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Examples directory

Every runnable example under examples/synthetic/, what it teaches, and what's tweakable. All six are hardware-free - the process_launcher panel spawns myogestic.tools.emg_generator for you, so one terminal is enough.

For VHI integration, install once with python -m myogestic.tools.install_vhi (see Install the Virtual Hand). Without it, the launcher button errors at click time and everything else still runs.

Running

uv sync --extra examples              # core demos
uv sync --extra examples --extra grpc # adds the gRPC-control examples
uv run python examples/synthetic/<name>.py

The six examples

emg_classification.py - start here

The simplest end-to-end loop: 8-channel synthetic EMG → MyoVerse RMS+MAV features → CatBoost binary classifier → smoothed 9-vec to the VHI predicted hand via the MyoGestic_Output LSL outlet. No gRPC. This is the canonical first read, and its line-by-line companion is the EMG classification tutorial.

uv run python examples/synthetic/emg_classification.py

What to tweak: swap rms/mav from myogestic.contrib.features for your own feature, change CLASSES, replace CatBoost with any sklearn-shaped classifier.

emg_classification_grpc.py - add the gRPC control plane

Same classifier, plus the VhiControlClient gRPC plane: each predicted class change fires SetMovement(name) via an EdgeTrigger, and a VhiMovementPanel in the UI lets the user click movements directly. Demonstrates the dual-plane idiom (continuous LSL pose + discrete gRPC events) on a single script.

uv run python examples/synthetic/emg_classification_grpc.py

What to tweak: wrap vhi_client.set_movement in a custom callback to layer a session-label snap; swap the EdgeTrigger value from class name to (class_name, intensity_bin) for hysteresis on multiple fields.

emg_regression.py - continuous-target regression

CatBoost regressor maps EMG features to a 5-DoF kinematic target. Recorded with cycle=False so VHI snaps to and holds each movement's end pose - regression needs the trainee to physically reach and hold the target, not sweep through a cycle. RMS + MAV + waveform length features.

uv run python examples/synthetic/emg_regression.py

What to tweak: add or remove DoFs in the kinematic target; swap CatBoost for sklearn's MultiOutputRegressor to compare model families.

emg_regression_raulnet.py - RaulNet via Lightning

Same regression flow but with RaulNetV17 - a PyTorch Lightning CNN that takes a sliding-RMS feature stack (channels, time) and predicts 5-DoF kinematics. Trains with Trainer(precision="32-true") (TorchScript backward has hard-coded fp32 checks; mixed-precision fails), SWA, ModelCheckpoint, and per-epoch log lines streamed to the pipeline panel's autoscroll-and-popout log box.

uv run python examples/synthetic/emg_regression_raulnet.py

What to tweak: change RaulNetV17 hyperparameters, increase the window size, switch the device to MPS (Apple Silicon) or CUDA - the training callback streams the same per-epoch log either way.

emg_32ch_multi_model.py - multi-classifier comparison

32-channel EMG with a selectable classifier - compare CatBoost, sklearn LDA, sklearn SVM, etc. live without re-running the script. Adds the Save/Load model panel so a tuned model survives a restart, and the pose-lookup pattern for mapping multiple gestures to canonical poses.

uv run python examples/synthetic/emg_32ch_multi_model.py

What to tweak: plug another classifier into the model registry, add more gestures to the pose-lookup, increase the channel count (the generator scales to any --channels).

emg_popout_layout.py - dockable layout reference

Same flow as emg_32ch_multi_model.py but every block is a tear-off pop-out window via App(docking=True) + app.popout(...). The Prediction panel gets its own floating window; the training log can pop out independently. Reference layout for multi-monitor experiments.

uv run python examples/synthetic/emg_popout_layout.py

What to tweak: rearrange the app.popout() call sites; combine docking with the Grid layout from Grid layout for the in-window panels.

Choosing where to start

  • Brand new - Anatomy of an app → EMG classification tutorial → emg_classification.py.
  • Want gRPC discrete control - emg_classification_grpc.py next.
  • Regression flow - EMG regression with VHI tutorial → emg_regression.py → swap in _raulnet for the deep variant.
  • Comparing models - emg_32ch_multi_model.py.
  • Multi-monitor / docking - emg_popout_layout.py.
  • Custom extension point - skip the examples and read the how-to guides - each is a recipe for one extension point.