sensPy

A Python port of the R package sensR for Thurstonian models in sensory discrimination analysis.

---

About

sensPy provides a comprehensive Python implementation of sensR, the R package for sensory discrimination methods. This project ensures:

• Numerical parity with sensR (validated against R outputs)
• Modern Python practices (type hints, dataclasses, NumPy/SciPy integration)
• Full test coverage with 500+ tests

Installation

# Install with pip
pip install senspy

# Or install from source
git clone https://github.com/aigorahub/sensPy.git
cd sensPy
pip install -e .

Features

Discrimination Protocols

All standard protocols with single and double variants:

Protocol	Single	Double
Triangle	✅	✅
Duo-Trio	✅	✅
2-AFC	✅	✅
3-AFC	✅	✅
Tetrad	✅	✅
Hexad	✅	-
2-out-of-5	✅	-

Statistical Models

• discrim() - D-prime estimation with confidence intervals and p-values
• betabin() - Beta-binomial model for overdispersed data
• twoac() - 2-Alternative Certainty model
• samediff() - Same-different model
• dod() - Degree-of-difference model
• anota() - A-not-A signal detection model

Power & Sample Size

• discrim_power(), dprime_power() - Power analysis
• discrim_sample_size(), dprime_sample_size() - Sample size calculation
• samediff_power() - Same-different power (simulation-based)
• twoac_power() - 2-AC exact power
• dod_power() - DOD power analysis

Inference & Comparison

• dprime_test() - Test d-prime hypotheses
• dprime_compare() - Compare d-primes across groups
• posthoc() - Post-hoc pairwise comparisons

ROC & Signal Detection

• roc() - ROC curve computation
• auc() - Area under the ROC curve
• sdt() - Signal detection theory transforms

Simulation

• discrim_sim() - Simulate discrimination experiments
• samediff_sim() - Simulate same-different data
• dod_sim() - Simulate DOD data

Visualization

• plot_psychometric() - Psychometric functions
• plot_roc() - ROC curves
• plot_power_curve() - Power curves
• plot_profile_likelihood() - Profile likelihood

Quick Start

from senspy import discrim, discrim_power

# Analyze a Triangle test result (80 correct out of 100)
result = discrim(correct=80, total=100, method="triangle")
print(f"d-prime: {result.d_prime:.3f}")
print(f"95% CI: [{result.ci_lower:.3f}, {result.ci_upper:.3f}]")
print(f"p-value: {result.p_value:.4g}")

# Calculate power for a future test
power = discrim_power(d_prime=1.5, sample_size=100, method="triangle")
print(f"Power: {power:.1%}")

from senspy import samediff, dprime_compare

# Same-different analysis
sd = samediff(nsamesame=45, ndiffsame=5, nsamediff=20, ndiffdiff=30)
print(f"delta: {sd.delta:.3f}, tau: {sd.tau:.3f}")

# Compare d-primes across products
result = dprime_compare(
    correct=[80, 65, 90],
    total=[100, 100, 100],
    method="triangle"
)
print(f"Chi-square: {result.statistic:.2f}, p={result.p_value:.4f}")

License

This project is licensed under the GNU General Public License version 2.0 or later. See LICENSE for details.

Citation

If you use sensPy in your research, please cite:

@software{senspy,
  title = {sensPy: Python port of sensR for Thurstonian models},
  author = {Aigora},
  year = {2025},
  url = {https://github.com/aigorahub/sensPy}
}

Acknowledgments

This package is a port of sensR by Per Bruun Brockhoff and Rune Haubo Bojesen Christensen.