Example Execution

This guide shows you how to run OSeMOSYS-RDM step-by-step using Anaconda. Specifically, we focus on the automated pipeline, using DVC, to reproduce the Uganda analysis. This is a beginner-friendly guide that assumes no prior experience with command-line interfaces.

Step-by-Step Execution Guide

Step 1: Open Anaconda Prompt

First, open the Anaconda Prompt terminal. This is a special command-line interface that comes with Anaconda.

Opening the Anaconda Prompt terminal

Tip

You can find Anaconda Prompt in your Windows Start menu by searching for “Anaconda Prompt”.

Step 2: Navigate to Repository

Change to the repository directory using the cd command (which stands for “change directory”).

Command explanation:

• cd followed by a path tells the terminal to move to that location on your computer

• Replace the path in the image with your actual repository location

Type the command and press Enter to execute it.

Accessing the repository directory path

Important

Make sure to replace the path with the actual location where you cloned or downloaded the repository on your computer.

Step 3: Run the Model

Execute the model using the appropriate command. Type the command and press Enter to start the execution.

Command explanation:

python run.py rdm

• python tells the computer to use Python to run a program

• run.py is the main program file that controls the workflow

• rdm tells the program to run the RDM (Robust Decision Making) pipeline

Other available options:

• python run.py prim - Runs only the PRIM analysis (requires RDM results already available)

• python run.py all - Runs both RDM and PRIM pipelines sequentially

Command to execute the process

Step 4: Process Starts

After pressing Enter, the process will begin executing the pipeline stages. You’ll see text appearing on the screen showing the progress.

Beginning of the execution process

Note

Don’t close the window while the process is running. The terminal will show you updates as each stage completes.

Step 5: Process Completes

The process will finish when all stages are complete. You’ll see a final message indicating success.

End of the execution process

Note

In this example, the process completes very quickly because the last model executed is cached in memory. DVC (Data Version Control) detects that re-running would produce the same results, so it skips unnecessary computations. Your first run may take longer.

Understanding the Output

After the process completes successfully, your results will be available in the src/Results/ directory. You can find:

• CSV files with consolidated outputs

• Parquet files for efficient data storage

Changes to the Environment Name

If the name of the environment changes in environment.yaml, you need to also modify the name of ENV_NAME_DEFAULT in run.py.