9 Download - Dhd Toolbox

¹ Department of Computer Science, University of Cambridge, United Kingdom ² Institute for Systems Engineering, Universidad Politécnica de Madrid, Spain ³ School of Information Technology, Indian Institute of Technology Bombay, India

Alexandra M. Chen¹, Javier L. Ortega², Maya R. Patel³ dhd toolbox 9 download

class DHDModule: @staticmethod def inputs() -> List[SignalSpec]: ... @staticmethod def outputs() -> List[SignalSpec]: ... def configure(self, cfg: dict) -> None: ... def run(self, data: DataSlice) -> DataSlice: ... The modularity permits community contributions (e.g., dhd‑gait , dhd‑driverstate ) without modifying the core codebase. The visual editor is built on Qt 6 and the Node‑Graph library. Users drag‑and‑drop module nodes, connect ports, and execute pipelines either interactively or in headless mode ( dhd flow run pipeline.yaml ). The editor automatically generates reproducible YAML specifications. 4. Core Modules and Capabilities | Category | Module | Description | Example API | |----------|--------|-------------|-------------| | Signal Pre‑processing | dhd.signal.filter | FIR/IIR filters, wavelet denoising, adaptive noise cancellation. | filter.lowpass(data, cutoff=30, order=4) | | Kinematic Reconstruction | dhd.motion.reconstruct | Marker‑gap filling, inverse kinematics (IK) using OpenSim backend. | reconstruct.ik(c3d, model='gait2392') | | Physiological Analysis | dhd.physio.hr | Heart‑rate extraction from ECG, HRV metrics (RMSSD, LF/HF). | hr.compute_hr(ecg, fs=1000) | | Eye‑Tracking | dhd.vision.gaze | Pupil‑center detection, gaze‑vector mapping to 3D scenes. | gaze.map(pupil, calibration) | | Machine Learning | dhd.ml.pipeline | Scikit‑learn and PyTorch wrappers, automated hyper‑parameter search (Optuna). | pipeline.fit(X_train, y_train) | | ROS 2 Bridge | dhd.ros.bridge | Subscribes/publishes DHD topics ( /dhd/imu , /dhd/mocap ). | bridge.subscribe('/imu', callback) | | GPU Accelerated | dhd.gpu.spectra | Real‑time spectrogram computation via CuPy. | spectra.cwt(signal, scales=np.arange(1,128)) | ¹ Department of Computer Science, University of Cambridge,

All modules expose type hints and docstrings that are automatically rendered in the online documentation (https://dhd-toolbox.org/docs). 5.1 System Requirements | Requirement | Minimum | Recommended | |-------------|---------|-------------| | OS | Windows 10 / Ubuntu 20.04 | Linux (Ubuntu 22.04) or macOS 13 | | Python | 3.10 | 3.11 | | CPU | 4‑core (2 GHz) | 8‑core (3.2 GHz) | | RAM | 8 GB | 32 GB | | GPU | — | NVIDIA RTX 3060 (CUDA 11.8) | | Disk | 5 GB | 20 GB SSD | 5.2 Obtaining the Toolbox The official source distribution is hosted on the public GitHub organization dhd-toolbox (https://github.com/dhd-toolbox). The latest stable tag is v9.0.2 . The recommended acquisition workflow is: def run(self, data: DataSlice) -> DataSlice: