Analyzing results

The solution object

On successfully solving a model, Calliope creates a solution, which is a multi-dimensional xarray.Dataset, with the model and run configuration stored as AttrDict attributes of the dataset (config_model and config_run).

The analysis tools included with Calliope expect to operate on a dataset.

The solution contains model variables such as rs, s, e_cap, r_area, etc, as well as variables derived from them such as capacity_factor and levelized_cost. It also contains several two-dimensional summary and metadata tables:

  • metadata: metadata for each technology (such as its stack_weight or color), used for analysis and plotting.
  • groups: definition of technology groups and their members.
  • shares: technology and group based shares of production, consumption and installed capacity (index is y).
  • summary: summary information on each technology.

Reading solutions

Calliope provides functionality to read a solution from a single NetCDF file or a collection of CSV files and re-construct a solution object for further analysis in a Python session:

solution_from_netcdf ='')

solution_from_csv ='path/to/output_directory')

Reading results from parallel runs

A successfully completed parallel run will contain multiple solutions inside its “Output” directory. To read all solutions, including information about the iterations they correspond to, use:

results ='path/to/Output')

The results variable is an AttrDict with two keys:

  • iterations: a DataFrame containing the iterations from this parallel run
  • solutions: an AttrDict with iteration IDs as keys and the individual solution objects as values

This allows easy access to and analysis of solutions.

Analyzing solutions

Refer to the API documentation for the analysis module for an overview of available analysis functionality.

Refer to the tutorials for some basic analysis techniques.


The built-in analysis and plotting functionality is still experimental. More documentation on it will be added in a future release.

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