Quick Start Guide
Launch a basic OpenScope experiment, then layer in optional metadata validation and post-acquisition tooling.
Prerequisites
Windows 10 or 11 with Python 3.8 or newer
Rig configuration
.tomldescribing hardware defaultsExperiment parameter
.json(start withparams/example_minimalist_params.json)
Install
pip install -e .
Configuration Layers
Rig Config (TOML) – shared hardware + default values.
Parameter File (JSON) – experiment-specific overrides.
Runtime Prompts – final confirmation during launch.
Precedence order is runtime prompt > parameter file > rig config. Rig values can be referenced inside script_parameters using {rig_param:KEY}.
Prepare Parameters
Adjust the minimal example or copy a template:
{
"subject_id": "mouse_001",
"user_id": "operator",
"script_path": "C:/local/scripts/task.py",
"output_root_folder": "D:/OpenScopeData"
}
Launch a Session
python run_launcher.py --param_file params/example_minimalist_params.json
Follow the prompts; a session folder is created with launcher_metadata/processed_parameters.json and launcher_metadata/end_state.json.
Add Modules (Optional)
Extend the workflow by listing modules in your parameter file:
Metadata service checks – see
params/example_metadata_pipeline.jsonfor subject, procedures, and project validation prior to acquisition. Modules default to the in-rig hosthttp://aind-metadata-service; override viametadata_service_base_urlwhen your deployment uses a different metadata host.Experiment notes workflow –
params/experiment_notes_pipeline.jsonpreviews notes before the run and requires confirmation after the run.Session archiver – add
session_archivertopost_acquisition_pipelineto copy data to backup storage with checksum and throughput logging. Providesession_dir(usually{output_session_folder}), plusnetwork_dirandbackup_dir.
You can exercise a module independently via:
python run_module.py --module_type post_acquisition --module_name session_archiver --param_file params/example_metadata_pipeline.json
The param file you pass must include session_dir in addition to the archive destinations.
Outputs
Every run produces:
launcher_metadata/processed_parameters.json– merged config and promptslauncher_metadata/end_state.json– flattened end-state summarylauncher_metadata/debug_state.jsonwhen a crash occurs
Post-acquisition modules may add artifacts such as session.json or archived data manifests.
Next Steps
Parameter Files – full parameter schema and module references
Rig Configuration – rig TOML structure and placeholder usage
Pre- and Post-Acquisition Modules – metadata integrations, notes workflow, session archiver, and more
MATLAB Launcher – configure the shared-engine MATLAB workflow