utilities

This module provides various convenience functions and classes.

ema_workbench.util.utilities.load_results(file_name)

load the specified bz2 file. the file is assumed to be saves using save_results.

Parameters:

file_name (str) – the path to the file

Raises:

IOError if file not found –

ema_workbench.util.utilities.merge_results(results1, results2)

convenience function for merging the return from perform_experiments().

The function merges results2 with results1. For the experiments, it generates an empty array equal to the size of the sum of the experiments. As dtype is uses the dtype from the experiments in results1. The function assumes that the ordering of dtypes and names is identical in both results.

A typical use case for this function is in combination with experiments_to_cases(). Using experiments_to_cases() one extracts the cases from a first set of experiments. One then performs these cases on a different model or policy, and then one wants to merge these new results with the old result for further analysis.

Parameters:

• results1 (tuple) – first results to be merged

• results2 (tuple) – second results to be merged

Return type:

the merged results

ema_workbench.util.utilities.process_replications(data, aggregation_func=<function mean>)

Convenience function for processing the replications of a stochastic model’s outcomes.

The default behavior is to take the mean of the replications. This reduces the dimensionality of the outcomes from (experiments * replications * outcome_shape) to (experiments * outcome_shape), where outcome_shape is 0-d for scalars, 1-d for time series, and 2-d for arrays.

The function can take either the outcomes (dictionary: keys are outcomes of interest, values are arrays of data) or the results (tuple: experiments as DataFrame, outcomes as dictionary) of a set of simulation experiments.

Parameters:

• data (dict, tuple) – outcomes or results of a set of experiments

• aggregation_func (callabale, optional) – aggregation function to be applied, defaults to np.mean.

Return type:

dict, tuple

ema_workbench.util.utilities.save_results(results, file_name)

save the results to the specified tar.gz file.

The way in which results are stored depends. Experiments are saved as csv. Outcomes depend on the outcome type. Scalar, and <3D arrays are saved as csv files. Higher dimensional arrays are stored as .npy files.

Parameters:

• results (tuple) – the return of perform_experiments

• file_name (str) – the path of the file

Raises:

IOError if file not found –