This project is currently in Alpha development. Precise content and included features are still open to debate. Consider this if you are interested in using this package.
This project is currently being released to work with pip for an easy install.
To install ETMeta use the following command (in your venv of choice).
pip install https://github.com/thesethtruth/ETMeta/archive/main.zip
Dependencies are specified in the setup.py without versioning to retain flexibility for users. Please make sure that you have latest versions of the modules listed below.
pandasnumpyrequestsbeautifulsoup4openpyxlseaborn
If you want to use ema_workbench, please use the documentation to install this package and optional dependencies.
To make give scenario generation more overview, this packages uses Excel worksheets to generate ETM slider values and track sources within the Excel in any way the user sees fit. A collection of scenarios slider settings can be requested from the ETM and listed within a single Excel file. The same Excel can later be used to create new scenarios based on values within the Excel sheet. Just refer to the columns of the to-be-created scenarios as range or list. Column letters will automatically be converted (e.g. 'A' == 0 index) and column headers will be used as scenario titles, unless titles are explicitly supplied.
In the tutorials below minimal examples are given with some more comments on specific arguments for various downloads/uploads to/from Excel sheets. Inspect the sheets folder to get an idea of how you can work with this function.
Exploratory Modelling and Analysis (EMA) Workbench is a great tool that allows us to implement parametric experiment on nearly any Python function. Using the package and the ETM API, we can write simple wrappers that use the ET Engine API (ETE) to generate higher-level results based on varying inputs. For extended documentation on EMA, please refer to its official documentation.
In the tutorials below a minimal example is given which varies the ratio of solar to wind for a given total energy production from VRE sources in the KEA scenario. Inspect the images to get a glance of possible insight you could gain from using parametric experiments.