reeds-visualizer

A Tableau-like platform for visualizing data from the ReEDS model leveraging PyGWalker and streamlit.

Requirements

Users must have the following installed:

• git
• A python distribution (Anaconda or Mamba)

Environment Set Up

1. Open a terminal window (terminal on a Unix system or Command Prompt on Windows).

2. Clone the repository by typing (or copy-pasting) the following command:

git clone https://github.com/ucsusa/reeds-visualizer.git

3. Navigate into the newly created directory with

cd reeds-visualizer

4. Create a new Python environment with (and follow the prompts)

conda env create

5. Activate the newly created Python environment

conda activate reeds-viz

6. Start the visualizer, which will open a new browser window, using

streamlit run visualizer.py

Pulling recent changes

When issues or tickets lead to code changes, those changes will not automatically be present on a local copy of the repository.

In order to pull down the changes, make sure you are the in reeds-visualizer working directory in your command prompt or terminal window. Then you can use the following command (assuming the desired changes are on the main branch):

git pull origin main

Note

You cannot pull changes or execute any commands in the same session that is running the visualizer. In other words, you must shut down the visualizer, pull the changes, and restart it for the changes to take effect.

Compiling results into a spreadsheet for analysis

There are two scripts which can be run sequentially to aid in analyzing results.

1. Run results_list.py to create a matrix of results, with rows for scenarios and columns for metrics (generation, capacity, emissions, etc). "TRUE" indicates that an output file exists for the given combination of scenario and metric.
2. Edit analysis/results_list.csv to limit which files will be included in the final spreadsheet. This can be acomplished by deleting entire rows/columns, and/or deleting "TRUE" from specific combinations that aren't needed. Limiting which files are included will greatly reduce the processing time and the file size of the final result. Very large files may be unstable.
3. Run results_spreadsheet.py to create a spreadsheet with the desired results. If there is an existing version of results_spreadsheet.xlsx, the script will append it, and overwrite existing sheets. Otherwise, a new copy will be created.