Experiment Data¶

Experiment data lives in a dedicated GitHub repository (e.g., my-flu-experiment-repo) and provides everything Stage A needs to build simulation tasks.

Repository Structure¶

{experiment-repo}/
├── experiments/
│   └── {EXP_ID}/
│       └── config/
│           ├── basemodel.yaml       # Model structure, parameters, populations
│           ├── modelset.yaml         # Populations, calibration or sampling settings (optional)
│           ├── output.yaml                  # Output generation settings (optional)
│           └── output_projection.yaml       # Alternative output config (optional)
├── common-data/                            # Shared data files
│   ├── surveillance/                       # Surveillance data (e.g., ILI rates)
│   └── parameters/                         # Shared parameter files
└── functions/                              # Custom Python modules
    └── custom_models.py                    # User-defined model functions

Loading experiment data¶

Experiment data are loaded from different sources depending on the execution mode.

In cloud mode, the forecast repository is configured in the profile config:

# profiles/flu.yaml
github:
  forecast_repo: your-org/my-flu-experiment-repo  # GitHub repo to clone
  forecast_repo_ref: ""                            # Branch/tag/commit (empty = default branch)

In this case, Stage A clones your-org/my-flu-experiment-repo from GitHub at the default branch into /data/forecast/ inside the container. Experiment configs are then available at /data/forecast/experiments/{EXP_ID}/config/. Setting forecast_repo_ref to a branch, tag, or commit pins the clone to that specific version.

In local mode, experiment data must be placed in ./local/forecast/ before running. This can be done by writing configs directly or copying from an existing experiment repository. Docker Compose mounts ./local/forecast/ to /data/forecast/ inside the container.

Configuration Files¶