Data Processing Pipeline

Psiexperiment uses a sophisticated pipeline system for handling incoming and outgoing data. This ensures that signal processing is efficient, traceable, and modular.

Input Processing

All analog input data is streamed through a series of operations that perform transformations in real-time. This is defined in your IO Manifest.

Data Objects

Data is passed through the pipeline as specialized NumPy-like objects that carry additional metadata, such as the sampling rate and timestamp of the first sample.

Pipeline Clearing

If an engine needs to reset (e.g., after a hardware error or an experiment restart), a special “clear” signal is sent through the pipeline to ensure that any buffered filters or accumulators are reset.

Calibration

Psiexperiment was built with auditory research in mind, so calibration is a first-class citizen. Every input and output channel can have an associated calibration object.

How Calibration Works

When you specify a stimulus level (e.g., 80 dB SPL), the system uses the calibration data to calculate the required voltage for the hardware output. Conversely, when acquiring data from a microphone, it uses the calibration to convert measured voltages back into sound pressure levels.

Special Calibration Classes

UnityCalibration Used when you want to express or measure values relative to 1V (dB re 1V). This is useful for non-acoustic signals or when a system is already pre-calibrated.

Example usage: * calibration.get_sf(frequency, level_db): Calculates the scale factor (V) for a given frequency and level. * calibration.get_spl(frequency, voltage): Calculates the level (dB) for a given frequency and voltage.

Data Sinks and Storage

Data is saved by contributing a Sink to the psi.data.sinks extension point.

• Continuous Sinks: Save every sample acquired (e.g., to a Zarr or HDF5 file).

• Event Sinks: Save discrete experimental events and their associated context values (e.g., to a CSV or SQLite database).

• Trial Sinks: Automatically extract epochs around specific triggers and save them as independent trials.

By combining these sinks, you can ensure that you have both the raw high-speed data and a summarized version of the experiment for quick analysis.