Creates a sample using Latin Hypercube and assuming uniform distributions for parameters defined in config/parameters.csv. Creates a new datapackage modified with the sample values from a template datapackage (path provided in config/config.yaml) and creates the LP file using the osemosys model (also provided in config/config.yaml). The model run is solved in parallel using CBC and results are post-processed and placed in results folder with the modelrun name.
I've removed the rule-specific conda environments, which means it should be possible to run this on Windows. Follow the installation instructions in the readme to create a new conda environment containing all the dependencies needed for all the rules.
Creates a sample using Latin Hypercube and assuming uniform distributions for parameters defined in
config/parameters.csv. Creates a new datapackage modified with the sample values from a template datapackage (path provided inconfig/config.yaml) and creates the LP file using the osemosys model (also provided inconfig/config.yaml). The model run is solved in parallel using CBC and results are post-processed and placed inresultsfolder with the modelrun name.I've removed the rule-specific conda environments, which means it should be possible to run this on Windows. Follow the installation instructions in the readme to create a new conda environment containing all the dependencies needed for all the rules.