For version 0.2.0, the outputlib has been refactored. Tools for plotting optimization results that were part of the outputlib in earlier versions are no longer part of this module as the requirements to plotting functions greatly depend on individial requirements.

Basic functions for plotting of optimisation results are now found in a separate repository oemof_visio.

The main purpose of the outputlib is to collect and organise results. It gives back the results as a python dictionary holding pandas Series for scalar values and pandas DataFrames for all nodes and flows between them. This way we can make use of the full power of the pandas package available to process the results.

See the pandas documentation to learn how to visualise, read or write or how to access parts of the DataFrame to process them.

The documentation of the outputlib consists of three parts:

The first step is the processing of the results (Collecting results). This is followed by basic examples of the general analysis of the results (General approach) and finally the use of functionalities already included in the outputlib for providing a quick access to your results (Easy access). Especially for larger energy systems the general approach will help you to write your own results processing functions.

Collecting results

Collecting results can be done with the help of the processing module:

results = outputlib.processing.results(om)

The scalars and sequences describe nodes (with keys like (node, None)) and flows between nodes (with keys like (node_1, node_2)). You can directly extract the data in the dictionary by using these keys, where “node” is the name of the object you want to address. Processing the results is the prerequisite for the examples in the following sections.

General approach

As stated above, after processing you will get a dictionary with all result data. If you want to access your results directly via labels, you can continue with Easy access. For a systematic analysis list comprehensions are the easiest way of filtering and analysing your results.

The keys of the results dictionary are tuples containing two nodes. Since flows have a starting node and an ending node, you get a list of all flows by filtering the results using the following expression:

flows = [x for x in results.keys() if x[1] is not None]

On the same way you can get a list of all nodes by applying:

nodes = [x for x in results.keys() if x[1] is None]

Probably you will just get storages as nodes, if you have some in your energy system. Note, that just nodes containing decision variables are listed, e.g. a Source or a Transformer object does not have decision variables. These are in the flows from or to the nodes.

All items within the results dictionary are dictionaries and have two items with ‘scalars’ and ‘sequences’ as keys:

for flow in flows:

There many options of filtering the flows and nodes as you prefer. The following will give you all flows which are outputs of transformer:

flows_from_transformer = [x for x in flows if isinstance(
    x[0], solph.Transformer)]

You can filter your flows, if the label of in- or output contains a given string, e.g.:

flows_to_elec = [x for x in results.keys() if 'elec' in x[1].label]

Getting all labels of the starting node of your investment flows:

flows_invest = [x[0].label for x in flows if hasattr(
    results[x]['scalars'], 'invest')]

Easy access

The outputlib provides some functions which will help you to access your results directly via labels, which is helpful especially for small energy systems. So, if you want to address objects by their label, you can convert the results dictionary such that the keys are changed to strings given by the labels:

print(results[('wind', 'bus_electricity')]['sequences']

Another option is to access data belonging to a grouping by the name of the grouping (note also this section on groupings. Given the label of an object, e.g. ‘wind’ you can access the grouping by its label and use this to extract data from the results dictionary.

node_wind = energysystem.groups['wind']
print(results[(node_wind, bus_electricity)])

However, in many situations it might be convenient to use the views module to collect information on a specific node. You can request all data related to a specific node by using either the node’s variable name or its label:

data_wind = outputlib.views.node(results, 'wind')

A function for collecting and printing meta results, i.e. information on the objective function, the problem and the solver, is provided as well:

meta_results = outputlib.processing.meta_results(om)