respyra
A general-purpose respiratory tracking toolbox for interoception research
respyra is a Python toolbox that integrates a Vernier Go Direct Respiration Belt (GDX-RB) with PsychoPy to enable real-time respiratory tracking experiments. Participants follow a sinusoidal target dot with their breathing while receiving continuous visual biofeedback. The toolbox supports configurable experimental conditions including multi-frequency target waveforms and visuomotor perturbations (visual gain manipulation).
Full documentation | PyPI | Paper
Task Schematic
Task Screenshots
| Range Calibration | Baseline |
|---|---|
| Countdown | Tracking (good) | Tracking (poor) |
|---|---|---|
Installation
From PyPI (recommended)
pip install respyra
For post-session visualization (adds pandas and matplotlib):
pip install "respyra[vis]"
Development install
git clone https://github.com/embodied-computation-group/respyra.git
cd respyra
Create a virtual environment with Python 3.10:
# Windows (with Python Launcher)
py -3.10 -m venv .venv
.venv\Scripts\activate
# macOS / Linux
python3.10 -m venv .venv
source .venv/bin/activate
Install in editable mode with dev extras:
pip install -e ".[dev,vis]"
Running tests
pytest tests/ -v
PsychoPy and godirect are mocked at the sys.modules level so the test suite
runs without hardware or heavy dependencies. See CONTRIBUTING.md
for details on the mock strategy and adding new tests.
Requirements
- Python 3.10 -- PsychoPy does not yet support 3.11+
- Vernier Go Direct Respiration Belt (GDX-RB) -- required for hardware experiments; display demos run without a belt
Quick start
Run a no-hardware display demo to verify PsychoPy is working:
python -m respyra.demos.demo_display
With a belt connected, run the full experiment:
respyra-task
See the full documentation for detailed installation, quickstart, and user guide.
Running the experiment
python -m respyra.scripts.breath_tracking_task
# or, after pip install:
respyra-task
Session flow
- Belt connection -- BLE with automatic USB fallback (connects before PsychoPy to avoid Windows COM conflicts)
- Participant info dialog -- enter participant ID and session number
- Range calibration (15 s) -- comfortable deep breaths to establish breathing range, with percentile-based outlier rejection and sensor saturation detection
- Trial loop (per condition x N reps):
- Baseline (10 s) -- breathe naturally
- Countdown (3 s) -- target dot blends from current position into the target waveform
- Tracking (30 s) -- follow the sinusoidal target dot with breathing
- Feedback -- mean absolute tracking error for the trial
- Data saved to
data/as CSV (one row per sample, flushed incrementally)
Experimental conditions
Conditions are defined in respyra/configs/breath_tracking.py using composable frequency segments:
| Condition | Pattern | Feedback gain |
|---|---|---|
slow_steady |
3 cycles at 0.1 Hz (30 s) | 1.0 (veridical) |
mixed_rhythm |
3 cycles at 0.1 Hz + 1 cycle at 0.3 Hz | 1.0 (veridical) |
perturbed_slow |
3 cycles at 0.1 Hz (30 s) | 1.5 (amplified trace) |
The feedback gain perturbation multiplies the displayed breathing trace around the participant's center, similar to cursor rotation in visuomotor reaching studies. The target dot, tracking error, and color feedback remain based on the true (unperturbed) signal -- only the visual trace is distorted.
Visual feedback
The target dot changes color based on real-time tracking error:
- Graded mode (default) -- continuous green (good) to yellow to red (poor) using HSV interpolation
- Binary mode -- yellow/red threshold
- Trinary mode -- yellow/orange/red with two thresholds
Post-session visualization
python -m respyra.utils.vis.plot_session data/sub-01_ses-001_2026-02-24.csv
# or, after pip install:
respyra-plot data/sub-01_ses-001_2026-02-24.csv
Generates a 6-panel summary figure saved as {csv_stem}_summary.png:
- Full session force trace with target overlay
- Signed tracking error per trial
- Per-trial mean absolute error (bar chart)
- Error distribution by condition (box plot)
- Baseline calibration stability across trials
- Summary statistics (MAE, RMSE, per-condition breakdown)
Project structure
respyra/
core/ Reusable modules
breath_belt.py Non-blocking belt I/O (threaded reader + queue)
display.py PsychoPy window, SignalTrace waveform renderer
data_logger.py Incremental CSV logging with crash resilience
events.py Keyboard input helpers
target_generator.py Sinusoidal target waveform from segment definitions
gdx/ Vernier gdx wrapper (from godirect-examples, not on PyPI)
configs/ Experiment parameters (no magic numbers in scripts)
scripts/ Runnable experiment sessions
demos/ Standalone single-feature test scripts
utils/vis/ Post-session visualization
docs/ Sphinx documentation source
media/ Stimulus assets and icons
data/ Session output (gitignored)
Demos
python -m respyra.demos.demo_belt_connection # Test belt connectivity (terminal only)
python -m respyra.demos.demo_display # PsychoPy display with synthetic data
python -m respyra.demos.demo_threaded_belt # Threaded belt queue-draining pattern
Documentation
Full documentation is available at embodied-computation-group.github.io/respyra, including:
- Installation guide with platform-specific notes
- Quick start tutorial
- User guide with detailed experiment configuration
- API reference generated from source docstrings
- Troubleshooting for common issues
Why the Vernier belt?
The Vernier Go Direct Respiration Belt (GDX-RB) was chosen because it is inexpensive, well-documented, and available worldwide through educational science suppliers. It provides wireless (BLE) respiratory force measurement out of the box, making it accessible to researchers and teaching labs without specialized biomedical equipment budgets. While not a research-grade instrument, it offers a reliable and practical solution for respiratory tracking and interoception research at low cost. For applications requiring more sophisticated monitoring (e.g., dual-band respiratory inductance plethysmography or spirometry), respyra's modular sensor interface is designed to accommodate alternative hardware.
Platform notes
Windows BLE: The Vernier belt's BLE scanner (Bleak) requires COM in MTA mode on the main thread. PsychoPy sets COM to STA on import. The framework handles this by connecting the belt before importing PsychoPy.
Linux: Requires udev rules for USB access. See the installation guide.
macOS: Works with both BLE and USB out of the box.
Contributing
Contributions are welcome! See CONTRIBUTING.md for development setup, testing, linting, and pull request guidelines.
License
The respyra/core/gdx/ module is derived from VernierST/godirect-examples and is licensed under the BSD 3-Clause License.
Metadata
Release files for respyra 0.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| respyra-0.4.0.tar.gz | 37.3 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| respyra-0.4.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 37.4 MB
Release files / respyra-0.4.0.tar.gz
| Download URL | respyra-0.4.0.tar.gz |
|---|---|
| Size | 37.3 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
125b54e51cd81e67444264bacfce33ef9563118f9cb337c64268836a4948f354
|
|
BLAKE2b-256 checksum How to use checksums |
abdfdf94df313b9cbd104b0e004ab0941c453366d58593bbcce4e3b197bbba19
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Feb 25, 2026.
Transparency logRelease files / respyra-0.4.0-py3-none-any.whl
| Download URL | respyra-0.4.0-py3-none-any.whl |
|---|---|
| Size | 71.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b1856a06d43a8b14802b60f26b9fad1ba0a7bff1acacd7a3c6e50093fc61c539
|
|
BLAKE2b-256 checksum How to use checksums |
df2ffdf8f49e4d9de49f9952d85663a4299509fad7e37b7e9f2f170c3f2b6e1a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Feb 25, 2026.
Transparency log