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Analysing a model

Calliope inputs and results are designed for easy handling. Whatever software you prefer to use for data processing, either the NetCDF or CSV output options should provide a path to importing your Calliope results.

Example

Refer to the examples and tutorials section section for a more practical look at how to analyse model results.

Easiest approach: Using Calligraph

The most straightforward approach to analysing model results is to use Calligraph, our separate tool for visualising Calliope results:

After having run your model and saved its results to NetCDF (e.g., calliope run my_model.yaml --save_netcdf=results.nc), you can open the resulting NetCDF file with Calligraph simply by running:

$ calligraph results.nc

This will open up Calligraph's interactive in-browser interface. For more, see the Calligraph documentation.

Accessing model data and results within Python

A model which solved successfully has two primary [xarray.Dataset][]s with data of interest:

  • model.inputs: contains all input data, such as renewable resource capacity factors.
  • model.results: contains all results data, such as dispatch decisions and installed capacities. It also includes results calculated in postprocessing, such as levelised cost of electricity (LCOE) and capacity factor.

Both of these are an [xarray.Dataset][] and can be further processed with Python.

Data is indexed over a subset of the core Calliope dimensions, e.g. techs (technologies), nodes, timesteps. Not all combinations of the dimensions items will contain data; if you did not define your PV technology at node X1 then there will be no data for model.inputs.flow_cap.sel(techs="PV", nodes="X1"). In fact, there is likely to be more empty (NaN) data points than filled ones. In Python you can quickly "densify" your data to look at only filled data points: model.inputs.flow_cap.to_series().dropna()

Note

On saving to CSV, each data variable is saved to its own file with all empty data points removed.

Reading solutions

Calliope provides functionality to read a previously-saved model from a single NetCDF file:

solved_model = calliope.read_netcdf('my_saved_model.nc')

Once loaded, the input and results data can be accessed as above (i.e., solved_model.inputs and solved_model.results).

Warning

Calliope's NetCDF files can be processed with any other tool that can deal with NetCDF. However, certain model attributes are serialised on saving the model to ensure the convention is followed. To view the model as intended, use Calliope functionality to read the NetCDF.

See also

The [xarray][] documentation should be consulted for further information on dealing with Datasets.

Visualisation

You can visualise Calliope results with: