench.em_framework.util.combine(*args) → dict

Combine scenario and policy into a single experiment dict.

Parameters:

args (two or more dicts that need to be combined)

Return type:

a single unified dict containing the entries from all dicts

Raises:

EMAError – if a keyword argument exists in more than one dict

ema_workbench.em_framework.util.determine_objects(models, attribute: Literal['uncertainties', 'levers', 'outcomes'], union=True)

Determine the parameters over which to sample.

Parameters:

• models (a collection of AbstractModel instances)

• attribute ({'uncertainties', 'levers', 'outcomes'})

• union (bool, optional) – in case of multiple models, sample over the union of levers, or over the intersection of the levers

Return type:

collection of Parameter instances

ema_workbench.em_framework.util.representation(named_dict)

Helper function for generating repr based names for NamedDicts.