Python wrapper for MLDS R package

Contents

It contains:

• a python implementation that wraps the R package MLDS. This wrapper makes easier to analyse the data obtained in MLDS experiments. It also provides the extra functionality to using multi-thread, making the bootstrap calculation much faster.

• utilities for designing MLDS experiments (method of triads and quadruples)

• functions to simulate an observer performing an MLDS experiment (so far only for the method of triads).

Requirements

• Python >= 3.7

• Python modules: numpy, subprocess, multiprocessing, rpy2 (>=2.3.10)

• R (>=3.0), with the MLDS, psyphy and snow packages

Python module dependencies are installed automatically.

R packages must be installed manually, either from CRAN (see below) or using the files provided in this repository (mlds/CRAN).

Installation

• Install R and the requirements within R: install.packages(c("MLDS", "psyphy", "snow"))

For users

• Simply run pip install https://github.com/computational-psychology/mlds/tarball/master. Missing python dependencies are installed automatically.

For developers

• Clone the repository from github (git clone https://github.com/computational-psychology/mlds.git)
• Go to the root of the repository and run python setup.py install -f (you can also run pip install -e .)

Testing

In a Python console, run:

import mlds
mlds.test() # this should take around a minute

Usage examples

• examples/example.py gives usage example for MLDS analysis
• examples/example_stim_generation.py gives usage example for designing the triads or quadruples.
• examples/example_simulation.py gives usage example for simulating an observer performing the method of triads.
• examples/example_predict_thresholds.py gives usage example for predicting discrimination thresholds from a perceptual scale, using signal detection theory assumptions. See Aguilar, Wichmann & Maertens (2017) for details.

Contact

Questions? Feedback? Don't hesitate to ask Guillermo Aguilar ([email protected])

This repository has so far only been tested in Linux (Debian 9, 10 and Ubuntu Xenial, Bionic, Focal). Automatic testing using Travis test the package in Ubuntu Focal, for all major versions of python >=3.6.