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)

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}

ema_workbench.em_framework.util.combine(*args)

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, 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